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"source_file": "troubleshoot.md", + "language_code": "pt-BR" + } +} \ No newline at end of file diff --git a/translations/pt-BR/AGENTS.md b/translations/pt-BR/AGENTS.md new file mode 100644 index 00000000..4b13bcf6 --- /dev/null +++ b/translations/pt-BR/AGENTS.md @@ -0,0 +1,317 @@ +# AGENTS.md + +## Visão Geral do Projeto + +AI for Beginners é um currículo abrangente de 12 semanas e 24 aulas que cobre os fundamentos da Inteligência Artificial. Este repositório educacional inclui lições práticas usando Jupyter Notebooks, quizzes e laboratórios práticos. O currículo aborda: + +- IA Simbólica com Representação de Conhecimento e Sistemas Especialistas +- Redes Neurais e Aprendizado Profundo com TensorFlow e PyTorch +- Técnicas e arquiteturas de Visão Computacional +- Processamento de Linguagem Natural (NLP), incluindo transformers e BERT +- Tópicos especializados: Algoritmos Genéticos, Aprendizado por Reforço, Sistemas Multiagentes +- Ética em IA e princípios de IA Responsável + +**Principais Tecnologias:** Python 3, Jupyter Notebooks, TensorFlow, PyTorch, Keras, OpenCV, Vue.js (para o aplicativo de quiz) + +**Arquitetura:** Repositório de conteúdo educacional com Jupyter Notebooks organizados por áreas temáticas, complementado por um aplicativo de quiz baseado em Vue.js e suporte extensivo a múltiplos idiomas. + +## Comandos de Configuração + +### Ambiente de Desenvolvimento Principal (Python/Jupyter) + +O currículo foi projetado para ser executado com Python e Jupyter Notebooks. A abordagem recomendada é usar miniconda: + +```bash +# Clone the repository +git clone https://github.com/microsoft/ai-for-beginners +cd ai-for-beginners + +# Create and activate conda environment +conda env create --name ai4beg --file environment.yml +conda activate ai4beg + +# Start Jupyter Notebook +jupyter notebook +# OR +jupyter lab +``` + +### Alternativa: Usando devcontainer + +```bash +# Open in VS Code and select "Reopen in Container" when prompted +# The devcontainer will automatically set up the environment +``` + +### Configuração do Aplicativo de Quiz + +O aplicativo de quiz é um aplicativo Vue.js separado localizado em `etc/quiz-app/`: + +```bash +cd etc/quiz-app +npm install +npm run serve # Development server +npm run build # Production build +npm run lint # Lint and fix files +``` + +## Fluxo de Trabalho de Desenvolvimento + +### Trabalhando com Jupyter Notebooks + +1. **Desenvolvimento Local:** + - Ative o ambiente conda: `conda activate ai4beg` + - Inicie o Jupyter: `jupyter notebook` ou `jupyter lab` + - Navegue até as pastas de lições e abra os arquivos `.ipynb` + - Execute as células interativamente para acompanhar as lições + +2. **VS Code com Extensão Python:** + - Abra o repositório no VS Code + - Instale a extensão Python + - O VS Code detecta e usa automaticamente o ambiente conda + - Abra os arquivos `.ipynb` diretamente no VS Code + +3. **Desenvolvimento na Nuvem:** + - **GitHub Codespaces:** Clique em "Code" → "Codespaces" → "Create codespace on main" + - **Binder:** Use o badge do Binder no README para iniciar no navegador + - Nota: O Binder tem recursos limitados e algumas restrições de acesso à web + +### Suporte a GPU para Lições Avançadas + +As lições posteriores se beneficiam significativamente da aceleração por GPU: + +- **Azure Data Science VM:** Use VMs da série NC com suporte a GPU +- **Azure Machine Learning:** Use os recursos de notebook com computação GPU +- **Google Colab:** Faça upload dos notebooks individualmente (tem suporte gratuito a GPU) + +### Desenvolvimento do Aplicativo de Quiz + +```bash +cd etc/quiz-app +npm run serve # Hot-reload development server at http://localhost:8080 +``` + +## Instruções de Teste + +Este é um repositório educacional focado em conteúdo de aprendizado, em vez de testes de software. Não há suíte de testes tradicional. + +### Abordagens de Validação: + +1. **Jupyter Notebooks:** Execute as células sequencialmente para verificar se os exemplos de código funcionam +2. **Teste do Aplicativo de Quiz:** Teste manual via servidor de desenvolvimento +3. **Validação de Tradução:** Verifique o conteúdo traduzido na pasta `translations/` +4. **Linting do Aplicativo de Quiz:** `npm run lint` em `etc/quiz-app/` + +### Executando Exemplos de Código: + +```bash +# Activate environment first +conda activate ai4beg + +# Run Python scripts directly +python lessons/4-ComputerVision/07-ConvNets/pytorchcv.py + +# Or execute notebooks +jupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb +``` + +## Estilo de Código + +### Estilo de Código Python + +- Convenções padrão de Python para código educacional +- Código claro e legível, priorizando o aprendizado em vez da otimização +- Comentários explicando conceitos-chave +- Compatível com Jupyter Notebook: as células devem ser autossuficientes sempre que possível +- Sem requisitos rigorosos de linting para conteúdo de lições + +### JavaScript/Vue.js (Aplicativo de Quiz) + +- Configuração do ESLint em `etc/quiz-app/package.json` +- Execute `npm run lint` para verificar e corrigir problemas automaticamente +- Convenções do Vue 2.x +- Arquitetura baseada em componentes + +### Organização de Arquivos + +``` +lessons/ + ├── 0-course-setup/ # Setup instructions + ├── 1-Intro/ # Introduction to AI + ├── 2-Symbolic/ # Symbolic AI + ├── 3-NeuralNetworks/ # Neural Networks basics + ├── 4-ComputerVision/ # Computer Vision + ├── 5-NLP/ # Natural Language Processing + ├── 6-Other/ # Other AI techniques + ├── 7-Ethics/ # AI Ethics + └── X-Extras/ # Additional content + +etc/ + ├── quiz-app/ # Vue.js quiz application + └── quiz-src/ # Quiz source files + +translations/ # Multi-language translations +``` + +## Build e Implantação + +### Conteúdo Jupyter + +Nenhum processo de build é necessário - os Jupyter Notebooks são executados diretamente. + +### Aplicativo de Quiz + +```bash +cd etc/quiz-app + +# Development +npm run serve + +# Production build +npm run build # Outputs to etc/quiz-app/dist/ + +# Deploy to Azure Static Web Apps +# Azure automatically creates GitHub Actions workflow +# See etc/quiz-app/README.md for detailed deployment instructions +``` + +### Site de Documentação + +O repositório usa Docsify para documentação: +- `index.html` serve como ponto de entrada +- Nenhum build é necessário - servido diretamente via GitHub Pages +- Acesse em: https://microsoft.github.io/AI-For-Beginners/ + +## Diretrizes de Contribuição + +### Processo de Pull Request + +1. **Formato do Título:** Títulos claros e descritivos que descrevam a alteração +2. **Requisito de CLA:** O CLA da Microsoft deve ser assinado (verificação automatizada) +3. **Diretrizes de Conteúdo:** + - Mantenha o foco educacional e a abordagem amigável para iniciantes + - Teste todos os exemplos de código nos notebooks + - Certifique-se de que os notebooks sejam executados de ponta a ponta + - Atualize as traduções se modificar o conteúdo em inglês +4. **Alterações no Aplicativo de Quiz:** Execute `npm run lint` antes de fazer o commit + +### Contribuições de Tradução + +- As traduções são automatizadas via GitHub Actions usando co-op-translator +- Traduções manuais vão em `translations//` +- Traduções do quiz em `etc/quiz-app/src/assets/translations/` +- Idiomas suportados: mais de 40 idiomas (veja o README para a lista completa) + +### Áreas Ativas de Contribuição + +Veja `etc/CONTRIBUTING.md` para necessidades atuais: +- Seções de Aprendizado por Reforço Profundo +- Melhorias em Detecção de Objetos +- Exemplos de Reconhecimento de Entidades Nomeadas +- Amostras de treinamento de embeddings personalizados + +## Configuração do Ambiente + +### Dependências Necessárias + +```bash +# Core Python packages (from requirements.txt) +tensorflow==2.17.0 +torch (via conda) +torchvision (via conda) +keras==3.5.0 +opencv (via conda) +scikit-learn +numpy==1.26 +pandas==2.2.2 +matplotlib==3.9 +jupyter +``` + +### Variáveis de Ambiente + +Nenhuma variável de ambiente especial é necessária para uso básico. + +Para implantações no Azure (aplicativo de quiz): +- `AZURE_STATIC_WEB_APPS_API_TOKEN` (configurado automaticamente pelo Azure) + +## Depuração e Solução de Problemas + +### Problemas Comuns + +**Problema:** Falha na criação do ambiente conda +- **Solução:** Atualize o conda primeiro: `conda update conda -y` +- Certifique-se de ter espaço em disco suficiente (recomendado 50GB) + +**Problema:** Kernel do Jupyter não encontrado +- **Solução:** + ```bash + conda activate ai4beg + python -m ipykernel install --user --name ai4beg + ``` + +**Problema:** GPU não detectada nos notebooks +- **Solução:** + - Verifique a instalação do CUDA: `nvidia-smi` + - Verifique a GPU no PyTorch: `python -c "import torch; print(torch.cuda.is_available())"` + - Verifique a GPU no TensorFlow: `python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"` + +**Problema:** Aplicativo de quiz não inicia +- **Solução:** + ```bash + cd etc/quiz-app + rm -rf node_modules package-lock.json + npm install + npm run serve + ``` + +**Problema:** Binder expira ou bloqueia downloads +- **Solução:** Use GitHub Codespaces ou configuração local para melhor acesso a recursos + +### Problemas de Memória + +Algumas lições exigem uma quantidade significativa de RAM (recomendado 8GB+): +- Use VMs na nuvem para lições que exigem muitos recursos +- Feche outros aplicativos ao treinar modelos +- Reduza os tamanhos de lote nos notebooks se estiver com falta de memória + +## Notas Adicionais + +### Para Instrutores do Curso + +- Veja `lessons/0-course-setup/for-teachers.md` para orientações de ensino +- As lições são autossuficientes e podem ser ensinadas em sequência ou selecionadas individualmente +- Tempo estimado: 12 semanas com 2 lições por semana + +### Recursos na Nuvem + +- **Azure for Students:** Créditos gratuitos disponíveis para estudantes +- **Microsoft Learn:** Caminhos de aprendizado suplementares vinculados ao longo do curso +- **Binder:** Gratuito, mas com recursos limitados e algumas restrições de rede + +### Opções de Execução de Código + +1. **Local (Recomendado):** Controle total, melhor desempenho, suporte a GPU +2. **GitHub Codespaces:** VS Code baseado na nuvem, bom para acesso rápido +3. **Binder:** Jupyter baseado no navegador, gratuito, mas limitado +4. **Azure ML Notebooks:** Opção empresarial com suporte a GPU +5. **Google Colab:** Faça upload dos notebooks individualmente, disponível nível gratuito de GPU + +### Trabalhando com Notebooks + +- Os notebooks são projetados para serem executados célula por célula para aprendizado +- Muitos notebooks baixam conjuntos de dados na primeira execução (pode levar tempo) +- Alguns modelos exigem GPU para tempos de treinamento razoáveis +- Modelos pré-treinados são usados sempre que possível para reduzir os requisitos de computação + +### Considerações de Desempenho + +- Lições posteriores de visão computacional (CNNs, GANs) se beneficiam de GPU +- Lições de transformers em NLP podem exigir muita RAM +- Treinar do zero é educativo, mas demorado +- Exemplos de aprendizado por transferência minimizam o tempo de treinamento + +--- + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/README.md b/translations/pt-BR/README.md new file mode 100644 index 00000000..7453d264 --- /dev/null +++ b/translations/pt-BR/README.md @@ -0,0 +1,225 @@ +[![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE) +[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/) +[![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/) +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) + +[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/) +[![GitHub forks](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/) +[![GitHub stars](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD) +[![Gitter](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) + +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +# Inteligência Artificial para Iniciantes - Um Currículo + +|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/pt-BR/ai-overview.0857791951d19500.webp)| +|:---:| +| AI Para Iniciantes - _Sketchnote por [@girlie_mac](https://twitter.com/girlie_mac)_ | + +Explore o mundo da **Inteligência Artificial** (IA) com nosso currículo de 12 semanas e 24 aulas! Inclui lições práticas, questionários e laboratórios. O currículo é amigável para iniciantes e cobre ferramentas como TensorFlow e PyTorch, além de ética em IA + + +### 🌐 Suporte Multilíngue + +#### Suportado via GitHub Action (Automatizado e Sempre Atualizado) + + +[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](./README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md) + +> **Prefere Clonar Localmente?** + +> Este repositório inclui mais de 50 traduções de idiomas, o que aumenta significativamente o tamanho do download. Para clonar sem traduções, use sparse checkout: +> ```bash +> git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git +> cd AI-For-Beginners +> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' +> ``` +> Isso lhe dá tudo o que precisa para completar o curso com um download muito mais rápido. + + +**Se desejar ter suporte para idiomas adicionais, eles estão listados [aqui](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)** + +## Junte-se à Comunidade +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +## O que você vai aprender + +**[Mapa Mental do Curso](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)** + +Neste currículo, você aprenderá: + +* Diferentes abordagens para Inteligência Artificial, incluindo a abordagem "boa e velha" simbólica com **Representação do Conhecimento** e raciocínio ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)). +* **Redes Neurais** e **Aprendizado Profundo**, que são o núcleo da IA moderna. Vamos ilustrar os conceitos por trás desses temas importantes usando código em dois dos frameworks mais populares - [TensorFlow](http://Tensorflow.org) e [PyTorch](http://pytorch.org). +* **Arquiteturas Neurais** para trabalhar com imagens e texto. Cobriremos modelos recentes, embora possam estar um pouco defasados em relação ao estado da arte. +* Abordagens menos populares de IA, como **Algoritmos Genéticos** e **Sistemas Multi-Agentes**. + +O que não será abordado neste currículo: + +> [Encontre todos os recursos adicionais para este curso em nossa coleção Microsoft Learn](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) + +* Casos de uso de **IA nos Negócios**. Considere fazer o caminho de aprendizado [Introdução à IA para usuários de negócios](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum) no Microsoft Learn, ou a [Escola de Negócios de IA](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum), desenvolvida em cooperação com a [INSEAD](https://www.insead.edu/). +* **Aprendizado de Máquina Clássico**, que está bem descrito em nosso [Currículo de Aprendizado de Máquina para Iniciantes](http://github.com/Microsoft/ML-for-Beginners). +* Aplicações práticas de IA construídas usando **[Serviços Cognitivos](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**. Para isso, recomendamos que comece com os módulos Microsoft Learn para [visão](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum), [processamento de linguagem natural](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum), **[IA Generativa com Azure OpenAI Service](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)** e outros. +* **Frameworks de ML em Nuvem** específicos, como [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum), [Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum), ou [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum). Considere usar os caminhos de aprendizado [Construir e operar soluções de aprendizado de máquina com Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum) e [Construir e operar soluções de aprendizado de máquina com Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum). +* **IA Conversacional** e **Chat Bots**. Há um caminho de aprendizado separado [Criar soluções de IA conversacional](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum), e você também pode consultar [este post no blog](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) para mais detalhes. +* **Matemática Avançada** por trás do aprendizado profundo. Para isso, recomendamos o livro [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) de Ian Goodfellow, Yoshua Bengio e Aaron Courville, que também está disponível online em [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/). + +Para uma introdução suave aos tópicos de _IA na Nuvem_, você pode considerar fazer o caminho de aprendizado [Comece com inteligência artificial no Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum). + +# Conteúdo + +| | Link da Aula | PyTorch/Keras/TensorFlow | Laboratório | +| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | +| 0 | [Configuração do Curso](./lessons/0-course-setup/setup.md) | [Configure Seu Ambiente de Desenvolvimento](./lessons/0-course-setup/how-to-run.md) | | +| I | [**Introdução à IA**](./lessons/1-Intro/README.md) | | | +| 01 | [Introdução e História da IA](./lessons/1-Intro/README.md) | - | - | +| II | **IA Simbólica** | +| 02 | [Representação do Conhecimento e Sistemas Especialistas](./lessons/2-Symbolic/README.md) | [Sistemas Especialistas](./lessons/2-Symbolic/Animals.ipynb) / [Ontologia](./lessons/2-Symbolic/FamilyOntology.ipynb) /[Grafo de Conceitos](./lessons/2-Symbolic/MSConceptGraph.ipynb) | | +| III | [**Introdução a Redes Neurais**](./lessons/3-NeuralNetworks/README.md) ||| +| 03 | [Perceptron](./lessons/3-NeuralNetworks/03-Perceptron/README.md) | [Notebook](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb) | [Lab](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) | +| 04 | [Perceptron Multicamadas e Criando nosso Próprio Framework](./lessons/3-NeuralNetworks/04-OwnFramework/README.md) | [Notebook](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb) | [Lab](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) | +| 05 | [Introdução a Frameworks (PyTorch/TensorFlow) e Overfitting](./lessons/3-NeuralNetworks/05-Frameworks/README.md) | [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) | +| IV | [**Visão Computacional**](./lessons/4-ComputerVision/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [Explore Visão Computacional no Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) | +| 06 | [Introdução à Visão Computacional. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md) | [Notebook](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb) | [Lab](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) | +| 07 | [Redes Neurais Convolucionais](./lessons/4-ComputerVision/07-ConvNets/README.md) & [Arquiteturas CNN](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) | [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb) | [Lab](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) | +| 08 | [Redes Pré-treinadas e Transferência de Aprendizado](./lessons/4-ComputerVision/08-TransferLearning/README.md) e [Truques de Treinamento](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) | [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb) | [Lab](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) | +| 09 | [Autoencoders e VAEs](./lessons/4-ComputerVision/09-Autoencoders/README.md) | [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb) | | +| 10 | [Redes Adversariais Generativas & Transferência de Estilo Artístico](./lessons/4-ComputerVision/10-GANs/README.md) | [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb) | | +| 11 | [Detecção de Objetos](./lessons/4-ComputerVision/11-ObjectDetection/README.md) | [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb) | [Lab](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) | +| 12 | [Segmentação Semântica. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md) | [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb) | | +| V | [**Processamento de Linguagem Natural**](./lessons/5-NLP/README.md) | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [Explore Processamento de Linguagem Natural no Microsoft Azure](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)| +| 13 | [Representação de Texto. Bow/TF-IDF](./lessons/5-NLP/13-TextRep/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb) | | +| 14 | [Embeddings Semânticos. Word2Vec e GloVe](./lessons/5-NLP/14-Embeddings/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb) | | +| 15 | [Modelagem de Linguagem. Treinando seus Próprios Embeddings](./lessons/5-NLP/15-LanguageModeling/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb) | [Lab](./lessons/5-NLP/15-LanguageModeling/lab/README.md) | +| 16 | [Redes Neurais Recorrentes](./lessons/5-NLP/16-RNN/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb) | | +| 17 | [Redes Recorrentes Generativas](./lessons/5-NLP/17-GenerativeNetworks/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb) | [Lab](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) | +| 18 | [Transformers. BERT.](./lessons/5-NLP/18-Transformers/README.md) | [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb) | | +| 19 | [Reconhecimento de Entidades Nomeadas](./lessons/5-NLP/19-NER/README.md) | [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb) | [Lab](./lessons/5-NLP/19-NER/lab/README.md) | +| 20 | [Modelos de Linguagem de Grande Porte, Programação por Prompt e Tarefas Few-Shot](./lessons/5-NLP/20-LangModels/README.md) | [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | | +| VI | **Outras Técnicas de IA** || | +| 21 | [Algoritmos Genéticos](./lessons/6-Other/21-GeneticAlgorithms/README.md) | [Notebook](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | | +| 22 | [Aprendizado por Reforço Profundo](./lessons/6-Other/22-DeepRL/README.md) | [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb) | [Lab](./lessons/6-Other/22-DeepRL/lab/README.md) | +| 23 | [Sistemas Multiagentes](./lessons/6-Other/23-MultiagentSystems/README.md) | | | +| VII | **Ética em IA** | | | +| 24 | [Ética em IA e IA Responsável](./lessons/7-Ethics/README.md) | [Microsoft Learn: Princípios de IA Responsável](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | | +| IX | **Extras** | | | +| 25 | [Redes Multimodais, CLIP e VQGAN](./lessons/X-Extras/X1-MultiModal/README.md) | [Notebook](./lessons/X-Extras/X1-MultiModal/Clip.ipynb) | | + +## Cada lição contém + +* Material de pré-leitura +* Notebooks Jupyter executáveis, que frequentemente são específicos para o framework (**PyTorch** ou **TensorFlow**). O notebook executável também contém muito material teórico, então para entender o tópico você precisa passar por pelo menos uma versão do notebook (seja PyTorch ou TensorFlow). +* **Labs** disponíveis para alguns tópicos, que te dão a oportunidade de tentar aplicar o material que aprendeu em um problema específico. +* Algumas seções contêm links para módulos do [**MS Learn**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) que cobrem tópicos relacionados. + +## Começando + +### 🎯 Novo em IA? Comece Aqui! + +Se você é completamente novo em IA e quer exemplos práticos rápidos, confira nossos [**Exemplos para Iniciantes**](./examples/README.md)! Estes incluem: + +- 🌟 **Olá Mundo IA** - Seu primeiro programa de IA (reconhecimento de padrões) +- 🧠 **Rede Neural Simples** - Construa uma rede neural do zero + +- 🖼️ **Classificador de Imagens** - Classifique imagens com comentários detalhados +- 💬 **Sentimento de Texto** - Analise texto positivo/negativo + +Estes exemplos foram criados para ajudá-lo a entender conceitos de IA antes de mergulhar no currículo completo. + +### 📚 Configuração do Currículo Completo + +- Criamos uma [lição de configuração](./lessons/0-course-setup/setup.md) para ajudá-lo a configurar seu ambiente de desenvolvimento. - Para Educadores, também criamos uma [lição de configuração do currículo](./lessons/0-course-setup/for-teachers.md)! +- Como [Executar o código no VSCode ou em um Codespace](./lessons/0-course-setup/how-to-run.md) + +Siga estes passos: + +Faça um Fork do Repositório: Clique no botão "Fork" no canto superior direito desta página. + +Clone o Repositório: `git clone https://github.com/microsoft/AI-For-Beginners.git` + +Não esqueça de dar uma estrela (🌟) neste repositório para encontrá-lo mais facilmente depois. + +## Conheça outros Aprendizes + +Junte-se ao nosso [servidor oficial de AI no Discord](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum) para conhecer e fazer networking com outros alunos que estão fazendo este curso e obter suporte. + +Se você tiver feedback do produto ou perguntas durante a construção, visite nosso [Fórum de Desenvolvedores do Azure AI Foundry](https://aka.ms/foundry/forum) + +## Questionários + +> **Uma nota sobre questionários**: Todos os questionários estão contidos na pasta Quiz-app em etc\quiz-app, ou [Online Aqui](https://ff-quizzes.netlify.app/) Eles estão vinculados dentro das lições e o aplicativo de quiz pode ser executado localmente ou implantado no Azure; siga as instruções na pasta `quiz-app`. Eles estão sendo gradualmente localizados. + +## Precisamos de Ajuda + +Você tem sugestões ou encontrou erros ortográficos ou de código? Abra uma issue ou crie um pull request. + +## Agradecimentos Especiais + +* **✍️ Autor Principal:** [Dmitry Soshnikov](http://soshnikov.com), PhD +* **🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD +* **🎨 Ilustrador Sketchnote:** [Tomomi Imura](https://twitter.com/girlie_mac) +* **✅ Criador do Quiz:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) +* **🙏 Colaboradores Principais:** [Evgenii Pishchik](https://github.com/Pe4enIks) + +## Outros Currículos + +Nossa equipe produz outros currículos! Confira: + + +### LangChain +[![LangChain4j para Iniciantes](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners) +[![LangChain.js para Iniciantes](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin) + +--- + +### Azure / Edge / MCP / Agentes +[![AZD para Iniciantes](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Edge AI para Iniciantes](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![MCP para Iniciantes](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst) +[![Agentes de IA para Iniciantes](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst) + +--- + +### Série de IA Generativa +[![IA Generativa para Iniciantes](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst) +[![IA Generativa (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst) + +--- + +### Aprendizagem Principal +[![ML para Iniciantes](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst) +[![Ciência de Dados para Iniciantes](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst) +[![IA para Iniciantes](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst) +[![Cibersegurança para Iniciantes](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung) +[![Desenvolvimento Web para Iniciantes](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst) +[![IoT para Iniciantes](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst) +[![Desenvolvimento XR para Iniciantes](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst) + +--- + +### Série Copilot +[![Copilot para Programação em Par com IA](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst) +[![Copilot para C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst) +[![Aventura Copilot](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst) + + +## Obtenha Ajuda + +Se ficar travado ou tiver alguma dúvida sobre como construir aplicativos de IA, junte-se a outros alunos e desenvolvedores experientes nas discussões sobre MCP. É uma comunidade de apoio onde perguntas são bem-vindas e o conhecimento é compartilhado livremente. + +[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG) + +Se você tiver feedback do produto ou erros durante a construção visite: + +[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum) + +--- + + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos empenhemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/SECURITY.md b/translations/pt-BR/SECURITY.md new file mode 100644 index 00000000..98b5a8f7 --- /dev/null +++ b/translations/pt-BR/SECURITY.md @@ -0,0 +1,40 @@ +## Segurança + +A Microsoft leva a segurança de seus produtos e serviços de software muito a sério, incluindo todos os repositórios de código-fonte gerenciados por meio de nossas organizações no GitHub, que incluem [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin) e [nossas organizações no GitHub](https://opensource.microsoft.com/). + +Se você acredita ter encontrado uma vulnerabilidade de segurança em qualquer repositório de propriedade da Microsoft que atenda à [definição de vulnerabilidade de segurança da Microsoft](https://aka.ms/opensource/security/definition), por favor, informe-nos conforme descrito abaixo. + +## Relatando Problemas de Segurança + +**Por favor, não relate vulnerabilidades de segurança por meio de issues públicas no GitHub.** + +Em vez disso, informe-as ao Microsoft Security Response Center (MSRC) em [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report). + +Se preferir enviar sem fazer login, envie um e-mail para [secure@microsoft.com](mailto:secure@microsoft.com). Se possível, criptografe sua mensagem com nossa chave PGP; faça o download na [página de Chave PGP do Microsoft Security Response Center](https://aka.ms/opensource/security/pgpkey). + +Você deve receber uma resposta dentro de 24 horas. Se, por algum motivo, não receber, entre em contato novamente por e-mail para garantir que recebemos sua mensagem original. Informações adicionais podem ser encontradas em [microsoft.com/msrc](https://aka.ms/opensource/security/msrc). + +Inclua as informações solicitadas abaixo (o máximo que puder fornecer) para nos ajudar a entender melhor a natureza e o alcance do possível problema: + + * Tipo de problema (por exemplo, buffer overflow, SQL injection, cross-site scripting, etc.) + * Caminhos completos dos arquivos de código-fonte relacionados à manifestação do problema + * A localização do código-fonte afetado (tag/branch/commit ou URL direto) + * Qualquer configuração especial necessária para reproduzir o problema + * Instruções passo a passo para reproduzir o problema + * Código de prova de conceito ou exploit (se possível) + * Impacto do problema, incluindo como um atacante poderia explorar o problema + +Essas informações nos ajudarão a priorizar seu relatório mais rapidamente. + +Se você estiver relatando para um programa de recompensa por bugs, relatórios mais completos podem contribuir para uma recompensa maior. Visite nossa página do [Programa de Recompensa por Bugs da Microsoft](https://aka.ms/opensource/security/bounty) para mais detalhes sobre nossos programas ativos. + +## Idiomas Preferidos + +Preferimos que todas as comunicações sejam feitas em inglês. + +## Política + +A Microsoft segue o princípio de [Divulgação Coordenada de Vulnerabilidades](https://aka.ms/opensource/security/cvd). + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/CODE_OF_CONDUCT.md b/translations/pt-BR/etc/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..2a58de8d --- /dev/null +++ b/translations/pt-BR/etc/CODE_OF_CONDUCT.md @@ -0,0 +1,12 @@ +# Código de Conduta de Código Aberto da Microsoft + +Este projeto adotou o [Código de Conduta de Código Aberto da Microsoft](https://opensource.microsoft.com/codeofconduct/). + +Recursos: + +- [Código de Conduta de Código Aberto da Microsoft](https://opensource.microsoft.com/codeofconduct/) +- [FAQ do Código de Conduta da Microsoft](https://opensource.microsoft.com/codeofconduct/faq/) +- Entre em contato com [opencode@microsoft.com](mailto:opencode@microsoft.com) para dúvidas ou preocupações + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/CONTRIBUTING.md b/translations/pt-BR/etc/CONTRIBUTING.md new file mode 100644 index 00000000..a85a6816 --- /dev/null +++ b/translations/pt-BR/etc/CONTRIBUTING.md @@ -0,0 +1,22 @@ +# Contribuindo + +Este projeto aceita contribuições e sugestões. A maioria das contribuições exige que você concorde com um Acordo de Licença de Contribuidor (CLA), declarando que você tem o direito de, e realmente concede a nós, os direitos de usar sua contribuição. Para mais detalhes, visite https://cla.microsoft.com. + +Ao enviar um pull request, um CLA-bot determinará automaticamente se você precisa fornecer um CLA e decorará o PR apropriadamente (por exemplo, com etiqueta ou comentário). Basta seguir as instruções fornecidas pelo bot. Você só precisará fazer isso uma vez em todos os repositórios que utilizam nosso CLA. + +Este projeto adotou o [Código de Conduta de Código Aberto da Microsoft](https://opensource.microsoft.com/codeofconduct/). +Para mais informações, veja as [Perguntas Frequentes sobre o Código de Conduta](https://opensource.microsoft.com/codeofconduct/faq/) +ou entre em contato pelo e-mail [opencode@microsoft.com](mailto:opencode@microsoft.com) para quaisquer dúvidas ou comentários adicionais. + +# Procurando Contribuições + +Atualmente, estamos buscando ativamente contribuições nos seguintes tópicos: + +- [ ] Escrever seção sobre Aprendizado por Reforço Profundo +- [ ] Melhorar seção + notebook sobre Detecção de Objetos +- [ ] PyTorch Lightning (para [esta seção](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/README.md)) +- [ ] Escrever seção + exemplos sobre Reconhecimento de Entidades Nomeadas +- [ ] Criar exemplos para treinar nossos próprios embeddings para [esta seção](https://github.com/microsoft/AI-For-Beginners/tree/main/5-NLP/15-LanguageModeling) + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/Mindmap.md b/translations/pt-BR/etc/Mindmap.md new file mode 100644 index 00000000..ace459e3 --- /dev/null +++ b/translations/pt-BR/etc/Mindmap.md @@ -0,0 +1,78 @@ +# IA + +## [Introdução à IA](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md) + - Definição de IA + - História da IA + - Abordagens para IA + - Top-down/Simbólica + - Bottom-up/Neural + - Evolutiva + - Sinergética / IA Emergente + - [Microsoft AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste) + +## [IA Simbólica](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/README.md) + - Representação de Conhecimento + - [Sistemas Especialistas](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) + - [Ontologias](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) + - Web Semântica + +## [Redes Neurais](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/README.md) + - [Perceptron](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/03-Perceptron/README.md) + - [Redes Multicamadas](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/04-OwnFramework/README.md) + - [Introdução a Frameworks](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/README.md) + - [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) + - [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md) + - [Overfitting](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md) + +## [Visão Computacional](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/README.md) + - No MS Learn + - [Fundamentos de IA: Explore Visão Computacional](https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-77998-cacaste) + - [Visão Computacional com PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) + - [Visão Computacional com TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste) + - [Introdução à Visão Computacional. OpenCV](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/06-IntroCV/README.md) + - [Redes Convolucionais](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/README.md) + - [Arquiteturas de CNN](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md) + - [Transferência de Aprendizado](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/08-TransferLearning/README.md) + - [Dicas de Treinamento](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md) + - [Autoencoders e VAEs](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/09-Autoencoders/README.md) + - [Redes Adversárias Generativas](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/README.md) + - [Transferência de Estilo](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/StyleTransfer.ipynb) + - [Detecção de Objetos](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/11-ObjectDetection/README.md) + - [Segmentação](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/12-Segmentation/README.md) + +## [Processamento de Linguagem Natural](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/README.md) + - No MS Learn + - [Fundamentos de IA: Explore PLN](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste) + - [PLN com PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) + - [PLN com TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) + - [Representação de Texto](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/README.md) + - Bag of Words + - TF/IDF + - [Embeddings Semânticos](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/README.md) + - Word2Vec + - GloVE + - [Modelagem de Linguagem](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling) + - [Redes Neurais Recorrentes](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/README.md) + - LSTM + - GRU + - [Redes Recorrentes Generativas](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/README.md) + - [Transformers e BERT](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/README.md) + - [Reconhecimento de Entidades Nomeadas](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/19-NER/README.md) + - [Geração de Texto e GPT](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/20-LanguageModels/README.md) + +## Outras Técnicas + - [Algoritmos Genéticos](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/21-GeneticAlgorithms/README.md) + - [Aprendizado por Reforço Profundo](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/22-DeepRL/README.md) + - [Sistemas Multiagentes](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/23-MultiagentSystems/README.md) + +## [Ética em IA](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/7-Ethics/README.md) + - [MS Learn sobre IA Responsável](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) + +## Extras + - [Redes Multimodais](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/README.md) + - [CLIP](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/Clip.ipynb) + - DALL-E + - VQ-GAN + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/SUPPORT.md b/translations/pt-BR/etc/SUPPORT.md new file mode 100644 index 00000000..952a437e --- /dev/null +++ b/translations/pt-BR/etc/SUPPORT.md @@ -0,0 +1,14 @@ +# Suporte + +## Como registrar problemas e obter ajuda + +Este projeto utiliza o GitHub Issues para rastrear bugs e solicitações de recursos. Por favor, pesquise os problemas existentes antes de registrar novos, para evitar duplicações. Para novos problemas, registre seu bug ou solicitação de recurso como um novo Issue. + +Para obter ajuda e tirar dúvidas sobre o uso deste projeto, utilize os Fóruns de Discussão. + +## Política de Suporte da Microsoft + +O suporte para este projeto é limitado aos recursos listados acima. + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/TRANSLATIONS.md b/translations/pt-BR/etc/TRANSLATIONS.md new file mode 100644 index 00000000..b43d5de8 --- /dev/null +++ b/translations/pt-BR/etc/TRANSLATIONS.md @@ -0,0 +1,36 @@ +# Contribua traduzindo as lições + +Agradecemos traduções para as lições deste currículo! + +## Diretrizes + +Existem pastas em cada pasta de lição e pasta de introdução de lição que contêm os arquivos markdown traduzidos. + +> Nota: por favor, não traduza nenhum código nos arquivos de exemplo de código; as únicas coisas a serem traduzidas são README, tarefas e os questionários. Obrigado! + +Os arquivos traduzidos devem seguir esta convenção de nomenclatura: + +**README._[language]_.md** + +onde _[language]_ é uma abreviação de dois caracteres do idioma seguindo o padrão ISO 639-1 (por exemplo, `README.es.md` para Espanhol e `README.nl.md` para Holandês). + +**assignment._[language]_.md** + +Semelhante aos Readme's, por favor, traduza também as tarefas. + +**Questionários** + +1. Adicione sua tradução ao quiz-app adicionando um arquivo aqui: https://github.com/microsoft/AI-For-Beginners/tree/main/etc/quiz-app/src/assets/translations, com a convenção de nomenclatura adequada (en.json, fr.json). **Por favor, não localize as palavras 'true' ou 'false'. Obrigado!** + +2. Adicione o código do seu idioma ao menu suspenso no arquivo App.vue do quiz-app. + +3. Edite o [arquivo index.js de traduções](https://github.com/microsoft/AI-For-Beginners/blob/main/etc/quiz-app/src/assets/translations/index.js) do quiz-app para adicionar seu idioma. + +4. Por fim, edite TODOS os links de questionários nos seus arquivos README.md traduzidos para apontar diretamente para o questionário traduzido: https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/1 se torna https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/1?loc=id + +**OBRIGADO** + +Agradecemos muito seus esforços! + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/etc/quiz-app/README.md b/translations/pt-BR/etc/quiz-app/README.md new file mode 100644 index 00000000..e2413650 --- /dev/null +++ b/translations/pt-BR/etc/quiz-app/README.md @@ -0,0 +1,128 @@ +# Questionários + +Esses questionários são os questionários pré e pós-aula para o currículo de IA em https://aka.ms/ai-beginners + +## Adicionando um conjunto de questionários traduzidos + +Adicione uma tradução de questionário criando estruturas de questionários correspondentes nas pastas `assets/translations`. Os questionários originais estão em `assets/translations/en`. Os questionários estão divididos em vários agrupamentos por lição. Certifique-se de alinhar a numeração com a seção correta do questionário. Há um total de 40 questionários neste currículo, começando a contagem em 0. + +Após editar as traduções, edite o arquivo index.js na pasta de tradução para importar todos os arquivos seguindo as convenções em `en`. + +Edite o arquivo `index.js` em `assets/translations` para importar os novos arquivos traduzidos. + +Depois, edite o menu suspenso em `App.vue` neste aplicativo para adicionar seu idioma. Combine a abreviação localizada com o nome da pasta do seu idioma. + +Por fim, edite todos os links dos questionários nas lições traduzidas, se existirem, para incluir essa localização como um parâmetro de consulta: `?loc=fr`, por exemplo. + +## Configuração do projeto + +``` +npm install +``` + +### Compila e recarrega automaticamente para desenvolvimento + +``` +npm run serve +``` + +### Compila e minimiza para produção + +``` +npm run build +``` + +### Verifica e corrige arquivos + +``` +npm run lint +``` + +### Personalizar configuração + +Veja [Referência de Configuração](https://cli.vuejs.org/config/). + +Créditos: Agradecimentos à versão original deste aplicativo de questionário: https://github.com/arpan45/simple-quiz-vue + +## Implantando no Azure + +Aqui está um guia passo a passo para ajudá-lo a começar: + +1. Faça um fork do repositório GitHub +Certifique-se de que o código do seu aplicativo web estático esteja no seu repositório GitHub. Faça um fork deste repositório. + +2. Crie um aplicativo web estático no Azure +- Crie uma [conta no Azure](http://azure.microsoft.com) +- Acesse o [portal do Azure](https://portal.azure.com) +- Clique em “Criar um recurso” e procure por “Aplicativo Web Estático”. +- Clique em “Criar”. + +3. Configure o aplicativo web estático +- Básico: + - Assinatura: Selecione sua assinatura do Azure. + - Grupo de Recursos: Crie um novo grupo de recursos ou use um existente. + - Nome: Forneça um nome para seu aplicativo web estático. + - Região: Escolha a região mais próxima dos seus usuários. + +- #### Detalhes de Implantação: + - Fonte: Selecione “GitHub”. + - Conta do GitHub: Autorize o Azure a acessar sua conta do GitHub. + - Organização: Selecione sua organização no GitHub. + - Repositório: Escolha o repositório que contém seu aplicativo web estático. + - Branch: Selecione o branch do qual deseja implantar. + +- #### Detalhes de Build: + - Presets de Build: Escolha o framework com o qual seu aplicativo foi construído (ex.: React, Angular, Vue, etc.). + - Localização do Aplicativo: Especifique a pasta que contém o código do seu aplicativo (ex.: / se estiver na raiz). + - Localização da API: Se você tiver uma API, especifique sua localização (opcional). + - Localização de Saída: Especifique a pasta onde a saída do build é gerada (ex.: build ou dist). + +4. Revisar e Criar +Revise suas configurações e clique em “Criar”. O Azure configurará os recursos necessários e criará um workflow do GitHub Actions no seu repositório. + +5. Workflow do GitHub Actions +O Azure criará automaticamente um arquivo de workflow do GitHub Actions no seu repositório (.github/workflows/azure-static-web-apps-.yml). Este workflow lidará com o processo de build e implantação. + +6. Monitorar a Implantação +Acesse a aba “Actions” no seu repositório GitHub. +Você deverá ver um workflow em execução. Este workflow irá construir e implantar seu aplicativo web estático no Azure. +Assim que o workflow for concluído, seu aplicativo estará ativo no URL fornecido pelo Azure. + +### Exemplo de Arquivo de Workflow + +Aqui está um exemplo de como o arquivo de workflow do GitHub Actions pode ser: +name: Azure Static Web Apps CI/CD +``` +on: + push: + branches: + - main + pull_request: + types: [opened, synchronize, reopened, closed] + branches: + - main + +jobs: + build_and_deploy_job: + runs-on: ubuntu-latest + name: Build and Deploy Job + steps: + - uses: actions/checkout@v2 + - name: Build And Deploy + id: builddeploy + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN }} + repo_token: ${{ secrets.GITHUB_TOKEN }} + action: "upload" + app_location: "etc/quiz-app # App source code path" + api_location: ""API source code path optional + output_location: "dist" #Built app content directory - optional +``` + +### Recursos Adicionais +- [Documentação de Aplicativos Web Estáticos do Azure](https://learn.microsoft.com/azure/static-web-apps/getting-started) +- [Documentação do GitHub Actions](https://docs.github.com/actions/use-cases-and-examples/deploying/deploying-to-azure-static-web-app) + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/examples/03-image-classifier.ipynb b/translations/pt-BR/examples/03-image-classifier.ipynb new file mode 100644 index 00000000..c478a462 --- /dev/null +++ b/translations/pt-BR/examples/03-image-classifier.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classificador de Imagens Simples\n", + "\n", + "Este notebook mostra como classificar imagens usando uma rede neural pré-treinada.\n", + "\n", + "**O que você vai aprender:**\n", + "- Como carregar e usar um modelo pré-treinado\n", + "- Pré-processamento de imagens\n", + "- Fazer previsões em imagens\n", + "- Entender as pontuações de confiança\n", + "\n", + "**Caso de uso:** Identificar objetos em imagens (como \"gato\", \"cachorro\", \"carro\", etc.)\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etapa 1: Importar Bibliotecas Necessárias\n", + "\n", + "Vamos importar as ferramentas que precisamos. Não se preocupe se você ainda não entender todas elas!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Core libraries\n", + "import numpy as np\n", + "from PIL import Image\n", + "import requests\n", + "from io import BytesIO\n", + "\n", + "# TensorFlow for deep learning\n", + "try:\n", + " import tensorflow as tf\n", + " from tensorflow.keras.applications import MobileNetV2\n", + " from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n", + " print(\"✅ TensorFlow loaded successfully!\")\n", + " print(f\" Version: {tf.__version__}\")\n", + "except ImportError:\n", + " print(\"❌ Please install TensorFlow: pip install tensorflow\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etapa 2: Carregar o Modelo Pré-treinado\n", + "\n", + "Vamos usar **MobileNetV2**, uma rede neural já treinada em milhões de imagens.\n", + "\n", + "Isso é chamado de **Aprendizado por Transferência** - usar um modelo que outra pessoa já treinou!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"📦 Loading pre-trained MobileNetV2 model...\")\n", + "print(\" This may take a minute on first run (downloading weights)...\")\n", + "\n", + "# Load the model\n", + "# include_top=True means we use the classification layer\n", + "# weights='imagenet' means it was trained on ImageNet dataset\n", + "model = MobileNetV2(weights='imagenet', include_top=True)\n", + "\n", + "print(\"✅ Model loaded!\")\n", + "print(f\" The model can recognize 1000 different object categories\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etapa 3: Funções Auxiliares\n", + "\n", + "Vamos criar funções para carregar e preparar imagens para o nosso modelo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_image_from_url(url):\n", + " \"\"\"\n", + " Load an image from a URL.\n", + " \n", + " Args:\n", + " url: Web address of the image\n", + " \n", + " Returns:\n", + " PIL Image object\n", + " \"\"\"\n", + " response = requests.get(url)\n", + " img = Image.open(BytesIO(response.content))\n", + " return img\n", + "\n", + "\n", + "def prepare_image(img):\n", + " \"\"\"\n", + " Prepare an image for the model.\n", + " \n", + " Steps:\n", + " 1. Resize to 224x224 (model's expected size)\n", + " 2. Convert to array\n", + " 3. Add batch dimension\n", + " 4. Preprocess for MobileNetV2\n", + " \n", + " Args:\n", + " img: PIL Image\n", + " \n", + " Returns:\n", + " Preprocessed image array\n", + " \"\"\"\n", + " # Resize to 224x224 pixels\n", + " img = img.resize((224, 224))\n", + " \n", + " # Convert to numpy array\n", + " img_array = np.array(img)\n", + " \n", + " # Add batch dimension (model expects multiple images)\n", + " img_array = np.expand_dims(img_array, axis=0)\n", + " \n", + " # Preprocess for MobileNetV2\n", + " img_array = preprocess_input(img_array)\n", + " \n", + " return img_array\n", + "\n", + "\n", + "def classify_image(img):\n", + " \"\"\"\n", + " Classify an image and return top predictions.\n", + " \n", + " Args:\n", + " img: PIL Image\n", + " \n", + " Returns:\n", + " List of (class_name, confidence) tuples\n", + " \"\"\"\n", + " # Prepare the image\n", + " img_array = prepare_image(img)\n", + " \n", + " # Make prediction\n", + " predictions = model.predict(img_array, verbose=0)\n", + " \n", + " # Decode predictions to human-readable labels\n", + " # top=5 means we get the top 5 most likely classes\n", + " decoded = decode_predictions(predictions, top=5)[0]\n", + " \n", + " # Convert to simpler format\n", + " results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n", + " \n", + " return results\n", + "\n", + "\n", + "print(\"✅ Helper functions ready!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etapa 4: Teste em Imagens de Exemplo\n", + "\n", + "Vamos tentar classificar algumas imagens da internet!\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sample images to classify\n", + "# These are from Unsplash (free stock photos)\n", + "test_images = [\n", + " {\n", + " \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n", + " \"description\": \"A cat\"\n", + " },\n", + " {\n", + " \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n", + " \"description\": \"A dog\"\n", + " },\n", + " {\n", + " \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n", + " \"description\": \"A car\"\n", + " },\n", + "]\n", + "\n", + "print(f\"🧪 Testing on {len(test_images)} images...\")\n", + "print(\"=\" * 70)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classifique Cada Imagem\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i, img_data in enumerate(test_images, 1):\n", + " print(f\"\\n📸 Image {i}: {img_data['description']}\")\n", + " print(\"-\" * 70)\n", + " \n", + " try:\n", + " # Load image\n", + " img = load_image_from_url(img_data['url'])\n", + " \n", + " # Display image\n", + " display(img.resize((200, 200))) # Show smaller version\n", + " \n", + " # Classify\n", + " results = classify_image(img)\n", + " \n", + " # Show predictions\n", + " print(\"\\n🎯 Top 5 Predictions:\")\n", + " for rank, (label, confidence) in enumerate(results, 1):\n", + " # Create a visual bar\n", + " bar_length = int(confidence * 50)\n", + " bar = \"█\" * bar_length\n", + " \n", + " print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error: {e}\")\n", + "\n", + "print(\"\\n\" + \"=\" * 70)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Etapa 5: Experimente suas próprias imagens!\n", + "\n", + "Substitua o URL abaixo por qualquer URL de imagem que você queira classificar.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Try your own image!\n", + "# Replace this URL with any image URL\n", + "custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\" # A flower\n", + "\n", + "print(\"🖼️ Classifying your custom image...\")\n", + "print(\"=\" * 70)\n", + "\n", + "try:\n", + " # Load and show image\n", + " img = load_image_from_url(custom_image_url)\n", + " display(img.resize((300, 300)))\n", + " \n", + " # Classify\n", + " results = classify_image(img)\n", + " \n", + " # Show results\n", + " print(\"\\n🎯 Top 5 Predictions:\")\n", + " print(\"-\" * 70)\n", + " for rank, (label, confidence) in enumerate(results, 1):\n", + " bar_length = int(confidence * 50)\n", + " bar = \"█\" * bar_length\n", + " print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n", + " \n", + " # Highlight top prediction\n", + " top_label, top_confidence = results[0]\n", + " print(\"\\n\" + \"=\" * 70)\n", + " print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n", + " \n", + "except Exception as e:\n", + " print(f\"❌ Error: {e}\")\n", + " print(\" Make sure the URL points to a valid image!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 💡 O que Acabou de Acontecer?\n", + "\n", + "1. **Carregamos um modelo pré-treinado** - O MobileNetV2 foi treinado com milhões de imagens\n", + "2. **Pré-processamos as imagens** - Redimensionamos e formatamos para o modelo\n", + "3. **O modelo fez previsões** - Ele gerou probabilidades para 1000 classes de objetos\n", + "4. **Decodificamos os resultados** - Convertendo números em rótulos legíveis para humanos\n", + "\n", + "### Entendendo as Pontuações de Confiança\n", + "\n", + "- **90-100%**: Muito confiante (quase certamente correto)\n", + "- **70-90%**: Confiante (provavelmente correto)\n", + "- **50-70%**: Moderadamente confiante (pode estar correto)\n", + "- **Abaixo de 50%**: Pouco confiante (incerto)\n", + "\n", + "### Por que as previsões podem estar erradas?\n", + "\n", + "- **Ângulo ou iluminação incomuns** - O modelo foi treinado com fotos típicas\n", + "- **Objetos múltiplos** - O modelo espera um objeto principal\n", + "- **Objetos raros** - O modelo conhece apenas 1000 categorias\n", + "- **Imagem de baixa qualidade** - Imagens borradas ou pixeladas são mais difíceis\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🚀 Próximos Passos\n", + "\n", + "1. **Experimente imagens diferentes:**\n", + " - Encontre imagens no [Unsplash](https://unsplash.com)\n", + " - Clique com o botão direito → \"Copiar endereço da imagem\" para obter a URL\n", + "\n", + "2. **Faça experimentos:**\n", + " - O que acontece com arte abstrata?\n", + " - Ele consegue reconhecer objetos de diferentes ângulos?\n", + " - Como lida com múltiplos objetos?\n", + "\n", + "3. **Aprenda mais:**\n", + " - Explore as [lições de Visão Computacional](../lessons/4-ComputerVision/README.md)\n", + " - Aprenda a treinar seu próprio classificador de imagens\n", + " - Entenda como funcionam as CNNs (Redes Neurais Convolucionais)\n", + "\n", + "---\n", + "\n", + "## 🎉 Parabéns!\n", + "\n", + "Você acabou de construir um classificador de imagens usando uma rede neural de última geração!\n", + "\n", + "Essa mesma técnica é usada em:\n", + "- Google Fotos (organização de suas fotos)\n", + "- Carros autônomos (reconhecimento de objetos)\n", + "- Diagnóstico médico (análise de raios X)\n", + "- Controle de qualidade (detecção de defeitos)\n", + "\n", + "Continue explorando e aprendendo! 🚀\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "coopTranslator": { + "original_hash": "1d472141d9df46b751542b3c29f88677", + "translation_date": "2025-10-03T11:44:26+00:00", + "source_file": "examples/03-image-classifier.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/translations/pt-BR/examples/README.md b/translations/pt-BR/examples/README.md new file mode 100644 index 00000000..dffaa735 --- /dev/null +++ b/translations/pt-BR/examples/README.md @@ -0,0 +1,85 @@ +# Exemplos de IA para Iniciantes + +Bem-vindo! Este diretório contém exemplos simples e independentes para ajudá-lo a começar com IA e aprendizado de máquina. Cada exemplo foi projetado para ser acessível a iniciantes, com comentários detalhados e explicações passo a passo. + +## 📚 Visão Geral dos Exemplos + +| Exemplo | Descrição | Dificuldade | Pré-requisitos | +|---------|-------------|------------|---------------| +| [Hello AI World](../../../examples/01-hello-ai-world.py) | Seu primeiro programa de IA - reconhecimento de padrões simples | ⭐ Iniciante | Noções básicas de Python | +| [Rede Neural Simples](../../../examples/02-simple-neural-network.py) | Construa uma rede neural do zero | ⭐⭐ Iniciante+ | Python, matemática básica | +| [Classificador de Imagens](./03-image-classifier.ipynb) | Classifique imagens com um modelo pré-treinado | ⭐⭐ Iniciante+ | Python, numpy | +| [Sentimento de Texto](../../../examples/04-text-sentiment.py) | Analise o sentimento de textos (positivo/negativo) | ⭐⭐ Iniciante+ | Python | + +## 🚀 Começando + +### Pré-requisitos + +Certifique-se de ter o Python instalado (recomenda-se a versão 3.8 ou superior). Instale os pacotes necessários: + +```bash +# For Python scripts +pip install numpy + +# For Jupyter notebooks (image classifier) +pip install jupyter numpy pillow tensorflow +``` + +Ou use o ambiente conda do currículo principal: + +```bash +conda env create --name ai4beg --file ../environment.yml +conda activate ai4beg +``` + +### Executando os Exemplos + +**Para scripts Python (.py):** +```bash +python 01-hello-ai-world.py +``` + +**Para notebooks Jupyter (.ipynb):** +```bash +jupyter notebook 03-image-classifier.ipynb +``` + +## 📖 Caminho de Aprendizado + +Recomendamos seguir os exemplos na ordem: + +1. **Comece com "Hello AI World"** - Aprenda o básico sobre reconhecimento de padrões +2. **Construa uma Rede Neural Simples** - Entenda como funcionam as redes neurais +3. **Experimente o Classificador de Imagens** - Veja a IA em ação com imagens reais +4. **Analise o Sentimento de Texto** - Explore o processamento de linguagem natural + +## 💡 Dicas para Iniciantes + +- **Leia os comentários do código com atenção** - Eles explicam o que cada linha faz +- **Experimente!** - Tente alterar valores e veja o que acontece +- **Não se preocupe em entender tudo de imediato** - Aprender leva tempo +- **Faça perguntas** - Use o [Fórum de Discussão](https://github.com/microsoft/AI-For-Beginners/discussions) + +## 🔗 Próximos Passos + +Depois de concluir esses exemplos, explore o currículo completo: +- [Introdução à IA](../lessons/1-Intro/README.md) +- [Redes Neurais](../lessons/3-NeuralNetworks/README.md) +- [Visão Computacional](../lessons/4-ComputerVision/README.md) +- [Processamento de Linguagem Natural](../lessons/5-NLP/README.md) + +## 🤝 Contribuindo + +Achou esses exemplos úteis? Ajude-nos a melhorá-los: +- Relate problemas ou sugira melhorias +- Adicione mais exemplos para iniciantes +- Melhore a documentação e os comentários + +--- + +*Lembre-se: Todo especialista já foi um iniciante. Boa aprendizagem! 🎓* + +--- + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/0-course-setup/for-teachers.md b/translations/pt-BR/lessons/0-course-setup/for-teachers.md new file mode 100644 index 00000000..376f0775 --- /dev/null +++ b/translations/pt-BR/lessons/0-course-setup/for-teachers.md @@ -0,0 +1,26 @@ +# Para Educadores + +Gostaria de usar este currículo em sua sala de aula? Fique à vontade! + +Na verdade, você pode utilizá-lo diretamente no GitHub, usando o GitHub Classroom. + +Para isso, faça um fork deste repositório. Você precisará criar um repositório para cada aula, então será necessário extrair cada pasta em um repositório separado. Dessa forma, o [GitHub Classroom](https://classroom.github.com/classrooms) poderá identificar cada aula individualmente. + +Estas [instruções completas](https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/) darão uma ideia de como configurar sua sala de aula. + +## Usando o repositório como está + +Se você preferir usar este repositório no formato atual, sem utilizar o GitHub Classroom, isso também é possível. Você precisará comunicar aos seus alunos qual aula seguir juntos. + +Em um formato online (Zoom, Teams ou outro), você pode criar salas de grupo para os questionários e orientar os alunos para ajudá-los a se prepararem para aprender. Depois, convide os alunos para os questionários e peça que enviem suas respostas como 'issues' em um horário determinado. Você pode fazer o mesmo com as tarefas, caso queira que os alunos trabalhem colaborativamente de forma aberta. + +Se preferir um formato mais privado, peça aos seus alunos que façam o fork do currículo, aula por aula, para seus próprios repositórios privados no GitHub, e concedam acesso a você. Assim, eles podem completar os questionários e tarefas de forma privada e enviá-los para você como issues no repositório da sua sala de aula. + +Existem muitas maneiras de fazer isso funcionar em um formato de sala de aula online. Por favor, compartilhe conosco o que funciona melhor para você! + +## Por favor, compartilhe suas opiniões + +Queremos que este currículo funcione para você e seus alunos. Por favor, envie seu feedback nos fóruns de discussão! + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/0-course-setup/how-to-run.md b/translations/pt-BR/lessons/0-course-setup/how-to-run.md new file mode 100644 index 00000000..50824b6c --- /dev/null +++ b/translations/pt-BR/lessons/0-course-setup/how-to-run.md @@ -0,0 +1,71 @@ +# Como Executar o Código + +Este currículo contém muitos exemplos executáveis e laboratórios que você vai querer executar. Para isso, você precisa da capacidade de executar código Python nos Jupyter Notebooks fornecidos como parte deste currículo. Você tem várias opções para executar o código: + +## Executar localmente no seu computador + +Para executar o código localmente no seu computador, é necessária uma instalação do Python. Uma recomendação é instalar o **[miniconda](https://conda.io/en/latest/miniconda.html)** - é uma instalação bastante leve que suporta o gerenciador de pacotes `conda` para diferentes **ambientes virtuais** Python. + +Depois de instalar o miniconda, clone o repositório e crie um ambiente virtual para ser usado neste curso: + +```bash +git clone http://github.com/microsoft/ai-for-beginners +cd ai-for-beginners +conda env create --name ai4beg --file .devcontainer/environment.yml +conda activate ai4beg +``` + +### Usando Visual Studio Code com Extensão Python + +Este currículo é melhor utilizado ao abri-lo no [Visual Studio Code](http://code.visualstudio.com/?WT.mc_id=academic-77998-cacaste) com a [Extensão Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python&WT.mc_id=academic-77998-cacaste). + +> **Nota**: Após clonar e abrir o diretório no VS Code, ele automaticamente sugerirá a instalação das extensões Python. Você também precisará instalar o miniconda conforme descrito acima. + +> **Nota**: Se o VS Code sugerir reabrir o repositório em um contêiner, você deve recusar para usar a instalação local do Python. + +### Usando Jupyter no Navegador + +Você também pode usar um ambiente Jupyter pelo navegador em seu próprio computador. Tanto o Jupyter clássico quanto o JupyterHub fornecem um ambiente de desenvolvimento conveniente com auto-completar, realce de código, etc. + +Para iniciar o Jupyter localmente, vá para o diretório do curso e execute: + +```bash +jupyter notebook +``` +ou +```bash +jupyterhub +``` +Depois você pode navegar para qualquer arquivo `.ipynb`, abri-lo e começar a trabalhar. + +### Executando em contêiner + +Uma alternativa à instalação do Python seria executar o código em um contêiner. Como nosso repositório fornece uma pasta especial `.devcontainer` que instrui como construir um contêiner para este repositório, o VS Code oferece a oportunidade de reabrir o código em um contêiner. Isso requer a instalação do Docker, e também seria mais complexo, então recomendamos isso para usuários mais experientes. + +## Executando na Nuvem + +Se você não quiser instalar o Python localmente e tiver acesso a alguns recursos na nuvem - uma boa alternativa seria executar o código na nuvem. Existem várias formas de fazer isso: + +* Usando **[GitHub Codespaces](https://github.com/features/codespaces)**, que é um ambiente virtual criado para você no GitHub, acessível através da interface do navegador do VS Code. Se você tem acesso ao Codespaces, basta clicar no botão **Code** no repositório, iniciar um codespace e começar a trabalhar rapidamente. +* Usando **[Binder](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)**. [Binder](https://mybinder.org) oferece recursos computacionais gratuitos na nuvem para pessoas como você testarem algum código no GitHub. Há um botão na página principal para abrir o repositório no Binder - isso deve levá-lo rapidamente ao site do binder, que construirá um contêiner subjacente e iniciará uma interface web Jupyter para você de forma transparente. + +> **Nota**: Para evitar uso indevido, o Binder tem acesso a alguns recursos web bloqueado. Isso pode impedir o funcionamento de algum código que busca modelos e/ou conjuntos de dados na internet pública. Você pode precisar encontrar algumas soluções alternativas. Além disso, os recursos computacionais oferecidos pelo Binder são bastante básicos, então o treinamento será lento, especialmente em aulas mais complexas. + +## Executando na Nuvem com GPU + +Algumas das aulas posteriores deste currículo se beneficiariam muito do suporte a GPU. O treinamento de modelos, por exemplo, pode ser dolorosamente lento sem isso. Existem algumas opções que você pode seguir, especialmente se tiver acesso à nuvem pela [Azure for Students](https://azure.microsoft.com/free/students/?WT.mc_id=academic-77998-cacaste) ou pela sua instituição: + +* Crie uma [Máquina Virtual de Ciência de Dados](https://docs.microsoft.com/learn/modules/intro-to-azure-data-science-virtual-machine/?WT.mc_id=academic-77998-cacaste) e conecte-se a ela via Jupyter. Você pode então clonar o repositório diretamente na máquina e começar a aprender. VMs da série NC têm suporte a GPU. + +> **Nota**: Algumas assinaturas, incluindo Azure for Students, não oferecem suporte a GPU por padrão. Você pode precisar solicitar núcleos GPU adicionais por meio de um pedido de suporte técnico. + +* Crie um [Workspace do Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) e use o recurso Notebook nele. [Este vídeo](https://azure-for-academics.github.io/quickstart/azureml-papers/) mostra como clonar um repositório dentro do notebook Azure ML e começar a usar. + +Você também pode usar o Google Colab, que oferece algum suporte gratuito a GPU, e fazer upload dos Jupyter Notebooks para executá-los um por um. + +--- + + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomendamos a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/lessons/0-course-setup/setup.md b/translations/pt-BR/lessons/0-course-setup/setup.md new file mode 100644 index 00000000..89f0b1ad --- /dev/null +++ b/translations/pt-BR/lessons/0-course-setup/setup.md @@ -0,0 +1,49 @@ +# Começando com este Currículo + +## Você é um estudante? + +Comece com os seguintes recursos: + +* [Página do Student Hub](https://docs.microsoft.com/learn/student-hub?WT.mc_id=academic-77998-cacaste) Nesta página, você encontrará recursos para iniciantes, pacotes para estudantes e até maneiras de obter um voucher gratuito para certificação. Esta é uma página que você vai querer adicionar aos favoritos e verificar de tempos em tempos, pois atualizamos o conteúdo pelo menos mensalmente. +* [Microsoft Student Learn Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77998-cacaste) Junte-se a uma comunidade global de embaixadores estudantis, esta pode ser sua porta de entrada para a Microsoft. + +**Estudantes**, há algumas maneiras de usar o currículo. Primeiramente, você pode simplesmente ler o texto e examinar o código diretamente no GitHub. Se quiser executar o código em algum dos notebooks - [leia nossas instruções](./how-to-run.md) e encontre mais dicas sobre como fazer isso [neste post do blog](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). + +> **Note**: [Instruções sobre como executar o código neste currículo](./how-to-run.md) + +## Estudo Autodidata + +No entanto, se você preferir fazer o curso como um projeto de estudo autodidata, sugerimos que você faça um fork de todo o repositório para sua própria conta do GitHub e complete os exercícios por conta própria ou com um grupo: + +* Comece com um questionário pré-aula. +* Leia o texto introdutório da aula. +* Se a aula tiver notebooks adicionais, passe por eles, lendo e executando o código. Se forem fornecidos notebooks tanto para TensorFlow quanto para PyTorch, você pode focar em um deles - escolha o framework de sua preferência. +* Os notebooks frequentemente contêm alguns desafios que exigem que você ajuste o código um pouco para experimentar. +* Faça o questionário pós-aula. +* Se houver um laboratório anexado ao módulo - complete a tarefa. +* Visite o [Fórum de Discussão](https://github.com/microsoft/AI-For-Beginners/discussions) para "aprender em voz alta". + +> Para estudos adicionais, recomendamos seguir estes módulos e trilhas de aprendizado do [Microsoft Learn](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste). + +**Professores**, incluímos [algumas sugestões](./for-teachers.md) sobre como usar este currículo. + +--- + +## Pedagogia + +Escolhemos dois princípios pedagógicos ao construir este currículo: garantir que ele seja **baseado em projetos práticos** e que inclua **questionários frequentes**. + +Ao garantir que o conteúdo esteja alinhado com projetos, o processo se torna mais envolvente para os estudantes e a retenção dos conceitos será aumentada. Além disso, um questionário de baixa pressão antes de uma aula direciona a intenção do estudante para aprender um tópico, enquanto um segundo questionário após a aula garante uma maior retenção. Este currículo foi projetado para ser flexível e divertido, podendo ser realizado total ou parcialmente. Os projetos começam pequenos e se tornam progressivamente mais complexos ao final do ciclo de 12 semanas. + +> **Uma nota sobre os questionários**: Todos os questionários estão contidos [neste aplicativo](https://red-field-0a6ddfd03.1.azurestaticapps.net/), com um total de 50 questionários de três perguntas cada. Eles estão vinculados dentro das lições, mas o aplicativo de questionários pode ser executado localmente; siga as instruções na pasta `etc/quiz-app`. + +## Acesso offline + +Você pode acessar esta documentação offline usando o [Docsify](https://docsify.js.org/#/). Faça um fork deste repositório, [instale o Docsify](https://docsify.js.org/#/quickstart) em sua máquina local e, em seguida, na pasta raiz deste repositório, digite `docsify serve`. O site será servido na porta 3000 do seu localhost: `localhost:3000`. Um PDF do currículo está disponível [neste link](../../../../../../../../../etc/pdf/readme.pdf). + +--- + + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/lessons/1-Intro/README.md b/translations/pt-BR/lessons/1-Intro/README.md new file mode 100644 index 00000000..6a996694 --- /dev/null +++ b/translations/pt-BR/lessons/1-Intro/README.md @@ -0,0 +1,161 @@ +# Introdução à IA + +![Resumo do conteúdo de Introdução à IA em um doodle](../../../../translated_images/pt-BR/ai-intro.bf28d1ac4235881c.webp) + +> Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/1) + +**Inteligência Artificial** é uma disciplina científica empolgante que estuda como podemos fazer os computadores exibirem comportamentos inteligentes, ou seja, realizarem coisas que os seres humanos fazem bem. + +Originalmente, os computadores foram inventados por [Charles Babbage](https://en.wikipedia.org/wiki/Charles_Babbage) para operar com números seguindo um procedimento bem definido - um algoritmo. Os computadores modernos, embora significativamente mais avançados que o modelo original proposto no século XIX, ainda seguem a mesma ideia de cálculos controlados. Assim, é possível programar um computador para fazer algo se soubermos a sequência exata de passos necessários para alcançar o objetivo. + +![Foto de uma pessoa](../../../../translated_images/pt-BR/dsh_age.d212a30d4e54fb5f.webp) + +> Foto por [Vickie Soshnikova](http://twitter.com/vickievalerie) + +> ✅ Definir a idade de uma pessoa a partir de sua fotografia é uma tarefa que não pode ser explicitamente programada, porque não sabemos como chegamos a um número em nossa cabeça ao fazer isso. + +--- + +Existem algumas tarefas, no entanto, que não sabemos resolver explicitamente. Considere determinar a idade de uma pessoa a partir de sua fotografia. De alguma forma, aprendemos a fazer isso porque vimos muitos exemplos de pessoas de diferentes idades, mas não conseguimos explicar explicitamente como fazemos isso, nem podemos programar o computador para fazê-lo. Este é exatamente o tipo de tarefa que interessa à **Inteligência Artificial** (IA). + +✅ Pense em algumas tarefas que você poderia delegar a um computador e que se beneficiariam da IA. Considere os campos de finanças, medicina e artes - como esses campos estão se beneficiando hoje da IA? + +## IA Fraca vs. IA Forte + +IA Fraca | IA Forte +---------------------------------------|------------------------------------- +IA Fraca refere-se a sistemas de IA projetados e treinados para uma tarefa específica ou um conjunto restrito de tarefas.|IA Forte, ou Inteligência Artificial Geral (AGI), refere-se a sistemas de IA com inteligência e compreensão em nível humano. +Esses sistemas de IA não são geralmente inteligentes; eles se destacam em realizar uma tarefa predefinida, mas carecem de verdadeira compreensão ou consciência.|Esses sistemas de IA têm a capacidade de realizar qualquer tarefa intelectual que um ser humano pode fazer, adaptar-se a diferentes domínios e possuir uma forma de consciência ou autoconsciência. +Exemplos de IA fraca incluem assistentes virtuais como Siri ou Alexa, algoritmos de recomendação usados por serviços de streaming e chatbots projetados para tarefas específicas de atendimento ao cliente.|Alcançar a IA Forte é um objetivo de longo prazo da pesquisa em IA e exigiria o desenvolvimento de sistemas de IA que possam raciocinar, aprender, compreender e se adaptar a uma ampla gama de tarefas e contextos. +IA Fraca é altamente especializada e não possui habilidades cognitivas semelhantes às humanas ou capacidades gerais de resolução de problemas além de seu domínio restrito.|IA Forte é atualmente um conceito teórico, e nenhum sistema de IA atingiu esse nível de inteligência geral. + +Para mais informações, consulte **[Inteligência Artificial Geral](https://en.wikipedia.org/wiki/Artificial_general_intelligence)** (AGI). + +## A Definição de Inteligência e o Teste de Turing + +Um dos problemas ao lidar com o termo **[Inteligência](https://en.wikipedia.org/wiki/Intelligence)** é que não há uma definição clara para esse termo. Pode-se argumentar que inteligência está conectada ao **pensamento abstrato** ou à **autoconsciência**, mas não conseguimos defini-la adequadamente. + +![Foto de um gato](../../../../translated_images/pt-BR/photo-cat.8c8e8fb760ffe457.webp) + +> [Foto](https://unsplash.com/photos/75715CVEJhI) por [Amber Kipp](https://unsplash.com/@sadmax) do Unsplash + +Para ver a ambiguidade do termo *inteligência*, tente responder à pergunta: "Um gato é inteligente?". Diferentes pessoas tendem a dar respostas diferentes a essa pergunta, já que não há um teste universalmente aceito para provar se a afirmação é verdadeira ou não. E se você acha que há - tente aplicar um teste de QI ao seu gato... + +✅ Pense por um minuto sobre como você define inteligência. Um corvo que consegue resolver um labirinto para alcançar comida é inteligente? Uma criança é inteligente? + +--- + +Ao falar sobre AGI, precisamos ter alguma forma de dizer se criamos um sistema verdadeiramente inteligente. [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing) propôs uma maneira chamada **[Teste de Turing](https://en.wikipedia.org/wiki/Turing_test)**, que também funciona como uma definição de inteligência. O teste compara um sistema a algo inerentemente inteligente - um ser humano real, e como qualquer comparação automática pode ser burlada por um programa de computador, usamos um interrogador humano. Assim, se um ser humano não conseguir distinguir entre uma pessoa real e um sistema de computador em um diálogo baseado em texto - o sistema é considerado inteligente. + +> Um chatbot chamado [Eugene Goostman](https://en.wikipedia.org/wiki/Eugene_Goostman), desenvolvido em São Petersburgo, chegou perto de passar no Teste de Turing em 2014 usando um truque de personalidade inteligente. Ele anunciou desde o início que era um garoto ucraniano de 13 anos, o que explicaria a falta de conhecimento e algumas discrepâncias no texto. O bot convenceu 30% dos juízes de que era humano após um diálogo de 5 minutos, uma métrica que Turing acreditava que uma máquina seria capaz de alcançar até 2000. No entanto, deve-se entender que isso não indica que criamos um sistema inteligente ou que um sistema de computador enganou o interrogador humano - o sistema não enganou os humanos, mas sim os criadores do bot! + +✅ Você já foi enganado por um chatbot pensando que estava conversando com um humano? Como ele te convenceu? + +## Diferentes Abordagens para IA + +Se quisermos que um computador se comporte como um humano, precisamos de alguma forma modelar dentro do computador nossa maneira de pensar. Consequentemente, precisamos tentar entender o que torna um ser humano inteligente. + +> Para ser capaz de programar inteligência em uma máquina, precisamos entender como nossos próprios processos de tomada de decisão funcionam. Se você fizer um pouco de autoanálise, perceberá que há alguns processos que acontecem subconscientemente – por exemplo, conseguimos distinguir um gato de um cachorro sem pensar sobre isso - enquanto outros envolvem raciocínio. + +Existem duas abordagens possíveis para esse problema: + +Abordagem de Cima para Baixo (Raciocínio Simbólico) | Abordagem de Baixo para Cima (Redes Neurais) +---------------------------------------|------------------------------------- +Uma abordagem de cima para baixo modela a maneira como uma pessoa raciocina para resolver um problema. Envolve extrair **conhecimento** de um ser humano e representá-lo em uma forma legível por computador. Também precisamos desenvolver uma maneira de modelar o **raciocínio** dentro de um computador. | Uma abordagem de baixo para cima modela a estrutura do cérebro humano, consistindo em um grande número de unidades simples chamadas **neurônios**. Cada neurônio age como uma média ponderada de suas entradas, e podemos treinar uma rede de neurônios para resolver problemas úteis fornecendo **dados de treinamento**. + +Existem também outras abordagens possíveis para inteligência: + +* Uma abordagem **Emergente**, **Sinergética** ou **multiagente** baseia-se no fato de que comportamentos inteligentes complexos podem ser obtidos pela interação de um grande número de agentes simples. De acordo com a [cibernética evolutiva](https://en.wikipedia.org/wiki/Global_brain#Evolutionary_cybernetics), a inteligência pode *emergir* de comportamentos mais simples e reativos no processo de *transição de metasistema*. + +* Uma abordagem **Evolutiva**, ou **algoritmo genético**, é um processo de otimização baseado nos princípios da evolução. + +Consideraremos essas abordagens mais tarde no curso, mas agora focaremos em duas direções principais: de cima para baixo e de baixo para cima. + +### A Abordagem de Cima para Baixo + +Na abordagem **de cima para baixo**, tentamos modelar nosso raciocínio. Como podemos acompanhar nossos pensamentos ao raciocinar, podemos tentar formalizar esse processo e programá-lo dentro do computador. Isso é chamado de **raciocínio simbólico**. + +As pessoas tendem a ter algumas regras em sua mente que orientam seus processos de tomada de decisão. Por exemplo, quando um médico está diagnosticando um paciente, ele ou ela pode perceber que a pessoa tem febre e, portanto, pode haver alguma inflamação ocorrendo no corpo. Aplicando um grande conjunto de regras a um problema específico, o médico pode ser capaz de chegar ao diagnóstico final. + +Essa abordagem depende muito da **representação de conhecimento** e do **raciocínio**. Extrair conhecimento de um especialista humano pode ser a parte mais difícil, porque um médico, em muitos casos, não saberia exatamente por que está chegando a um diagnóstico específico. Às vezes, a solução simplesmente surge em sua mente sem pensar explicitamente. Algumas tarefas, como determinar a idade de uma pessoa a partir de uma fotografia, não podem ser reduzidas à manipulação de conhecimento. + +### Abordagem de Baixo para Cima + +Alternativamente, podemos tentar modelar os elementos mais simples dentro de nosso cérebro – um neurônio. Podemos construir uma **rede neural artificial** dentro de um computador e, em seguida, tentar ensiná-la a resolver problemas fornecendo exemplos. Esse processo é semelhante ao modo como um recém-nascido aprende sobre seu ambiente ao fazer observações. + +✅ Pesquise um pouco sobre como os bebês aprendem. Quais são os elementos básicos do cérebro de um bebê? + +> | E o ML? | | +> |--------------|-----------| +> | Parte da Inteligência Artificial que se baseia no aprendizado do computador para resolver um problema com base em alguns dados é chamada de **Machine Learning**. Não consideraremos o aprendizado de máquina clássico neste curso - recomendamos o currículo separado [Machine Learning para Iniciantes](http://aka.ms/ml-beginners). | ![ML para Iniciantes](../../../../translated_images/pt-BR/ml-for-beginners.9e4fed176fd5817d.webp) | + +## Um Breve Histórico da IA + +A Inteligência Artificial começou como um campo no meio do século XX. Inicialmente, o raciocínio simbólico era a abordagem predominante, e isso levou a uma série de sucessos importantes, como sistemas especialistas – programas de computador capazes de atuar como especialistas em alguns domínios de problemas limitados. No entanto, logo ficou claro que essa abordagem não escala bem. Extrair o conhecimento de um especialista, representá-lo em um computador e manter essa base de conhecimento precisa acaba sendo uma tarefa muito complexa e cara demais para ser prática em muitos casos. Isso levou ao chamado [Inverno da IA](https://en.wikipedia.org/wiki/AI_winter) na década de 1970. + +Breve Histórico da IA + +> Imagem por [Dmitry Soshnikov](http://soshnikov.com) + +Com o passar do tempo, os recursos computacionais se tornaram mais baratos e mais dados ficaram disponíveis, então as abordagens de redes neurais começaram a demonstrar grande desempenho ao competir com seres humanos em muitas áreas, como visão computacional ou compreensão de fala. Na última década, o termo Inteligência Artificial tem sido usado principalmente como sinônimo de Redes Neurais, porque a maioria dos sucessos da IA que ouvimos falar são baseados nelas. + +Podemos observar como as abordagens mudaram, por exemplo, na criação de um programa de computador para jogar xadrez: + +* Os primeiros programas de xadrez eram baseados em busca – um programa tentava explicitamente estimar os possíveis movimentos de um oponente para um determinado número de próximos movimentos e selecionava um movimento ideal com base na posição ideal que poderia ser alcançada em alguns movimentos. Isso levou ao desenvolvimento do chamado algoritmo de busca [alpha-beta pruning](https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning). +* Estratégias de busca funcionam bem no final do jogo, onde o espaço de busca é limitado por um pequeno número de movimentos possíveis. No entanto, no início do jogo, o espaço de busca é enorme, e o algoritmo pode ser melhorado aprendendo com partidas existentes entre jogadores humanos. Experimentos subsequentes empregaram o chamado [raciocínio baseado em casos](https://en.wikipedia.org/wiki/Case-based_reasoning), onde o programa procurava casos na base de conhecimento muito semelhantes à posição atual no jogo. +* Programas modernos que vencem jogadores humanos são baseados em redes neurais e [aprendizado por reforço](https://en.wikipedia.org/wiki/Reinforcement_learning), onde os programas aprendem a jogar apenas jogando por muito tempo contra si mesmos e aprendendo com seus próprios erros – muito parecido com o que os seres humanos fazem ao aprender a jogar xadrez. No entanto, um programa de computador pode jogar muito mais partidas em muito menos tempo e, assim, aprender muito mais rápido. + +✅ Pesquise um pouco sobre outros jogos que foram jogados por IA. + +Da mesma forma, podemos ver como a abordagem para criar “programas que falam” (que podem passar no Teste de Turing) mudou: + +* Programas iniciais desse tipo, como [Eliza](https://en.wikipedia.org/wiki/ELIZA), eram baseados em regras gramaticais muito simples e na reformulação da frase de entrada em uma pergunta. +* Assistentes modernos, como Cortana, Siri ou Google Assistant, são todos sistemas híbridos que usam redes neurais para converter fala em texto e reconhecer nossa intenção, e então empregam algum raciocínio ou algoritmos explícitos para realizar as ações necessárias. +* No futuro, podemos esperar um modelo completamente baseado em redes neurais para lidar com diálogos por conta própria. As recentes redes neurais da família GPT e [Turing-NLG](https://www.microsoft.com/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft) mostram grande sucesso nisso. + +a evolução do teste de Turing +> Imagem de Dmitry Soshnikov, [foto](https://unsplash.com/photos/r8LmVbUKgns) por [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash + +## Pesquisas Recentes em IA + +O grande crescimento recente na pesquisa de redes neurais começou por volta de 2010, quando grandes conjuntos de dados públicos começaram a se tornar disponíveis. Uma enorme coleção de imagens chamada [ImageNet](https://en.wikipedia.org/wiki/ImageNet), que contém cerca de 14 milhões de imagens anotadas, deu origem ao [Desafio de Reconhecimento Visual em Grande Escala do ImageNet](https://image-net.org/challenges/LSVRC/). + +![Precisão do ILSVRC](../../../../lessons/1-Intro/images/ilsvrc.gif) + +> Imagem de [Dmitry Soshnikov](http://soshnikov.com) + +Em 2012, [Redes Neurais Convolucionais](../4-ComputerVision/07-ConvNets/README.md) foram usadas pela primeira vez na classificação de imagens, o que levou a uma queda significativa nos erros de classificação (de quase 30% para 16,4%). Em 2015, a arquitetura ResNet da Microsoft Research [alcançou precisão em nível humano](https://doi.org/10.1109/ICCV.2015.123). + +Desde então, as Redes Neurais demonstraram um comportamento muito bem-sucedido em muitas tarefas: + +--- + +Ano | Paridade Humana alcançada +-----|-------- +2015 | [Classificação de Imagens](https://doi.org/10.1109/ICCV.2015.123) +2016 | [Reconhecimento de Fala Conversacional](https://arxiv.org/abs/1610.05256) +2018 | [Tradução Automática de Máquinas](https://arxiv.org/abs/1803.05567) (Chinês para Inglês) +2020 | [Legenda de Imagens](https://arxiv.org/abs/2009.13682) + +Nos últimos anos, testemunhamos grandes sucessos com modelos de linguagem de grande escala, como BERT e GPT-3. Isso aconteceu principalmente devido ao fato de haver uma grande quantidade de dados textuais gerais disponíveis, permitindo treinar modelos para capturar a estrutura e o significado dos textos, pré-treiná-los em coleções de textos gerais e, em seguida, especializar esses modelos para tarefas mais específicas. Vamos aprender mais sobre [Processamento de Linguagem Natural](../5-NLP/README.md) mais adiante neste curso. + +## 🚀 Desafio + +Faça uma pesquisa na internet para determinar onde, na sua opinião, a IA é mais eficazmente utilizada. É em um aplicativo de mapeamento, algum serviço de conversão de fala para texto ou em um videogame? Pesquise como o sistema foi construído. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/2) + +## Revisão e Autoestudo + +Revise a história da IA e do ML lendo [esta lição](https://github.com/microsoft/ML-For-Beginners/tree/main/1-Introduction/2-history-of-ML). Escolha um elemento do sketchnote no início dessa lição ou desta e pesquise mais a fundo para entender o contexto cultural que informa sua evolução. + +**Tarefa**: [Game Jam](assignment.md) + +--- + + +**Aviso Legal**: +Este documento foi traduzido usando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/lessons/1-Intro/assignment.md b/translations/pt-BR/lessons/1-Intro/assignment.md new file mode 100644 index 00000000..cde20b1f --- /dev/null +++ b/translations/pt-BR/lessons/1-Intro/assignment.md @@ -0,0 +1,10 @@ +# Game Jam + +Os jogos são uma área que foi fortemente influenciada pelos avanços em IA e ML. Nesta tarefa, escreva um breve artigo sobre um jogo que você gosta e que foi impactado pela evolução da IA. Deve ser um jogo antigo o suficiente para ter sido influenciado por vários tipos de sistemas de processamento de computador. Um bom exemplo é Xadrez ou Go, mas também considere videogames como Pong ou Pac-Man. Escreva um ensaio que discuta o passado, o presente e o futuro da IA no jogo. + +--- + + +**Aviso Legal**: +Este documento foi traduzido usando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/lessons/2-Symbolic/Animals.ipynb b/translations/pt-BR/lessons/2-Symbolic/Animals.ipynb new file mode 100644 index 00000000..7eff832a --- /dev/null +++ b/translations/pt-BR/lessons/2-Symbolic/Animals.ipynb @@ -0,0 +1,477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Implementando um Sistema Especialista de Animais\n", + "\n", + "Um exemplo do [Currículo AI para Iniciantes](http://github.com/microsoft/ai-for-beginners).\n", + "\n", + "Neste exemplo, implementaremos um sistema baseado em conhecimento simples para determinar um animal com base em algumas características físicas. O sistema pode ser representado pela seguinte árvore AND-OR (esta é uma parte da árvore inteira, podemos facilmente adicionar mais algumas regras):\n", + "\n", + "![](../../../../../../translated_images/pt-BR/AND-OR-Tree.5592d2c70187f283.webp)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nosso próprio shell de sistemas especialistas com inferência reversa\n", + "\n", + "Vamos tentar definir uma linguagem simples para representação de conhecimento baseada em regras de produção. Usaremos classes do Python como palavras-chave para definir as regras. Basicamente, haverá 3 tipos de classes:\n", + "* `Ask` representa uma pergunta que precisa ser feita ao usuário. Ela contém o conjunto de respostas possíveis.\n", + "* `If` representa uma regra, e é apenas um açúcar sintático para armazenar o conteúdo da regra\n", + "* `AND`/`OR` são classes para representar ramos E/OU da árvore. Eles apenas armazenam a lista de argumentos dentro. Para simplificar o código, toda funcionalidade é definida na classe pai `Content`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "class Ask():\n", + " def __init__(self,choices=['y','n']):\n", + " self.choices = choices\n", + " def ask(self):\n", + " if max([len(x) for x in self.choices])>1:\n", + " for i,x in enumerate(self.choices):\n", + " print(\"{0}. {1}\".format(i,x),flush=True)\n", + " x = int(input())\n", + " return self.choices[x]\n", + " else:\n", + " print(\"/\".join(self.choices),flush=True)\n", + " return input()\n", + "\n", + "class Content():\n", + " def __init__(self,x):\n", + " self.x=x\n", + " \n", + "class If(Content):\n", + " pass\n", + "\n", + "class AND(Content):\n", + " pass\n", + "\n", + "class OR(Content):\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Em nosso sistema, a memória de trabalho conteria a lista de **fatos** como **pares atributo-valor**. A base de conhecimento pode ser definida como um grande dicionário que associa ações (novos fatos que devem ser inseridos na memória de trabalho) a condições, expressas como expressões AND-OR. Além disso, alguns fatos podem ser `Perguntados`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "rules = {\n", + " 'default': Ask(['y','n']),\n", + " 'color' : Ask(['red-brown','black and white','other']),\n", + " 'pattern' : Ask(['dark stripes','dark spots']),\n", + " 'mammal': If(OR(['hair','gives milk'])),\n", + " 'carnivor': If(OR([AND(['sharp teeth','claws','forward-looking eyes']),'eats meat'])),\n", + " 'ungulate': If(['mammal',OR(['has hooves','chews cud'])]),\n", + " 'bird': If(OR(['feathers',AND(['flies','lies eggs'])])),\n", + " 'animal:monkey' : If(['mammal','carnivor','color:red-brown','pattern:dark spots']),\n", + " 'animal:tiger' : If(['mammal','carnivor','color:red-brown','pattern:dark stripes']),\n", + " 'animal:giraffe' : If(['ungulate','long neck','long legs','pattern:dark spots']),\n", + " 'animal:zebra' : If(['ungulate','pattern:dark stripes']),\n", + " 'animal:ostrich' : If(['bird','long nech','color:black and white','cannot fly']),\n", + " 'animal:pinguin' : If(['bird','swims','color:black and white','cannot fly']),\n", + " 'animal:albatross' : If(['bird','flies well'])\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para realizar a inferência reversa, definiremos a classe `Knowledgebase`. Ela conterá:\n", + "* `memory` de trabalho - um dicionário que mapeia atributos para valores\n", + "* `rules` do Knowledgebase no formato definido acima\n", + "\n", + "Dois métodos principais são:\n", + "* `get` para obter o valor de um atributo, realizando a inferência se necessário. Por exemplo, `get('color')` obteria o valor de um slot de cor (ele perguntará se necessário e armazenará o valor para uso posterior na memória de trabalho). Se perguntarmos `get('color:blue')`, ele perguntará pela cor e então retornará o valor `y`/`n` dependendo da cor.\n", + "* `eval` realiza a inferência real, ou seja, percorre a árvore AND/OR, avalia sub-objetivos etc.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "class KnowledgeBase():\n", + " def __init__(self,rules):\n", + " self.rules = rules\n", + " self.memory = {}\n", + " \n", + " def get(self,name):\n", + " if ':' in name:\n", + " k,v = name.split(':')\n", + " vv = self.get(k)\n", + " return 'y' if v==vv else 'n'\n", + " if name in self.memory.keys():\n", + " return self.memory[name]\n", + " for fld in self.rules.keys():\n", + " if fld==name or fld.startswith(name+\":\"):\n", + " # print(\" + proving {}\".format(fld))\n", + " value = 'y' if fld==name else fld.split(':')[1]\n", + " res = self.eval(self.rules[fld],field=name)\n", + " if res!='y' and res!='n' and value=='y':\n", + " self.memory[name] = res\n", + " return res\n", + " if res=='y':\n", + " self.memory[name] = value\n", + " return value\n", + " # field is not found, using default\n", + " res = self.eval(self.rules['default'],field=name)\n", + " self.memory[name]=res\n", + " return res\n", + " \n", + " def eval(self,expr,field=None):\n", + " # print(\" + eval {}\".format(expr))\n", + " if isinstance(expr,Ask):\n", + " print(field)\n", + " return expr.ask()\n", + " elif isinstance(expr,If):\n", + " return self.eval(expr.x)\n", + " elif isinstance(expr,AND) or isinstance(expr,list):\n", + " expr = expr.x if isinstance(expr,AND) else expr\n", + " for x in expr:\n", + " if self.eval(x)=='n':\n", + " return 'n'\n", + " return 'y'\n", + " elif isinstance(expr,OR):\n", + " for x in expr.x:\n", + " if self.eval(x)=='y':\n", + " return 'y'\n", + " return 'n'\n", + " elif isinstance(expr,str):\n", + " return self.get(expr)\n", + " else:\n", + " print(\"Unknown expr: {}\".format(expr))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora vamos definir nossa base de conhecimento sobre animais e realizar a consulta. Note que esta chamada fará perguntas a você. Você pode responder digitando `y`/`n` para perguntas de sim ou não, ou especificando um número (0..N) para perguntas com respostas múltipla escolha mais longas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hair\n", + "y/n\n", + "sharp teeth\n", + "y/n\n", + "claws\n", + "y/n\n", + "forward-looking eyes\n", + "y/n\n", + "color\n", + "0. red-brown\n", + "1. black and white\n", + "2. other\n", + "has hooves\n", + "y/n\n", + "long neck\n", + "y/n\n", + "long legs\n", + "y/n\n", + "pattern\n", + "0. dark stripes\n", + "1. dark spots\n" + ] + }, + { + "data": { + "text/plain": [ + "'giraffe'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kb = KnowledgeBase(rules)\n", + "kb.get('animal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usando Experta para Inferência Direta\n", + "\n", + "No próximo exemplo, tentaremos implementar inferência direta usando uma das bibliotecas para representação de conhecimento, [Experta](https://github.com/nilp0inter/experta). **Experta** é uma biblioteca para criação de sistemas de inferência direta em Python, que foi projetada para ser semelhante ao clássico sistema antigo [CLIPS](http://www.clipsrules.net/index.html).\n", + "\n", + "Também poderíamos ter implementado encadeamento para frente nós mesmos sem muitos problemas, mas implementações ingênuas geralmente não são muito eficientes. Para uma correspondência de regras mais eficaz, um algoritmo especial [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) é usado.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/nilp0inter/experta\n", + " Cloning https://github.com/nilp0inter/experta to /tmp/pip-req-build-7qurtwk3\n", + " Running command git clone --filter=blob:none --quiet https://github.com/nilp0inter/experta /tmp/pip-req-build-7qurtwk3\n", + " Resolved https://github.com/nilp0inter/experta to commit c6d5834b123861f5ae09e7d07027dc98bec58741\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: frozendict~=2.4.6 in /opt/conda/envs/ai4beg/lib/python3.12/site-packages (from experta==1.9.5.dev1) (2.4.7)\n", + "Collecting schema~=0.6.7 (from experta==1.9.5.dev1)\n", + " Downloading schema-0.6.8-py2.py3-none-any.whl.metadata (14 kB)\n", + "Downloading schema-0.6.8-py2.py3-none-any.whl (14 kB)\n", + "Building wheels for collected packages: experta\n", + " Building wheel for experta (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for experta: filename=experta-1.9.5.dev1-py3-none-any.whl size=34804 sha256=888c459512a5e713f4b674caa9a0f96cfdf07ec0d6eb56cc318ce0653d218014\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-1eeii9zy/wheels/3d/e8/bb/22d7956359603fa8dd679aa09f5b8efb3f29991c3986fdc787\n", + "Successfully built experta\n", + "Installing collected packages: schema, experta\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [experta]\n", + "\u001b[1A\u001b[2KSuccessfully installed experta-1.9.5.dev1 schema-0.6.8\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install git+https://github.com/nilp0inter/experta" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from experta import *\n", + "#import experta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nós definiremos nosso sistema como uma classe que herda de `KnowledgeEngine`. Cada regra é definida por uma função separada com a anotação `@Rule`, que especifica quando a regra deve ser acionada. Dentro da regra, podemos adicionar novos fatos usando a função `declare`, e a adição desses fatos resultará na chamada de mais regras pelo motor de inferência direta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "class Animals(KnowledgeEngine):\n", + " @Rule(OR(\n", + " AND(Fact('sharp teeth'),Fact('claws'),Fact('forward looking eyes')),\n", + " Fact('eats meat')))\n", + " def cornivor(self):\n", + " self.declare(Fact('carnivor'))\n", + " \n", + " @Rule(OR(Fact('hair'),Fact('gives milk')))\n", + " def mammal(self):\n", + " self.declare(Fact('mammal'))\n", + "\n", + " @Rule(Fact('mammal'),\n", + " OR(Fact('has hooves'),Fact('chews cud')))\n", + " def hooves(self):\n", + " self.declare('ungulate')\n", + " \n", + " @Rule(OR(Fact('feathers'),AND(Fact('flies'),Fact('lays eggs'))))\n", + " def bird(self):\n", + " self.declare('bird')\n", + " \n", + " @Rule(Fact('mammal'),Fact('carnivor'),\n", + " Fact(color='red-brown'),\n", + " Fact(pattern='dark spots'))\n", + " def monkey(self):\n", + " self.declare(Fact(animal='monkey'))\n", + "\n", + " @Rule(Fact('mammal'),Fact('carnivor'),\n", + " Fact(color='red-brown'),\n", + " Fact(pattern='dark stripes'))\n", + " def tiger(self):\n", + " self.declare(Fact(animal='tiger'))\n", + "\n", + " @Rule(Fact('ungulate'),\n", + " Fact('long neck'),\n", + " Fact('long legs'),\n", + " Fact(pattern='dark spots'))\n", + " def giraffe(self):\n", + " self.declare(Fact(animal='giraffe'))\n", + "\n", + " @Rule(Fact('ungulate'),\n", + " Fact(pattern='dark stripes'))\n", + " def zebra(self):\n", + " self.declare(Fact(animal='zebra'))\n", + "\n", + " @Rule(Fact('bird'),\n", + " Fact('long neck'),\n", + " Fact('cannot fly'),\n", + " Fact(color='black and white'))\n", + " def straus(self):\n", + " self.declare(Fact(animal='ostrich'))\n", + "\n", + " @Rule(Fact('bird'),\n", + " Fact('swims'),\n", + " Fact('cannot fly'),\n", + " Fact(color='black and white'))\n", + " def pinguin(self):\n", + " self.declare(Fact(animal='pinguin'))\n", + "\n", + " @Rule(Fact('bird'),\n", + " Fact('flies well'))\n", + " def albatros(self):\n", + " self.declare(Fact(animal='albatross'))\n", + " \n", + " @Rule(Fact(animal=MATCH.a))\n", + " def print_result(self,a):\n", + " print('Animal is {}'.format(a))\n", + " \n", + " def factz(self,l):\n", + " for x in l:\n", + " self.declare(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uma vez que definimos uma base de conhecimento, populamos nossa memória de trabalho com alguns fatos iniciais e então chamamos o método `run()` para realizar a inferência. Você pode ver como resultado que novos fatos inferidos são adicionados à memória de trabalho, incluindo o fato final sobre o animal (se configurarmos todos os fatos iniciais corretamente).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Animal is tiger\n" + ] + }, + { + "data": { + "text/plain": [ + "FactList([(0, InitialFact()),\n", + " (1, Fact(color='red-brown')),\n", + " (2, Fact(pattern='dark stripes')),\n", + " (3, Fact('sharp teeth')),\n", + " (4, Fact('claws')),\n", + " (5, Fact('forward looking eyes')),\n", + " (6, Fact('gives milk')),\n", + " (7, Fact('mammal')),\n", + " (8, Fact('carnivor')),\n", + " (9, Fact(animal='tiger'))])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ex1 = Animals()\n", + "ex1.reset()\n", + "ex1.factz([\n", + " Fact(color='red-brown'),\n", + " Fact(pattern='dark stripes'),\n", + " Fact('sharp teeth'),\n", + " Fact('claws'),\n", + " Fact('forward looking eyes'),\n", + " Fact('gives milk')])\n", + "ex1.run()\n", + "ex1.facts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução automática [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit (conda)", + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "coopTranslator": { + "original_hash": "8ef43db4b9182239fd150a76bd494fdb", + "translation_date": "2026-01-15T14:03:48+00:00", + "source_file": "lessons/2-Symbolic/Animals.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/2-Symbolic/FamilyOntology.ipynb b/translations/pt-BR/lessons/2-Symbolic/FamilyOntology.ipynb new file mode 100644 index 00000000..33265fe7 --- /dev/null +++ b/translations/pt-BR/lessons/2-Symbolic/FamilyOntology.ipynb @@ -0,0 +1,593 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Ontologia de Relações Familiares\n", + "\n", + "Este exemplo faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners) e foi inspirado por [este post no blog](https://habr.com/post/270857/).\n", + "\n", + "Sempre acho difícil lembrar das diferentes relações entre pessoas em uma família. Neste exemplo, vamos usar uma ontologia que define relações familiares e a árvore genealógica real, e mostrar como podemos realizar inferências automáticas para encontrar todos os parentes.\n", + "\n", + "### Obtendo a Árvore Genealógica\n", + "\n", + "Como exemplo, vamos usar a árvore genealógica da [Família Romanov dos Czares](https://en.wikipedia.org/wiki/House_of_Romanov). O formato mais comum para descrever relações familiares é o [GEDCOM](https://en.wikipedia.org/wiki/GEDCOM). Vamos usar a árvore genealógica da família Romanov no formato GEDCOM:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 HEAD\n", + "1 CHAR UTF8\n", + "1 GEDC\n", + "2 VERS 5.5\n", + "0 @0@ INDI\n", + "1 NAME Mihail Fedorovich /Romanov/\n", + "1 SEX M\n", + "1 BIRT\n", + "2 DATE 1613\n", + "1 DEAT \n", + "2 DATE 1645\n", + "1 FAMS @41@\n", + "0 @1@ INDI\n", + "1 NAME Evdokija Lukjanovna /Streshneva/\n", + "1 SEX F\n" + ] + } + ], + "source": [ + "!head -15 data/tsars.ged" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para usar o arquivo GEDCOM, podemos usar a biblioteca `python-gedcom`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting python-gedcom\n", + " Downloading python_gedcom-1.0.0-py2.py3-none-any.whl (35 kB)\n", + "Installing collected packages: python-gedcom\n", + "Successfully installed python-gedcom-1.0.0\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install python-gedcom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Esta biblioteca elimina alguns dos problemas técnicos com a análise de arquivos, mas ainda nos dá acesso em nível bastante baixo a todos os indivíduos e famílias na árvore. Aqui está como podemos analisar o arquivo e mostrar a lista de todos os indivíduos:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "from gedcom.parser import Parser\n", + "from gedcom.element.individual import IndividualElement\n", + "from gedcom.element.family import FamilyElement\n", + "g = Parser()\n", + "g.parse_file('data/tsars.ged')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true, + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('@0@', ('Mihail Fedorovich', 'Romanov')),\n", + " ('@1@', ('Evdokija Lukjanovna', 'Streshneva')),\n", + " ('@2@', ('Aleksej Mihajlovich', 'Romanov')),\n", + " ('@3@', ('Marija Ilinichna', 'Miloslavskaja')),\n", + " ('@4@', ('Natalja Kirillovna', 'Naryshkina')),\n", + " ('@5@', ('Marfa Matveevna', 'Apraksina')),\n", + " ('@6@', ('Fedor Alekseevich', 'Romanov')),\n", + " ('@7@', ('Sofja Aleksevna', 'Romanova')),\n", + " ('@8@', ('Ivan V Alekseevich', 'Romanov')),\n", + " ('@9@', ('Praskovja Fedorovna', 'Saltykova')),\n", + " ('@10@', ('Ekaterina Ivanovna', 'Romanova')),\n", + " ('@11@', ('Anna Ivanovna', 'Romanova')),\n", + " ('@12@', ('Fridrih Vilgelm', 'Kurlandskij')),\n", + " ('@13@', ('Karl Leopold', 'Meklenburg-Shverinskij')),\n", + " ('@14@', ('Anna Leopoldovna', 'Meklenburg-Shverinskaja')),\n", + " ('@15@', ('Anton Ulrih', 'Braunshvejg-Volfenbjuttelskij')),\n", + " ('@16@', ('Ivan VI Antonovich', 'Braunshvejg-Volfenbjuttelskij')),\n", + " ('@17@', ('Petr I Alekseevich', 'Romanov')),\n", + " ('@18@', ('Evdokija Fedorovna', 'Lopuhina')),\n", + " ('@19@', ('Ekaterina I Alekseevna', 'Mihajlova')),\n", + " ('@20@', ('Aleksej Petrovich', 'Romanov')),\n", + " ('@21@', ('Sharlotta Kristina', 'Braunshvejg-Volfenbjuttelskaja')),\n", + " ('@22@', ('Petr II Alekseevich', 'Romanov')),\n", + " ('@23@', ('Anna Petrovna', 'Romanova')),\n", + " ('@24@', ('Elizaveta Petrovna', 'Romanova')),\n", + " ('@25@', ('Karl Fridrih', 'Golshtejn-Gottorpskij')),\n", + " ('@26@', ('Petr III Fedorovich', 'Romanov')),\n", + " ('@27@', ('Ekaterina II', 'Alekseevna')),\n", + " ('@28@', ('Pavel I Petrovich', 'Romanov')),\n", + " ('@29@', ('Natalja Alekseevna', 'Gessen-Darmshtadskaja')),\n", + " ('@30@', ('Marija Fedorovna', 'Vjurtembergskaja')),\n", + " ('@31@', ('Aleksandr I Pavlovich', 'Romanov')),\n", + " ('@32@', ('Elizaveta Alekseevna', 'Baden-Durlahskaja')),\n", + " ('@33@', ('Nikolaj I Pavlovich', 'Romanov')),\n", + " ('@34@', ('Aleksandra Fedorovna', 'Prusskaja')),\n", + " ('@35@', ('Aleksandr II Nikolaevich', 'Romanov')),\n", + " ('@36@', ('Marija Aleksandrovna', 'Gessenskaja')),\n", + " ('@37@', ('Aleksandr III Aleksandrovich', 'Romanov')),\n", + " ('@38@', ('Marija Fedorovna', 'Datskaja')),\n", + " ('@39@', ('Nikolaj II Aleksandrovich', 'Romanov')),\n", + " ('@40@', ('Aleksandra Fedorovna', 'Gessenskaja'))]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = g.get_element_dictionary()\n", + "[ (k,v.get_name()) for k,v in d.items() if isinstance(v,IndividualElement)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aqui está como podemos obter informações sobre famílias. Observe que isso nos dá uma lista de **identificadores**, e precisamos convertê-los em nomes se quisermos mais clareza:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('@41@', ['@0@', '@1@', '@2@']),\n", + " ('@42@', ['@2@', '@3@', '@6@', '@7@', '@8@']),\n", + " ('@43@', ['@8@', '@9@', '@10@', '@11@']),\n", + " ('@44@', ['@13@', '@10@', '@14@']),\n", + " ('@45@', ['@15@', '@14@', '@16@']),\n", + " ('@46@', ['@2@', '@4@', '@17@']),\n", + " ('@47@', ['@17@', '@18@', '@20@']),\n", + " ('@48@', ['@20@', '@21@', '@22@']),\n", + " ('@49@', ['@17@', '@19@', '@23@', '@24@']),\n", + " ('@50@', ['@25@', '@23@', '@26@']),\n", + " ('@51@', ['@26@', '@27@', '@28@']),\n", + " ('@52@', ['@28@', '@30@', '@31@', '@33@']),\n", + " ('@53@', ['@33@', '@34@', '@35@']),\n", + " ('@54@', ['@35@', '@36@', '@37@']),\n", + " ('@55@', ['@37@', '@38@', '@39@'])]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = g.get_element_dictionary()\n", + "[ (k,[x.get_value() for x in v.get_child_elements()]) for k,v in d.items() if isinstance(v,FamilyElement)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtendo a Ontologia Familiar\n", + "\n", + "Agora, vamos dar uma olhada na [ontologia familiar](https://raw.githubusercontent.com/blokhin/genealogical-trees/master/data/header.ttl) definida como um conjunto de triplas da Web Semântica. Essa ontologia define relações como `isUncleOf`, `isCousinOf` e muitas outras. Todas essas relações são definidas em termos de predicados básicos `isMotherOf`, `isFatherOf`, `isBrotherOf` e `isSisterOf`. Usaremos raciocínio automático para deduzir todas as outras relações utilizando a ontologia.\n", + "\n", + "Aqui está um exemplo de definição da propriedade `isAuntOf`, que é definida como uma composição de `isSisterOf` e `isParentOf` (*Tia é a irmã de um dos pais*).\n", + "\n", + "```\n", + "fhkb:isAuntOf a owl:ObjectProperty ;\n", + " rdfs:domain fhkb:Woman ;\n", + " rdfs:range fhkb:Person ;\n", + " owl:propertyChainAxiom ( fhkb:isSisterOf fhkb:isParentOf ) .\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "@prefix fhkb: .\n", + "@prefix owl: .\n", + "@prefix rdf: .\n", + "@prefix rdfs: .\n", + "@prefix xml: .\n", + "@prefix xsd: .\n", + "\n", + " a owl:Ontology .\n", + "\n", + "fhkb:DomainEntity a owl:Class .\n", + "\n", + "fhkb:Man a owl:Class ;\n", + " owl:equivalentClass [ a owl:Class ;\n", + " owl:intersectionOf ( fhkb:Person [ a owl:Restriction ;\n", + " owl:onProperty fhkb:hasSex ;\n", + " owl:someValuesFrom fhkb:Male ] ) ] .\n", + "\n", + "fhkb:Woman a owl:Class ;\n", + " owl:equivalentClass [ a owl:Class ;\n", + " owl:intersectionOf ( fhkb:Person [ a owl:Restriction ;\n" + ] + } + ], + "source": [ + "!head -20 data/onto.ttl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construindo Ontologia para Inferência\n", + "\n", + "Para simplificar, criaremos um único arquivo de ontologia que incluirá as regras originais da ontologia de família e os fatos sobre indivíduos do nosso arquivo GEDCOM. Vamos analisar o arquivo GEDCOM, extrair informações sobre famílias e indivíduos e convertê-las em triplas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "!cp data/onto.ttl .\n", + "\n", + "gedcom_dict = g.get_element_dictionary()\n", + "individuals, marriages = {}, {}\n", + "\n", + "def term2id(el):\n", + " return \"i\" + el.get_pointer().replace('@', '').lower()\n", + "\n", + "out = open(\"onto.ttl\",\"a\")\n", + "\n", + "for k, v in gedcom_dict.items():\n", + " if isinstance(v,IndividualElement):\n", + " children, siblings = set(), set()\n", + " idx = term2id(v)\n", + "\n", + " title = v.get_name()[0] + \" \" + v.get_name()[1]\n", + " title = title.replace('\"', '').replace('[', '').replace(']', '').replace('(', '').replace(')', '').strip()\n", + "\n", + " own_families = g.get_families(v, 'FAMS')\n", + " for fam in own_families:\n", + " children |= set(term2id(i) for i in g.get_family_members(fam, \"CHIL\"))\n", + "\n", + " parent_families = g.get_families(v, 'FAMC')\n", + " if len(parent_families):\n", + " for member in g.get_family_members(parent_families[0], \"CHIL\"): # NB adoptive families i.e len(parent_families)>1 are not considered (TODO?)\n", + " if member.get_pointer() == v.get_pointer():\n", + " continue\n", + " siblings.add(term2id(member))\n", + "\n", + " if idx in individuals:\n", + " children |= individuals[idx].get('children', set())\n", + " siblings |= individuals[idx].get('siblings', set())\n", + " individuals[idx] = {'sex': v.get_gender().lower(), 'children': children, 'siblings': siblings, 'title': title}\n", + "\n", + " elif isinstance(v,FamilyElement):\n", + " wife, husb, children = None, None, set()\n", + " children = set(term2id(i) for i in g.get_family_members(v, \"CHIL\"))\n", + "\n", + " try:\n", + " wife = g.get_family_members(v, \"WIFE\")[0]\n", + " wife = term2id(wife)\n", + " if wife in individuals: individuals[wife]['children'] |= children\n", + " else: individuals[wife] = {'children': children}\n", + " except IndexError: pass\n", + " try:\n", + " husb = g.get_family_members(v, \"HUSB\")[0]\n", + " husb = term2id(husb)\n", + " if husb in individuals: individuals[husb]['children'] |= children\n", + " else: individuals[husb] = {'children': children}\n", + " except IndexError: pass\n", + "\n", + " if wife and husb: marriages[wife + husb] = (term2id(v), wife, husb)\n", + "\n", + "for idx, val in individuals.items():\n", + " added_terms = ''\n", + " if val['sex'] == 'f':\n", + " parent_predicate, sibl_predicate = \"isMotherOf\", \"isSisterOf\"\n", + " else:\n", + " parent_predicate, sibl_predicate = \"isFatherOf\", \"isBrotherOf\"\n", + " if len(val['children']):\n", + " added_terms += \" ;\\n fhkb:\" + parent_predicate + \" \" + \", \".join([\"fhkb:\" + i for i in val['children']])\n", + " if len(val['siblings']):\n", + " added_terms += \" ;\\n fhkb:\" + sibl_predicate + \" \" + \", \".join([\"fhkb:\" + i for i in val['siblings']])\n", + " out.write(\"fhkb:%s a owl:NamedIndividual, owl:Thing%s ;\\n rdfs:label \\\"%s\\\" .\\n\" % (idx, added_terms, val['title']))\n", + "\n", + "for k, v in marriages.items():\n", + " out.write(\"fhkb:%s a owl:NamedIndividual, owl:Thing ;\\n fhkb:hasFemalePartner fhkb:%s ;\\n fhkb:hasMalePartner fhkb:%s .\\n\" % v)\n", + "\n", + "out.write(\"[] a owl:AllDifferent ;\\n owl:distinctMembers (\")\n", + "for idx in individuals.keys():\n", + " out.write(\" fhkb:\" + idx)\n", + "for k, v in marriages.items():\n", + " out.write(\" fhkb:\" + v[0])\n", + "out.write(\" ) .\")\n", + "out.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " fhkb:hasFemalePartner fhkb:i34 ;\n", + " fhkb:hasMalePartner fhkb:i33 .\n", + "fhkb:i54 a owl:NamedIndividual, owl:Thing ;\n", + " fhkb:hasFemalePartner fhkb:i36 ;\n", + " fhkb:hasMalePartner fhkb:i35 .\n", + "fhkb:i55 a owl:NamedIndividual, owl:Thing ;\n", + " fhkb:hasFemalePartner fhkb:i38 ;\n", + " fhkb:hasMalePartner fhkb:i37 .\n", + "[] a owl:AllDifferent ;\n", + " owl:distinctMembers ( fhkb:i0 fhkb:i1 fhkb:i2 fhkb:i3 fhkb:i4 fhkb:i5 fhkb:i6 fhkb:i7 fhkb:i8 fhkb:i9 fhkb:i10 fhkb:i11 fhkb:i12 fhkb:i13 fhkb:i14 fhkb:i15 fhkb:i16 fhkb:i17 fhkb:i18 fhkb:i19 fhkb:i20 fhkb:i21 fhkb:i22 fhkb:i23 fhkb:i24 fhkb:i25 fhkb:i26 fhkb:i27 fhkb:i28 fhkb:i29 fhkb:i30 fhkb:i31 fhkb:i32 fhkb:i33 fhkb:i34 fhkb:i35 fhkb:i36 fhkb:i37 fhkb:i38 fhkb:i39 fhkb:i40 fhkb:i41 fhkb:i42 fhkb:i43 fhkb:i44 fhkb:i45 fhkb:i46 fhkb:i47 fhkb:i48 fhkb:i49 fhkb:i50 fhkb:i51 fhkb:i52 fhkb:i53 fhkb:i54 fhkb:i55 ) ." + ] + } + ], + "source": [ + "!tail onto.ttl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fazendo Inferência\n", + "\n", + "Agora queremos ser capazes de usar esta ontologia para inferência e consulta. Utilizaremos [RDFLib](https://github.com/RDFLib), uma biblioteca para leitura de gráficos RDF em diferentes formatos, realização de consultas, etc.\n", + "\n", + "Para inferência lógica, usaremos a biblioteca [OWL-RL](https://github.com/RDFLib/OWL-RL), que nos permite construir o **Closure** do gráfico RDF, ou seja, adicionar todos os conceitos e relações possíveis que podem ser inferidos.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: rdflib in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (6.3.2)\n", + "Requirement already satisfied: isodate<0.7.0,>=0.6.0 in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from rdflib) (0.6.1)\n", + "Requirement already satisfied: pyparsing<4,>=2.1.0 in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from rdflib) (3.0.9)\n", + "Requirement already satisfied: six in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from isodate<0.7.0,>=0.6.0->rdflib) (1.16.0)\n", + "Collecting git+https://github.com/RDFLib/OWL-RL.git\n", + " Cloning https://github.com/RDFLib/OWL-RL.git to /tmp/pip-req-build-lbfzwi3m\n", + " Running command git clone --filter=blob:none --quiet https://github.com/RDFLib/OWL-RL.git /tmp/pip-req-build-lbfzwi3m\n", + " Resolved https://github.com/RDFLib/OWL-RL.git to commit a77e1791b88b54aace609bc6000aac14c7add4ff\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: rdflib>=6.0.2 in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from owlrl==6.0.2) (6.3.2)\n", + "Requirement already satisfied: isodate<0.7.0,>=0.6.0 in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from rdflib>=6.0.2->owlrl==6.0.2) (0.6.1)\n", + "Requirement already satisfied: pyparsing<4,>=2.1.0 in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from rdflib>=6.0.2->owlrl==6.0.2) (3.0.9)\n", + "Requirement already satisfied: six in /home/rg/anaconda3/envs/ai4beg/lib/python3.11/site-packages (from isodate<0.7.0,>=0.6.0->rdflib>=6.0.2->owlrl==6.0.2) (1.16.0)\n" + ] + } + ], + "source": [ + "!{sys.executable} -m pip install rdflib\n", + "!{sys.executable} -m pip install git+https://github.com/RDFLib/OWL-RL.git" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos abrir o arquivo de ontologia e ver quantos triplos ele contém:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Triplets found:669\n" + ] + } + ], + "source": [ + "import rdflib\n", + "from owlrl import DeductiveClosure, OWLRL_Extension\n", + "\n", + "g = rdflib.Graph()\n", + "g.parse(\"onto.ttl\", format=\"turtle\")\n", + "\n", + "print(\"Triplets found:%d\" % len(g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Triplets after inference:4246\n" + ] + } + ], + "source": [ + "DeductiveClosure(OWLRL_Extension).expand(g)\n", + "print(\"Triplets after inference:%d\" % len(g))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Consultando Parentes\n", + "\n", + "Agora podemos consultar o grafo para ver diferentes relações entre as pessoas. Podemos usar a linguagem **SPARQL** junto com o método `query`. No nosso caso, vamos ver todos os **tios** na nossa árvore genealógica:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fedor Alekseevich Romanov is uncle of Ekaterina Ivanovna Romanova\n", + "Aleksandr I Pavlovich Romanov is uncle of Aleksandr II Nikolaevich Romanov\n", + "Fedor Alekseevich Romanov is uncle of Anna Ivanovna Romanova\n" + ] + } + ], + "source": [ + "qres = g.query(\n", + " \"\"\"SELECT DISTINCT ?aname ?bname\n", + " WHERE {\n", + " ?a fhkb:isUncleOf ?b .\n", + " ?a rdfs:label ?aname .\n", + " ?b rdfs:label ?bname .\n", + " }\"\"\")\n", + "\n", + "for row in qres:\n", + " print(\"%s is uncle of %s\" % row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sinta-se à vontade para experimentar diferentes relações familiares. Por exemplo, você pode dar uma olhada na relação `isAncestorOf`, que define recursivamente todos os ancestrais de uma determinada pessoa.\n", + "\n", + "Por fim, vamos finalizar!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "!rm onto.ttl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + }, + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "coopTranslator": { + "original_hash": "6537d5597320e27b6052b4377b8ff8bb", + "translation_date": "2025-08-28T13:32:30+00:00", + "source_file": "lessons/2-Symbolic/FamilyOntology.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/2-Symbolic/MSConceptGraph.ipynb b/translations/pt-BR/lessons/2-Symbolic/MSConceptGraph.ipynb new file mode 100644 index 00000000..aec68c2e --- /dev/null +++ b/translations/pt-BR/lessons/2-Symbolic/MSConceptGraph.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Microsoft Concept Graph - ESTA API NÃO ESTÁ MAIS DISPONÍVEL, MAS CONSULTE O NOTEBOOK PARA ENTENDER O CONCEITO\n", + "\n", + "[Microsoft Concept Graph](https://concept.research.microsoft.com/) é uma grande taxonomia de termos extraídos da internet, com relações de `é-um` entre conceitos.\n", + "\n", + "O Context Graph está disponível em duas formas:\n", + " * Arquivo de texto grande para download\n", + " * API REST\n", + "\n", + "Estatísticas:\n", + " * 5.401.933 conceitos únicos\n", + " * 12.551.613 instâncias únicas\n", + " * 87.603.947 relações de `é-um`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usando o Serviço Web\n", + "\n", + "O serviço web oferece diferentes chamadas para estimar a probabilidade de um conceito pertencer a diferentes grupos. Mais informações estão disponíveis [aqui](https://concept.research.microsoft.com/Home/Api). \n", + "Aqui está o URL de exemplo para realizar a chamada: `https://concept.research.microsoft.com/api/Concept/ScoreByProb?instance=microsoft&topK=10`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'company': 0.6105356614382954,\n", + " 'vendor': 0.08858636677518003,\n", + " 'client': 0.048239124001183784,\n", + " 'firm': 0.045476965571668145,\n", + " 'large company': 0.043109401203511886,\n", + " 'organization': 0.043010752688172046,\n", + " 'corporation': 0.035908059583703265,\n", + " 'brand': 0.03383644076156654,\n", + " 'software company': 0.027522935779816515,\n", + " 'technology company': 0.023774292196902438}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import urllib\n", + "import json\n", + "import ssl\n", + "\n", + "def http(x):\n", + " ssl._create_default_https_context = ssl._create_unverified_context\n", + " response = urllib.request.urlopen(x)\n", + " data = response.read()\n", + " return data.decode('utf-8')\n", + "\n", + "def query(x):\n", + " return json.loads(http(\"https://concept.research.microsoft.com/api/Concept/ScoreByProb?instance={}&topK=10\".format(urllib.parse.quote(x))))\n", + "\n", + "query('microsoft')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos tentar categorizar os títulos das notícias usando conceitos principais. Para obter os títulos das notícias, usaremos o serviço [NewsApi.org](http://newsapi.org). Você precisa obter sua própria chave de API para usar o serviço - acesse o site e registre-se no plano de desenvolvedor gratuito.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "newsapi_key = ''\n", + "def get_news(country='us'):\n", + " res = json.loads(http(\"https://newsapi.org/v2/top-headlines?country={0}&apiKey={1}\".format(country,newsapi_key)))\n", + " return res['articles']\n", + "\n", + "all_titles = [x['title'] for x in get_news('us')+get_news('gb')]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Covid-19 Live Updates: Vaccines and Boosters News - The New York Times',\n", + " 'Ukrainians Flee Mariupol as Russian Forces Push to Take Port City - The Wall Street Journal',\n", + " 'Bond Yields Jump, Stock Futures Rise After Powell Says Fed Is Ready to Be More Aggressive - The Wall Street Journal',\n", + " 'Putin critic Alexei Navalny found guilty by Russian court - New York Post ',\n", + " \"Supreme Court nominee Ketanji Brown Jackson will face questions at confirmation hearing's second day - CNN\",\n", + " '2 teachers killed at Swedish high school, student arrested - ABC News',\n", + " 'Clues to Covid-19’s Next Moves Come From Sewers - The Wall Street Journal',\n", + " 'Republicans to roll dice by grilling Jackson over child-pornography sentencing decisions | TheHill - The Hill',\n", + " '‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent',\n", + " 'NASA confirms there are 5,000 planets outside our solar system - Daily Mail',\n", + " \"US stocks whipsawed overnight after Fed Chair Powell's remarks - Fox Business\",\n", + " \"'We've learned absolutely nothing': Tests could again be in short supply if Covid surges - POLITICO\",\n", + " \"Duchess of Cambridge swaps khaki jungle gear for Vampire's Wife dress on Belize trip - Daily Mail\",\n", + " 'China searches for victims, flight recorders after first plane crash in 12 years - Reuters',\n", + " 'Second superyacht linked to Russian oligarch Abramovich docks in Turkey - Reuters',\n", + " 'Live updates: Russia stops talks with Japan over sanctions - The Associated Press - en Español',\n", + " 'Powers Remain and Threats Lurk as Women’s Sweet 16 Is Set - The New York Times',\n", + " 'Webb Space Telescope Begins Multi-Instrument Alignment - SciTechDaily',\n", + " \"UConn vs UCF - NCAA women's tournament second-round highlights - March Madness\",\n", + " 'Bucking Republican Trend, Indiana Governor Vetoes Transgender Sports Bill - The New York Times',\n", + " \"Maggie Fox dead: Coronation Street and Shameless actress dies after 'sudden accident' - Mirror Online - The Mirror\",\n", + " 'China plane crash – live: Search for survivors continues as witness describes moment flight fell from sky - The Independent',\n", + " 'Daniel Morgan murder: damning report condemns Met police - The Guardian',\n", + " 'What to expect from Rishi Sunak’s Spring Statement - BBC.com',\n", + " 'UK and Republic of Ireland in line to host Euro 2028 after no one else bids - The Guardian',\n", + " \"Friends beg Vladimir Putin's 'lover' to persuade him to end Ukraine invasion - The Mirror\",\n", + " 'Brass Eye’s outtakes show the brutal TV comedy was the tip of an iceberg - The Guardian',\n", + " \"Vladimir Putin threatens civilians to break Mariupol's spirit - The Times\",\n", + " 'Shell U-turn on Cambo oilfield would threaten green targets, say campaigners - The Guardian',\n", + " 'St Helens dog attack: Girl aged 17 months killed at home - BBC',\n", + " \"PlayStation to buy 'Assassin's Creed' veteran Jade Raymond's Haven Studios - NME\",\n", + " '‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent',\n", + " 'NASA confirms there are 5,000 planets outside our solar system - Daily Mail',\n", + " 'Nintendo Switch finally has folders • Eurogamer.net - Eurogamer.net',\n", + " 'FA to “find a solution” as Liverpool fan group blasts “shambolic” Wembley travel - This Is Anfield',\n", + " 'Manchester United transfer news LIVE Erik ten Hag latest and Man Utd manager updates - Manchester Evening News',\n", + " 'Inflation raises cost of UK government borrowing in February; crude oil up again – business live - The Guardian',\n", + " 'Alexei Navalny: Kremlin critic found guilty of large-scale fraud and contempt of court by Russian court - Sky News',\n", + " \"UK prepares to nationalize Russia natural gas giant Gazprom's retail unit - Business Insider\",\n", + " 'Zaghari-Ratcliffe: Hunt calls for inquiry into delay over Iran debt payment - The Guardian']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_titles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Primeiramente, queremos ser capazes de extrair substantivos de títulos de notícias. Usaremos a biblioteca `TextBlob` para fazer isso, o que simplifica muito tarefas típicas de PLN como esta.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: textblob in c:\\winapp\\miniconda3\\lib\\site-packages (0.17.1)\n", + "Requirement already satisfied: nltk>=3.1 in c:\\winapp\\miniconda3\\lib\\site-packages (from textblob) (3.5)\n", + "Requirement already satisfied: joblib in c:\\winapp\\miniconda3\\lib\\site-packages (from nltk>=3.1->textblob) (1.0.1)\n", + "Requirement already satisfied: regex in c:\\winapp\\miniconda3\\lib\\site-packages (from nltk>=3.1->textblob) (2021.11.10)\n", + "Requirement already satisfied: tqdm in c:\\winapp\\miniconda3\\lib\\site-packages (from nltk>=3.1->textblob) (4.61.2)\n", + "Requirement already satisfied: click in c:\\winapp\\miniconda3\\lib\\site-packages (from nltk>=3.1->textblob) (8.0.3)\n", + "Requirement already satisfied: colorama in c:\\winapp\\miniconda3\\lib\\site-packages (from click->nltk>=3.1->textblob) (0.4.4)\n", + "Finished.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package brown to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package brown is already up-to-date!\n", + "[nltk_data] Downloading package punkt to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n", + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n", + "[nltk_data] Downloading package conll2000 to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package conll2000 is already up-to-date!\n", + "[nltk_data] Downloading package movie_reviews to\n", + "[nltk_data] C:\\Users\\dmitryso\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package movie_reviews is already up-to-date!\n" + ] + } + ], + "source": [ + "import sys\n", + "!{sys.executable} -m pip install textblob\n", + "!{sys.executable} -m textblob.download_corpora\n", + "from textblob import TextBlob" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'covid-19 live updates': 1,\n", + " 'vaccines': 1,\n", + " 'boosters': 1,\n", + " 'york': 4,\n", + " 'ukrainians flee mariupol': 1,\n", + " 'forces push': 1,\n", + " 'port city': 1,\n", + " 'wall street journal': 3,\n", + " 'bond yields': 1,\n", + " 'futures rise': 1,\n", + " 'powell says fed': 1,\n", + " 'ready': 1,\n", + " 'be': 1,\n", + " 'aggressive': 1,\n", + " 'putin': 3,\n", + " 'alexei navalny': 2,\n", + " 'russian': 2,\n", + " 'supreme court nominee': 1,\n", + " 'ketanji brown jackson': 1,\n", + " \"confirmation hearing 's\": 1,\n", + " 'cnn': 1,\n", + " 'swedish': 1,\n", + " 'high school': 1,\n", + " 'abc': 1,\n", + " 'clues': 1,\n", + " 'covid-19': 1,\n", + " '’ s': 2,\n", + " 'moves': 1,\n", + " 'sewers': 1,\n", + " 'roll dice': 1,\n", + " 'jackson': 1,\n", + " 'decisions |': 1,\n", + " 'thehill': 1,\n", + " 'clear': 2,\n", + " 'chemical weapons': 2,\n", + " 'ukraine': 3,\n", + " 'claims president': 2,\n", + " 'biden': 2,\n", + " 'nasa': 2,\n", + " 'solar system': 2,\n", + " 'daily mail': 3,\n", + " 'us stocks': 1,\n", + " 'fed chair powell': 1,\n", + " \"'s remarks\": 1,\n", + " 'fox': 1,\n", + " \"'we 've\": 1,\n", + " 'tests': 1,\n", + " 'covid': 1,\n", + " 'politico': 1,\n", + " 'duchess': 1,\n", + " 'cambridge': 1,\n", + " 'swaps khaki jungle gear': 1,\n", + " 'vampire': 1,\n", + " 'wife': 1,\n", + " 'belize': 1,\n", + " 'china': 2,\n", + " 'flight recorders': 1,\n", + " 'plane crash': 1,\n", + " 'reuters': 2,\n", + " 'russian oligarch': 1,\n", + " 'abramovich': 1,\n", + " 'live': 1,\n", + " 'russia': 2,\n", + " 'stops talks': 1,\n", + " 'japan': 1,\n", + " 'español': 1,\n", + " 'powers remain': 1,\n", + " 'threats lurk': 1,\n", + " 'set': 1,\n", + " 'webb': 1,\n", + " 'telescope begins multi-instrument alignment': 1,\n", + " 'scitechdaily': 1,\n", + " 'uconn': 1,\n", + " 'ucf': 1,\n", + " 'ncaa': 1,\n", + " \"women 's tournament second-round highlights\": 1,\n", + " 'march madness': 1,\n", + " 'bucking republican trend': 1,\n", + " 'indiana': 1,\n", + " 'vetoes transgender': 1,\n", + " 'bill': 1,\n", + " 'maggie fox': 1,\n", + " 'coronation': 1,\n", + " 'shameless': 1,\n", + " \"'sudden accident\": 1,\n", + " 'mirror online': 1,\n", + " 'mirror': 2,\n", + " 'plane crash –': 1,\n", + " 'search': 1,\n", + " 'moment flight': 1,\n", + " 'daniel morgan': 1,\n", + " 'report condemns': 1,\n", + " 'met': 1,\n", + " 'guardian': 6,\n", + " 'rishi sunak': 1,\n", + " '’ s spring': 1,\n", + " 'statement': 1,\n", + " 'bbc.com': 1,\n", + " 'uk': 3,\n", + " 'ireland': 1,\n", + " 'euro': 1,\n", + " 'vladimir putin': 2,\n", + " \"'s 'lover\": 1,\n", + " 'brass eye': 1,\n", + " '’ s outtakes': 1,\n", + " 'brutal tv comedy': 1,\n", + " 'threatens civilians': 1,\n", + " 'mariupol': 1,\n", + " \"'s spirit\": 1,\n", + " 'shell u-turn': 1,\n", + " 'cambo': 1,\n", + " 'green targets': 1,\n", + " 'st helens': 1,\n", + " 'dog attack': 1,\n", + " 'girl': 1,\n", + " 'bbc': 1,\n", + " 'playstation': 1,\n", + " \"'assassin 's\": 1,\n", + " 'creed': 1,\n", + " 'jade raymond': 1,\n", + " 'haven studios': 1,\n", + " 'nme': 1,\n", + " 'nintendo switch': 1,\n", + " 'folders •': 1,\n", + " 'eurogamer.net': 2,\n", + " 'fa': 1,\n", + " 'solution ”': 1,\n", + " 'liverpool': 1,\n", + " 'fan group blasts “ shambolic ”': 1,\n", + " 'wembley': 1,\n", + " 'anfield': 1,\n", + " 'manchester': 1,\n", + " 'live erik': 1,\n", + " 'hag': 1,\n", + " 'utd': 1,\n", + " 'manager updates': 1,\n", + " 'manchester evening': 1,\n", + " 'inflation': 1,\n", + " 'government borrowing': 1,\n", + " 'february': 1,\n", + " 'crude oil': 1,\n", + " '– business': 1,\n", + " 'kremlin': 1,\n", + " 'large-scale fraud': 1,\n", + " 'sky': 1,\n", + " 'natural gas': 1,\n", + " 'gazprom': 1,\n", + " 'retail unit': 1,\n", + " 'insider': 1,\n", + " 'zaghari-ratcliffe': 1,\n", + " 'hunt': 1,\n", + " 'iran': 1,\n", + " 'debt payment': 1}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w = {}\n", + "for x in all_titles:\n", + " for n in TextBlob(x).noun_phrases:\n", + " if n in w:\n", + " w[n].append(x)\n", + " else:\n", + " w[n]=[x]\n", + "{ x:len(w[x]) for x in w.keys()}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver que os substantivos não nos fornecem grandes grupos temáticos. Vamos substituir os substantivos por termos mais gerais obtidos a partir do gráfico de conceitos. Isso levará algum tempo, porque estamos fazendo chamadas REST para cada frase nominal.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "trusted": true + }, + "outputs": [], + "source": [ + "w = {}\n", + "for x in all_titles:\n", + " for noun in TextBlob(x).noun_phrases:\n", + " terms = query(noun.replace(' ','%20'))\n", + " for term in [u for u in terms.keys() if terms[u]>0.1]:\n", + " if term in w:\n", + " w[term].append(x)\n", + " else:\n", + " w[term]=[x]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'city': 9,\n", + " 'brand': 4,\n", + " 'place': 9,\n", + " 'town': 4,\n", + " 'factor': 4,\n", + " 'film': 4,\n", + " 'nation': 11,\n", + " 'state': 5,\n", + " 'person': 4,\n", + " 'organization': 5,\n", + " 'publication': 10,\n", + " 'market': 5,\n", + " 'economy': 4,\n", + " 'company': 6,\n", + " 'newspaper': 6,\n", + " 'relationship': 6}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{ x:len(w[x]) for x in w.keys() if len(w[x])>3}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "ECONOMY:\n", + "China searches for victims, flight recorders after first plane crash in 12 years - Reuters\n", + "Live updates: Russia stops talks with Japan over sanctions - The Associated Press - en Español\n", + "China plane crash – live: Search for survivors continues as witness describes moment flight fell from sky - The Independent\n", + "UK prepares to nationalize Russia natural gas giant Gazprom's retail unit - Business Insider\n", + "\n", + "NATION:\n", + "‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent\n", + "Duchess of Cambridge swaps khaki jungle gear for Vampire's Wife dress on Belize trip - Daily Mail\n", + "China searches for victims, flight recorders after first plane crash in 12 years - Reuters\n", + "Live updates: Russia stops talks with Japan over sanctions - The Associated Press - en Español\n", + "Live updates: Russia stops talks with Japan over sanctions - The Associated Press - en Español\n", + "China plane crash – live: Search for survivors continues as witness describes moment flight fell from sky - The Independent\n", + "UK and Republic of Ireland in line to host Euro 2028 after no one else bids - The Guardian\n", + "Friends beg Vladimir Putin's 'lover' to persuade him to end Ukraine invasion - The Mirror\n", + "‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent\n", + "UK prepares to nationalize Russia natural gas giant Gazprom's retail unit - Business Insider\n", + "Zaghari-Ratcliffe: Hunt calls for inquiry into delay over Iran debt payment - The Guardian\n", + "\n", + "PERSON:\n", + "‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent\n", + "Duchess of Cambridge swaps khaki jungle gear for Vampire's Wife dress on Belize trip - Daily Mail\n", + "Second superyacht linked to Russian oligarch Abramovich docks in Turkey - Reuters\n", + "‘Clear sign’ Putin considering using chemical weapons in Ukraine, claims President Biden - The Independent\n" + ] + } + ], + "source": [ + "print('\\nECONOMY:\\n'+'\\n'.join(w['economy']))\n", + "print('\\nNATION:\\n'+'\\n'.join(w['nation']))\n", + "print('\\nPERSON:\\n'+'\\n'.join(w['person']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit (conda)", + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "coopTranslator": { + "original_hash": "7b3f1fc049371bcf8649ac9fefdf0413", + "translation_date": "2025-10-03T18:54:00+00:00", + "source_file": "lessons/2-Symbolic/MSConceptGraph.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/2-Symbolic/README.md b/translations/pt-BR/lessons/2-Symbolic/README.md new file mode 100644 index 00000000..095f42a1 --- /dev/null +++ b/translations/pt-BR/lessons/2-Symbolic/README.md @@ -0,0 +1,247 @@ +# Representação do Conhecimento e Sistemas Especialistas + +![Resumo do conteúdo de IA Simbólica](../../../../translated_images/pt-BR/ai-symbolic.715a30cb610411a6.webp) + +> Sketchnote por [Tomomi Imura](https://twitter.com/girlie_mac) + +A busca pela inteligência artificial é baseada na busca pelo conhecimento, para compreender o mundo de forma semelhante aos humanos. Mas como você pode fazer isso? + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/3) + +Nos primeiros dias da IA, a abordagem top-down para criar sistemas inteligentes (discutida na lição anterior) era popular. A ideia era extrair o conhecimento das pessoas para alguma forma legível por máquina e então usá-lo para resolver problemas automaticamente. Essa abordagem foi baseada em duas grandes ideias: + +* Representação do Conhecimento +* Raciocínio + +## Representação do Conhecimento + +Um dos conceitos importantes na IA Simbólica é o **conhecimento**. É importante diferenciar conhecimento de *informação* ou *dados*. Por exemplo, pode-se dizer que livros contêm conhecimento, porque é possível estudar os livros e tornar-se um especialista. No entanto, o que os livros contêm é na verdade chamado de *dados*, e ao ler livros e integrar esses dados em nosso modelo do mundo, nós convertemos esses dados em conhecimento. + +> ✅ **Conhecimento** é algo que está contido em nossa cabeça e representa nossa compreensão do mundo. É obtido por um processo ativo de **aprendizagem**, que integra pedaços de informação que recebemos em nosso modelo ativo do mundo. + +Na maioria das vezes, não definimos conhecimento de forma estrita, mas o alinhamos com outros conceitos relacionados usando a [Pirâmide DIKW](https://en.wikipedia.org/wiki/DIKW_pyramid). Ela contém os seguintes conceitos: + +* **Dados** são algo representado em mídia física, como texto escrito ou palavras faladas. Dados existem independentemente dos seres humanos e podem ser passados entre as pessoas. +* **Informação** é como interpretamos dados em nossa mente. Por exemplo, quando ouvimos a palavra *computador*, temos alguma compreensão do que é. +* **Conhecimento** é a informação integrada em nosso modelo do mundo. Por exemplo, uma vez que aprendemos o que é um computador, começamos a ter algumas ideias sobre como ele funciona, quanto custa e para que pode ser usado. Essa rede de conceitos inter-relacionados forma nosso conhecimento. +* **Sabedoria** é ainda um nível a mais da nossa compreensão do mundo, e representa *meta-conhecimento*, por exemplo, alguma noção sobre como e quando o conhecimento deve ser usado. + + + +*Imagem [da Wikipedia](https://commons.wikimedia.org/w/index.php?curid=37705247), Por Longlivetheux - Trabalho próprio, CC BY-SA 4.0* + +Assim, o problema da **representação do conhecimento** é encontrar algum meio eficaz para representar conhecimento dentro de um computador na forma de dados, para torná-lo automaticamente utilizável. Isso pode ser visto como um espectro: + +![Espectro de representação do conhecimento](../../../../translated_images/pt-BR/knowledge-spectrum.b60df631852c0217.webp) + +> Imagem por [Dmitry Soshnikov](http://soshnikov.com) + +* À esquerda, há tipos muito simples de representação do conhecimento que podem ser efetivamente usados por computadores. O mais simples é o algorítmico, quando o conhecimento é representado por um programa de computador. Isso, no entanto, não é a melhor forma de representar o conhecimento, porque não é flexível. O conhecimento dentro da nossa cabeça muitas vezes não é algorítmico. +* À direita, há representações como o texto natural. É a mais poderosa, mas não pode ser usada para raciocínio automático. + +> ✅ Pense por um minuto sobre como você representa conhecimento em sua cabeça e o converte em anotações. Existe algum formato particular que funcione bem para você ajudar na retenção? + +## Classificando Representações de Conhecimento de Computadores + +Podemos classificar diferentes métodos de representação do conhecimento em computadores nas seguintes categorias: + +* **Representações em rede** baseiam-se no fato de que temos uma rede de conceitos inter-relacionados dentro da nossa cabeça. Podemos tentar reproduzir as mesmas redes como um grafo dentro do computador - uma chamada **rede semântica**. + +1. **Triplas Objeto-Atributo-Valor** ou **pares atributo-valor**. Como um grafo pode ser representado em computador como uma lista de nós e arestas, podemos representar uma rede semântica por uma lista de triplas, contendo objetos, atributos e valores. Por exemplo, construímos as seguintes triplas sobre linguagens de programação: + +Objeto | Atributo | Valor +-------|-----------|------ +Python | é | Linguagem Não Tipada +Python | inventada-por | Guido van Rossum +Python | sintaxe-de-bloco | indentação +Linguagem Não Tipada | não tem | definições de tipos + +> ✅ Pense como triplas podem ser usadas para representar outros tipos de conhecimento. + +2. **Representações hierárquicas** enfatizam o fato de que muitas vezes criamos uma hierarquia de objetos em nossa mente. Por exemplo, sabemos que o canário é um pássaro, e todos os pássaros têm asas. Também temos alguma noção de qual é a cor usual de um canário, e qual é sua velocidade de voo. + + - **Representação em frames** baseia-se em representar cada objeto ou classe de objetos como um **frame** que contém **slots**. Slots têm valores padrão possíveis, restrições de valor ou procedimentos armazenados que podem ser chamados para obter o valor de um slot. Todos os frames formam uma hierarquia semelhante a uma hierarquia de objetos em linguagens de programação orientadas a objetos. + - **Cenários** são um tipo especial de frame que representam situações complexas que podem se desenrolar no tempo. + +**Python** + +Slot | Valor | Valor padrão | Intervalo | +-----|-------|---------------|----------| +Nome | Python | | | +É-Um | Linguagem Não Tipada | | | +Caso de Variável | | CamelCase | | +Comprimento do Programa | | | 5-5000 linhas | +Sintaxe de Bloco | Indentação | | | + +3. **Representações procedurais** baseiam-se em representar o conhecimento por uma lista de ações que podem ser executadas quando uma certa condição ocorre. + - Regras de produção são declarações do tipo se-então que nos permitem tirar conclusões. Por exemplo, um médico pode ter uma regra dizendo que **SE** um paciente tem febre alta **OU** nível alto de proteína C-reativa no exame de sangue **ENTÃO** ele tem uma inflamação. Uma vez que encontramos uma das condições, podemos concluir que há inflamação e usar isso em raciocínios posteriores. + - Algoritmos podem ser considerados outra forma de representação procedural, embora quase nunca sejam usados diretamente em sistemas baseados em conhecimento. + +4. **Lógica** foi originalmente proposta por Aristóteles como uma forma de representar conhecimento universal humano. + - A Lógica de Predicados, como teoria matemática, é rica demais para ser computável, portanto algum subconjunto dela é normalmente usado, como cláusulas de Horn usadas em Prolog. + - Lógica Descritiva é uma família de sistemas lógicos usados para representar e raciocinar sobre hierarquias de objetos e representações distribuídas de conhecimento, como a *web semântica*. + +## Sistemas Especialistas + +Um dos primeiros sucessos da IA simbólica foram os chamados **sistemas especialistas** - sistemas computacionais desenhados para atuar como um especialista em algum domínio de problema limitado. Eles foram baseados em uma **base de conhecimento** extraída de um ou mais especialistas humanos, e continham um **motor de inferência** que realizava algum raciocínio sobre ela. + +![Arquitetura Humana](../../../../translated_images/pt-BR/arch-human.5d4d35f1bba3ab1c.webp) | ![Sistema Baseado em Conhecimento](../../../../translated_images/pt-BR/arch-kbs.3ec5c150b09fa8da.webp) +---------------------------------------------|------------------------------------------------ +Estrutura simplificada do sistema neural humano | Arquitetura de um sistema baseado em conhecimento + +Sistemas especialistas são construídos como o sistema de raciocínio humano, que contém **memória de curto prazo** e **memória de longo prazo**. De maneira semelhante, em sistemas baseados em conhecimento distinguimos os seguintes componentes: + +* **Memória do problema**: contém o conhecimento sobre o problema que está sendo atualmente resolvido, ou seja, a temperatura ou pressão arterial de um paciente, se ele tem inflamação ou não, etc. Esse conhecimento também é chamado de **conhecimento estático**, porque contém um instantâneo do que sabemos atualmente sobre o problema - o chamado *estado do problema*. +* **Base de conhecimento**: representa o conhecimento de longo prazo sobre um domínio de problema. É extraída manualmente de especialistas humanos, e não muda de consulta para consulta. Como permite navegar de um estado do problema para outro, também é chamada de **conhecimento dinâmico**. +* **Motor de inferência**: orquestra o processo todo de busca no espaço de estados do problema, fazendo perguntas ao usuário quando necessário. Também é responsável por encontrar as regras certas a serem aplicadas para cada estado. + +Como exemplo, vamos considerar o seguinte sistema especialista para determinar um animal com base em suas características físicas: + +![Árvore AND-OR](../../../../translated_images/pt-BR/AND-OR-Tree.5592d2c70187f283.webp) + +> Imagem por [Dmitry Soshnikov](http://soshnikov.com) + +Esse diagrama é chamado de **árvore AND-OR**, e é uma representação gráfica de um conjunto de regras de produção. Desenhar uma árvore é útil no início da extração de conhecimento do especialista. Para representar o conhecimento dentro do computador, é mais conveniente usar regras: + +``` +IF the animal eats meat +OR (animal has sharp teeth + AND animal has claws + AND animal has forward-looking eyes +) +THEN the animal is a carnivore +``` + +Você pode notar que cada condição no lado esquerdo da regra e a ação são essencialmente triplas objeto-atributo-valor (OAV). A **memória de trabalho** contém o conjunto de triplas OAV que correspondem ao problema atualmente sendo resolvido. Um **motor de regras** procura as regras cujas condições são satisfeitas e as aplica, adicionando outra tripla à memória de trabalho. + +> ✅ Escreva sua própria árvore AND-OR sobre um tema que você goste! + +### Inferência Direta vs. Inferência Reversa + +O processo descrito acima é chamado de **inferência direta**. Ele começa com alguns dados iniciais sobre o problema disponíveis na memória de trabalho, e então executa o seguinte ciclo de raciocínio: + +1. Se o atributo alvo está presente na memória de trabalho - pare e dê o resultado +2. Procure todas as regras cuja condição está atualmente satisfeita - obtenha o **conjunto de conflito** de regras +3. Realize a **resolução de conflitos** - selecione uma regra que será executada nesta etapa. Podem existir diferentes estratégias de resolução de conflito: + - Selecionar a primeira regra aplicável na base de conhecimento + - Selecionar uma regra aleatória + - Selecionar uma regra *mais específica*, ou seja, aquela que satisfaz o maior número de condições no "lado esquerdo" (LHS) +4. Aplique a regra selecionada e insira novo conhecimento no estado do problema +5. Repita a partir do passo 1. + +Contudo, em alguns casos podemos querer começar com nenhum conhecimento sobre o problema, e fazer perguntas que nos ajudarão a chegar à conclusão. Por exemplo, quando fazemos diagnóstico médico, geralmente não realizamos todos os exames médicos antecipadamente antes de começar a diagnosticar o paciente. Preferimos realizar exames quando uma decisão precisa ser tomada. + +Esse processo pode ser modelado usando **inferência reversa**. Ela é guiada pelo **objetivo** - o valor do atributo que estamos buscando: + +1. Selecione todas as regras que podem nos dar o valor de um objetivo (ou seja, com o objetivo no Lado Direito ("right-hand-side")) - um conjunto de conflito +1. Se não houver regras para esse atributo, ou houver uma regra dizendo que devemos perguntar o valor ao usuário - pergunte, caso contrário: +1. Use a estratégia de resolução de conflitos para selecionar uma regra que usaremos como *hipótese* - tentaremos prová-la +1. Repita recorrentemente o processo para todos os atributos no LHS da regra, tentando prová-los como objetivos +1. Se em algum ponto o processo falhar - use outra regra no passo 3. + +> ✅ Em quais situações a inferência direta é mais apropriada? E a inferência reversa? + +### Implementando Sistemas Especialistas + +Sistemas especialistas podem ser implementados usando diferentes ferramentas: + +* Programando-os diretamente em alguma linguagem de programação de alto nível. Isso não é a melhor ideia, porque a principal vantagem de um sistema baseado em conhecimento é que o conhecimento está separado da inferência, e potencialmente um especialista no domínio do problema deve ser capaz de escrever regras sem entender os detalhes do processo de inferência +* Usando **shell para sistemas especialistas**, ou seja, um sistema especificamente desenhado para ser povoado com conhecimento usando alguma linguagem de representação de conhecimento. + +## ✍️ Exercício: Inferência de Animais + +Veja [Animals.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb) para um exemplo de implementação de sistema especialista com inferência direta e reversa. + +> **Nota**: Este exemplo é relativamente simples, e apenas dá a ideia de como um sistema especialista se parece. Quando você começar a criar tal sistema, só notará algum comportamento *inteligente* nele quando alcançar certo número de regras, em torno de 200+. Em algum ponto, as regras se tornam complexas demais para manter todas na mente, e neste ponto você pode começar a se perguntar porque um sistema toma determinadas decisões. No entanto, a característica importante dos sistemas baseados em conhecimento é que você sempre pode *explicar* exatamente como qualquer decisão foi tomada. + +## Ontologias e a Web Semântica + +No final do século 20 houve uma iniciativa para usar representação do conhecimento para anotar recursos da Internet, para que fosse possível encontrar recursos que corresponderiam a consultas muito específicas. Esse movimento foi chamado de **Web Semântica**, e se apoiou em vários conceitos: + +- Uma representação especial do conhecimento baseada em **[lógicas descritivas](https://en.wikipedia.org/wiki/Description_logic)** (DL). É similar à representação em frames, porque constrói uma hierarquia de objetos com propriedades, mas tem semântica lógica formal e inferência. Existe uma família inteira de DLs que equilibram entre expressividade e complexidade algorítmica da inferência. +- Representação distribuída do conhecimento, onde todos os conceitos são representados por um identificador global URI, tornando possível criar hierarquias de conhecimento que abrangem a internet. +- Uma família de linguagens baseadas em XML para descrição de conhecimento: RDF (Resource Description Framework), RDFS (RDF Schema), OWL (Ontology Web Language). + +Um conceito central na Web Semântica é o conceito de **Ontologia**. Refere-se a uma especificação explícita de um domínio do problema usando alguma representação formal de conhecimento. A ontologia mais simples pode ser apenas uma hierarquia de objetos em um domínio do problema, mas ontologias mais complexas incluirão regras que podem ser usadas para inferência. + +Na web semântica, todas as representações são baseadas em tripletas. Cada objeto e cada relação são identificados unicamente pela URI. Por exemplo, se quisermos afirmar o fato de que este Currículo de IA foi desenvolvido por Dmitry Soshnikov em 1º de janeiro de 2022 - aqui estão as tripletas que podemos usar: + + + +``` +http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 1, 2022” +http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com +``` + +> ✅ Aqui `http://www.example.com/terms/creation-date` e `http://purl.org/dc/elements/1.1/creator` são algumas URIs bem conhecidas e universalmente aceitas para expressar os conceitos de *criador* e *data de criação*. + +Em um caso mais complexo, se quisermos definir uma lista de criadores, podemos usar algumas estruturas de dados definidas em RDF. + + + +> Diagramas acima por [Dmitry Soshnikov](http://soshnikov.com) + +O progresso na construção da Web Semântica foi de certa forma desacelerado pelo sucesso dos motores de busca e técnicas de processamento de linguagem natural, que permitem extrair dados estruturados do texto. Contudo, em algumas áreas ainda há esforços significativos para manter ontologias e bases de conhecimento. Alguns projetos que merecem destaque: + +* [WikiData](https://wikidata.org/) é uma coleção de bases de conhecimento legíveis por máquina associadas à Wikipedia. A maior parte dos dados é extraída dos *InfoBoxes* da Wikipedia, pedaços de conteúdo estruturado dentro das páginas da Wikipedia. Você pode [consultar](https://query.wikidata.org/) o wikidata em SPARQL, uma linguagem especial de consulta para a Web Semântica. Aqui está uma consulta de exemplo que mostra as cores de olhos mais populares entre humanos: + +```sparql +#defaultView:BubbleChart +SELECT ?eyeColorLabel (COUNT(?human) AS ?count) +WHERE +{ + ?human wdt:P31 wd:Q5. # human instance-of homo sapiens + ?human wdt:P1340 ?eyeColor. # human eye-color ?eyeColor + SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } +} +GROUP BY ?eyeColorLabel +``` + +* [DBpedia](https://www.dbpedia.org/) é outro esforço similar ao WikiData. + +> ✅ Se você quiser experimentar criar suas próprias ontologias, ou abrir as existentes, existe um ótimo editor visual de ontologias chamado [Protégé](https://protege.stanford.edu/). Faça o download ou use online. + + + +*Editor Web Protégé aberto com a ontologia da Família Romanov. Captura de tela por Dmitry Soshnikov* + +## ✍️ Exercício: Uma Ontologia de Família + +Veja [FamilyOntology.ipynb](https://github.com/Ezana135/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb) para um exemplo do uso de técnicas da Web Semântica para raciocinar sobre relacionamentos familiares. Iremos pegar uma árvore genealógica representada no formato comum GEDCOM e uma ontologia de relacionamentos familiares e construir um grafo de todos os relacionamentos familiares para um conjunto dado de indivíduos. + +## Microsoft Concept Graph + +Na maioria dos casos, ontologias são cuidadosamente criadas manualmente. Entretanto, também é possível **extrair** ontologias de dados não estruturados, por exemplo, de textos em linguagem natural. + +Uma dessas tentativas foi feita pela Microsoft Research e resultou no [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste). + +É uma grande coleção de entidades agrupadas usando relacionamento de herança `é-um`. Isso permite responder perguntas como "O que é a Microsoft?" - a resposta seria algo como "uma empresa com probabilidade 0,87, e uma marca com probabilidade 0,75". + +O Grafo está disponível tanto como REST API, quanto como um grande arquivo de texto para download que lista todos os pares de entidades. + +## ✍️ Exercício: Um Grafo de Conceitos + +Experimente o notebook [MSConceptGraph.ipynb](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/MSConceptGraph.ipynb) para ver como podemos usar o Microsoft Concept Graph para agrupar artigos de notícias em várias categorias. + +## Conclusão + +Hoje em dia, IA é frequentemente considerada sinônimo de *Aprendizado de Máquina* ou *Redes Neurais*. Contudo, um ser humano também exibe raciocínio explícito, algo que atualmente não é tratado pelas redes neurais. Em projetos do mundo real, o raciocínio explícito ainda é usado para realizar tarefas que requerem explicações ou a capacidade de modificar o comportamento do sistema de forma controlada. + +## 🚀 Desafio + +No notebook Ontologia de Família associado a esta lição, há a oportunidade de experimentar outras relações familiares. Tente descobrir novas conexões entre pessoas na árvore genealógica. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/4) + +## Revisão & Autoestudo + +Faça uma pesquisa na internet para descobrir áreas onde humanos tentaram quantificar e codificar conhecimento. Veja a Taxonomia de Bloom, e volte na história para aprender como os humanos tentaram entender seu mundo. Explore o trabalho de Lineu para criar uma taxonomia de organismos, e observe como Dmitri Mendeleev criou uma forma para os elementos químicos serem descritos e agrupados. Quais outros exemplos interessantes você pode encontrar? + +**Tarefa**: [Construa uma Ontologia](assignment.md) + +--- + + +**Aviso Legal**: +Este documento foi traduzido usando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para alcançar a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução. + \ No newline at end of file diff --git a/translations/pt-BR/lessons/2-Symbolic/assignment.md b/translations/pt-BR/lessons/2-Symbolic/assignment.md new file mode 100644 index 00000000..91c12a25 --- /dev/null +++ b/translations/pt-BR/lessons/2-Symbolic/assignment.md @@ -0,0 +1,6 @@ +# Construir uma Ontologia + +Construir uma base de conhecimento envolve categorizar um modelo que representa fatos sobre um determinado tema. Escolha um tema - como uma pessoa, um lugar ou um objeto - e, em seguida, construa um modelo sobre esse tema. Utilize algumas das técnicas e estratégias de construção de modelos descritas nesta lição. Um exemplo seria criar uma ontologia de uma sala de estar com móveis, luzes, e assim por diante. Como a sala de estar se diferencia da cozinha? Do banheiro? Como você sabe que é uma sala de estar e não uma sala de jantar? Use o [Protégé](https://protege.stanford.edu/) para construir sua ontologia. + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb new file mode 100644 index 00000000..e080a6d5 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb @@ -0,0 +1,1219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Perceptron\n", + "\n", + "> Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners). Visite o repositório para o conjunto completo de materiais de aprendizado.\n", + "\n", + "Como discutimos, o perceptron permite resolver o problema de **classificação binária**, ou seja, classificar exemplos de entrada em duas classes - podemos chamá-las de **positiva** e **negativa**.\n", + "\n", + "Primeiro, vamos importar algumas bibliotecas necessárias.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pylab\n", + "from matplotlib import gridspec\n", + "from sklearn.datasets import make_classification\n", + "import numpy as np\n", + "from ipywidgets import interact, interactive, fixed\n", + "import ipywidgets as widgets\n", + "import pickle\n", + "import os\n", + "import gzip\n", + "\n", + "# pick the seed for reproducability - change it to explore the effects of random variations\n", + "np.random.seed(1)\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Problema Simples\n", + "\n", + "Para começar, vamos iniciar com um problema simples, onde temos duas características de entrada. Por exemplo, na medicina, podemos querer classificar tumores como benignos ou malignos, dependendo do seu tamanho e idade.\n", + "\n", + "Vamos gerar um conjunto de dados de classificação aleatório usando a função `make_classification` da biblioteca SciKit Learn:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features:\n", + " [[-1.7441838 -1.3952037 ]\n", + " [ 2.5921783 -0.08124504]\n", + " [ 0.9218062 0.91789985]\n", + " [-0.8437018 -0.18738253]]\n", + "Labels:\n", + " [-1 -1 1 -1]\n" + ] + } + ], + "source": [ + "n = 50\n", + "X, Y = make_classification(n_samples = n, n_features=2,\n", + " n_redundant=0, n_informative=2, flip_y=0)\n", + "Y = Y*2-1 # convert initial 0/1 values into -1/1\n", + "X = X.astype(np.float32); Y = Y.astype(np.int32) # features - float, label - int\n", + "\n", + "# Split the dataset into training and test\n", + "train_x, test_x = np.split(X, [ n*8//10])\n", + "train_labels, test_labels = np.split(Y, [n*8//10])\n", + "print(\"Features:\\n\",train_x[0:4])\n", + "print(\"Labels:\\n\",train_labels[0:4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos também plotar o conjunto de dados:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":11: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_dataset(suptitle, features, labels):\n", + " # prepare the plot\n", + " fig, ax = pylab.subplots(1, 1)\n", + " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\n", + " fig.suptitle(suptitle, fontsize = 16)\n", + " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n", + " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n", + "\n", + " colors = ['r' if l>0 else 'b' for l in labels]\n", + " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n", + " fig.show()\n", + "\n", + "plot_dataset('Training data', train_x, train_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Perceptron\n", + "\n", + "Como o perceptron é um classificador binário, para cada vetor de entrada $x$, a saída do nosso perceptron será +1 ou -1, dependendo da classe. A saída será calculada usando a fórmula\n", + "\n", + "$$y(\\mathbf{x}) = f(\\mathbf{w}^{\\mathrm{T}}\\mathbf{x})$$\n", + "\n", + "onde $\\mathbf{w}$ é um vetor de pesos, e $f$ é uma função de ativação degrau:\n", + "$$\n", + "f(x) = \\begin{cases}\n", + " +1 & x \\geq 0 \\\\\n", + " -1 & x < 0\n", + " \\end{cases} \\\\\n", + "$$\n", + "\n", + "No entanto, um modelo linear genérico também deve ter um termo de viés, ou seja, idealmente deveríamos calcular $y$ como $y=f(\\mathbf{w}^{\\mathrm{T}}\\mathbf{x}+\\mathbf{b})$. Para simplificar nosso modelo, podemos eliminar esse termo de viés adicionando mais uma dimensão às nossas características de entrada, que sempre será igual a 1:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.92180622 0.91789985 1. ]\n", + " [-1.06435513 1.49764717 1. ]\n", + " [ 0.32839951 2.25677919 1. ]]\n" + ] + } + ], + "source": [ + "pos_examples = np.array([ [t[0], t[1], 1] for i,t in enumerate(train_x) \n", + " if train_labels[i]>0])\n", + "neg_examples = np.array([ [t[0], t[1], 1] for i,t in enumerate(train_x) \n", + " if train_labels[i]<0])\n", + "print(pos_examples[0:3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Algoritmo de Treinamento\n", + "\n", + "Para treinar o perceptron, precisamos encontrar os pesos $\\mathbf{w}$ que minimizem o erro. O erro é definido usando o **critério do perceptron**:\n", + "\n", + "$$E(\\mathbf{w}) = -\\sum_{n \\in \\mathcal{M}}\\mathbf{w}^{\\mathrm{T}}\\mathbf{x}_{n}t_{n}$$\n", + " \n", + " * $t_{n} \\in \\{-1, +1\\}$ para amostras de treinamento negativas e positivas, respectivamente\n", + " * $\\mathcal{M}$ - um conjunto de exemplos classificados incorretamente\n", + " \n", + "Usaremos o processo de **descida do gradiente**. Começando com alguns pesos iniciais aleatórios $\\mathbf{w}^{(0)}$, ajustaremos os pesos a cada etapa do treinamento usando o gradiente de $E$:\n", + "\n", + "$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} - \\eta \\nabla E(\\mathbf{w}) = \\mathbf{w}^{\\tau} + \\eta\\sum_{n \\in \\mathcal{M}}\\mathbf{x}_{n} t_{n}$$\n", + "\n", + "onde $\\eta$ é a **taxa de aprendizado**, e $\\tau\\in\\mathbb{N}$ - número da iteração.\n", + "\n", + "Vamos definir este algoritmo em Python:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def train(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n", + " num_dims = positive_examples.shape[1]\n", + " \n", + " # Initialize weights. \n", + " # We initialize with 0 for simplicity, but random initialization is also a good idea\n", + " weights = np.zeros((num_dims,1)) \n", + " \n", + " pos_count = positive_examples.shape[0]\n", + " neg_count = negative_examples.shape[0]\n", + " \n", + " report_frequency = 10\n", + " \n", + " for i in range(num_iterations):\n", + " # Pick one positive and one negative example\n", + " pos = random.choice(positive_examples)\n", + " neg = random.choice(negative_examples)\n", + "\n", + " z = np.dot(pos, weights) \n", + " if z < 0: # positive example was classified as negative\n", + " weights = weights + learning_rate * pos.reshape(weights.shape)\n", + "\n", + " z = np.dot(neg, weights)\n", + " if z >= 0: # negative example was classified as positive\n", + " weights = weights - learning_rate * neg.reshape(weights.shape)\n", + " \n", + " # Periodically, print out the current accuracy on all examples \n", + " if i % report_frequency == 0: \n", + " pos_out = np.dot(positive_examples, weights)\n", + " neg_out = np.dot(negative_examples, weights) \n", + " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", + " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", + " print(\"Iteration={}, pos correct={}, neg correct={}\".format(i,pos_correct,neg_correct))\n", + "\n", + " return weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Nota sobre a Taxa de Aprendizado**: O parâmetro `learning_rate` (padrão `0.01`) controla o quanto ajustamos os pesos durante cada etapa de treinamento. Isso implementa a fórmula de atualização do gradiente descendente:\n", + "\n", + "$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} + \\eta \\mathbf{x}_{n} t_{n}$$\n", + "\n", + "- Uma taxa de aprendizado maior (por exemplo, `1.0`) faz o perceptron aprender mais rápido, mas pode ultrapassar a solução ideal\n", + "- Uma taxa de aprendizado menor (por exemplo, `0.001`) aprende mais devagar, mas pode convergir de forma mais precisa\n", + "- Você pode experimentar chamando: `train(pos_examples, neg_examples, learning_rate=0.1)`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora vamos executar o treinamento em nosso conjunto de dados:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration=0, pos correct=0.2631578947368421, neg correct=0.6190476190476191\n", + "Iteration=10, pos correct=0.8947368421052632, neg correct=0.8571428571428571\n", + "Iteration=20, pos correct=0.8421052631578947, neg correct=1.0\n", + "Iteration=30, pos correct=0.8947368421052632, neg correct=0.9523809523809523\n", + "Iteration=40, pos correct=0.8947368421052632, neg correct=0.9523809523809523\n", + "Iteration=50, pos correct=0.9473684210526315, neg correct=0.9047619047619048\n", + "Iteration=60, pos correct=0.8947368421052632, neg correct=0.9523809523809523\n", + "Iteration=70, pos correct=0.8947368421052632, neg correct=0.9047619047619048\n", + "Iteration=80, pos correct=0.8947368421052632, neg correct=0.6190476190476191\n", + "Iteration=90, pos correct=0.8421052631578947, neg correct=1.0\n", + "[[-0.66042328 4.90850882 -1. ]]\n" + ] + } + ], + "source": [ + "wts = train(pos_examples,neg_examples)\n", + "print(wts.transpose())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como você pode ver, a precisão inicial está em torno de 50%, mas rapidamente aumenta para valores mais altos, próximos de 90%.\n", + "\n", + "Vamos visualizar como as classes estão separadas. Nossa função de classificação é representada por $\\mathbf{w}^Tx$, e ela é maior que 0 para uma classe e menor que 0 para outra. Assim, a linha de separação das classes é definida por $\\mathbf{w}^Tx = 0$. Como temos apenas duas dimensões, $x_0$ e $x_1$, a equação da linha seria $w_0x_0+w_1x_1+w_2 = 0$ (lembre-se de que definimos explicitamente uma dimensão extra $x_2=1$). Vamos plotar essa linha:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def plot_boundary(positive_examples, negative_examples, weights):\n", + " if np.isclose(weights[1], 0):\n", + " if np.isclose(weights[0], 0):\n", + " x = y = np.array([-6, 6], dtype = 'float32')\n", + " else:\n", + " y = np.array([-6, 6], dtype='float32')\n", + " x = -(weights[1] * y + weights[2])/weights[0]\n", + " else:\n", + " x = np.array([-6, 6], dtype='float32')\n", + " y = -(weights[0] * x + weights[2])/weights[1]\n", + "\n", + " pylab.xlim(-6, 6)\n", + " pylab.ylim(-6, 6) \n", + " pylab.plot(positive_examples[:,0], positive_examples[:,1], 'bo')\n", + " pylab.plot(negative_examples[:,0], negative_examples[:,1], 'ro')\n", + " pylab.plot(x, y, 'g', linewidth=2.0)\n", + " pylab.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_boundary(pos_examples,neg_examples,wts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experimentando com Taxas de Aprendizado\n", + "\n", + "Agora vamos explorar como diferentes taxas de aprendizado afetam o processo de treinamento. A taxa de aprendizado controla o tamanho do passo no gradiente descendente - um hiperparâmetro crucial que afeta tanto a velocidade de convergência quanto a estabilidade.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare different learning rates\n", + "learning_rates = [0.001, 0.01, 0.1, 1.0]\n", + "fig, axes = pylab.subplots(2, 2, figsize=(12, 10))\n", + "fig.suptitle('Effect of Different Learning Rates', fontsize=16)\n", + "\n", + "for idx, lr in enumerate(learning_rates):\n", + " ax = axes[idx // 2, idx % 2]\n", + " \n", + " # Train with this learning rate\n", + " weights_lr = train(pos_examples, neg_examples, num_iterations=100, learning_rate=lr)\n", + " \n", + " # Plot decision boundary\n", + " if np.isclose(weights_lr[1], 0):\n", + " if np.isclose(weights_lr[0], 0):\n", + " x = y = np.array([-6, 6], dtype='float32')\n", + " else:\n", + " y = np.array([-6, 6], dtype='float32')\n", + " x = -(weights_lr[1] * y + weights_lr[2])/weights_lr[0]\n", + " else:\n", + " x = np.array([-6, 6], dtype='float32')\n", + " y = -(weights_lr[0] * x + weights_lr[2])/weights_lr[1]\n", + " \n", + " ax.set_xlim(-6, 6)\n", + " ax.set_ylim(-6, 6)\n", + " ax.plot(pos_examples[:, 0], pos_examples[:, 1], 'bo', label='Positive', alpha=0.7)\n", + " ax.plot(neg_examples[:, 0], neg_examples[:, 1], 'ro', label='Negative', alpha=0.7)\n", + " ax.plot(x, y, 'g-', linewidth=2)\n", + " ax.set_title(f'Learning Rate = {lr}')\n", + " ax.set_xlabel('Feature 1')\n", + " ax.set_ylabel('Feature 2')\n", + " ax.legend()\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "pylab.tight_layout()\n", + "pylab.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Experimento Interativo de Taxa de Aprendizado\n", + "\n", + "Use o controle deslizante abaixo para experimentar interativamente diferentes taxas de aprendizado e veja como elas afetam a fronteira de decisão:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_and_plot_with_lr(learning_rate=0.01):\n", + " \"\"\"Train perceptron with specified learning rate and plot results\"\"\"\n", + " weights_lr = train(pos_examples, neg_examples, num_iterations=100, learning_rate=learning_rate)\n", + " \n", + " fig, (ax1, ax2) = pylab.subplots(1, 2, figsize=(14, 5))\n", + " \n", + " # Plot 1: Decision boundary\n", + " if np.isclose(weights_lr[1], 0):\n", + " if np.isclose(weights_lr[0], 0):\n", + " x = y = np.array([-6, 6], dtype='float32')\n", + " else:\n", + " y = np.array([-6, 6], dtype='float32')\n", + " x = -(weights_lr[1] * y + weights_lr[2])/weights_lr[0]\n", + " else:\n", + " x = np.array([-6, 6], dtype='float32')\n", + " y = -(weights_lr[0] * x + weights_lr[2])/weights_lr[1]\n", + " \n", + " ax1.set_xlim(-6, 6)\n", + " ax1.set_ylim(-6, 6)\n", + " ax1.plot(pos_examples[:, 0], pos_examples[:, 1], 'bo', label='Positive', s=100, alpha=0.6)\n", + " ax1.plot(neg_examples[:, 0], neg_examples[:, 1], 'ro', label='Negative', s=100, alpha=0.6)\n", + " ax1.plot(x, y, 'g-', linewidth=3, label='Decision Boundary')\n", + " ax1.set_title(f'Decision Boundary (lr={learning_rate})', fontsize=14)\n", + " ax1.set_xlabel('Feature 1')\n", + " ax1.set_ylabel('Feature 2')\n", + " ax1.legend()\n", + " ax1.grid(True, alpha=0.3)\n", + " \n", + " # Plot 2: Weight values\n", + " ax2.bar(['w0', 'w1', 'bias'], weights_lr.flatten(), color=['blue', 'green', 'red'], alpha=0.7)\n", + " ax2.set_title('Final Weight Values', fontsize=14)\n", + " ax2.set_ylabel('Weight Value')\n", + " ax2.grid(True, alpha=0.3, axis='y')\n", + " ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n", + " \n", + " pylab.tight_layout()\n", + " pylab.show()\n", + " \n", + " print(f\"Final weights: {weights_lr.flatten()}\")\n", + "\n", + "# Create interactive widget\n", + "interact(train_and_plot_with_lr, \n", + " learning_rate=widgets.FloatSlider(value=0.01, min=0.001, max=1.0, step=0.001, \n", + " description='Learning Rate:', continuous_update=False))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Avaliar no Conjunto de Dados de Teste\n", + "\n", + "No início, reservamos alguns dados para o conjunto de teste. Vamos verificar quão preciso nosso classificador é nesse conjunto de teste. Para isso, também expandimos o conjunto de teste com uma dimensão extra, multiplicamos pela matriz de pesos e garantimos que o valor obtido tenha o mesmo sinal que o rótulo (+1 ou -1). Em seguida, somamos todos os valores booleanos e dividimos pelo tamanho da amostra de teste para obter a precisão:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def accuracy(weights, test_x, test_labels):\n", + " res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)\n", + " return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))\n", + "\n", + "accuracy(wts, test_x, test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Observando o processo de treinamento\n", + "\n", + "Já vimos antes como a precisão diminui durante o treinamento. Seria interessante observar como a linha de separação se comporta durante o treinamento. O código abaixo irá visualizar tudo em um único gráfico, e você poderá mover o controle deslizante para \"viajar no tempo\" através do processo de treinamento.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def train_graph(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n", + " num_dims = positive_examples.shape[1]\n", + " weights = np.zeros((num_dims,1)) # initialize weights\n", + " \n", + " pos_count = positive_examples.shape[0]\n", + " neg_count = negative_examples.shape[0]\n", + " \n", + " report_frequency = 15;\n", + " snapshots = []\n", + " \n", + " for i in range(num_iterations):\n", + " pos = random.choice(positive_examples)\n", + " neg = random.choice(negative_examples)\n", + "\n", + " z = np.dot(pos, weights) \n", + " if z < 0:\n", + " weights = weights + learning_rate * pos.reshape(weights.shape)\n", + "\n", + " z = np.dot(neg, weights)\n", + " if z >= 0:\n", + " weights = weights - learning_rate * neg.reshape(weights.shape)\n", + " \n", + " if i % report_frequency == 0: \n", + " pos_out = np.dot(positive_examples, weights)\n", + " neg_out = np.dot(negative_examples, weights) \n", + " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", + " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", + " snapshots.append([np.copy(weights).flatten(), (pos_correct+neg_correct)/2.0])\n", + "\n", + " return np.array(snapshots, dtype=object)\n", + "\n", + "snapshots = train_graph(pos_examples,neg_examples)\n", + "\n", + "def plotit(pos_examples,neg_examples,snapshots,step):\n", + " fig = pylab.figure(figsize=(10,4))\n", + " fig.add_subplot(1, 2, 1)\n", + " plot_boundary(pos_examples, neg_examples, snapshots[step][0])\n", + " fig.add_subplot(1, 2, 2)\n", + " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n", + " pylab.ylabel('Accuracy')\n", + " pylab.xlabel('Iteration')\n", + " pylab.plot(step, snapshots[step,1], \"bo\")\n", + " pylab.show()\n", + "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8561af1ae77c421f9ca068fe0bdac566", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='step', max=6), Output()), _dom_classes=('widget-interact…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interact(pl1, step=widgets.IntSlider(value=0, min=0, max=len(snapshots)-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Limitações do Perceptron\n", + "\n", + "Como você viu acima, o perceptron é um **classificador linear**. Ele consegue distinguir bem entre duas classes se elas forem **linearmente separáveis**, ou seja, podem ser separadas por uma linha reta. Caso contrário, o processo de treinamento do perceptron não irá convergir.\n", + "\n", + "Um exemplo mais evidente de um problema que não pode ser resolvido por um perceptron é o chamado **problema XOR**. Queremos que nosso perceptron aprenda a função booleana XOR, que possui a seguinte tabela verdade:\n", + "\n", + "| | 0 | 1 |\n", + "|---|---|---|\n", + "| 0 | 0 | 1 | \n", + "| 1 | 1 | 0 |\n", + "\n", + "Vamos tentar fazer isso! Vamos preencher manualmente todos os exemplos de treinamento positivos e negativos e, em seguida, chamar nossa função de treinamento definida acima:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "pos_examples_xor = np.array([[1,0,1],[0,1,1]])\n", + "neg_examples_xor = np.array([[1,1,1],[0,0,1]])\n", + "\n", + "snapshots_xor = train_graph(pos_examples_xor,neg_examples_xor,1000)\n", + "def pl2(step): plotit(pos_examples_xor,neg_examples_xor,snapshots_xor,step)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8f45bf78e4c8471fbc6eea233dde2bf6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='step', max=6), Output()), _dom_classes=('widget-interact…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interact(pl2, step=widgets.IntSlider(value=0, min=0, max=len(snapshots)-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Como você pode ver no gráfico acima, a precisão nunca ultrapassa 75%, porque é impossível traçar uma linha reta de forma a acertar todos os exemplos possíveis.\n", + "\n", + "O problema XOR é um exemplo clássico das limitações do perceptron, e foi apontado por Marvin Minsky e Seymour Papert em 1969 no livro [Perceptrons](https://en.wikipedia.org/wiki/Perceptrons_(book)). Essa observação limitou as pesquisas na área de redes neurais por quase 10 anos, embora - e veremos isso na próxima seção do nosso curso - perceptrons multicamadas sejam perfeitamente capazes de resolver tais problemas.\n", + "\n", + "## Exemplo Complexo - MNIST\n", + "\n", + "Embora o perceptron não consiga resolver o problema XOR, ele pode resolver problemas muito mais complexos, como o reconhecimento de caracteres manuscritos.\n", + "\n", + "Um conjunto de dados frequentemente utilizado ao aprender machine learning é chamado [MNIST](https://en.wikipedia.org/wiki/MNIST_database). Ele foi criado pelo Instituto Nacional de Padrões e Tecnologia Modificado e contém um conjunto de treinamento com 60.000 dígitos manuscritos, coletados de cerca de 250 estudantes e funcionários do instituto. Há também um conjunto de teste com 10.000 dígitos, coletados de diferentes indivíduos.\n", + "\n", + "Todos os dígitos são representados por imagens em escala de cinza de tamanho 28x28 pixels.\n", + "\n", + "> O conjunto de dados MNIST está disponível como uma competição de treinamento no [Kaggle](https://www.kaggle.com/c/digit-recognizer), um site que hospeda competições e desafios de machine learning. Assim que você aprender a classificar os dígitos do MNIST, pode enviar sua solução ao Kaggle para ver como ela é avaliada entre outros participantes.\n", + "\n", + "Começamos carregando o conjunto de dados MNIST:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "# If you are not running this notebook from a cloned repository, you may need to grab the binary dataset file first\n", + "# !wget https://github.com/microsoft/AI-For-Beginners/raw/main/data/mnist.pkl.gz?raw=true\n", + "# In this case correct the link to the dataset below as well.\n", + "\n", + "with gzip.open('../../../data/mnist.pkl.gz', 'rb') as mnist_pickle:\n", + " MNIST = pickle.load(mnist_pickle, encoding='latin1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos agora plotar o conjunto de dados:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 0 188 255 94 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 191 250 253 93 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + "1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(MNIST['Train']['Features'][0][130:180])\n", + "print(MNIST['Train']['Labels'][0])\n", + "features = MNIST['Train']['Features'].astype(np.float32) / 256.0\n", + "labels = MNIST['Train']['Labels']\n", + "fig = pylab.figure(figsize=(10,5))\n", + "for i in range(10):\n", + " ax = fig.add_subplot(1,10,i+1)\n", + " pylab.imshow(features[i].reshape(28,28))\n", + "pylab.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Porque o perceptron é um classificador binário, limitaremos nosso problema ao reconhecimento de apenas dois dígitos. A função abaixo irá preencher os arrays de amostras positivas e negativas com dois dígitos fornecidos (e também mostrará amostras desses dígitos para maior clareza).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "def set_mnist_pos_neg(positive_label, negative_label):\n", + " positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n", + " if j == positive_label]\n", + " negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n", + " if j == negative_label]\n", + "\n", + " positive_images = MNIST['Train']['Features'][positive_indices]\n", + " negative_images = MNIST['Train']['Features'][negative_indices]\n", + "\n", + " fig = pylab.figure()\n", + " ax = fig.add_subplot(1, 2, 1)\n", + " pylab.imshow(positive_images[0].reshape(28,28), cmap='gray', interpolation='nearest')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " ax = fig.add_subplot(1, 2, 2)\n", + " pylab.imshow(negative_images[0].reshape(28,28), cmap='gray', interpolation='nearest')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " pylab.show()\n", + " \n", + " return positive_images, negative_images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos começar tentando classificar entre 0 e 1:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pos1,neg1 = set_mnist_pos_neg(1,0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def plotit2(snapshots_mn,step):\n", + " fig = pylab.figure(figsize=(10,4))\n", + " ax = fig.add_subplot(1, 2, 1)\n", + " pylab.imshow(snapshots_mn[step][0].reshape(28, 28), interpolation='nearest')\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " pylab.colorbar()\n", + " ax = fig.add_subplot(1, 2, 2)\n", + " ax.set_ylim([0,1])\n", + " pylab.plot(np.arange(len(snapshots_mn[:,1])), snapshots_mn[:,1])\n", + " pylab.plot(step, snapshots_mn[step,1], \"bo\")\n", + " pylab.show()\n", + "def pl3(step): plotit2(snapshots_mn,step)\n", + "def pl4(step): plotit2(snapshots_mn2,step) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "afbc754ef0d04c95a1af039574b46a6b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='step', max=66), Output()), _dom_classes=('widget-interac…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snapshots_mn = train_graph(pos1,neg1,1000) \n", + "interact(pl3, step=widgets.IntSlider(value=0, min=0, max=len(snapshots_mn) - 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe como a precisão aumenta rapidamente para quase 100%.\n", + "\n", + "Por favor, mova o controle deslizante para uma posição mais próxima do final do treinamento e observe a matriz de pesos plotada à esquerda. Essa matriz permitirá que você entenda como o perceptron realmente funciona. Você pode ver os valores altos de peso no meio do campo, que correspondem a pixels que geralmente estão presentes no dígito 1, e valores negativos baixos nas laterais, onde estão partes do dígito 0. Assim, se o dígito apresentado ao perceptron for de fato 1, a parte central será multiplicada por valores altos, produzindo um resultado positivo. Por outro lado, quando o perceptron observa o dígito 0, os pixels correspondentes serão multiplicados por números negativos.\n", + "\n", + "> Você pode notar que, se apresentarmos ao perceptron um dígito 1 ligeiramente deslocado horizontalmente, de forma que seus pixels ocupem o lugar onde há partes verticais do dígito 0, podemos obter um resultado incorreto. Isso ocorre porque a natureza do nosso conjunto de dados MNIST é tal que todos os dígitos estão centralizados e posicionados corretamente, e o perceptron depende disso para distinguir entre os dígitos.\n", + "\n", + "Agora vamos tentar diferentes dígitos: \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pos2,neg2 = set_mnist_pos_neg(2,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bf6f25d14c3b4d548ac026af97f70e2a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='step', max=66), Output()), _dom_classes=('widget-interac…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "snapshots_mn2 = train_graph(pos2,neg2,1000)\n", + "interact(pl4, step=widgets.IntSlider(value=0, min=0, max=len(snapshots_mn2) - 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Discussão\n", + "\n", + "Por algum motivo, 2 e 5 não são tão facilmente separáveis. Mesmo que obtenhamos uma precisão relativamente alta (acima de 85%), podemos claramente perceber que o perceptron para de aprender em determinado momento.\n", + "\n", + "Para entender por que isso acontece, podemos tentar usar a [Análise de Componentes Principais](https://pt.wikipedia.org/wiki/An%C3%A1lise_de_componentes_principais) (PCA). É uma técnica de aprendizado de máquina usada para reduzir a dimensionalidade do conjunto de dados de entrada, de forma a obter a melhor separabilidade entre as classes.\n", + "\n", + "No nosso caso, uma imagem de entrada tem 784 pixels (características de entrada), e queremos usar o PCA para reduzir o número de parâmetros para apenas 2, para que possamos plotá-los em um gráfico. Esses dois parâmetros seriam uma combinação linear das características originais, e podemos visualizar esse procedimento como uma \"rotação\" do nosso espaço original de 784 dimensões, observando sua projeção no espaço 2D, até obtermos a melhor visualização que separa as classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "def pca_analysis(positive_label, negative_label):\n", + " positive_images, negative_images = set_mnist_pos_neg(positive_label, negative_label)\n", + " M = np.append(positive_images, negative_images, 0)\n", + "\n", + " mypca = PCA(n_components=2)\n", + " mypca.fit(M)\n", + " \n", + " pos_points = mypca.transform(positive_images[:200])\n", + " neg_points = mypca.transform(negative_images[:200])\n", + "\n", + " pylab.plot(pos_points[:,0], pos_points[:,1], 'bo')\n", + " pylab.plot(neg_points[:,0], neg_points[:,1], 'ro')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pca_analysis(2,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Como você pode ver, 0 e 1 podem ser claramente separados por uma linha reta. Isso indica que, no espaço original de 784 dimensões, os pontos correspondentes aos dígitos também são linearmente separáveis. No caso de 2 e 5, não conseguimos encontrar uma boa projeção que separe os dígitos claramente, e, portanto, há alguns casos de classificação incorreta.\n", + "\n", + "> Mais adiante neste curso, aprenderemos como criar classificadores não lineares usando Redes Neurais e como lidar com o problema de dígitos que não estão devidamente alinhados. Muito em breve, alcançaremos mais de 99% de precisão na classificação de dígitos do MNIST, classificando-os em 10 diferentes classes.\n", + "\n", + "## Conclusão\n", + "\n", + " * Aprendemos sobre a arquitetura mais simples de rede neural - o perceptron de uma camada.\n", + " * Implementamos o perceptron \"manualmente\", usando um procedimento de treinamento simples baseado no gradiente descendente.\n", + " * Apesar da simplicidade, o perceptron de uma camada pode resolver problemas relativamente complexos de reconhecimento de dígitos manuscritos.\n", + " * O perceptron de uma camada é um classificador linear e, portanto, oferece o mesmo poder de classificação que a regressão logística.\n", + " * No espaço amostral, o perceptron pode separar duas classes de dados de entrada usando um hiperplano.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Créditos\n", + "\n", + "Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners) e foi preparado por [Dmitry Soshnikov](http://soshnikov.com). Ele é inspirado no Workshop de Redes Neurais da Microsoft Research Cambridge. Parte do código e materiais ilustrativos foram retirados de apresentações de [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/) e [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/), além do repositório [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n\n\n**Aviso Legal**: \nEste documento foi traduzido usando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução.\n\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "interpreter": { + "hash": "16aeaa504b544176258e5caf576fc030dfd6fff62d0c15825e7863ff13e121ff" + }, + "kernelspec": { + "display_name": "Python 3.8.0 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "coopTranslator": { + "original_hash": "2a7a86d0006b9dd5c26493a7ed51e511", + "translation_date": "2025-12-12T18:15:41+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/README.md new file mode 100644 index 00000000..680f2646 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/README.md @@ -0,0 +1,95 @@ +# Introdução às Redes Neurais: Perceptron + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/5) + +Uma das primeiras tentativas de implementar algo semelhante a uma rede neural moderna foi realizada por Frank Rosenblatt, do Laboratório Aeronáutico de Cornell, em 1957. Foi uma implementação em hardware chamada "Mark-1", projetada para reconhecer figuras geométricas primitivas, como triângulos, quadrados e círculos. + +| | | +|--------------|-----------| +|Frank Rosenblatt | O Perceptron Mark 1| + +> Imagens [da Wikipedia](https://en.wikipedia.org/wiki/Perceptron) + +Uma imagem de entrada era representada por uma matriz de fotocélulas de 20x20, então a rede neural tinha 400 entradas e uma saída binária. Uma rede simples continha um único neurônio, também chamado de **unidade lógica de limiar**. Os pesos da rede neural funcionavam como potenciômetros que precisavam ser ajustados manualmente durante a fase de treinamento. + +> ✅ Um potenciômetro é um dispositivo que permite ao usuário ajustar a resistência de um circuito. + +> O New York Times escreveu sobre o perceptron na época: *o embrião de um computador eletrônico que [a Marinha] espera que seja capaz de andar, falar, ver, escrever, reproduzir-se e estar consciente de sua existência.* + +## Modelo de Perceptron + +Suponha que temos N características em nosso modelo, caso em que o vetor de entrada seria um vetor de tamanho N. Um perceptron é um modelo de **classificação binária**, ou seja, ele pode distinguir entre duas classes de dados de entrada. Vamos assumir que, para cada vetor de entrada x, a saída do nosso perceptron será +1 ou -1, dependendo da classe. A saída será calculada usando a fórmula: + +y(x) = f(wTx) + +onde f é uma função de ativação em degrau + + + + +## Treinando o Perceptron + +Para treinar um perceptron, precisamos encontrar um vetor de pesos w que classifique a maioria dos valores corretamente, ou seja, que resulte no menor **erro**. Este erro E é definido pelo **critério do perceptron** da seguinte maneira: + +E(w) = -∑wTxiti + +onde: + +* a soma é feita sobre os pontos de dados de treinamento i que resultam em classificação incorreta +* xi é o dado de entrada, e ti é -1 ou +1 para exemplos negativos e positivos, respectivamente. + +Este critério é considerado como uma função dos pesos w, e precisamos minimizá-lo. Frequentemente, é usado um método chamado **descida de gradiente**, no qual começamos com alguns pesos iniciais w(0), e então, a cada passo, atualizamos os pesos de acordo com a fórmula: + +w(t+1) = w(t) - η∇E(w) + +Aqui, η é a chamada **taxa de aprendizado**, e ∇E(w) denota o **gradiente** de E. Após calcular o gradiente, obtemos: + +w(t+1) = w(t) + ∑ηxiti + +O algoritmo em Python se parece com isto: + +```python +def train(positive_examples, negative_examples, num_iterations = 100, eta = 1): + + weights = [0,0,0] # Initialize weights (almost randomly :) + + for i in range(num_iterations): + pos = random.choice(positive_examples) + neg = random.choice(negative_examples) + + z = np.dot(pos, weights) # compute perceptron output + if z < 0: # positive example classified as negative + weights = weights + eta*weights.shape + + z = np.dot(neg, weights) + if z >= 0: # negative example classified as positive + weights = weights - eta*weights.shape + + return weights +``` + +## Conclusão + +Nesta aula, você aprendeu sobre o perceptron, que é um modelo de classificação binária, e como treiná-lo usando um vetor de pesos. + +## 🚀 Desafio + +Se você quiser tentar construir seu próprio perceptron, experimente [este laboratório no Microsoft Learn](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-77998-cacaste), que utiliza o [Azure ML designer](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-77998-cacaste). + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/6) + +## Revisão e Autoestudo + +Para ver como podemos usar o perceptron para resolver um problema simples, bem como problemas da vida real, e para continuar aprendendo - acesse o notebook [Perceptron](Perceptron.ipynb). + +Aqui está um [artigo interessante sobre perceptrons](https://towardsdatascience.com/what-is-a-perceptron-basics-of-neural-networks-c4cfea20c590). + +## [Tarefa](lab/README.md) + +Nesta aula, implementamos um perceptron para uma tarefa de classificação binária e o usamos para classificar entre dois dígitos manuscritos. Neste laboratório, você será solicitado a resolver o problema de classificação de dígitos completamente, ou seja, determinar qual dígito é mais provável de corresponder a uma imagem dada. + +* [Instruções](lab/README.md) +* [Notebook](lab/PerceptronMultiClass.ipynb) + +--- + diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb new file mode 100644 index 00000000..d437adc4 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Classificação Multi-Classe com Perceptron\n", + "\n", + "Atividade prática do [Currículo de IA para Iniciantes](https://github.com/microsoft/ai-for-beginners).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pickle\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode usar o seguinte código de treinamento do perceptron da aula:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def train(positive_examples, negative_examples, num_iterations = 100):\n", + " num_dims = positive_examples.shape[1]\n", + " weights = np.zeros((num_dims,1)) # initialize weights\n", + " \n", + " pos_count = positive_examples.shape[0]\n", + " neg_count = negative_examples.shape[0]\n", + " \n", + " report_frequency = 10\n", + " \n", + " for i in range(num_iterations):\n", + " pos = random.choice(positive_examples)\n", + " neg = random.choice(negative_examples)\n", + "\n", + " z = np.dot(pos, weights) \n", + " if z < 0:\n", + " weights = weights + pos.reshape(weights.shape)\n", + "\n", + " z = np.dot(neg, weights)\n", + " if z >= 0:\n", + " weights = weights - neg.reshape(weights.shape)\n", + " \n", + " if i % report_frequency == 0: \n", + " pos_out = np.dot(positive_examples, weights)\n", + " neg_out = np.dot(negative_examples, weights) \n", + " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n", + " neg_correct = (neg_out < 0).sum() / float(neg_count)\n", + " print(\"Iteration={}, pos correct={}, neg correct={}\".format(i,pos_correct,neg_correct))\n", + "\n", + " return weights" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def accuracy(weights, test_x, test_labels):\n", + " res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)\n", + " return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))\n", + "\n", + "accuracy(wts, test_x, test_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Lendo o Conjunto de Dados\n", + "\n", + "Este código baixa o conjunto de dados do repositório na internet. Você também pode copiar manualmente o conjunto de dados do diretório `/data` do repositório AI Curriculum.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "!rm *.pkl\n", + "https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz", + "!gzip -d mnist.pkl.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "with open('mnist.pkl', 'rb') as mnist_pickle:\n", + " MNIST = pickle.load(mnist_pickle)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 0 188 255 94 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 191 250 253 93 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", + "1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(MNIST['Train']['Features'][0][130:180])\n", + "print(MNIST['Train']['Labels'][0])\n", + "features = MNIST['Train']['Features'].astype(np.float32) / 256.0\n", + "labels = MNIST['Train']['Labels']\n", + "fig = plt.figure(figsize=(10,5))\n", + "for i in range(10):\n", + " ax = fig.add_subplot(1,10,i+1)\n", + " plt.imshow(features[i].reshape(28,28))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Código para criar *one-vs-other* dataset para classificação de dois dígitos. Você precisa modificar este código para criar *one-vs-all* dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "def set_mnist_pos_neg(positive_label, negative_label):\n", + " positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n", + " if j == positive_label]\n", + " negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n", + " if j == negative_label]\n", + "\n", + " positive_images = MNIST['Train']['Features'][positive_indices]\n", + " negative_images = MNIST['Train']['Features'][negative_indices]\n", + "\n", + " return positive_images, negative_images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora você precisa: \n", + "1. Criar 10 conjuntos de dados *um-contra-todos* para todos os dígitos \n", + "1. Treinar 10 perceptrons \n", + "1. Definir a função `classify` para realizar a classificação de dígitos \n", + "1. Medir a precisão da classificação e imprimir a *matriz de confusão* \n", + "1. [Opcional] Criar uma função `classify` aprimorada que realize a classificação usando uma única multiplicação de matriz. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "coopTranslator": { + "original_hash": "346132688ca5e351b17b56c6166067e2", + "translation_date": "2025-08-28T13:45:16+00:00", + "source_file": "lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md new file mode 100644 index 00000000..444c59ff --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md @@ -0,0 +1,24 @@ +# Classificação Multi-Classe com Perceptron + +Atividade prática do [Currículo de IA para Iniciantes](https://github.com/microsoft/ai-for-beginners). + +## Tarefa + +Usando o código que desenvolvemos nesta lição para classificação binária de dígitos manuscritos do MNIST, crie um classificador multi-classe que seja capaz de reconhecer qualquer dígito. Calcule a precisão da classificação nos conjuntos de dados de treinamento e teste, e exiba a matriz de confusão. + +## Dicas + +1. Para cada dígito, crie um conjunto de dados para o classificador binário de "este dígito vs. todos os outros dígitos". +1. Treine 10 perceptrons diferentes para classificação binária (um para cada dígito). +1. Defina uma função que classifique um dígito de entrada. + +> **Dica**: Se combinarmos os pesos de todos os 10 perceptrons em uma matriz, devemos ser capazes de aplicar todos os 10 perceptrons aos dígitos de entrada por meio de uma única multiplicação de matriz. O dígito mais provável pode então ser encontrado simplesmente aplicando a operação `argmax` no resultado. + +## Notebook Inicial + +Inicie a atividade abrindo [PerceptronMultiClass.ipynb](PerceptronMultiClass.ipynb) + +--- + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb new file mode 100644 index 00000000..26f4d374 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb @@ -0,0 +1,1336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Perceptrons Multicamadas\n", + "## Construindo nossa própria estrutura neural\n", + "\n", + "> Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners). Visite o repositório para acessar o conjunto completo de materiais de aprendizado.\n", + "\n", + "Neste notebook, vamos construir gradualmente nossa própria estrutura neural capaz de resolver tarefas de classificação multiclasse, bem como regressão, utilizando perceptrons multicamadas.\n", + "\n", + "Primeiro, vamos importar algumas bibliotecas necessárias.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "%matplotlib nbagg\n", + "import matplotlib.pyplot as plt \n", + "from matplotlib import gridspec\n", + "from sklearn.datasets import make_classification\n", + "import numpy as np\n", + "# pick the seed for reproducibility - change it to explore the effects of random variations\n", + "np.random.seed(0)\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Conjunto de Dados de Exemplo\n", + "\n", + "Como antes, começaremos com um conjunto de dados de exemplo simples com dois parâmetros.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [], + "source": [ + "n = 100\n", + "X, Y = make_classification(n_samples = n, n_features=2,\n", + " n_redundant=0, n_informative=2, flip_y=0.2)\n", + "X = X.astype(np.float32)\n", + "Y = Y.astype(np.int32)\n", + "\n", + "# Split into train and test dataset\n", + "train_x, test_x = np.split(X, [n*8//10])\n", + "train_labels, test_labels = np.split(Y, [n*8//10])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def plot_dataset(suptitle, features, labels):\n", + " # prepare the plot\n", + " fig, ax = plt.subplots(1, 1)\n", + " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\n", + " fig.suptitle(suptitle, fontsize = 16)\n", + " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n", + " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n", + "\n", + " colors = ['r' if l else 'b' for l in labels]\n", + " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. 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It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset('Scatterplot of the training data', train_x, train_labels)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.3382818 -0.98613256]\n", + " [ 0.5128146 0.43299454]\n", + " [-0.4473693 -0.2680512 ]\n", + " [-0.9865851 -0.28692 ]\n", + " [-1.0693829 0.41718036]]\n", + "[1 1 0 0 0]\n" + ] + } + ], + "source": [ + "print(train_x[:5])\n", + "print(train_labels[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Problema de Machine Learning\n", + "\n", + "Suponha que temos um conjunto de dados de entrada $\\langle X,Y\\rangle$, onde $X$ é um conjunto de características e $Y$ são os rótulos correspondentes. Para um problema de regressão, $y_i\\in\\mathbb{R}$, e para classificação, ele é representado por um número de classe $y_i\\in\\{0,\\dots,n\\}$.\n", + "\n", + "Qualquer modelo de machine learning pode ser representado por uma função $f_\\theta(x)$, onde $\\theta$ é um conjunto de **parâmetros**. Nosso objetivo é encontrar os parâmetros $\\theta$ de forma que nosso modelo se ajuste ao conjunto de dados da melhor maneira possível. O critério é definido por uma **função de perda** $\\mathcal{L}$, e precisamos encontrar o valor ótimo\n", + "\n", + "$$\n", + "\\theta = \\mathrm{argmin}_\\theta \\mathcal{L}(f_\\theta(X),Y)\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "A função de perda depende do problema que está sendo resolvido.\n", + "\n", + "### Funções de perda para regressão\n", + "\n", + "Para regressão, frequentemente usamos o **erro absoluto** $\\mathcal{L}_{abs}(\\theta) = \\sum_{i=1}^n |y_i - f_{\\theta}(x_i)|$, ou o **erro quadrático médio**: $\\mathcal{L}_{sq}(\\theta) = \\sum_{i=1}^n (y_i - f_{\\theta}(x_i))^2$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "# helper function for plotting various loss functions\n", + "def plot_loss_functions(suptitle, functions, ylabels, xlabel):\n", + " fig, ax = plt.subplots(1,len(functions), figsize=(9, 3))\n", + " plt.subplots_adjust(bottom=0.2, wspace=0.4)\n", + " fig.suptitle(suptitle)\n", + " for i, fun in enumerate(functions):\n", + " ax[i].set_xlabel(xlabel)\n", + " if len(ylabels) > i:\n", + " ax[i].set_ylabel(ylabels[i])\n", + " ax[i].plot(x, fun)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; 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position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? 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\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-2, 2, 101)\n", + "plot_loss_functions(\n", + " suptitle = 'Common loss functions for regression',\n", + " functions = [np.abs(x), np.power(x, 2)],\n", + " ylabels = ['$\\mathcal{L}_{abs}}$ (absolute loss)',\n", + " '$\\mathcal{L}_{sq}$ (squared loss)'],\n", + " xlabel = '$y - f(x_i)$')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Funções de perda para classificação\n", + "\n", + "Vamos considerar a classificação binária por um momento. Nesse caso, temos duas classes, numeradas como 0 e 1. A saída da rede $f_\\theta(x_i)\\in [0,1]$ essencialmente define a probabilidade de escolher a classe 1.\n", + "\n", + "**Perda 0-1**\n", + "\n", + "A perda 0-1 é a mesma coisa que calcular a acurácia do modelo - computamos o número de classificações corretas:\n", + "\n", + "$$\\mathcal{L}_{0-1} = \\sum_{i=1}^n l_i \\quad l_i = \\begin{cases}\n", + " 0 & (f(x_i)<0.5 \\land y_i=0) \\lor (f(x_i)<0.5 \\land y_i=1) \\\\\n", + " 1 & \\mathrm{ caso \\ contrário}\n", + " \\end{cases} \\\\\n", + "$$\n", + "\n", + "No entanto, a acurácia por si só não mostra o quão longe estamos da classificação correta. Pode ser que tenhamos errado a classe correta por muito pouco, e isso é, de certa forma, \"melhor\" (no sentido de que precisamos ajustar os pesos muito menos) do que errar significativamente. Por isso, mais frequentemente é usada a perda logística, que leva isso em consideração.\n", + "\n", + "**Perda Logística**\n", + "\n", + "$$\\mathcal{L}_{log} = \\sum_{i=1}^n -y\\log(f_{\\theta}(x_i)) - (1-y)\\log(1-f_\\theta(x_i))$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(0,1,100)\n", + "def zero_one(d):\n", + " if d < 0.5:\n", + " return 0\n", + " return 1\n", + "zero_one_v = np.vectorize(zero_one)\n", + "\n", + "def logistic_loss(fx):\n", + " # assumes y == 1\n", + " return -np.log(fx)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_55820/331859503.py:10: RuntimeWarning: divide by zero encountered in log\n", + " return -np.log(fx)\n" + ] + }, + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_loss_functions(suptitle = 'Common loss functions for classification (class=1)',\n", + " functions = [zero_one_v(x), logistic_loss(x)],\n", + " ylabels = ['$\\mathcal{L}_{0-1}}$ (0-1 loss)',\n", + " '$\\mathcal{L}_{log}$ (logistic loss)'],\n", + " xlabel = '$p$')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para entender a perda logística, considere dois casos para o resultado esperado:\n", + "* Se esperamos que o resultado seja 1 ($y=1$), então a perda é $-log f_\\theta(x_i)$. A perda é 0 quando a rede prevê 1 com probabilidade 1, e aumenta conforme a probabilidade de 1 diminui.\n", + "* Se esperamos que o resultado seja 0 ($y=0$), a perda é $-log(1-f_\\theta(x_i))$. Aqui, $1-f_\\theta(x_i)$ é a probabilidade de 0 prevista pela rede, e o significado da perda logarítmica é o mesmo descrito no caso anterior.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Arquitetura de Rede Neural\n", + "\n", + "Geramos um conjunto de dados para um problema de classificação binária. No entanto, vamos considerá-lo como um problema de classificação multiclasse desde o início, para que possamos facilmente adaptar nosso código para classificação multiclasse. Nesse caso, nosso perceptron de uma camada terá a seguinte arquitetura:\n", + "\n", + "Os dois outputs da rede correspondem a duas classes, e a classe com o maior valor entre os dois outputs corresponde à solução correta.\n", + "\n", + "O modelo é definido como\n", + "$$\n", + "f_\\theta(x) = W\\times x + b\n", + "$$\n", + "onde $$\\theta = \\langle W,b\\rangle$$ são os parâmetros.\n", + "\n", + "Definiremos essa camada linear como uma classe Python com uma função `forward` que realiza o cálculo. Ela recebe o valor de entrada $x$ e produz o output da camada. Os parâmetros `W` e `b` são armazenados dentro da classe da camada e são inicializados na criação com valores aleatórios e zeros, respectivamente.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.77202116, -0.25384488],\n", + " [ 0.28370828, -0.39610552],\n", + " [-0.30097433, 0.30513182],\n", + " [-0.8120485 , 0.56079421],\n", + " [-1.23519653, 0.3394973 ]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Linear:\n", + " def __init__(self,nin,nout):\n", + " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n", + " self.b = np.zeros((1,nout))\n", + " \n", + " def forward(self, x):\n", + " return np.dot(x, self.W.T) + self.b\n", + " \n", + "net = Linear(2,2)\n", + "net.forward(train_x[0:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Em muitos casos, é mais eficiente operar não com um único valor de entrada, mas com um vetor de valores de entrada. Como utilizamos operações do Numpy, podemos passar um vetor de valores de entrada para nossa rede, e ela nos retornará um vetor de valores de saída.\n", + "\n", + "## Softmax: Transformando Saídas em Probabilidades\n", + "\n", + "Como você pode ver, nossas saídas não são probabilidades - elas podem assumir qualquer valor. Para convertê-las em probabilidades, precisamos normalizar os valores entre todas as classes. Isso é feito utilizando a função **softmax**: $$\\sigma(\\mathbf{z}_c) = \\frac{e^{z_c}}{\\sum_{j} e^{z_j}}, \\quad\\mathrm{para}\\quad c\\in 1 .. |C|$$\n", + "\n", + "\n", + "\n", + "> A saída da rede $\\sigma(\\mathbf{z})$ pode ser interpretada como uma distribuição de probabilidade no conjunto de classes $C$: $q = \\sigma(\\mathbf{z}_c) = \\hat{p}(c | x)$\n", + "\n", + "Definiremos a camada `Softmax` da mesma forma, como uma classe com a função `forward`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.88348621, 0.11651379],\n", + " [0.66369714, 0.33630286],\n", + " [0.35294795, 0.64705205],\n", + " [0.20216095, 0.79783905],\n", + " [0.17154828, 0.82845172],\n", + " [0.24279153, 0.75720847],\n", + " [0.18915732, 0.81084268],\n", + " [0.17282951, 0.82717049],\n", + " [0.13897531, 0.86102469],\n", + " [0.72746882, 0.27253118]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class Softmax:\n", + " def forward(self,z):\n", + " zmax = z.max(axis=1,keepdims=True)\n", + " expz = np.exp(z-zmax)\n", + " Z = expz.sum(axis=1,keepdims=True)\n", + " return expz / Z\n", + "\n", + "softmax = Softmax()\n", + "softmax.forward(net.forward(train_x[0:10]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Agora podemos ver que estamos obtendo probabilidades como resultados, ou seja, a soma de cada vetor de saída é exatamente 1.\n", + "\n", + "No caso de termos mais de 2 classes, o softmax irá normalizar as probabilidades entre todas elas. Aqui está um diagrama da arquitetura de rede que realiza a classificação de dígitos do MNIST:\n", + "\n", + "![Classificador MNIST](../../../../../translated_images/pt-BR/Cross-Entropy-Loss.7acff482d48cc41b2d3ef85ff0b263fbab299da128a462fa9b0640c5b7bc12ef.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Perda por Entropia Cruzada\n", + "\n", + "Uma função de perda em classificação é tipicamente uma função logística, que pode ser generalizada como **perda por entropia cruzada**. A perda por entropia cruzada é uma função que pode calcular a similaridade entre duas distribuições de probabilidade arbitrárias. Você pode encontrar uma discussão mais detalhada sobre isso [na Wikipedia](https://en.wikipedia.org/wiki/Cross_entropy).\n", + "\n", + "No nosso caso, a primeira distribuição é a saída probabilística da nossa rede, e a segunda é a chamada distribuição **one-hot**, que especifica que uma determinada classe $c$ tem probabilidade correspondente de 1 (todas as outras sendo 0). Nesse caso, a perda por entropia cruzada pode ser calculada como $-\\log p_c$, onde $c$ é a classe esperada, e $p_c$ é a probabilidade correspondente dessa classe fornecida pela nossa rede neural.\n", + "\n", + "> Se a rede retornar probabilidade 1 para a classe esperada, a perda por entropia cruzada será 0. Quanto mais próxima de 0 for a probabilidade da classe real, maior será a perda por entropia cruzada (e ela pode chegar ao infinito!).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def plot_cross_ent():\n", + " p = np.linspace(0.01, 0.99, 101) # estimated probability p(y|x)\n", + " cross_ent_v = np.vectorize(cross_ent)\n", + " f3, ax = plt.subplots(1,1, figsize=(8, 3))\n", + " l1, = plt.plot(p, cross_ent_v(p, 1), 'r--')\n", + " l2, = plt.plot(p, cross_ent_v(p, 0), 'r-')\n", + " plt.legend([l1, l2], ['$y = 1$', '$y = 0$'], loc = 'upper center', ncol = 2)\n", + " plt.xlabel('$\\hat{p}(y|x)$', size=18)\n", + " plt.ylabel('$\\mathcal{L}_{CE}$', size=18)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. 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Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def cross_ent(prediction, ground_truth):\n", + " t = 1 if ground_truth > 0.5 else 0\n", + " return -t * np.log(prediction) - (1 - t) * np.log(1 - prediction)\n", + "plot_cross_ent()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A perda de entropia cruzada será definida novamente como uma camada separada, mas a função `forward` terá dois valores de entrada: a saída das camadas anteriores da rede `p` e a classe esperada `y`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.429664938969559" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class CrossEntropyLoss:\n", + " def forward(self,p,y):\n", + " self.p = p\n", + " self.y = y\n", + " p_of_y = p[np.arange(len(y)), y]\n", + " log_prob = np.log(p_of_y)\n", + " return -log_prob.mean() # average over all input samples\n", + "\n", + "cross_ent_loss = CrossEntropyLoss()\n", + "p = softmax.forward(net.forward(train_x[0:10]))\n", + "cross_ent_loss.forward(p,train_labels[0:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Até este momento, definimos diferentes classes para diferentes camadas da rede. A composição dessas camadas pode ser representada como **grafo computacional**. Agora podemos calcular a perda para um conjunto de dados de treinamento (ou parte dele) da seguinte maneira:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.429664938969559\n" + ] + } + ], + "source": [ + "z = net.forward(train_x[0:10])\n", + "p = softmax.forward(z)\n", + "loss = cross_ent_loss.forward(p,train_labels[0:10])\n", + "print(loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Problema de Minimização de Perda e Treinamento de Redes\n", + "\n", + "Uma vez que definimos nossa rede como $f_\\theta$, e dada a função de perda $\\mathcal{L}(Y,f_\\theta(X))$, podemos considerar $\\mathcal{L}$ como uma função de $\\theta$ em nosso conjunto de dados de treinamento fixo: $\\mathcal{L}(\\theta) = \\mathcal{L}(Y,f_\\theta(X))$\n", + "\n", + "Nesse caso, o treinamento da rede seria um problema de minimização de $\\mathcal{L}$ em relação ao argumento $\\theta$:\n", + "$$\n", + "\\theta = \\mathrm{argmin}_{\\theta} \\mathcal{L}(Y,f_\\theta(X))\n", + "$$\n", + "\n", + "Existe um método bem conhecido de otimização de funções chamado **descida do gradiente**. A ideia é que podemos calcular a derivada (no caso multidimensional, chamada de **gradiente**) da função de perda em relação aos parâmetros e ajustar os parâmetros de forma que o erro diminua.\n", + "\n", + "A descida do gradiente funciona da seguinte maneira:\n", + " * Inicialize os parâmetros com alguns valores aleatórios $w^{(0)}$, $b^{(0)}$\n", + " * Repita o seguinte passo várias vezes:\n", + "\n", + " $$\\begin{align}\n", + " W^{(i+1)}&=W^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W}\\\\\n", + " b^{(i+1)}&=b^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b}\n", + " \\end{align}\n", + " $$\n", + "\n", + "Durante o treinamento, os passos de otimização devem ser calculados considerando todo o conjunto de dados (lembre-se de que a perda é calculada como uma soma/média de todas as amostras de treinamento). No entanto, na prática, utilizamos pequenas porções do conjunto de dados chamadas de **minibatches**, e calculamos os gradientes com base em um subconjunto dos dados. Como o subconjunto é escolhido aleatoriamente a cada vez, esse método é chamado de **descida do gradiente estocástica** (SGD).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Propagação para Trás\n", + "\n", + "\n", + "\n", + "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n", + "\\begin{align}\n", + "\\zz{\\L}{W} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{W}\\cr\n", + "\\zz{\\L}{b} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{b}\n", + "\\end{align}\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Para calcular $\\partial\\mathcal{L}/\\partial W$, podemos usar a **regra da cadeia** para derivadas de uma função composta, como mostrado nas fórmulas acima. Isso corresponde à seguinte ideia:\n", + "\n", + "* Suponha que, dado um input, obtemos a perda $\\Delta\\mathcal{L}$\n", + "* Para minimizá-la, precisaríamos ajustar a saída softmax $p$ pelo valor $\\Delta p = (\\partial\\mathcal{L}/\\partial p)\\Delta\\mathcal{L}$ \n", + "* Isso corresponde às alterações no nó $z$ por $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n", + "* Para minimizar esse erro, precisamos ajustar os parâmetros de forma correspondente: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (e o mesmo para $b$)\n", + "\n", + "Esse processo começa distribuindo o erro da perda da saída da rede de volta para seus parâmetros. Assim, o processo é chamado de **retropropagação**.\n", + "\n", + "Uma iteração do treinamento da rede consiste em duas partes:\n", + "* **Passagem direta**, quando calculamos o valor da função de perda para um minibatch de entrada dado\n", + "* **Passagem reversa**, quando tentamos minimizar esse erro distribuindo-o de volta para os parâmetros do modelo através do grafo computacional.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Implementação da Retropropagação\n", + "\n", + "* Vamos adicionar a função `backward` a cada um dos nossos nós, que calculará a derivada e propagará o erro durante a passagem para trás.\n", + "* Também precisamos implementar as atualizações de parâmetros de acordo com o procedimento descrito acima.\n", + "\n", + "Precisamos calcular as derivadas para cada camada manualmente, por exemplo, para a camada linear $z = x\\times W+b$:\n", + "$$\\begin{align}\n", + "\\frac{\\partial z}{\\partial W} &= x \\\\\n", + "\\frac{\\partial z}{\\partial b} &= 1 \\\\\n", + "\\end{align}$$\n", + "\n", + "Se precisarmos compensar o erro $\\Delta z$ na saída da camada, precisamos atualizar os pesos de acordo:\n", + "$$\\begin{align}\n", + "\\Delta x &= \\Delta z \\times W \\\\\n", + "\\Delta W &= \\frac{\\partial z}{\\partial W} \\Delta z = \\Delta z \\times x \\\\\n", + "\\Delta b &= \\frac{\\partial z}{\\partial b} \\Delta z = \\Delta z \\\\\n", + "\\end{align}$$\n", + "\n", + "**IMPORTANTE:** Os cálculos não são feitos para cada amostra de treinamento individualmente, mas sim para todo o **minilote**. As atualizações de parâmetros necessárias $\\Delta W$ e $\\Delta b$ são calculadas para todo o minilote, e os respectivos vetores têm as dimensões: $x\\in\\mathbb{R}^{\\mathrm{minibatch}\\, \\times\\, \\mathrm{nclass}}$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "class Linear:\n", + " def __init__(self,nin,nout):\n", + " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n", + " self.b = np.zeros((1,nout))\n", + " self.dW = np.zeros_like(self.W)\n", + " self.db = np.zeros_like(self.b)\n", + " \n", + " def forward(self, x):\n", + " self.x=x\n", + " return np.dot(x, self.W.T) + self.b\n", + " \n", + " def backward(self, dz):\n", + " dx = np.dot(dz, self.W)\n", + " dW = np.dot(dz.T, self.x)\n", + " db = dz.sum(axis=0)\n", + " self.dW = dW\n", + " self.db = db\n", + " return dx\n", + " \n", + " def update(self,lr):\n", + " self.W -= lr*self.dW\n", + " self.b -= lr*self.db" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Da mesma forma, podemos definir a função `backward` para o restante de nossas camadas:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class Softmax:\n", + " def forward(self,z):\n", + " self.z = z\n", + " zmax = z.max(axis=1,keepdims=True)\n", + " expz = np.exp(z-zmax)\n", + " Z = expz.sum(axis=1,keepdims=True)\n", + " return expz / Z\n", + " def backward(self,dp):\n", + " p = self.forward(self.z)\n", + " pdp = p * dp\n", + " return pdp - p * pdp.sum(axis=1, keepdims=True)\n", + " \n", + "class CrossEntropyLoss:\n", + " def forward(self,p,y):\n", + " self.p = p\n", + " self.y = y\n", + " p_of_y = p[np.arange(len(y)), y]\n", + " log_prob = np.log(p_of_y)\n", + " return -log_prob.mean()\n", + " def backward(self,loss):\n", + " dlog_softmax = np.zeros_like(self.p)\n", + " dlog_softmax[np.arange(len(self.y)), self.y] -= 1.0/len(self.y)\n", + " return dlog_softmax / self.p" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Treinando o Modelo\n", + "\n", + "Agora estamos prontos para escrever o **loop de treinamento**, que percorrerá nosso conjunto de dados e realizará a otimização minibatch por minibatch. Uma passagem completa pelo conjunto de dados é frequentemente chamada de **uma época**:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial accuracy: 0.725\n", + "Final accuracy: 0.825\n" + ] + } + ], + "source": [ + "lin = Linear(2,2)\n", + "softmax = Softmax()\n", + "cross_ent_loss = CrossEntropyLoss()\n", + "\n", + "learning_rate = 0.1\n", + "\n", + "pred = np.argmax(lin.forward(train_x),axis=1)\n", + "acc = (pred==train_labels).mean()\n", + "print(\"Initial accuracy: \",acc)\n", + "\n", + "batch_size=4\n", + "for i in range(0,len(train_x),batch_size):\n", + " xb = train_x[i:i+batch_size]\n", + " yb = train_labels[i:i+batch_size]\n", + " \n", + " # forward pass\n", + " z = lin.forward(xb)\n", + " p = softmax.forward(z)\n", + " loss = cross_ent_loss.forward(p,yb)\n", + " \n", + " # backward pass\n", + " dp = cross_ent_loss.backward(loss)\n", + " dz = softmax.backward(dp)\n", + " dx = lin.backward(dz)\n", + " lin.update(learning_rate)\n", + " \n", + "pred = np.argmax(lin.forward(train_x),axis=1)\n", + "acc = (pred==train_labels).mean()\n", + "print(\"Final accuracy: \",acc)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "É interessante ver como podemos aumentar a precisão do modelo de cerca de 50% para aproximadamente 80% em apenas uma época.\n", + "\n", + "## Classe da Rede\n", + "\n", + "Como, em muitos casos, uma rede neural é apenas uma composição de camadas, podemos criar uma classe que nos permita empilhar camadas e realizar passagens para frente e para trás através delas sem precisar programar essa lógica explicitamente. Vamos armazenar a lista de camadas dentro da classe `Net` e usar a função `add()` para adicionar novas camadas:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "class Net:\n", + " def __init__(self):\n", + " self.layers = []\n", + " \n", + " def add(self,l):\n", + " self.layers.append(l)\n", + " \n", + " def forward(self,x):\n", + " for l in self.layers:\n", + " x = l.forward(x)\n", + " return x\n", + " \n", + " def backward(self,z):\n", + " for l in self.layers[::-1]:\n", + " z = l.backward(z)\n", + " return z\n", + " \n", + " def update(self,lr):\n", + " for l in self.layers:\n", + " if 'update' in l.__dir__():\n", + " l.update(lr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Com esta classe `Net`, a definição e o treinamento do nosso modelo tornam-se mais organizados:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss=0.6212072429381601, accuracy=0.6875: \n", + "Final loss=0.44369925927417986, accuracy=0.8: \n", + "Test loss=0.4767711377257787, accuracy=0.85: \n" + ] + } + ], + "source": [ + "net = Net()\n", + "net.add(Linear(2,2))\n", + "net.add(Softmax())\n", + "loss = CrossEntropyLoss()\n", + "\n", + "def get_loss_acc(x,y,loss=CrossEntropyLoss()):\n", + " p = net.forward(x)\n", + " l = loss.forward(p,y)\n", + " pred = np.argmax(p,axis=1)\n", + " acc = (pred==y).mean()\n", + " return l,acc\n", + "\n", + "print(\"Initial loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\n", + "\n", + "def train_epoch(net, train_x, train_labels, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n", + " for i in range(0,len(train_x),batch_size):\n", + " xb = train_x[i:i+batch_size]\n", + " yb = train_labels[i:i+batch_size]\n", + "\n", + " p = net.forward(xb)\n", + " l = loss.forward(p,yb)\n", + " dp = loss.backward(l)\n", + " dx = net.backward(dp)\n", + " net.update(lr)\n", + " \n", + "train_epoch(net,train_x,train_labels)\n", + " \n", + "print(\"Final loss={}, accuracy={}: \".format(*get_loss_acc(train_x,train_labels)))\n", + "print(\"Test loss={}, accuracy={}: \".format(*get_loss_acc(test_x,test_labels)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotando o Processo de Treinamento\n", + "\n", + "Seria interessante visualizar como a rede está sendo treinada! Vamos definir uma função `train_and_plot` para isso. Para visualizar o estado da rede, utilizaremos um mapa de níveis, ou seja, representaremos diferentes valores da saída da rede usando cores diferentes.\n", + "\n", + "> Não se preocupe se você não entender parte do código de plotagem abaixo - é mais importante compreender os conceitos fundamentais da rede neural.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def train_and_plot(n_epoch, net, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n", + " fig, ax = plt.subplots(2, 1)\n", + " ax[0].set_xlim(0, n_epoch + 1)\n", + " ax[0].set_ylim(0,1)\n", + "\n", + " train_acc = np.empty((n_epoch, 3))\n", + " train_acc[:] = np.NAN\n", + " valid_acc = np.empty((n_epoch, 3))\n", + " valid_acc[:] = np.NAN\n", + "\n", + " for epoch in range(1, n_epoch + 1):\n", + "\n", + " train_epoch(net,train_x,train_labels,loss,batch_size,lr)\n", + " tloss, taccuracy = get_loss_acc(train_x,train_labels,loss)\n", + " train_acc[epoch-1, :] = [epoch, tloss, taccuracy]\n", + " vloss, vaccuracy = get_loss_acc(test_x,test_labels,loss)\n", + " valid_acc[epoch-1, :] = [epoch, vloss, vaccuracy]\n", + " \n", + " ax[0].set_ylim(0, max(max(train_acc[:, 2]), max(valid_acc[:, 2])) * 1.1)\n", + "\n", + " plot_training_progress(train_acc[:, 0], (train_acc[:, 2],\n", + " valid_acc[:, 2]), fig, ax[0])\n", + " plot_decision_boundary(net, fig, ax[1])\n", + " fig.canvas.draw()\n", + " fig.canvas.flush_events()\n", + "\n", + " return train_acc, valid_acc" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import matplotlib.cm as cm\n", + "\n", + "def plot_decision_boundary(net, fig, ax):\n", + " draw_colorbar = True\n", + " # remove previous plot\n", + " while ax.collections:\n", + " ax.collections.pop()\n", + " draw_colorbar = False\n", + "\n", + " # generate countour grid\n", + " x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 1\n", + " y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 1\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n", + " np.arange(y_min, y_max, 0.1))\n", + " grid_points = np.c_[xx.ravel().astype('float32'), yy.ravel().astype('float32')]\n", + " n_classes = max(train_labels)+1\n", + " while train_x.shape[1] > grid_points.shape[1]:\n", + " # pad dimensions (plot only the first two)\n", + " grid_points = np.c_[grid_points,\n", + " np.empty(len(xx.ravel())).astype('float32')]\n", + " grid_points[:, -1].fill(train_x[:, grid_points.shape[1]-1].mean())\n", + "\n", + " # evaluate predictions\n", + " prediction = np.array(net.forward(grid_points))\n", + " # for two classes: prediction difference\n", + " if (n_classes == 2):\n", + " Z = np.array([0.5+(p[0]-p[1])/2.0 for p in prediction]).reshape(xx.shape)\n", + " else:\n", + " Z = np.array([p.argsort()[-1]/float(n_classes-1) for p in prediction]).reshape(xx.shape)\n", + " \n", + " # draw contour\n", + " levels = np.linspace(0, 1, 40)\n", + " cs = ax.contourf(xx, yy, Z, alpha=0.4, levels = levels)\n", + " if draw_colorbar:\n", + " fig.colorbar(cs, ax=ax, ticks = [0, 0.5, 1])\n", + " c_map = [cm.jet(x) for x in np.linspace(0.0, 1.0, n_classes) ]\n", + " colors = [c_map[l] for l in train_labels]\n", + " ax.scatter(train_x[:, 0], train_x[:, 1], marker='o', c=colors, s=60, alpha = 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "def plot_training_progress(x, y_data, fig, ax):\n", + " styles = ['k--', 'g-']\n", + " # remove previous plot\n", + " while ax.lines:\n", + " ax.lines.pop()\n", + " # draw updated lines\n", + " for i in range(len(y_data)):\n", + " ax.plot(x, y_data[i], styles[i])\n", + " ax.legend(ax.lines, ['training accuracy', 'validation accuracy'],\n", + " loc='upper center', ncol = 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. 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Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n // Prevent repeat events\n if (name === 'key_press') {\n if (event.key === this._key) {\n return;\n } else {\n this._key = event.key;\n }\n }\n if (name === 'key_release') {\n this._key = null;\n }\n\n var value = '';\n if (event.ctrlKey && event.key !== 'Control') {\n value += 'ctrl+';\n }\n else if (event.altKey && event.key !== 'Alt') {\n value += 'alt+';\n }\n else if (event.shiftKey && event.key !== 'Shift') {\n value += 'shift+';\n }\n\n value += 'k' + event.key;\n\n this._key_event_extra(event, name);\n\n this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n if (name === 'download') {\n this.handle_save(this, null);\n } else {\n this.send_message('toolbar_button', { name: name });\n }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n // Create a \"websocket\"-like object which calls the given IPython comm\n // object with the appropriate methods. Currently this is a non binary\n // socket, so there is still some room for performance tuning.\n var ws = {};\n\n ws.binaryType = comm.kernel.ws.binaryType;\n ws.readyState = comm.kernel.ws.readyState;\n function updateReadyState(_event) {\n if (comm.kernel.ws) {\n ws.readyState = comm.kernel.ws.readyState;\n } else {\n ws.readyState = 3; // Closed state.\n }\n }\n comm.kernel.ws.addEventListener('open', updateReadyState);\n comm.kernel.ws.addEventListener('close', updateReadyState);\n comm.kernel.ws.addEventListener('error', updateReadyState);\n\n ws.close = function () {\n comm.close();\n };\n ws.send = function (m) {\n //console.log('sending', m);\n comm.send(m);\n };\n // Register the callback with on_msg.\n comm.on_msg(function (msg) {\n //console.log('receiving', msg['content']['data'], msg);\n var data = msg['content']['data'];\n if (data['blob'] !== undefined) {\n data = {\n data: new Blob(msg['buffers'], { type: data['blob'] }),\n };\n }\n // Pass the mpl event to the overridden (by mpl) onmessage function.\n ws.onmessage(data);\n });\n return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n // This is the function which gets called when the mpl process\n // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n var id = msg.content.data.id;\n // Get hold of the div created by the display call when the Comm\n // socket was opened in Python.\n var element = document.getElementById(id);\n var ws_proxy = comm_websocket_adapter(comm);\n\n function ondownload(figure, _format) {\n window.open(figure.canvas.toDataURL());\n }\n\n var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n // web socket which is closed, not our websocket->open comm proxy.\n ws_proxy.onopen();\n\n fig.parent_element = element;\n fig.cell_info = mpl.find_output_cell(\"
\");\n if (!fig.cell_info) {\n console.error('Failed to find cell for figure', id, fig);\n return;\n }\n fig.cell_info[0].output_area.element.on(\n 'cleared',\n { fig: fig },\n fig._remove_fig_handler\n );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n var width = fig.canvas.width / fig.ratio;\n fig.cell_info[0].output_area.element.off(\n 'cleared',\n fig._remove_fig_handler\n );\n fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n // Update the output cell to use the data from the current canvas.\n fig.push_to_output();\n var dataURL = fig.canvas.toDataURL();\n // Re-enable the keyboard manager in IPython - without this line, in FF,\n // the notebook keyboard shortcuts fail.\n IPython.keyboard_manager.enable();\n fig.parent_element.innerHTML =\n '';\n fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n var manager = IPython.notebook.keyboard_manager;\n if (!manager) {\n manager = IPython.keyboard_manager;\n }\n\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib nbagg \n", + "net = Net()\n", + "net.add(Linear(2,2))\n", + "net.add(Softmax())\n", + "\n", + "res = train_and_plot(30,net,lr=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Depois de executar a célula acima, você deverá conseguir ver interativamente como a fronteira entre as classes muda durante o treinamento. Observe que escolhemos uma taxa de aprendizado muito pequena para que possamos acompanhar como o processo acontece.\n", + "\n", + "## Modelos com Múltiplas Camadas\n", + "\n", + "A rede acima foi construída com várias camadas, mas ainda tínhamos apenas uma camada `Linear`, que realiza a classificação propriamente dita. O que acontece se decidirmos adicionar várias dessas camadas?\n", + "\n", + "Surpreendentemente, nosso código funcionará! No entanto, é muito importante observar que, entre as camadas lineares, precisamos incluir uma **função de ativação** não linear, como `tanh`. Sem essa não linearidade, várias camadas lineares teriam o mesmo poder de expressão que apenas uma camada - porque a composição de funções lineares também é linear!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "class Tanh:\n", + " def forward(self,x):\n", + " y = np.tanh(x)\n", + " self.y = y\n", + " return y\n", + " def backward(self,dy):\n", + " return (1.0-self.y**2)*dy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adicionar várias camadas faz sentido, porque, ao contrário de uma rede de camada única, um modelo com múltiplas camadas será capaz de classificar com precisão conjuntos que não são linearmente separáveis. Ou seja, um modelo com várias camadas será **mais robusto**.\n", + "\n", + "> Pode-se demonstrar que, com um número suficiente de neurônios, um modelo de duas camadas é capaz de classificar qualquer conjunto convexo de pontos de dados, e uma rede de três camadas pode classificar praticamente qualquer conjunto.\n", + "\n", + "Matematicamente, o perceptron de múltiplas camadas seria representado por uma função mais complexa $f_\\theta$, que pode ser calculada em várias etapas:\n", + "* $z_1 = W_1\\times x+b_1$\n", + "* $z_2 = W_2\\times\\alpha(z_1)+b_2$\n", + "* $f = \\sigma(z_2)$\n", + "\n", + "Aqui, $\\alpha$ é uma **função de ativação não linear**, $\\sigma$ é uma função softmax, e $\\theta=\\langle W_1,b_1,W_2,b_2\\rangle$ são os parâmetros.\n", + "\n", + "O algoritmo de descida de gradiente permaneceria o mesmo, mas seria mais difícil calcular os gradientes. Dado o\n", + "regra da diferenciação em cadeia, podemos calcular as derivadas como:\n", + "\n", + "$$\\begin{align}\n", + "\\frac{\\partial\\mathcal{L}}{\\partial W_2} &= \\color{red}{\\frac{\\partial\\mathcal{L}}{\\partial\\sigma}\\frac{\\partial\\sigma}{\\partial z_2}}\\color{black}{\\frac{\\partial z_2}{\\partial W_2}} \\\\\n", + "\\frac{\\partial\\mathcal{L}}{\\partial W_1} &= \\color{red}{\\frac{\\partial\\mathcal{L}}{\\partial\\sigma}\\frac{\\partial\\sigma}{\\partial z_2}}\\color{black}{\\frac{\\partial z_2}{\\partial\\alpha}\\frac{\\partial\\alpha}{\\partial z_1}\\frac{\\partial z_1}{\\partial W_1}}\n", + "\\end{align}\n", + "$$\n", + "\n", + "Note que o início de todas essas expressões ainda é o mesmo, e assim podemos continuar a retropropagação além de uma camada linear para ajustar pesos adicionais ao longo do grafo computacional.\n", + "\n", + "Agora vamos experimentar com uma rede de duas camadas:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "net = Net()\n", + "net.add(Linear(2,10))\n", + "net.add(Tanh())\n", + "net.add(Linear(10,2))\n", + "net.add(Softmax())\n", + "loss = CrossEntropyLoss()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'mpl-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'mpl-button-group';\n continue;\n }\n\n var button = (fig.buttons[name] = document.createElement('button'));\n button.classList = 'mpl-widget';\n button.setAttribute('role', 'button');\n button.setAttribute('aria-disabled', 'false');\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n var icon_img = document.createElement('img');\n icon_img.src = '_images/' + image + '.png';\n icon_img.srcset = '_images/' + image + '_large.png 2x';\n icon_img.alt = tooltip;\n button.appendChild(icon_img);\n\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n var fmt_picker = document.createElement('select');\n fmt_picker.classList = 'mpl-widget';\n toolbar.appendChild(fmt_picker);\n this.format_dropdown = fmt_picker;\n\n for (var ind in mpl.extensions) {\n var fmt = mpl.extensions[ind];\n var option = document.createElement('option');\n option.selected = fmt === mpl.default_extension;\n option.innerHTML = fmt;\n fmt_picker.appendChild(option);\n }\n\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n // which will in turn request a refresh of the image.\n this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n properties['type'] = type;\n properties['figure_id'] = this.id;\n this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n if (!this.waiting) {\n this.waiting = true;\n this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n var format_dropdown = fig.format_dropdown;\n var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n var size = msg['size'];\n if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n fig._resize_canvas(size[0], size[1], msg['forward']);\n fig.send_message('refresh', {});\n }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n var x0 = msg['x0'] / fig.ratio;\n var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n var x1 = msg['x1'] / fig.ratio;\n var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n x0 = Math.floor(x0) + 0.5;\n y0 = Math.floor(y0) + 0.5;\n x1 = Math.floor(x1) + 0.5;\n y1 = Math.floor(y1) + 0.5;\n var min_x = Math.min(x0, x1);\n var min_y = Math.min(y0, y1);\n var width = Math.abs(x1 - x0);\n var height = Math.abs(y1 - y0);\n\n fig.rubberband_context.clearRect(\n 0,\n 0,\n fig.canvas.width / fig.ratio,\n fig.canvas.height / fig.ratio\n );\n\n fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n var cursor = msg['cursor'];\n switch (cursor) {\n case 0:\n cursor = 'pointer';\n break;\n case 1:\n cursor = 'default';\n break;\n case 2:\n cursor = 'crosshair';\n break;\n case 3:\n cursor = 'move';\n break;\n }\n fig.rubberband_canvas.style.cursor = cursor;\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n for (var key in msg) {\n if (!(key in fig.buttons)) {\n continue;\n }\n fig.buttons[key].disabled = !msg[key];\n fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n if (msg['mode'] === 'PAN') {\n fig.buttons['Pan'].classList.add('active');\n fig.buttons['Zoom'].classList.remove('active');\n } else if (msg['mode'] === 'ZOOM') {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.add('active');\n } else {\n fig.buttons['Pan'].classList.remove('active');\n fig.buttons['Zoom'].classList.remove('active');\n }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Called whenever the canvas gets updated.\n this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n return function socket_on_message(evt) {\n if (evt.data instanceof Blob) {\n var img = evt.data;\n if (img.type !== 'image/png') {\n /* FIXME: We get \"Resource interpreted as Image but\n * transferred with MIME type text/plain:\" errors on\n * Chrome. But how to set the MIME type? It doesn't seem\n * to be part of the websocket stream */\n img.type = 'image/png';\n }\n\n /* Free the memory for the previous frames */\n if (fig.imageObj.src) {\n (window.URL || window.webkitURL).revokeObjectURL(\n fig.imageObj.src\n );\n }\n\n fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n img\n );\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n } else if (\n typeof evt.data === 'string' &&\n evt.data.slice(0, 21) === 'data:image/png;base64'\n ) {\n fig.imageObj.src = evt.data;\n fig.updated_canvas_event();\n fig.waiting = false;\n return;\n }\n\n var msg = JSON.parse(evt.data);\n var msg_type = msg['type'];\n\n // Call the \"handle_{type}\" callback, which takes\n // the figure and JSON message as its only arguments.\n try {\n var callback = fig['handle_' + msg_type];\n } catch (e) {\n console.log(\n \"No handler for the '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n return Object.keys(original).reduce(function (obj, key) {\n if (typeof original[key] !== 'object') {\n obj[key] = original[key];\n }\n return obj;\n }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. 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i < ncells; i++) {\n var cell = cells[i];\n if (cell.cell_type === 'code') {\n for (var j = 0; j < cell.output_area.outputs.length; j++) {\n var data = cell.output_area.outputs[j];\n if (data.data) {\n // IPython >= 3 moved mimebundle to data attribute of output\n data = data.data;\n }\n if (data['text/html'] === html_output) {\n return [cell, data, j];\n }\n }\n }\n }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res = train_and_plot(30,net,lr=0.01)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Por que não usar sempre um modelo com múltiplas camadas?\n", + "\n", + "Vimos que um modelo com múltiplas camadas é mais *poderoso* e *expressivo* do que um modelo com uma única camada. Você pode estar se perguntando por que não usamos sempre um modelo com muitas camadas. A resposta para essa pergunta é o **overfitting**.\n", + "\n", + "Vamos abordar esse termo mais detalhadamente em seções posteriores, mas a ideia é a seguinte: **quanto mais poderoso o modelo, melhor ele pode aproximar os dados de treinamento, e mais dados ele precisa para generalizar adequadamente** para novos dados que ainda não viu antes.\n", + "\n", + "**Um modelo linear:**\n", + "* É provável que apresente uma perda de treinamento alta - o chamado **underfitting**, quando o modelo não tem poder suficiente para separar corretamente todos os dados.\n", + "* A perda de validação e a perda de treinamento são mais ou menos iguais. O modelo provavelmente generaliza bem para os dados de teste.\n", + "\n", + "**Modelo complexo com múltiplas camadas:**\n", + "* Baixa perda de treinamento - o modelo consegue aproximar bem os dados de treinamento, pois tem poder expressivo suficiente.\n", + "* A perda de validação pode ser muito maior do que a perda de treinamento e pode começar a aumentar durante o treinamento - isso ocorre porque o modelo \"memoriza\" os pontos de treinamento e perde a \"visão geral\".\n", + "\n", + "![Overfitting](../../../../../translated_images/pt-BR/overfit.a0bd57f717c15769.webp)\n", + "\n", + "> Nesta imagem, `x` representa os dados de treinamento, e `o` os dados de validação. À esquerda - modelo linear (uma camada), que aproxima bem a natureza dos dados. À direita - modelo com overfitting, que aproxima perfeitamente os dados de treinamento, mas deixa de fazer sentido com qualquer outro dado (o erro de validação é muito alto).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Principais pontos\n", + "\n", + "* Modelos simples (menos camadas, menos neurônios) com baixo número de parâmetros (\"baixa capacidade\") têm menor probabilidade de sofrer overfitting.\n", + "* Modelos mais complexos (mais camadas, mais neurônios em cada camada, alta capacidade) têm maior probabilidade de sofrer overfitting. É necessário monitorar o erro de validação para garantir que ele não comece a aumentar com o treinamento adicional.\n", + "* Modelos mais complexos precisam de mais dados para serem treinados.\n", + "* Você pode resolver o problema de overfitting de duas maneiras:\n", + " - simplificando seu modelo\n", + " - aumentando a quantidade de dados de treinamento\n", + "* **Compromisso entre viés e variância** é um termo que indica que você precisa encontrar o equilíbrio:\n", + " - entre o poder do modelo e a quantidade de dados,\n", + " - entre overfitting e underfitting.\n", + "* Não existe uma receita única para determinar quantas camadas ou parâmetros você precisa - a melhor abordagem é experimentar.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Créditos\n", + "\n", + "Este notebook faz parte do [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners) e foi preparado por [Dmitry Soshnikov](http://soshnikov.com). Ele é inspirado no Workshop de Redes Neurais da Microsoft Research Cambridge. Parte do código e dos materiais ilustrativos foram retirados de apresentações de [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/) e [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/), bem como do repositório [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + }, + "kernelspec": { + "display_name": "Python 3.9.5 64-bit ('base': conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "coopTranslator": { + "original_hash": "6d3fe3f89c3d7b37e5edced9858aa1cb", + "translation_date": "2025-08-28T13:37:18+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/README.md new file mode 100644 index 00000000..a4230a9e --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/README.md @@ -0,0 +1,89 @@ +# Introdução às Redes Neurais. Perceptron Multicamadas + +Na seção anterior, você aprendeu sobre o modelo de rede neural mais simples - o perceptron de uma camada, um modelo linear de classificação de duas classes. + +Nesta seção, vamos expandir esse modelo para um framework mais flexível, permitindo: + +* realizar **classificação multiclasse** além de classificação de duas classes +* resolver **problemas de regressão** além de classificação +* separar classes que não são linearmente separáveis + +Também desenvolveremos nosso próprio framework modular em Python que nos permitirá construir diferentes arquiteturas de redes neurais. + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/7) + +## Formalização de Aprendizado de Máquina + +Vamos começar formalizando o problema de Aprendizado de Máquina. Suponha que temos um conjunto de dados de treinamento **X** com rótulos **Y**, e precisamos construir um modelo *f* que faça previsões mais precisas. A qualidade das previsões é medida pela **função de perda** ℒ. As seguintes funções de perda são frequentemente usadas: + +* Para problemas de regressão, quando precisamos prever um número, podemos usar **erro absoluto** ∑i|f(x(i))-y(i)|, ou **erro quadrático** ∑i(f(x(i))-y(i))2 +* Para classificação, usamos **perda 0-1** (que é essencialmente o mesmo que **acurácia** do modelo), ou **perda logística**. + +Para o perceptron de uma camada, a função *f* foi definida como uma função linear *f(x)=wx+b* (aqui *w* é a matriz de pesos, *x* é o vetor de características de entrada, e *b* é o vetor de viés). Para diferentes arquiteturas de redes neurais, essa função pode assumir uma forma mais complexa. + +> No caso de classificação, muitas vezes é desejável obter probabilidades das classes correspondentes como saída da rede. Para converter números arbitrários em probabilidades (por exemplo, para normalizar a saída), frequentemente usamos a função **softmax** σ, e a função *f* torna-se *f(x)=σ(wx+b)* + +Na definição de *f* acima, *w* e *b* são chamados de **parâmetros** θ=⟨*w,b*⟩. Dado o conjunto de dados ⟨**X**,**Y**⟩, podemos calcular um erro geral em todo o conjunto de dados como uma função dos parâmetros θ. + +> ✅ **O objetivo do treinamento de redes neurais é minimizar o erro variando os parâmetros θ** + +## Otimização por Gradiente Descendente + +Existe um método bem conhecido de otimização de funções chamado **gradiente descendente**. A ideia é que podemos calcular uma derivada (no caso multidimensional chamada de **gradiente**) da função de perda em relação aos parâmetros e variar os parâmetros de forma que o erro diminua. Isso pode ser formalizado da seguinte maneira: + +* Inicializar os parâmetros com alguns valores aleatórios w(0), b(0) +* Repetir o seguinte passo várias vezes: + - w(i+1) = w(i)-η∂ℒ/∂w + - b(i+1) = b(i)-η∂ℒ/∂b + +Durante o treinamento, os passos de otimização devem ser calculados considerando todo o conjunto de dados (lembre-se de que a perda é calculada como uma soma de todas as amostras de treinamento). No entanto, na prática, usamos pequenas porções do conjunto de dados chamadas **minibatches**, e calculamos os gradientes com base em um subconjunto de dados. Como o subconjunto é escolhido aleatoriamente a cada vez, esse método é chamado de **gradiente descendente estocástico** (SGD). + +## Perceptrons Multicamadas e Retropropagação + +A rede de uma camada, como vimos acima, é capaz de classificar classes linearmente separáveis. Para construir um modelo mais rico, podemos combinar várias camadas da rede. Matematicamente, isso significaria que a função *f* teria uma forma mais complexa e seria calculada em várias etapas: +* z1=w1x+b1 +* z2=w2α(z1)+b2 +* f = σ(z2) + +Aqui, α é uma **função de ativação não linear**, σ é uma função softmax, e os parâmetros θ=<*w1,b1,w2,b2*>. + +O algoritmo de gradiente descendente permaneceria o mesmo, mas seria mais difícil calcular os gradientes. Dado o princípio da regra da cadeia de diferenciação, podemos calcular as derivadas como: + +* ∂ℒ/∂w2 = (∂ℒ/∂σ)(∂σ/∂z2)(∂z2/∂w2) +* ∂ℒ/∂w1 = (∂ℒ/∂σ)(∂σ/∂z2)(∂z2/∂α)(∂α/∂z1)(∂z1/∂w1) + +> ✅ A regra da cadeia de diferenciação é usada para calcular as derivadas da função de perda em relação aos parâmetros. + +Note que a parte mais à esquerda de todas essas expressões é a mesma, e assim podemos calcular as derivadas de forma eficiente começando pela função de perda e indo "para trás" através do grafo computacional. Assim, o método de treinamento de um perceptron multicamadas é chamado de **retropropagação**, ou 'backprop'. + +grafo computacional + +> TODO: citação da imagem + +> ✅ Vamos abordar a retropropagação com muito mais detalhes em nosso exemplo no notebook. + +## Conclusão + +Nesta lição, construímos nossa própria biblioteca de redes neurais e a usamos para uma tarefa simples de classificação bidimensional. + +## 🚀 Desafio + +No notebook que acompanha, você implementará seu próprio framework para construir e treinar perceptrons multicamadas. Você poderá ver em detalhes como as redes neurais modernas operam. + +Acesse o notebook [OwnFramework](OwnFramework.ipynb) e trabalhe nele. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/8) + +## Revisão e Autoestudo + +Retropropagação é um algoritmo comum usado em IA e ML, vale a pena estudar [em mais detalhes](https://wikipedia.org/wiki/Backpropagation) + +## [Tarefa](lab/README.md) + +Neste laboratório, você será solicitado a usar o framework que construiu nesta lição para resolver a classificação de dígitos manuscritos do MNIST. + +* [Instruções](lab/README.md) +* [Notebook](lab/MyFW_MNIST.ipynb) + +--- + diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb new file mode 100644 index 00000000..1248095e --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classificação de Dígitos MNIST com nosso próprio Framework\n", + "\n", + "Tarefa prática do [Currículo de IA para Iniciantes](https://github.com/microsoft/ai-for-beginners).\n", + "\n", + "### Lendo o Conjunto de Dados\n", + "\n", + "Este código baixa o conjunto de dados do repositório na internet. Você também pode copiar manualmente o conjunto de dados do diretório `/data` do repositório do Currículo de IA.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "\n", + " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", + "100 9.9M 100 9.9M 0 0 9.9M 0 0:00:01 --:--:-- 0:00:01 15.8M\n" + ] + } + ], + "source": [ + "!rm *.pkl\n", + "!wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz\n", + "!gzip -d mnist.pkl.gz" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open('mnist.pkl','rb') as f:\n", + " MNIST = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "labels = MNIST['Train']['Labels']\n", + "data = MNIST['Train']['Features']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos ver qual é o formato dos dados que temos:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(42000, 784)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dividindo os Dados\n", + "\n", + "Usaremos o Scikit Learn para dividir os dados entre conjunto de treinamento e conjunto de teste:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train samples: 33600, test samples: 8400\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "features_train, features_test, labels_train, labels_test = train_test_split(data,labels,test_size=0.2)\n", + "\n", + "print(f\"Train samples: {len(features_train)}, test samples: {len(features_test)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instruções\n", + "\n", + "1. Pegue o código base do framework da lição e cole neste notebook ou (ainda melhor) em um módulo Python separado.\n", + "1. Defina e treine um perceptron de uma camada, observando a precisão de treinamento e validação durante o processo.\n", + "1. Tente entender se houve overfitting e ajuste os parâmetros da camada para melhorar a precisão.\n", + "1. Repita os passos anteriores para perceptrons de 2 e 3 camadas. Experimente diferentes funções de ativação entre as camadas.\n", + "1. Tente responder às seguintes perguntas:\n", + " - A função de ativação entre as camadas afeta o desempenho da rede?\n", + " - Precisamos de uma rede de 2 ou 3 camadas para esta tarefa?\n", + " - Você enfrentou algum problema ao treinar a rede? Especialmente à medida que o número de camadas aumentou.\n", + " - Como os pesos da rede se comportam durante o treinamento? Você pode plotar o valor absoluto máximo dos pesos em relação às épocas para entender a relação.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit (conda)", + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "orig_nbformat": 2, + "coopTranslator": { + "original_hash": "6fa055f484eb5d6bdf41166a356d3abf", + "translation_date": "2025-08-28T13:41:16+00:00", + "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md new file mode 100644 index 00000000..5e1d1a61 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md @@ -0,0 +1,23 @@ +# Classificação MNIST com Nosso Próprio Framework + +Tarefa de Laboratório do [Currículo de IA para Iniciantes](https://github.com/microsoft/ai-for-beginners). + +## Tarefa + +Resolva o problema de classificação de dígitos manuscritos do MNIST usando perceptrons com 1, 2 e 3 camadas. Utilize o framework de rede neural que desenvolvemos na aula. + +## Notebook Inicial + +Inicie o laboratório abrindo [MyFW_MNIST.ipynb](../../../../../../lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb) + +## Perguntas + +Como resultado deste laboratório, tente responder às seguintes perguntas: + +- A função de ativação entre as camadas afeta o desempenho da rede? +- Precisamos de uma rede com 2 ou 3 camadas para esta tarefa? +- Você encontrou algum problema ao treinar a rede? Especialmente à medida que o número de camadas aumentava. +- Como os pesos da rede se comportam durante o treinamento? Você pode plotar o valor absoluto máximo dos pesos em relação às épocas para entender a relação. + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb new file mode 100644 index 00000000..e862806e --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb @@ -0,0 +1,763 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "En2vX4FuwHlu" + }, + "source": [ + "## Introdução mais Simples às Redes Neurais com Keras\n", + "\n", + "> Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners). Visite o repositório para acessar o conjunto completo de materiais de aprendizado.\n", + "\n", + "### Frameworks de Redes Neurais\n", + "\n", + "Existem vários frameworks para treinar redes neurais. No entanto, se você deseja começar rapidamente e não se aprofundar muito em como as coisas funcionam internamente - considere usar [Keras](https://keras.io/). Este tutorial curto vai ajudá-lo a começar, e se você quiser entender mais profundamente como as coisas funcionam - confira o notebook [Introdução ao Tensorflow e Keras](IntroKerasTF.ipynb).\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "8cACQoFMwHl3" + }, + "source": [ + "### Preparando as coisas\n", + "\n", + "Keras faz parte do framework Tensorflow 2.x. Vamos garantir que temos a versão 2.x.x do Tensorflow instalada:\n", + "```\n", + "pip install tensorflow\n", + "```\n", + "ou\n", + "```\n", + "conda install tensorflow\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xwqVx9-bwHl3", + "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensorflow version = 2.7.0\n", + "Keras version = 2.7.0\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "import numpy as np\n", + "from sklearn.datasets import make_classification\n", + "import matplotlib.pyplot as plt\n", + "print(f'Tensorflow version = {tf.__version__}')\n", + "print(f'Keras version = {keras.__version__}')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "6tp2xGV7wHl4" + }, + "source": [ + "## Conceitos Básicos: Tensor\n", + "\n", + "**Tensor** é um array multidimensional. É muito prático usar tensores para representar diferentes tipos de dados:\n", + "* 400x400 - imagem em preto e branco\n", + "* 400x400x3 - imagem colorida\n", + "* 16x400x400x3 - minibatch de 16 imagens coloridas\n", + "* 25x400x400x3 - um segundo de vídeo com 25 quadros por segundo (fps)\n", + "* 8x25x400x400x3 - minibatch de 8 vídeos de 1 segundo\n", + "\n", + "Os tensores nos oferecem uma maneira conveniente de representar dados de entrada/saída, assim como os pesos dentro da rede neural.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "A10prCPowHl7" + }, + "source": [ + "## Exemplo de Problema\n", + "\n", + "Vamos considerar um problema de classificação binária. Um bom exemplo desse tipo de problema seria a classificação de tumores entre malignos e benignos com base em seu tamanho e idade. Vamos começar gerando alguns dados de exemplo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "j0OTPkGpwHl7", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n", + "\n", + "n = 100\n", + "X, Y = make_classification(n_samples = n, n_features=2,\n", + " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n", + "X = X.astype(np.float32)\n", + "Y = Y.astype(np.int32)\n", + "\n", + "split = [ 70*n//100 ]\n", + "train_x, test_x = np.split(X, split)\n", + "train_labels, test_labels = np.split(Y, split)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "c-_BjSHPwHl8", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "def plot_dataset(features, labels, W=None, b=None):\n", + " # prepare the plot\n", + " fig, ax = plt.subplots(1, 1)\n", + " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n", + " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n", + " colors = ['r' if l else 'b' for l in labels]\n", + " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n", + " if W is not None:\n", + " min_x = min(features[:,0])\n", + " max_x = max(features[:,1])\n", + " min_y = min(features[:,1])*(1-.1)\n", + " max_y = max(features[:,1])*(1+.1)\n", + " cx = np.array([min_x,max_x],dtype=np.float32)\n", + " cy = (0.5-W[0]*cx-b)/W[1]\n", + " ax.plot(cx,cy,'g')\n", + " ax.set_ylim(min_y,max_y)\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "tq0vFchQwHl8", + "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff", + "scrolled": false, + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x, train_labels)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalizando Dados\n", + "\n", + "Antes do treinamento, é comum trazer nossas características de entrada para a faixa padrão de [0,1] (ou [-1,1]). Os motivos exatos para isso serão discutidos mais adiante no curso, mas, resumidamente, a razão é a seguinte: queremos evitar que os valores que passam pela nossa rede fiquem muito grandes ou muito pequenos, e normalmente concordamos em manter todos os valores em uma faixa pequena próxima de 0. Por isso, inicializamos os pesos com números aleatórios pequenos e mantemos os sinais na mesma faixa.\n", + "\n", + "Ao normalizar os dados, precisamos subtrair o valor mínimo e dividir pelo intervalo. Calculamos o valor mínimo e o intervalo usando os dados de treinamento e, em seguida, normalizamos o conjunto de teste/validação usando os mesmos valores de mínimo/intervalo do conjunto de treinamento. Isso ocorre porque, na vida real, teremos acesso apenas ao conjunto de treinamento e não a todos os novos valores que a rede será solicitada a prever. Ocasionalmente, o novo valor pode ficar fora da faixa [0,1], mas isso não é crucial.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))\n", + "test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "SjPlpf2-wHl8" + }, + "source": [ + "## Treinando uma Rede de Uma Camada (Perceptron)\n", + "\n", + "Em muitos casos, uma rede neural será uma sequência de camadas. Ela pode ser definida no Keras usando o modelo `Sequential` da seguinte maneira:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_2 (Dense) (None, 1) 3 \n", + " \n", + " activation_1 (Activation) (None, 1) 0 \n", + " \n", + "=================================================================\n", + "Total params: 3\n", + "Trainable params: 3\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential()\n", + "model.add(keras.Input(shape=(2,)))\n", + "model.add(keras.layers.Dense(1))\n", + "model.add(keras.layers.Activation(keras.activations.sigmoid))\n", + "\n", + "model.summary()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aqui, primeiro criamos o modelo e, em seguida, adicionamos camadas a ele:\n", + "* A primeira camada `Input` (que, estritamente falando, não é uma camada) contém a especificação do tamanho de entrada da rede.\n", + "* A camada `Dense` é o perceptron real que contém pesos treináveis.\n", + "* Por fim, há uma camada com a função de *ativação* `sigmoid` para trazer o resultado da rede para o intervalo de 0-1 (para transformá-lo em uma probabilidade).\n", + "\n", + "O tamanho de entrada, assim como a função de ativação, também podem ser especificados diretamente na camada `Dense` para maior concisão:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_9\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_9 (Dense) (None, 1) 3 \n", + " \n", + "=================================================================\n", + "Total params: 3\n", + "Trainable params: 3\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential()\n", + "model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))\n", + "model.summary()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Antes de treinar o modelo, precisamos **compilá-lo**, o que basicamente significa especificar:\n", + "* **Função de perda**, que define como a perda é calculada. Como temos um problema de classificação binária, usaremos a *perda de entropia cruzada binária*.\n", + "* **Otimizador** a ser usado. A opção mais simples seria usar `sgd` para *descida de gradiente estocástica*, ou você pode optar por otimizadores mais sofisticados, como `adam`.\n", + "* **Métricas** que queremos usar para medir o sucesso do nosso treinamento. Como se trata de uma tarefa de classificação, uma boa métrica seria `Accuracy` (ou `acc` para abreviar).\n", + "\n", + "Podemos especificar a função de perda, as métricas e o otimizador tanto como strings quanto fornecendo alguns objetos do framework Keras. No nosso exemplo, precisamos especificar o parâmetro `learning_rate` para ajustar a velocidade de aprendizado do nosso modelo e, por isso, fornecemos o nome completo do otimizador SGD do Keras.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depois de compilar o modelo, podemos realizar o treinamento propriamente dito chamando o método `fit`. Os parâmetros mais importantes são:\n", + "* `x` e `y` especificam os dados de treinamento, sendo `x` as características e `y` os rótulos, respectivamente\n", + "* Se quisermos que a validação seja realizada a cada época, podemos especificar o parâmetro `validation_data`, que deve ser uma tupla contendo características e rótulos\n", + "* `epochs` especifica o número de épocas\n", + "* Se quisermos que o treinamento ocorra em minibatches, podemos especificar o parâmetro `batch_size`. Também é possível pré-processar os dados em lotes manualmente antes de passá-los para `x`/`y`/`validation_data`, caso em que não será necessário usar o parâmetro `batch_size`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "70/70 [==============================] - 0s 4ms/step - loss: 0.3379 - acc: 0.9000 - val_loss: 0.3282 - val_acc: 0.9000\n", + "Epoch 2/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.3270 - acc: 0.9429 - val_loss: 0.3336 - val_acc: 0.9000\n", + "Epoch 3/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.3195 - acc: 0.9143 - val_loss: 0.3137 - val_acc: 0.9000\n", + "Epoch 4/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.3087 - acc: 0.9286 - val_loss: 0.2970 - val_acc: 0.9333\n", + "Epoch 5/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.3006 - acc: 0.9429 - val_loss: 0.3210 - val_acc: 0.9000\n", + "Epoch 6/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.3003 - acc: 0.9000 - val_loss: 0.2985 - val_acc: 0.9000\n", + "Epoch 7/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2956 - acc: 0.9286 - val_loss: 0.3037 - val_acc: 0.9000\n", + "Epoch 8/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2891 - acc: 0.9429 - val_loss: 0.3035 - val_acc: 0.9000\n", + "Epoch 9/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9000 - val_loss: 0.2815 - val_acc: 0.9000\n", + "Epoch 10/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9286 - val_loss: 0.2907 - val_acc: 0.9000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "s4_Atvn5K4K9" + }, + "source": [ + "Você pode experimentar diferentes parâmetros de treinamento para ver como eles afetam o treinamento:\n", + "* Definir `batch_size` como muito grande (ou não especificá-lo) pode resultar em um treinamento menos estável, porque com dados de baixa dimensão, tamanhos de lote pequenos fornecem uma direção mais precisa do gradiente para cada caso específico.\n", + "* Uma `learning_rate` muito alta pode resultar em overfitting ou em resultados menos estáveis, enquanto uma taxa de aprendizado muito baixa significa que serão necessárias mais épocas para alcançar o resultado.\n", + "\n", + "> Observe que você pode chamar a função `fit` várias vezes seguidas para continuar treinando a rede. Se você quiser começar o treinamento do zero, será necessário executar novamente a célula com a definição do modelo.\n", + "\n", + "Para garantir que nosso treinamento funcionou, vamos plotar a linha que separa as duas classes. A linha de separação é definida pela equação $W\\times x + b = 0.5$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "PgRTHttLwHl9", + "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b", + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "dvAiaj_JndyP" + }, + "source": [ + "## Plotando os gráficos de treinamento\n", + "\n", + "A função `fit` retorna um objeto `history` como resultado, que pode ser usado para observar a perda e as métricas em cada época. No exemplo abaixo, reiniciaremos o treinamento com uma taxa de aprendizado pequena e observaremos como a perda e a precisão se comportam.\n", + "\n", + "> **Note** que estamos usando uma sintaxe ligeiramente diferente para definir o modelo `Sequential`. Em vez de adicionar as camadas uma por uma com `add`, também podemos especificar a lista de camadas diretamente ao criar o modelo pela primeira vez - essa é uma sintaxe um pouco mais curta, e você pode preferir usá-la.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "70/70 [==============================] - 1s 5ms/step - loss: 0.6600 - acc: 0.6143 - val_loss: 0.6351 - val_acc: 0.8000\n", + "Epoch 2/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.6384 - acc: 0.7143 - val_loss: 0.6187 - val_acc: 0.8333\n", + "Epoch 3/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.6188 - acc: 0.7571 - val_loss: 0.6001 - val_acc: 0.8667\n", + "Epoch 4/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.6022 - acc: 0.7714 - val_loss: 0.5837 - val_acc: 0.9000\n", + "Epoch 5/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5860 - acc: 0.8571 - val_loss: 0.5673 - val_acc: 0.9000\n", + "Epoch 6/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5702 - acc: 0.8571 - val_loss: 0.5597 - val_acc: 0.8667\n", + "Epoch 7/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5568 - acc: 0.8286 - val_loss: 0.5458 - val_acc: 0.9000\n", + "Epoch 8/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5430 - acc: 0.8714 - val_loss: 0.5325 - val_acc: 0.9000\n", + "Epoch 9/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5308 - acc: 0.8714 - val_loss: 0.5234 - val_acc: 0.9000\n", + "Epoch 10/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.5175 - acc: 0.9143 - val_loss: 0.5170 - val_acc: 0.8667\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential([\n", + " keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])\n", + "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])\n", + "hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(hist.history['acc'])\n", + "plt.plot(hist.history['val_acc'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classificação Multiclasse\n", + "\n", + "Se você precisa resolver um problema de classificação multiclasse, sua rede terá mais de uma saída - correspondente ao número de classes $C$. Cada saída conterá a probabilidade de uma determinada classe.\n", + "\n", + "> Observe que você também pode usar uma rede com duas saídas para realizar classificação binária da mesma maneira. É exatamente isso que demonstraremos agora.\n", + "\n", + "Quando você espera que uma rede produza um conjunto de probabilidades $p_1,\\dots, p_C$, precisamos que todas elas somem 1. Para garantir isso, usamos `softmax` como função de ativação final na última camada. **Softmax** recebe um vetor como entrada e garante que todos os componentes desse vetor sejam transformados em probabilidades.\n", + "\n", + "Além disso, como a saída da rede é um vetor de dimensão $C$, precisamos que os rótulos tenham o mesmo formato. Isso pode ser alcançado utilizando **one-hot encoding**, onde o número de uma classe $i$ é convertido em um vetor de zeros, com 1 na posição $i$.\n", + "\n", + "Para comparar a saída de probabilidade da rede neural com o rótulo esperado codificado em one-hot, usamos a função de perda **cross-entropy loss**. Ela recebe duas distribuições de probabilidade e retorna um valor que indica o quão diferentes elas são.\n", + "\n", + "Resumindo, o que precisamos fazer para classificação multiclasse com $C$ classes:\n", + "* A rede deve ter $C$ neurônios na última camada\n", + "* A última função de ativação deve ser **softmax**\n", + "* A função de perda deve ser **cross-entropy loss**\n", + "* Os rótulos devem ser convertidos para **one-hot encoding** (isso pode ser feito usando `numpy` ou utilizando `to_categorical` dos utils do Keras)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "70/70 [==============================] - 1s 6ms/step - loss: 0.6524 - acc: 0.7000 - val_loss: 0.5936 - val_acc: 0.9000\n", + "Epoch 2/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.5715 - acc: 0.8286 - val_loss: 0.5255 - val_acc: 0.8333\n", + "Epoch 3/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.4820 - acc: 0.8714 - val_loss: 0.4213 - val_acc: 0.9000\n", + "Epoch 4/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.4426 - acc: 0.9000 - val_loss: 0.3694 - val_acc: 0.9333\n", + "Epoch 5/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.3602 - acc: 0.9000 - val_loss: 0.3454 - val_acc: 0.9000\n", + "Epoch 6/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.3209 - acc: 0.8857 - val_loss: 0.2862 - val_acc: 0.9333\n", + "Epoch 7/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2905 - acc: 0.9286 - val_loss: 0.2787 - val_acc: 0.9000\n", + "Epoch 8/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2698 - acc: 0.9000 - val_loss: 0.2381 - val_acc: 0.9333\n", + "Epoch 9/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2639 - acc: 0.8857 - val_loss: 0.2217 - val_acc: 0.9667\n", + "Epoch 10/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.2592 - acc: 0.9286 - val_loss: 0.2391 - val_acc: 0.9000\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential([\n", + " keras.layers.Dense(5,input_shape=(2,),activation='relu'),\n", + " keras.layers.Dense(2,activation='softmax')\n", + "])\n", + "model.compile(keras.optimizers.Adam(0.01),'categorical_crossentropy',['acc'])\n", + "\n", + "# Two ways to convert to one-hot encoding\n", + "train_labels_onehot = keras.utils.to_categorical(train_labels)\n", + "test_labels_onehot = np.eye(2)[test_labels]\n", + "\n", + "hist = model.fit(x=train_x_norm,y=train_labels_onehot,\n", + " validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Entropia Cruzada Categórica Esparsa\n", + "\n", + "Frequentemente, os rótulos em classificações multiclasse são representados por números de classe. O Keras também oferece suporte a outro tipo de função de perda chamada **entropia cruzada categórica esparsa**, que espera que os números de classe sejam inteiros, e não vetores one-hot. Usando esse tipo de função de perda, podemos simplificar nosso código de treinamento:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "70/70 [==============================] - 1s 6ms/step - loss: 0.2353 - acc: 0.9143 - val_loss: 0.2190 - val_acc: 0.9000\n", + "Epoch 2/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2243 - acc: 0.9286 - val_loss: 0.1886 - val_acc: 0.9333\n", + "Epoch 3/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.2366 - acc: 0.9143 - val_loss: 0.2262 - val_acc: 0.9000\n", + "Epoch 4/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.2259 - acc: 0.9429 - val_loss: 0.2124 - val_acc: 0.9000\n", + "Epoch 5/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.2061 - acc: 0.9429 - val_loss: 0.2691 - val_acc: 0.9000\n", + "Epoch 6/10\n", + "70/70 [==============================] - 0s 2ms/step - loss: 0.2200 - acc: 0.9286 - val_loss: 0.2344 - val_acc: 0.9000\n", + "Epoch 7/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2133 - acc: 0.9286 - val_loss: 0.1973 - val_acc: 0.9000\n", + "Epoch 8/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2062 - acc: 0.9429 - val_loss: 0.1893 - val_acc: 0.9000\n", + "Epoch 9/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2060 - acc: 0.9571 - val_loss: 0.2719 - val_acc: 0.9000\n", + "Epoch 10/10\n", + "70/70 [==============================] - 0s 3ms/step - loss: 0.2021 - acc: 0.9571 - val_loss: 0.2293 - val_acc: 0.9000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.compile(keras.optimizers.Adam(0.01),'sparse_categorical_crossentropy',['acc'])\n", + "model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classificação Multi-Rótulo\n", + "\n", + "Às vezes, temos casos em que nossos objetos podem pertencer a duas classes ao mesmo tempo. Como exemplo, suponha que queremos desenvolver um classificador para gatos e cachorros em uma imagem, mas também queremos permitir casos em que ambos, gatos e cachorros, estejam presentes.\n", + "\n", + "Com a classificação multi-rótulo, em vez de um vetor codificado em one-hot, teremos um vetor que possui 1 na posição correspondente a todas as classes relevantes para a amostra de entrada. Assim, a saída da rede não deve ter probabilidades normalizadas para todas as classes, mas sim para cada classe individualmente - o que corresponde ao uso da função de ativação **sigmoid**. A função de perda cross-entropy ainda pode ser usada como função de perda.\n", + "\n", + "> **Note** que isso é muito semelhante ao uso de **redes neurais diferentes** para realizar a classificação binária de cada classe específica - apenas a parte inicial da rede (até a camada final de classificação) é compartilhada entre todas as classes.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "BmHNhUU8bqEX" + }, + "source": [ + "## Resumo das Funções de Perda para Classificação\n", + "\n", + "Vimos que a classificação binária, multiclasse e multilabel diferem pelo tipo de função de perda e pela função de ativação na última camada da rede. Isso pode ser um pouco confuso se você está começando a aprender, mas aqui estão algumas regras para ter em mente:\n", + "* Se a rede tem uma saída (**classificação binária**), usamos a função de ativação **sigmoid**; para **classificação multiclasse**, usamos **softmax**.\n", + "* Se a classe de saída é representada como one-hot-encoding, a função de perda será **cross entropy loss** (entropia cruzada categórica); se a saída contém o número da classe, usamos **sparse categorical cross-entropy**. Para **classificação binária**, usamos **binary cross-entropy** (o mesmo que **log loss**).\n", + "* **Classificação multilabel** ocorre quando um objeto pode pertencer a várias classes ao mesmo tempo. Nesse caso, precisamos codificar os rótulos usando one-hot encoding e usar **sigmoid** como função de ativação, para que a probabilidade de cada classe esteja entre 0 e 1.\n", + "\n", + "| Classificação | Formato do Rótulo | Função de Ativação | Perda |\n", + "|---------------|-----------------------|-----------------|----------|\n", + "| Binária | Probabilidade da 1ª classe | sigmoid | binary crossentropy |\n", + "| Binária | One-hot encoding (2 saídas) | softmax | categorical crossentropy |\n", + "| Multiclasse | One-hot encoding | softmax | categorical crossentropy |\n", + "| Multiclasse | Número da Classe | softmax | sparse categorical crossentropy |\n", + "| Multilabel | One-hot encoding | sigmoid | categorical crossentropy |\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "gZ-kWx84bMDH" + }, + "source": [ + "**Tarefa**: \n", + "Use o Keras para treinar um classificador para os dígitos manuscritos do MNIST: \n", + "* Observe que o Keras contém alguns conjuntos de dados padrão, incluindo o MNIST. Para usar o MNIST no Keras, você só precisa de algumas linhas de código (mais informações [aqui](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist)) \n", + "* Experimente várias configurações de rede, com diferentes números de camadas/neurônios e funções de ativação. \n", + "\n", + "Qual foi a melhor precisão que você conseguiu alcançar? \n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "yX6hqiafwHl9" + }, + "source": [ + "## Principais pontos\n", + "\n", + "* **Keras** é altamente recomendado para iniciantes, pois permite construir redes a partir de camadas de forma bastante simples e treiná-las com apenas algumas linhas de código.\n", + "* Se for necessário usar uma arquitetura não padrão, será preciso se aprofundar um pouco mais no Tensorflow. Ou você pode pedir para alguém implementar uma lógica personalizada como uma camada do Keras e, em seguida, utilizá-la em modelos do Keras.\n", + "* Também é uma boa ideia dar uma olhada no PyTorch e comparar as abordagens.\n", + "\n", + "Um bom notebook de exemplo do criador do Keras sobre Keras e Tensorflow 2.0 pode ser encontrado [aqui](https://t.co/k694J95PI8).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "colab": { + "collapsed_sections": [], + "name": "IntroKerasTF.ipynb", + "provenance": [] + }, + "interpreter": { + "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5" + }, + "kernelspec": { + "display_name": "Python 3.8.12 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "coopTranslator": { + "original_hash": "265eb1809444eb06f3dd1da03e3d7724", + "translation_date": "2025-08-28T13:53:26+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb new file mode 100644 index 00000000..7af120c7 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb @@ -0,0 +1,1451 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "En2vX4FuwHlu" + }, + "source": [ + "## Introdução ao Tensorflow e Keras\n", + "\n", + "> Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners). Visite o repositório para acessar o conjunto completo de materiais de aprendizado.\n", + "\n", + "### Frameworks Neurais\n", + "\n", + "Aprendemos que, para treinar redes neurais, você precisa:\n", + "* Multiplicar matrizes (tensores) rapidamente\n", + "* Calcular gradientes para realizar a otimização por descida de gradiente\n", + "\n", + "O que os frameworks de redes neurais permitem que você faça:\n", + "* Operar com tensores em qualquer recurso de computação disponível, seja CPU, GPU ou até mesmo TPU\n", + "* Calcular gradientes automaticamente (eles são programados explicitamente para todas as funções de tensor integradas)\n", + "\n", + "Opcionalmente:\n", + "* Construtor de Redes Neurais / API de nível superior (descrever a rede como uma sequência de camadas)\n", + "* Funções simples de treinamento (`fit`, como no Scikit Learn)\n", + "* Vários algoritmos de otimização além da descida de gradiente\n", + "* Abstrações para manipulação de dados (que idealmente também funcionarão em GPU)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8cACQoFMwHl3" + }, + "source": [ + "### Frameworks Mais Populares\n", + "\n", + "* Tensorflow 1.x - primeiro framework amplamente disponível (Google). Permitia definir um gráfico de computação estático, enviá-lo para a GPU e avaliá-lo explicitamente\n", + "* PyTorch - um framework do Facebook que está ganhando popularidade\n", + "* Keras - API de nível superior sobre Tensorflow/PyTorch para unificar e simplificar o uso de redes neurais (Francois Chollet)\n", + "* Tensorflow 2.x + Keras - nova versão do Tensorflow com funcionalidade Keras integrada, que suporta **gráfico de computação dinâmico**, permitindo realizar operações com tensores de forma muito semelhante ao numpy (e PyTorch)\n", + "\n", + "Vamos considerar Tensorflow 2.x e Keras. Certifique-se de ter a versão 2.x.x do Tensorflow instalada:\n", + "```\n", + "pip install tensorflow\n", + "```\n", + "ou\n", + "```\n", + "conda install tensorflow\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xwqVx9-bwHl3", + "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.7.0\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6tp2xGV7wHl4" + }, + "source": [ + "## Conceitos Básicos: Tensor\n", + "\n", + "**Tensor** é um array multidimensional. É muito prático usar tensores para representar diferentes tipos de dados:\n", + "* 400x400 - imagem em preto e branco\n", + "* 400x400x3 - imagem colorida\n", + "* 16x400x400x3 - minibatch de 16 imagens coloridas\n", + "* 25x400x400x3 - um segundo de vídeo com 25 fps\n", + "* 8x25x400x400x3 - minibatch de 8 vídeos de 1 segundo\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qG2bsaR7wHl4" + }, + "source": [ + "### Tensores Simples\n", + "\n", + "Você pode criar tensores simples facilmente a partir de listas de np-arrays ou gerar tensores aleatórios:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ybpnk08HwHl4", + "outputId": "fad9ed4a-df82-44a0-84ea-324bc71ea46f", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[1 2]\n", + " [3 4]], shape=(2, 2), dtype=int32)\n", + "tf.Tensor(\n", + "[[-0.33552304 -1.8252622 -1.8532339 ]\n", + " [ 1.0871267 -1.2779568 0.5240014 ]\n", + " [-0.12793781 -1.8618349 -0.9020286 ]\n", + " [ 0.5948797 0.11144501 -2.0396452 ]\n", + " [ 0.47620854 1.1726047 -0.4405675 ]\n", + " [-0.27211484 -0.08985762 -0.03376012]\n", + " [ 0.64274263 0.53368104 -0.9006528 ]\n", + " [-0.43745974 -1.0081122 -0.13442488]\n", + " [ 0.36497566 1.3221073 -1.8739727 ]\n", + " [ 0.94821155 -0.02817811 1.3563292 ]], shape=(10, 3), dtype=float32)\n" + ] + } + ], + "source": [ + "a = tf.constant([[1,2],[3,4]])\n", + "print(a)\n", + "a = tf.random.normal(shape=(10,3))\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AXFMsV3r09Ux" + }, + "source": [ + "Você pode usar operações aritméticas em tensores, que são realizadas elemento a elemento, como no numpy. Tensores são automaticamente expandidos para a dimensão necessária, se necessário. Para extrair um array numpy de um tensor, use `.numpy()`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e5Nu5Xgj1DnQ", + "outputId": "0dfc8758-4ffd-4968-c7bf-6ba8d435df2e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[ 0. 0. 0. ]\n", + " [ 1.4226497 0.54730535 2.3772354 ]\n", + " [ 0.20758523 -0.03657269 0.9512053 ]\n", + " [ 0.93040276 1.9367073 -0.18641126]\n", + " [ 0.8117316 2.9978669 1.4126664 ]\n", + " [ 0.0634082 1.7354046 1.8194739 ]\n", + " [ 0.97826564 2.3589432 0.9525811 ]\n", + " [-0.1019367 0.81715 1.718809 ]\n", + " [ 0.7004987 3.1473694 -0.02073872]\n", + " [ 1.2837346 1.7970841 3.2095633 ]], shape=(10, 3), dtype=float32)\n", + "[0.71496403 0.16117539 0.15672949]\n" + ] + } + ], + "source": [ + "print(a-a[0])\n", + "print(tf.exp(a)[0].numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uQ5zN6cVyrG7" + }, + "source": [ + "## Variáveis\n", + "\n", + "As variáveis são úteis para representar valores de tensores que podem ser modificados usando `assign` e `assign_add`. Elas são frequentemente usadas para representar os pesos de redes neurais.\n", + "\n", + "Como exemplo, aqui está uma maneira simples de obter a soma de todas as linhas do tensor `a`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7pu0UZ-_yqfB", + "outputId": "6708c83e-02e6-4442-8757-45918eb1fbc2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "s = tf.Variable(tf.zeros_like(a[0]))\n", + "for i in a:\n", + " s.assign_add(i)\n", + "\n", + "print(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rIh1EHcezlNo" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aQIdWZ1kzn6P", + "outputId": "1c123d9a-ecd2-4f2e-828e-5ade85ac8f63" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.reduce_sum(a,axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-auwezDwHl6" + }, + "source": [ + "## Computando Gradientes\n", + "\n", + "Para a retropropagação, você precisa calcular os gradientes. Isso é feito usando o padrão `tf.GradientTape()`:\n", + " * Adicione um bloco `with tf.GradientTape` em torno de nossos cálculos\n", + " * Marque os tensores em relação aos quais precisamos calcular os gradientes chamando `tape.watch` (todas as variáveis são monitoradas automaticamente)\n", + " * Calcule o que for necessário (construa o grafo computacional)\n", + " * Obtenha os gradientes usando `tape.gradient`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m8vFOXr7wHl6", + "outputId": "860ac72e-50c7-4ff2-f258-747f27194f90", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[ 0.40935674 -0.3495818 ]\n", + " [ 0.94165146 -0.33209163]], shape=(2, 2), dtype=float32)\n" + ] + } + ], + "source": [ + "a = tf.random.normal(shape=(2, 2))\n", + "b = tf.random.normal(shape=(2, 2))\n", + "\n", + "with tf.GradientTape() as tape:\n", + " tape.watch(a) # Start recording the history of operations applied to `a`\n", + " c = tf.sqrt(tf.square(a) + tf.square(b)) # Do some math using `a`\n", + " # What's the gradient of `c` with respect to `a`?\n", + " dc_da = tape.gradient(c, a)\n", + " print(dc_da)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8sfjBMBu59B5" + }, + "source": [ + "## Exemplo 1: Regressão Linear\n", + "\n", + "Agora sabemos o suficiente para resolver o problema clássico de **Regressão Linear**. Vamos gerar um pequeno conjunto de dados sintético:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "j723455WwHl7", + "trusted": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_classification, make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "WJNK_J6v6I-Z", + "outputId": "eb4a66a6-6b9a-4c8a-bc24-d81eeb2d3f27" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(13) # pick the seed for reproducability - change it to explore the effects of random variations\n", + "\n", + "train_x = np.linspace(0, 3, 120)\n", + "train_labels = 2 * train_x + 0.9 + np.random.randn(*train_x.shape) * 0.5\n", + "\n", + "plt.scatter(train_x,train_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ng4rZmGc6oxk" + }, + "source": [ + "A regressão linear é definida por uma linha reta $f_{W,b}(x) = Wx+b$, onde $W, b$ são os parâmetros do modelo que precisamos encontrar. Um erro em nosso conjunto de dados $\\{x_i,y_u\\}_{i=1}^N$ (também chamado de **função de perda**) pode ser definido como o erro quadrático médio:\n", + "$$\n", + "\\mathcal{L}(W,b) = {1\\over N}\\sum_{i=1}^N (f_{W,b}(x_i)-y_i)^2\n", + "$$\n", + "\n", + "Vamos definir nosso modelo e função de perda:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "QxhI4GlB6aiH" + }, + "outputs": [], + "source": [ + "input_dim = 1\n", + "output_dim = 1\n", + "learning_rate = 0.1\n", + "\n", + "# This is our weight matrix\n", + "w = tf.Variable([[100.0]])\n", + "# This is our bias vector\n", + "b = tf.Variable(tf.zeros(shape=(output_dim,)))\n", + "\n", + "def f(x):\n", + " return tf.matmul(x,w) + b\n", + "\n", + "def compute_loss(labels, predictions):\n", + " return tf.reduce_mean(tf.square(labels - predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JUxwj3367gD2" + }, + "source": [ + "Vamos treinar o modelo em uma série de minibatches. Utilizaremos o método de descida de gradiente, ajustando os parâmetros do modelo usando as seguintes fórmulas:\n", + "$$\n", + "\\begin{array}{l}\n", + "W^{(n+1)}=W^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W} \\\\\n", + "b^{(n+1)}=b^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b} \\\\\n", + "\\end{array}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "-991PErM7fJU" + }, + "outputs": [], + "source": [ + "def train_on_batch(x, y):\n", + " with tf.GradientTape() as tape:\n", + " predictions = f(x)\n", + " loss = compute_loss(y, predictions)\n", + " # Note that `tape.gradient` works with a list as well (w, b).\n", + " dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n", + " w.assign_sub(learning_rate * dloss_dw)\n", + " b.assign_sub(learning_rate * dloss_db)\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idr2VEWb9rr0" + }, + "source": [ + "Vamos fazer o treinamento. Faremos várias passagens pelo conjunto de dados (os chamados **épocas**), dividiremos em minibatches e chamaremos a função definida acima:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "nOuu0qpx-wAp" + }, + "outputs": [], + "source": [ + "# Shuffle the data.\n", + "indices = np.random.permutation(len(train_x))\n", + "features = tf.constant(train_x[indices],dtype=tf.float32)\n", + "labels = tf.constant(train_labels[indices],dtype=tf.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3zdIf6c_85Ht", + "outputId": "43b04684-8b90-4c65-d5ff-20ebac61c73c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 94.5247\n", + "Epoch 1: last batch loss = 9.3428\n", + "Epoch 2: last batch loss = 1.4166\n", + "Epoch 3: last batch loss = 0.5224\n", + "Epoch 4: last batch loss = 0.3807\n", + "Epoch 5: last batch loss = 0.3495\n", + "Epoch 6: last batch loss = 0.3413\n", + "Epoch 7: last batch loss = 0.3390\n", + "Epoch 8: last batch loss = 0.3384\n", + "Epoch 9: last batch loss = 0.3382\n" + ] + } + ], + "source": [ + "batch_size = 4\n", + "for epoch in range(10):\n", + " for i in range(0,len(features),batch_size):\n", + " loss = train_on_batch(tf.reshape(features[i:i+batch_size],(-1,1)),tf.reshape(labels[i:i+batch_size],(-1,1)))\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora obtivemos os parâmetros otimizados $W$ e $b$. Note que seus valores são semelhantes aos valores originais usados ao gerar o conjunto de dados ($W=2, b=1$)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "US6q0nCBD-LL", + "outputId": "65a79620-a3eb-445b-aafb-60a60575ab0e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(,\n", + " )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w,b" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "_e6xRMZFDnyI", + "outputId": "d202b7fe-4383-4d82-b98e-a20f3180093e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(train_x,train_labels)\n", + "x = np.array([min(train_x),max(train_x)])\n", + "y = w.numpy()[0,0]*x+b.numpy()[0]\n", + "plt.plot(x,y,color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0giuwC9GHzi8" + }, + "source": [ + "## Grafo Computacional e Cálculos em GPU\n", + "\n", + "Sempre que calculamos uma expressão de tensor, o Tensorflow constrói um grafo computacional que pode ser executado no dispositivo de computação disponível, como CPU ou GPU. Como estávamos usando funções arbitrárias em Python no nosso código, elas não podem ser incluídas como parte do grafo computacional. Por isso, ao executar nosso código na GPU, precisaríamos transferir os dados entre a CPU e a GPU repetidamente e calcular a função personalizada na CPU.\n", + "\n", + "O Tensorflow permite que marquemos nossa função Python usando o decorador `@tf.function`, o que fará com que essa função faça parte do mesmo grafo computacional. Esse decorador pode ser aplicado a funções que utilizam operações padrão de tensores do Tensorflow.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "HK7HPLz3Hyrl" + }, + "outputs": [], + "source": [ + "@tf.function\n", + "def train_on_batch(x, y):\n", + " with tf.GradientTape() as tape:\n", + " predictions = f(x)\n", + " loss = compute_loss(y, predictions)\n", + " # Note that `tape.gradient` works with a list as well (w, b).\n", + " dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n", + " w.assign_sub(learning_rate * dloss_dw)\n", + " b.assign_sub(learning_rate * dloss_db)\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J7HusxWkGjLX" + }, + "source": [ + "O código não mudou, mas se você estivesse executando este código em uma GPU e em um conjunto de dados maior - teria notado a diferença na velocidade.\n", + "\n", + "## API de Conjunto de Dados\n", + "\n", + "O Tensorflow possui uma API prática para trabalhar com dados. Vamos tentar usá-la. Também iremos treinar nosso modelo do zero.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oYro9Lbr8q0M", + "outputId": "78c0a6de-71bd-4eef-8819-439495b28672" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 173.4585\n", + "Epoch 1: last batch loss = 13.8459\n", + "Epoch 2: last batch loss = 4.5407\n", + "Epoch 3: last batch loss = 3.7364\n", + "Epoch 4: last batch loss = 3.4334\n", + "Epoch 5: last batch loss = 3.1790\n", + "Epoch 6: last batch loss = 2.9458\n", + "Epoch 7: last batch loss = 2.7311\n", + "Epoch 8: last batch loss = 2.5332\n", + "Epoch 9: last batch loss = 2.3508\n" + ] + } + ], + "source": [ + "w.assign([[10.0]])\n", + "b.assign([0.0])\n", + "\n", + "# Create a tf.data.Dataset object for easy batched iteration\n", + "dataset = tf.data.Dataset.from_tensor_slices((train_x.astype(np.float32), train_labels.astype(np.float32)))\n", + "dataset = dataset.shuffle(buffer_size=1024).batch(256)\n", + "\n", + "for epoch in range(10):\n", + " for step, (x, y) in enumerate(dataset):\n", + " loss = train_on_batch(tf.reshape(x,(-1,1)), tf.reshape(y,(-1,1)))\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A10prCPowHl7" + }, + "source": [ + "## Exemplo 2: Classificação\n", + "\n", + "Agora vamos considerar um problema de classificação binária. Um bom exemplo desse tipo de problema seria a classificação de um tumor entre maligno e benigno com base no seu tamanho e idade.\n", + "\n", + "O modelo principal é semelhante ao de regressão, mas precisamos usar uma função de perda diferente. Vamos começar gerando dados de exemplo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "j0OTPkGpwHl7", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n", + "\n", + "n = 100\n", + "X, Y = make_classification(n_samples = n, n_features=2,\n", + " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n", + "X = X.astype(np.float32)\n", + "Y = Y.astype(np.int32)\n", + "\n", + "split = [ 70*n//100, (15+70)*n//100 ]\n", + "train_x, valid_x, test_x = np.split(X, split)\n", + "train_labels, valid_labels, test_labels = np.split(Y, split)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "c-_BjSHPwHl8", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "def plot_dataset(features, labels, W=None, b=None):\n", + " # prepare the plot\n", + " fig, ax = plt.subplots(1, 1)\n", + " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n", + " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n", + " colors = ['r' if l else 'b' for l in labels]\n", + " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n", + " if W is not None:\n", + " min_x = min(features[:,0])\n", + " max_x = max(features[:,1])\n", + " min_y = min(features[:,1])*(1-.1)\n", + " max_y = max(features[:,1])*(1+.1)\n", + " cx = np.array([min_x,max_x],dtype=np.float32)\n", + " cy = (0.5-W[0]*cx-b)/W[1]\n", + " ax.plot(cx,cy,'g')\n", + " ax.set_ylim(min_y,max_y)\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "tq0vFchQwHl8", + "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff", + "scrolled": false, + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66184/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x, train_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalizando Dados\n", + "\n", + "Antes do treinamento, é comum trazer nossas características de entrada para a faixa padrão de [0,1] (ou [-1,1]). Os motivos exatos para isso serão discutidos mais adiante no curso, mas, resumidamente, a razão é a seguinte: queremos evitar que os valores que passam pela nossa rede fiquem muito grandes ou muito pequenos, e normalmente concordamos em manter todos os valores em uma faixa pequena próxima de 0. Por isso, inicializamos os pesos com números aleatórios pequenos e mantemos os sinais na mesma faixa.\n", + "\n", + "Ao normalizar os dados, precisamos subtrair o valor mínimo e dividir pelo intervalo. Calculamos o valor mínimo e o intervalo usando os dados de treinamento e, em seguida, normalizamos o conjunto de teste/validação usando os mesmos valores de mínimo/intervalo do conjunto de treinamento. Isso ocorre porque, na vida real, só teremos conhecimento do conjunto de treinamento e não de todos os novos valores que a rede será solicitada a prever. Ocasionalmente, o novo valor pode ficar fora da faixa [0,1], mas isso não é crucial.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "train_x_norm = (train_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n", + "valid_x_norm = (valid_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n", + "test_x_norm = (test_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SjPlpf2-wHl8" + }, + "source": [ + "## Treinando Perceptron de Uma Camada\n", + "\n", + "Vamos usar o mecanismo de cálculo de gradientes do Tensorflow para treinar um perceptron de uma camada.\n", + "\n", + "Nossa rede neural terá 2 entradas e 1 saída. A matriz de pesos $W$ terá tamanho $2\\times1$, e o vetor de bias $b$ -- $1$.\n", + "\n", + "O modelo principal será o mesmo do exemplo anterior, mas a função de perda será uma perda logística. Para aplicar a perda logística, precisamos obter o valor da **probabilidade** como saída da nossa rede, ou seja, precisamos trazer a saída $z$ para o intervalo [0,1] usando a função de ativação `sigmoid`: $p=\\sigma(z)$.\n", + "\n", + "Se obtivermos a probabilidade $p_i$ para o i-ésimo valor de entrada correspondente à classe real $y_i\\in\\{0,1\\}$, calculamos a perda como $\\mathcal{L_i}=-(y_i\\log p_i + (1-y_i)log(1-p_i))$. \n", + "\n", + "No Tensorflow, ambos esses passos (aplicar sigmoid e depois a perda logística) podem ser realizados com uma única chamada à função `sigmoid_cross_entropy_with_logits`. Como estamos treinando nossa rede em minibatches, precisamos calcular a média da perda entre todos os elementos de um minibatch usando `reduce_mean`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "kdDxWeCqwHl8", + "trusted": true + }, + "outputs": [], + "source": [ + "W = tf.Variable(tf.random.normal(shape=(2,1)),dtype=tf.float32)\n", + "b = tf.Variable(tf.zeros(shape=(1,),dtype=tf.float32))\n", + "\n", + "learning_rate = 0.1\n", + "\n", + "@tf.function\n", + "def train_on_batch(x, y):\n", + " with tf.GradientTape() as tape:\n", + " z = tf.matmul(x, W) + b\n", + " loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=z))\n", + " dloss_dw, dloss_db = tape.gradient(loss, [W, b])\n", + " W.assign_sub(learning_rate * dloss_dw)\n", + " b.assign_sub(learning_rate * dloss_db)\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zAAgw0h6KzUd" + }, + "source": [ + "Usaremos minibatches de 16 elementos e faremos algumas épocas de treinamento:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PfyqjVb2wHl8", + "outputId": "308850b8-fe17-4cda-ac27-8bcda210f113", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.3823\n", + "Epoch 1: last batch loss = 0.5243\n", + "Epoch 2: last batch loss = 0.4510\n", + "Epoch 3: last batch loss = 0.3261\n", + "Epoch 4: last batch loss = 0.4177\n", + "Epoch 5: last batch loss = 0.3323\n", + "Epoch 6: last batch loss = 0.6294\n", + "Epoch 7: last batch loss = 0.6334\n", + "Epoch 8: last batch loss = 0.2571\n", + "Epoch 9: last batch loss = 0.3425\n" + ] + } + ], + "source": [ + "# Create a tf.data.Dataset object for easy batched iteration\n", + "dataset = tf.data.Dataset.from_tensor_slices((train_x_norm.astype(np.float32), train_labels.astype(np.float32)))\n", + "dataset = dataset.shuffle(128).batch(2)\n", + "\n", + "for epoch in range(10):\n", + " for step, (x, y) in enumerate(dataset):\n", + " loss = train_on_batch(x, tf.expand_dims(y,1))\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s4_Atvn5K4K9" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "PgRTHttLwHl9", + "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b", + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66184/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x,train_labels,W.numpy(),b.numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos ver como nosso modelo se comporta nos dados de validação.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "oEQswfCGrmHw", + "outputId": "3cf61882-60e1-4baa-8e51-0c31ea80875c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pred = tf.matmul(test_x,W)+b\n", + "fig,ax = plt.subplots(1,2)\n", + "ax[0].scatter(test_x[:,0],test_x[:,1],c=pred[:,0]>0.5)\n", + "ax[1].scatter(test_x[:,0],test_x[:,1],c=valid_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para calcular a precisão nos dados de validação, podemos converter o tipo booleano para float e calcular a média:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HUjdeIefsIsg", + "outputId": "f267f505-8ba4-43ef-9ebe-df124c3c05a1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.reduce_mean(tf.cast(((pred[0]>0.5)==test_labels),tf.float32))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vamos explicar o que acontece aqui:\n", + "* `pred` são os valores previstos pela rede. Eles não são exatamente probabilidades, porque não usamos uma função de ativação, mas valores maiores que 0.5 correspondem à classe 1, e menores - à classe 0.\n", + "* `pred[0]>0.5` cria um tensor booleano de resultados, onde `True` corresponde à classe 1, e `False` - à classe 0.\n", + "* Comparamos esse tensor com os rótulos esperados `valid_labels`, obtendo o vetor booleano de previsões corretas, onde `True` corresponde à previsão correta, e `False` - à incorreta.\n", + "* Convertemos esse tensor para ponto flutuante usando `tf.cast`.\n", + "* Em seguida, calculamos o valor médio usando `tf.reduce_mean` - que é exatamente a nossa precisão desejada.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_95qF9lY2kHp" + }, + "source": [ + "## Usando Otimizadores do TensorFlow/Keras\n", + "\n", + "O TensorFlow está intimamente integrado ao Keras, que contém muitas funcionalidades úteis. Por exemplo, podemos usar diferentes **algoritmos de otimização**. Vamos fazer isso e também exibir a precisão obtida durante o treinamento.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ups7nlV22ofp", + "outputId": "aa4dff06-82b9-4b2f-ca00-33970ea2b989" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 4.7787, acc = 1.0000\n", + "Epoch 1: last batch loss = 8.4343, acc = 0.5000\n", + "Epoch 2: last batch loss = 8.3255, acc = 0.5000\n", + "Epoch 3: last batch loss = 7.5579, acc = 0.5000\n", + "Epoch 4: last batch loss = 6.5254, acc = 0.5000\n", + "Epoch 5: last batch loss = 7.3800, acc = 0.5000\n", + "Epoch 6: last batch loss = 7.7586, acc = 0.5000\n", + "Epoch 7: last batch loss = 10.4724, acc = 0.0000\n", + "Epoch 8: last batch loss = 9.4423, acc = 0.5000\n", + "Epoch 9: last batch loss = 4.1888, acc = 1.0000\n", + "Epoch 10: last batch loss = 11.2127, acc = 0.0000\n", + "Epoch 11: last batch loss = 9.0417, acc = 0.5000\n", + "Epoch 12: last batch loss = 7.9847, acc = 0.5000\n", + "Epoch 13: last batch loss = 3.7879, acc = 1.0000\n", + "Epoch 14: last batch loss = 6.8455, acc = 0.5000\n", + "Epoch 15: last batch loss = 6.5204, acc = 0.5000\n", + "Epoch 16: last batch loss = 9.2386, acc = 0.5000\n", + "Epoch 17: last batch loss = 6.2447, acc = 0.5000\n", + "Epoch 18: last batch loss = 3.9107, acc = 1.0000\n", + "Epoch 19: last batch loss = 5.7645, acc = 1.0000\n" + ] + } + ], + "source": [ + "optimizer = tf.keras.optimizers.Adam(0.01)\n", + "\n", + "W = tf.Variable(tf.random.normal(shape=(2,1)))\n", + "b = tf.Variable(tf.zeros(shape=(1,),dtype=tf.float32))\n", + "\n", + "@tf.function\n", + "def train_on_batch(x, y):\n", + " vars = [W, b]\n", + " with tf.GradientTape() as tape:\n", + " z = tf.sigmoid(tf.matmul(x, W) + b)\n", + " loss = tf.reduce_mean(tf.keras.losses.binary_crossentropy(z,y))\n", + " correct_prediction = tf.equal(tf.round(y), tf.round(z))\n", + " acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + " grads = tape.gradient(loss, vars)\n", + " optimizer.apply_gradients(zip(grads,vars))\n", + " return loss,acc\n", + "\n", + "for epoch in range(20):\n", + " for step, (x, y) in enumerate(dataset):\n", + " loss,acc = train_on_batch(tf.reshape(x,(-1,2)), tf.reshape(y,(-1,1)))\n", + " print('Epoch %d: last batch loss = %.4f, acc = %.4f' % (epoch, float(loss),acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dvAiaj_JndyP" + }, + "source": [ + "**Tarefa 1**: Plote os gráficos da função de perda e da precisão nos dados de treinamento e validação durante o treinamento\n", + "\n", + "**Tarefa 2**: Tente resolver o problema de classificação MNIST usando este código. Dica: use `softmax_crossentropy_with_logits` ou `sparse_softmax_cross_entropy_with_logits` como função de perda. No primeiro caso, você precisa fornecer os valores esperados no formato *one hot encoding*, e no segundo caso - como número inteiro da classe.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "995iCprDrgYQ" + }, + "source": [ + "## Keras\n", + "### Deep Learning para Humanos\n", + "\n", + "* Keras é uma biblioteca originalmente desenvolvida por François Chollet para funcionar em cima do Tensorflow, CNTK e Theano, com o objetivo de unificar todos os frameworks de baixo nível. Ainda é possível instalar o Keras como uma biblioteca separada, mas isso não é recomendado.\n", + "* Atualmente, o Keras está incluído como parte da biblioteca Tensorflow.\n", + "* Você pode construir redes neurais facilmente a partir de camadas.\n", + "* Contém a função `fit` para realizar todo o treinamento, além de várias funções para trabalhar com dados típicos (imagens, texto, etc.).\n", + "* Muitos exemplos disponíveis.\n", + "* API Funcional vs. API Sequencial.\n", + "\n", + "Keras oferece abstrações de nível mais alto para redes neurais, permitindo que trabalhemos em termos de camadas, modelos e otimizadores, em vez de lidar diretamente com tensores e gradientes.\n", + "\n", + "Livro clássico de Deep Learning do criador do Keras: [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)\n", + "\n", + "### API Funcional\n", + "\n", + "Ao usar a API funcional, definimos a **entrada** da rede como `keras.Input` e, em seguida, calculamos a **saída** passando-a por uma série de operações. Por fim, definimos o **modelo** como um objeto que transforma a entrada em saída.\n", + "\n", + "Depois de obter o objeto **modelo**, precisamos:\n", + "* **Compilá-lo**, especificando a função de perda e o otimizador que queremos usar com nosso modelo.\n", + "* **Treiná-lo** chamando a função `fit` com os dados de treinamento (e possivelmente de validação).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QJWplVfy34Eo", + "outputId": "9be976f2-4f9a-495c-bddc-a7f9ec30989a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " input_1 (InputLayer) [(None, 2)] 0 \n", + " \n", + " dense (Dense) (None, 1) 3 \n", + " \n", + "=================================================================\n", + "Total params: 3\n", + "Trainable params: 3\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/15\n", + "9/9 [==============================] - 1s 2ms/step - loss: 0.7812 - accuracy: 0.2857\n", + "Epoch 2/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.7142 - accuracy: 0.4000\n", + "Epoch 3/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.6683 - accuracy: 0.6143\n", + "Epoch 4/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.6221 - accuracy: 0.8429\n", + "Epoch 5/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.5843 - accuracy: 0.8857\n", + "Epoch 6/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.5447 - accuracy: 0.9429\n", + "Epoch 7/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.5135 - accuracy: 0.9286\n", + "Epoch 8/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4878 - accuracy: 0.9429\n", + "Epoch 9/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4679 - accuracy: 0.9429\n", + "Epoch 10/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4446 - accuracy: 0.9429\n", + "Epoch 11/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4349 - accuracy: 0.8714\n", + "Epoch 12/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4156 - accuracy: 0.9286\n", + "Epoch 13/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.4019 - accuracy: 0.9429\n", + "Epoch 14/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.3908 - accuracy: 0.9286\n", + "Epoch 15/15\n", + "9/9 [==============================] - 0s 2ms/step - loss: 0.3777 - accuracy: 0.9286\n" + ] + } + ], + "source": [ + "inputs = tf.keras.Input(shape=(2,))\n", + "z = tf.keras.layers.Dense(1,kernel_initializer='glorot_uniform',activation='sigmoid')(inputs)\n", + "model = tf.keras.models.Model(inputs,z)\n", + "\n", + "model.compile(tf.keras.optimizers.Adam(0.1),'binary_crossentropy',['accuracy'])\n", + "model.summary()\n", + "h = model.fit(train_x_norm,train_labels,batch_size=8,epochs=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "K2Kf60IrZcqs", + "outputId": "b60b868d-3562-4715-f5d5-1f9764e45f09" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(h.history['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iJruFXmb_dur" + }, + "source": [ + "### API Sequencial\n", + "\n", + "Alternativamente, podemos começar a pensar em um modelo como uma **sequência de camadas**, e simplesmente especificar essas camadas adicionando-as ao objeto `model`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iWc_kSr8_YXt", + "outputId": "345dbe65-629d-468f-ed75-1d412c966340" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense_1 (Dense) (None, 5) 15 \n", + " \n", + " dense_2 (Dense) (None, 1) 6 \n", + " \n", + "=================================================================\n", + "Total params: 21\n", + "Trainable params: 21\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "Epoch 1/15\n", + "9/9 [==============================] - 1s 64ms/step - loss: 0.6994 - accuracy: 0.5000 - val_loss: 0.6719 - val_accuracy: 0.4667\n", + "Epoch 2/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.6635 - accuracy: 0.5429 - val_loss: 0.6531 - val_accuracy: 0.4667\n", + "Epoch 3/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.6469 - accuracy: 0.5857 - val_loss: 0.5775 - val_accuracy: 1.0000\n", + "Epoch 4/15\n", + "9/9 [==============================] - 0s 4ms/step - loss: 0.5639 - accuracy: 0.9143 - val_loss: 0.5395 - val_accuracy: 0.7333\n", + "Epoch 5/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.5236 - accuracy: 0.7143 - val_loss: 0.4498 - val_accuracy: 0.9333\n", + "Epoch 6/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.4573 - accuracy: 0.8714 - val_loss: 0.3584 - val_accuracy: 1.0000\n", + "Epoch 7/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.3867 - accuracy: 0.8714 - val_loss: 0.2989 - val_accuracy: 0.9333\n", + "Epoch 8/15\n", + "9/9 [==============================] - 0s 7ms/step - loss: 0.3388 - accuracy: 0.8857 - val_loss: 0.2204 - val_accuracy: 1.0000\n", + "Epoch 9/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.2815 - accuracy: 0.9429 - val_loss: 0.1957 - val_accuracy: 1.0000\n", + "Epoch 10/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.2692 - accuracy: 0.8857 - val_loss: 0.1323 - val_accuracy: 1.0000\n", + "Epoch 11/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.2591 - accuracy: 0.9429 - val_loss: 0.1105 - val_accuracy: 1.0000\n", + "Epoch 12/15\n", + "9/9 [==============================] - 0s 6ms/step - loss: 0.2229 - accuracy: 0.9286 - val_loss: 0.1051 - val_accuracy: 1.0000\n", + "Epoch 13/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.2146 - accuracy: 0.9143 - val_loss: 0.0919 - val_accuracy: 1.0000\n", + "Epoch 14/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.2031 - accuracy: 0.9429 - val_loss: 0.0859 - val_accuracy: 1.0000\n", + "Epoch 15/15\n", + "9/9 [==============================] - 0s 5ms/step - loss: 0.1997 - accuracy: 0.9429 - val_loss: 0.0829 - val_accuracy: 1.0000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(5,activation='sigmoid',input_shape=(2,)))\n", + "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n", + "\n", + "model.compile(tf.keras.optimizers.Adam(0.1),'binary_crossentropy',['accuracy'])\n", + "model.summary()\n", + "model.fit(train_x_norm,train_labels,validation_data=(test_x_norm,test_labels),batch_size=8,epochs=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BmHNhUU8bqEX" + }, + "source": [ + "## Funções de Perda para Classificação\n", + "\n", + "É importante especificar corretamente a função de perda e a função de ativação na última camada da rede. As principais regras são as seguintes:\n", + "* Se a rede possui uma saída (**classificação binária**), usamos a função de ativação **sigmoid**; para **classificação multiclasse**, usamos **softmax**.\n", + "* Se a classe de saída for representada como one-hot-encoding, a função de perda será **cross entropy loss** (entropia cruzada categórica); se a saída contiver o número da classe, será **sparse categorical cross-entropy**. Para **classificação binária**, use **binary cross-entropy** (o mesmo que **log loss**).\n", + "* **Classificação multilabel** ocorre quando um objeto pode pertencer a várias classes ao mesmo tempo. Nesse caso, precisamos codificar os rótulos usando one-hot encoding e usar **sigmoid** como função de ativação, para que a probabilidade de cada classe esteja entre 0 e 1.\n", + "\n", + "| Classificação | Formato do Rótulo | Função de Ativação | Perda |\n", + "|---------------|-----------------------|-----------------|----------|\n", + "| Binária | Probabilidade da 1ª classe | sigmoid | binary crossentropy |\n", + "| Binária | One-hot encoding (2 saídas) | softmax | categorical crossentropy |\n", + "| Multiclasse | One-hot encoding | softmax | categorical crossentropy |\n", + "| Multiclasse | Número da Classe | softmax | sparse categorical crossentropy |\n", + "| Multilabel | One-hot encoding | sigmoid | categorical crossentropy |\n", + "\n", + "> A classificação binária também pode ser tratada como um caso especial de classificação multiclasse com duas saídas. Nesse caso, precisamos usar **softmax**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gZ-kWx84bMDH" + }, + "source": [ + "**Tarefa 3**: \n", + "Use Keras para treinar um classificador MNIST: \n", + "* Observe que o Keras contém alguns conjuntos de dados padrão, incluindo o MNIST. Para usar o MNIST no Keras, você só precisa de algumas linhas de código (mais informações [aqui](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist)) \n", + "* Experimente várias configurações de rede, com diferentes números de camadas/neurônios e funções de ativação. \n", + "\n", + "Qual foi a melhor precisão que você conseguiu alcançar? \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yX6hqiafwHl9" + }, + "source": [ + "## Principais pontos\n", + "\n", + "* O TensorFlow permite operar em tensores em um nível baixo, oferecendo maior flexibilidade.\n", + "* Existem ferramentas convenientes para trabalhar com dados (`td.Data`) e camadas (`tf.layers`).\n", + "* Para iniciantes/tarefas típicas, é recomendado usar **Keras**, que permite construir redes a partir de camadas.\n", + "* Se for necessário uma arquitetura não padrão, você pode implementar sua própria camada Keras e usá-la em modelos Keras.\n", + "* É uma boa ideia também explorar o PyTorch e comparar as abordagens.\n", + "\n", + "Um bom notebook de exemplo do criador do Keras sobre Keras e TensorFlow 2.0 pode ser encontrado [aqui](https://t.co/k694J95PI8).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "colab": { + "collapsed_sections": [], + "name": "IntroKerasTF.ipynb", + "provenance": [] + }, + "interpreter": { + "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5" + }, + "kernelspec": { + "display_name": "Python 3.8.12 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "coopTranslator": { + "original_hash": "ef00e24b21d0887ad778265cd284473c", + "translation_date": "2025-08-28T13:57:29+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb new file mode 100644 index 00000000..e00c7d09 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb @@ -0,0 +1,13892 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "En2vX4FuwHlu" + }, + "source": [ + "## Introdução ao PyTorch\n", + "\n", + "> Este notebook faz parte do [Currículo de IA para Iniciantes](http://github.com/microsoft/ai-for-beginners). Visite o repositório para acessar o conjunto completo de materiais de aprendizado.\n", + "\n", + "### Frameworks Neurais\n", + "\n", + "Aprendemos que, para treinar redes neurais, você precisa:\n", + "* Multiplicar matrizes (tensores) rapidamente\n", + "* Calcular gradientes para realizar a otimização por descida de gradiente\n", + "\n", + "O que os frameworks de redes neurais permitem que você faça:\n", + "* Operar com tensores em qualquer recurso de computação disponível, seja CPU, GPU ou até mesmo TPU\n", + "* Calcular gradientes automaticamente (eles são programados explicitamente para todas as funções de tensor integradas)\n", + "\n", + "Opcionalmente:\n", + "* Construtor de Redes Neurais / API de nível superior (descrever a rede como uma sequência de camadas)\n", + "* Funções simples de treinamento (`fit`, como no Scikit Learn)\n", + "* Vários algoritmos de otimização além da descida de gradiente\n", + "* Abstrações para manipulação de dados (que idealmente também funcionarão na GPU)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8cACQoFMwHl3" + }, + "source": [ + "### Frameworks Mais Populares\n", + "\n", + "* Tensorflow 1.x - primeiro framework amplamente disponível (Google). Permitia definir um gráfico de computação estático, enviá-lo para a GPU e avaliá-lo explicitamente\n", + "* PyTorch - um framework do Facebook que está ganhando popularidade\n", + "* Keras - API de nível superior sobre Tensorflow/PyTorch para unificar e simplificar o uso de redes neurais (Francois Chollet)\n", + "* Tensorflow 2.x + Keras - nova versão do Tensorflow com funcionalidade Keras integrada, que suporta **gráfico de computação dinâmico**, permitindo realizar operações com tensores de forma muito semelhante ao numpy (e PyTorch)\n", + "\n", + "Neste Notebook, aprenderemos a usar o PyTorch. Você precisa garantir que tenha uma versão recente do PyTorch instalada - para isso, siga as [instruções no site deles](https://pytorch.org/get-started/locally/). Normalmente, é tão simples quanto executar\n", + "```\n", + "pip install torch torchvision\n", + "```\n", + "ou\n", + "```\n", + "conda install pytorch -c pytorch\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "xwqVx9-bwHl3", + "outputId": "7fdf1bd8-a54b-4eb0-cb09-dbb81a42d99c", + "tags": [] + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'1.11.0+cu113'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "import torch\n", + "torch.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6tp2xGV7wHl4" + }, + "source": [ + "## Conceitos Básicos: Tensor\n", + "\n", + "**Tensor** é um array multidimensional. É muito prático usar tensores para representar diferentes tipos de dados:\n", + "* 400x400 - imagem em preto e branco\n", + "* 400x400x3 - imagem colorida\n", + "* 16x400x400x3 - minibatch de 16 imagens coloridas\n", + "* 25x400x400x3 - um segundo de vídeo com 25 fps\n", + "* 8x25x400x400x3 - minibatch de 8 vídeos de 1 segundo\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qG2bsaR7wHl4" + }, + "source": [ + "### Tensores Simples\n", + "\n", + "Você pode criar tensores simples facilmente a partir de listas de np-arrays ou gerar tensores aleatórios:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ybpnk08HwHl4", + "outputId": "377e6e25-bc5a-4d2e-fe8e-17dac1d20c2c", + "trusted": true + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[1, 2],\n", + " [3, 4]])\n", + "tensor([[ 0.8995, -1.6137, 1.4489],\n", + " [-0.2796, -2.1443, -2.4618],\n", + " [-0.2358, -0.4249, -0.0716],\n", + " [-0.1267, -0.6382, 0.0593],\n", + " [-0.4956, 1.7054, 0.3874],\n", + " [ 1.3479, -1.6329, 0.2793],\n", + " [ 1.1211, -1.5430, 0.7186],\n", + " [-1.5197, 0.5559, -1.6421],\n", + " [ 0.1900, -0.4175, -0.3922],\n", + " [ 1.8994, 0.1497, -0.7039]])\n" + ] + } + ], + "source": [ + "a = torch.tensor([[1,2],[3,4]])\n", + "print(a)\n", + "a = torch.randn(size=(10,3))\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AXFMsV3r09Ux" + }, + "source": [ + "Você pode usar operações aritméticas em tensores, que são realizadas elemento por elemento, como no numpy. Tensores são automaticamente expandidos para a dimensão necessária, se necessário. Para extrair um array numpy de um tensor, use `.numpy()`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e5Nu5Xgj1DnQ", + "outputId": "c1fbcd86-dde6-40b6-8edf-7a37f9d60901" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 0.0000, 0.0000, 0.0000],\n", + " [-2.0583, -0.5631, 1.4932],\n", + " [-1.0613, -1.0738, 2.2078],\n", + " [-1.5101, 0.5896, 2.4722],\n", + " [-2.8219, -2.0846, 1.2405],\n", + " [ 0.8706, -0.2485, 2.3679],\n", + " [-1.6590, 0.1935, 1.8698],\n", + " [-0.3316, 0.8065, 1.6490],\n", + " [-1.5788, -1.1844, -0.4816],\n", + " [ 0.0680, -1.4526, 1.8159]])\n", + "[3.887189 2.1276016 0.17371987]\n" + ] + } + ], + "source": [ + "print(a-a[0])\n", + "print(torch.exp(a)[0].numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uQ5zN6cVyrG7" + }, + "source": [ + "## Operações in-place e out-of-place\n", + "\n", + "Operações com tensores, como `+`/`add`, retornam novos tensores. No entanto, às vezes é necessário modificar o tensor existente diretamente (in-place). A maioria das operações possui suas versões in-place, que terminam com `_`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Mjkbcw3-ACKS", + "outputId": "ca021008-9ab6-4b09-c5a5-bbe854cd1493" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result when adding out-of-place: tensor(8)\n", + "Result after adding in-place: tensor(8)\n" + ] + } + ], + "source": [ + "u = torch.tensor(5)\n", + "print(\"Result when adding out-of-place:\",u.add(torch.tensor(3)))\n", + "u.add_(torch.tensor(3))\n", + "print(\"Result after adding in-place:\", u)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DLPUcVsXACKT" + }, + "source": [ + "É assim que podemos calcular a soma de todas as linhas em uma matriz de maneira ingênua:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7pu0UZ-_yqfB", + "outputId": "bd2e8c6a-39e1-4f29-990b-9591e866936c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 3.4945, 2.5325, -2.8684])\n" + ] + } + ], + "source": [ + "s = torch.zeros_like(a[0])\n", + "for i in a:\n", + " s.add_(i)\n", + "\n", + "print(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rIh1EHcezlNo" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aQIdWZ1kzn6P", + "outputId": "89000bb4-f45e-493b-a7b0-39fa4e7d92c1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 3.4945, 2.5325, -2.8684])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.sum(a,axis=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5UzUmEZhACKT" + }, + "source": [ + "Você pode ler mais sobre tensores do PyTorch na [documentação oficial](https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U-auwezDwHl6" + }, + "source": [ + "## Computando Gradientes\n", + "\n", + "Para a retropropagação, você precisa calcular os gradientes. Podemos definir o atributo `requires_grad` de qualquer Tensor do PyTorch como `True`, o que fará com que todas as operações com esse tensor sejam rastreadas para cálculos de gradiente. Para calcular os gradientes, você precisa chamar o método `backward()`, após o qual o gradiente estará disponível usando o atributo `grad`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m8vFOXr7wHl6", + "outputId": "7054c2b1-0b61-4938-937d-813f75f0b195", + "trusted": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[-0.1728, 0.0913],\n", + " [-0.1666, -0.1942]])\n" + ] + } + ], + "source": [ + "a = torch.randn(size=(2, 2), requires_grad=True)\n", + "b = torch.randn(size=(2, 2))\n", + "\n", + "c = torch.mean(torch.sqrt(torch.square(a) + torch.square(b))) # Do some math using `a`\n", + "c.backward() # call backward() to compute all gradients\n", + "# What's the gradient of `c` with respect to `a`?\n", + "print(a.grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nPj3rtrtACKU" + }, + "source": [ + "Para ser mais preciso, o PyTorch **acumula** automaticamente os gradientes. Se você especificar `retain_graph=True` ao chamar `backward`, o grafo computacional será preservado, e um novo gradiente será adicionado ao campo `grad`. Para reiniciar o cálculo de gradientes do zero, precisamos redefinir o campo `grad` para 0 explicitamente chamando `zero_()`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "z_VIw8MoACKU", + "outputId": "36a28b11-6919-47ab-c3f9-c7f1d8500423" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[-0.5185, 0.2739],\n", + " [-0.4998, -0.5826]])\n", + "tensor([[-0.1728, 0.0913],\n", + " [-0.1666, -0.1942]])\n" + ] + } + ], + "source": [ + "c = torch.mean(torch.sqrt(torch.square(a) + torch.square(b)))\n", + "c.backward(retain_graph=True)\n", + "c.backward(retain_graph=True)\n", + "print(a.grad)\n", + "a.grad.zero_()\n", + "c.backward()\n", + "print(a.grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HM9sUkVgCiG9" + }, + "source": [ + "Para calcular gradientes, o PyTorch cria e mantém o **grafo computacional**. Para cada tensor que possui a flag `requires_grad` definida como `True`, o PyTorch mantém uma função especial chamada `grad_fn`, que calcula a derivada da expressão de acordo com a regra da diferenciação em cadeia:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PcxHb-7jC7Vv", + "outputId": "3b3fa138-6d09-4636-8a71-f4a4051c7827" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.9143, grad_fn=)\n" + ] + } + ], + "source": [ + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rvLfNiblACKV" + }, + "source": [ + "Aqui, `c` é calculado usando a função `mean`, portanto, `grad_fn` aponta para uma função chamada `MeanBackward`.\n", + "\n", + "Na maioria dos casos, queremos que o PyTorch calcule o gradiente de uma função escalar (como uma função de perda). No entanto, se quisermos calcular o gradiente de um tensor em relação a outro tensor, o PyTorch nos permite calcular o produto de uma matriz Jacobiana por um vetor fornecido.\n", + "\n", + "Suponha que temos uma função vetorial $\\vec{y}=f(\\vec{x})$, onde\n", + "$\\vec{x}=\\langle x_1,\\dots,x_n\\rangle$ e\n", + "$\\vec{y}=\\langle y_1,\\dots,y_m\\rangle$, então o gradiente de $\\vec{y}$ em relação a $\\vec{x}$ é definido por uma **Jacobiana**:\n", + "\n", + "$$\n", + "\\begin{align}J=\\left(\\begin{array}{ccc}\n", + " \\frac{\\partial y_{1}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{1}}{\\partial x_{n}}\\\\\n", + " \\vdots & \\ddots & \\vdots\\\\\n", + " \\frac{\\partial y_{m}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{m}}{\\partial x_{n}}\n", + "\\end{array}\\right)\\end{align}\n", + "$$\n", + "\n", + "Em vez de nos dar acesso à Jacobiana completa, o PyTorch calcula o produto $v^T\\cdot J$ da Jacobiana com algum vetor\n", + "$v=(v_1 \\dots v_m)$. Para fazer isso, precisamos chamar ``backward`` e passar `v` como argumento. O tamanho de `v` deve ser o mesmo que o tamanho do tensor original, em relação ao qual estamos calculando o gradiente.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VUNYiQCOACKV", + "outputId": "e3127c21-fce6-420d-f347-ec40cc827e7e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[-0.8642, 0.0913],\n", + " [-0.1666, -0.9710]])\n" + ] + } + ], + "source": [ + "c = torch.sqrt(torch.square(a) + torch.square(b))\n", + "c.backward(torch.eye(2)) # eye(2) means 2x2 identity matrix\n", + "print(a.grad)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dGHlkVlvACKV" + }, + "source": [ + "Mais informações sobre o cálculo de Jacobianos no PyTorch podem ser encontradas na [documentação oficial](https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FnVvj4LkD15r" + }, + "source": [ + "# Exemplo 0: Otimização Usando Descida do Gradiente\n", + "\n", + "Vamos tentar usar a diferenciação automática para encontrar um mínimo de uma função simples de duas variáveis $f(x_1,x_2)=(x_1-3)^2+(x_2+2)^2$. Deixe o tensor `x` armazenar as coordenadas atuais de um ponto. Começamos com um ponto inicial $x^{(0)}=(0,0)$ e calculamos o próximo ponto em uma sequência usando a fórmula da descida do gradiente:\n", + "$$\n", + "x^{(n+1)} = x^{(n)} - \\eta\\nabla f\n", + "$$\n", + "Aqui, $\\eta$ é a chamada **taxa de aprendizado** (que será denotada como `lr` no código), e $\\nabla f = (\\frac{\\partial f}{\\partial x_1},\\frac{\\partial f}{\\partial x_2})$ - o gradiente de $f$.\n", + "\n", + "Para começar, vamos definir o valor inicial de `x` e a função `f`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nDw5mV9KEeOa" + }, + "outputs": [], + "source": [ + "x = torch.zeros(2,requires_grad=True)\n", + "f = lambda x : (x-torch.tensor([3,-2])).pow(2).sum()\n", + "lr = 0.1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wt815LWdEj77" + }, + "source": [ + "Agora vamos fazer 15 iterações de descida do gradiente. Em cada iteração, atualizaremos as coordenadas `x` e as imprimiremos, para garantir que estamos nos aproximando do ponto mínimo em (3,-2):\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KfwMf555EyWJ", + "outputId": "67e2199c-61ff-4ad1-9c48-b4a646bf8bbd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 0: x[0]=0.6000000238418579, x[1]=-0.4000000059604645\n", + "Step 1: x[0]=1.0800000429153442, x[1]=-0.7200000286102295\n", + "Step 2: x[0]=1.4639999866485596, x[1]=-0.9760000705718994\n", + "Step 3: x[0]=1.7711999416351318, x[1]=-1.1808000802993774\n", + "Step 4: x[0]=2.0169599056243896, x[1]=-1.3446400165557861\n", + "Step 5: x[0]=2.2135679721832275, x[1]=-1.4757120609283447\n", + "Step 6: x[0]=2.370854377746582, x[1]=-1.5805696249008179\n", + "Step 7: x[0]=2.4966835975646973, x[1]=-1.6644556522369385\n", + "Step 8: x[0]=2.597346782684326, x[1]=-1.7315645217895508\n", + "Step 9: x[0]=2.677877426147461, x[1]=-1.7852516174316406\n", + "Step 10: x[0]=2.7423019409179688, x[1]=-1.8282012939453125\n", + "Step 11: x[0]=2.793841600418091, x[1]=-1.8625609874725342\n", + "Step 12: x[0]=2.835073232650757, x[1]=-1.8900487422943115\n", + "Step 13: x[0]=2.868058681488037, x[1]=-1.912039041519165\n", + "Step 14: x[0]=2.894446849822998, x[1]=-1.929631233215332\n" + ] + } + ], + "source": [ + "for i in range(15):\n", + " y = f(x)\n", + " y.backward()\n", + " gr = x.grad\n", + " x.data.add_(-lr*gr)\n", + " x.grad.zero_()\n", + " print(\"Step {}: x[0]={}, x[1]={}\".format(i,x[0],x[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8sfjBMBu59B5" + }, + "source": [ + "## Exemplo 1: Regressão Linear\n", + "\n", + "Agora sabemos o suficiente para resolver o problema clássico de **Regressão Linear**. Vamos gerar um pequeno conjunto de dados sintético:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j723455WwHl7", + "trusted": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_classification, make_regression\n", + "from sklearn.model_selection import train_test_split\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "WJNK_J6v6I-Z", + "outputId": "09e6386e-a6d4-4b81-c8d2-153f0acf9696" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(13) # pick the seed for reproducibility - change it to explore the effects of random variations\n", + "\n", + "train_x = np.linspace(0, 3, 120)\n", + "train_labels = 2 * train_x + 0.9 + np.random.randn(*train_x.shape) * 0.5\n", + "\n", + "plt.scatter(train_x,train_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ng4rZmGc6oxk" + }, + "source": [ + "A regressão linear é definida por uma linha reta $f_{W,b}(x) = Wx+b$, onde $W, b$ são os parâmetros do modelo que precisamos encontrar. Um erro em nosso conjunto de dados $\\{x_i,y_u\\}_{i=1}^N$ (também chamado de **função de perda**) pode ser definido como o erro quadrático médio:\n", + "$$\n", + "\\mathcal{L}(W,b) = {1\\over N}\\sum_{i=1}^N (f_{W,b}(x_i)-y_i)^2\n", + "$$\n", + "\n", + "Vamos definir nosso modelo e função de perda:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QxhI4GlB6aiH" + }, + "outputs": [], + "source": [ + "input_dim = 1\n", + "output_dim = 1\n", + "learning_rate = 0.1\n", + "\n", + "# This is our weight matrix\n", + "w = torch.tensor([100.0],requires_grad=True,dtype=torch.float32)\n", + "# This is our bias vector\n", + "b = torch.zeros(size=(output_dim,),requires_grad=True)\n", + "\n", + "def f(x):\n", + " return torch.matmul(x,w) + b\n", + "\n", + "def compute_loss(labels, predictions):\n", + " return torch.mean(torch.square(labels - predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JUxwj3367gD2" + }, + "source": [ + "Treinaremos o modelo em uma série de minibatches. Utilizaremos o método de descida do gradiente, ajustando os parâmetros do modelo usando as seguintes fórmulas:\n", + "$$\n", + "\\begin{array}{l}\n", + "W^{(n+1)}=W^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W} \\\\\n", + "b^{(n+1)}=b^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b} \\\\\n", + "\\end{array}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-991PErM7fJU" + }, + "outputs": [], + "source": [ + "def train_on_batch(x, y):\n", + " predictions = f(x)\n", + " loss = compute_loss(y, predictions)\n", + " loss.backward()\n", + " w.data.sub_(learning_rate * w.grad)\n", + " b.data.sub_(learning_rate * b.grad)\n", + " w.grad.zero_()\n", + " b.grad.zero_()\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idr2VEWb9rr0" + }, + "source": [ + "Vamos fazer o treinamento. Faremos várias passagens pelo conjunto de dados (os chamados **épocas**), dividiremos em minibatches e chamaremos a função definida acima:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nOuu0qpx-wAp" + }, + "outputs": [], + "source": [ + "# Shuffle the data.\n", + "indices = np.random.permutation(len(train_x))\n", + "features = torch.tensor(train_x[indices],dtype=torch.float32)\n", + "labels = torch.tensor(train_labels[indices],dtype=torch.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3zdIf6c_85Ht", + "outputId": "6520288c-da59-4a9f-c37e-cd99779c3073" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 94.5247\n", + "Epoch 1: last batch loss = 9.3428\n", + "Epoch 2: last batch loss = 1.4166\n", + "Epoch 3: last batch loss = 0.5224\n", + "Epoch 4: last batch loss = 0.3807\n", + "Epoch 5: last batch loss = 0.3495\n", + "Epoch 6: last batch loss = 0.3413\n", + "Epoch 7: last batch loss = 0.3390\n", + "Epoch 8: last batch loss = 0.3384\n", + "Epoch 9: last batch loss = 0.3382\n" + ] + } + ], + "source": [ + "batch_size = 4\n", + "for epoch in range(10):\n", + " for i in range(0,len(features),batch_size):\n", + " loss = train_on_batch(features[i:i+batch_size].view(-1,1),labels[i:i+batch_size])\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JPO9xs4bToPb" + }, + "source": [ + "Agora obtivemos os parâmetros otimizados $W$ e $b$. Note que seus valores são semelhantes aos valores originais usados ao gerar o conjunto de dados ($W=2, b=1$)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "US6q0nCBD-LL", + "outputId": "c804b779-3231-4f6f-c854-032d211b2853" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(tensor([1.8617], requires_grad=True), tensor([1.0711], requires_grad=True))" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w,b" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "id": "_e6xRMZFDnyI", + "outputId": "79e6c360-265a-401d-ce39-8f211917a13d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(train_x,train_labels)\n", + "x = np.array([min(train_x),max(train_x)])\n", + "with torch.no_grad():\n", + " y = w.numpy()*x+b.numpy()\n", + "plt.plot(x,y,color='red')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0giuwC9GHzi8" + }, + "source": [ + "## Cálculos na GPU\n", + "\n", + "Para usar a GPU em cálculos, o PyTorch permite mover tensores para a GPU e construir um grafo computacional para a GPU. Tradicionalmente, no início do nosso código, definimos o dispositivo de computação disponível `device` (que pode ser `cpu` ou `cuda`), e então movemos todos os tensores para esse dispositivo usando a chamada `.to(device)`. Também podemos criar tensores diretamente no dispositivo especificado, passando o parâmetro `device=...` para o código de criação do tensor. Esse tipo de código funciona sem alterações tanto na CPU quanto na GPU:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HK7HPLz3Hyrl", + "outputId": "7e14cccb-d376-4e59-be66-4ab3f5c3f6f4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Doing computations on cpu\n", + "Epoch 0: last batch loss = 94.5247\n", + "Epoch 1: last batch loss = 9.3428\n", + "Epoch 2: last batch loss = 1.4166\n", + "Epoch 3: last batch loss = 0.5224\n", + "Epoch 4: last batch loss = 0.3807\n", + "Epoch 5: last batch loss = 0.3495\n", + "Epoch 6: last batch loss = 0.3413\n", + "Epoch 7: last batch loss = 0.3390\n", + "Epoch 8: last batch loss = 0.3384\n", + "Epoch 9: last batch loss = 0.3382\n" + ] + } + ], + "source": [ + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "\n", + "print('Doing computations on '+device)\n", + "\n", + "### Changes here: indicate device\n", + "w = torch.tensor([100.0],requires_grad=True,dtype=torch.float32,device=device)\n", + "b = torch.zeros(size=(output_dim,),requires_grad=True,device=device)\n", + "\n", + "def f(x):\n", + " return torch.matmul(x,w) + b\n", + "\n", + "def compute_loss(labels, predictions):\n", + " return torch.mean(torch.square(labels - predictions))\n", + "\n", + "def train_on_batch(x, y):\n", + " predictions = f(x)\n", + " loss = compute_loss(y, predictions)\n", + " loss.backward()\n", + " w.data.sub_(learning_rate * w.grad)\n", + " b.data.sub_(learning_rate * b.grad)\n", + " w.grad.zero_()\n", + " b.grad.zero_()\n", + " return loss\n", + "\n", + "batch_size = 4\n", + "for epoch in range(10):\n", + " for i in range(0,len(features),batch_size):\n", + " ### Changes here: move data to required device\n", + " loss = train_on_batch(features[i:i+batch_size].view(-1,1).to(device),labels[i:i+batch_size].to(device))\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A10prCPowHl7" + }, + "source": [ + "## Exemplo 2: Classificação\n", + "\n", + "Agora vamos considerar um problema de classificação binária. Um bom exemplo desse tipo de problema seria a classificação de um tumor entre maligno e benigno com base no seu tamanho e idade.\n", + "\n", + "O modelo principal é semelhante ao de regressão, mas precisamos usar uma função de perda diferente. Vamos começar gerando dados de exemplo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j0OTPkGpwHl7", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n", + "\n", + "n = 100\n", + "X, Y = make_classification(n_samples = n, n_features=2,\n", + " n_redundant=0, n_informative=2, flip_y=0.1,class_sep=1.5)\n", + "X = X.astype(np.float32)\n", + "Y = Y.astype(np.int32)\n", + "\n", + "split = [ 70*n//100, (15+70)*n//100 ]\n", + "train_x, valid_x, test_x = np.split(X, split)\n", + "train_labels, valid_labels, test_labels = np.split(Y, split)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c-_BjSHPwHl8", + "scrolled": false, + "trusted": true + }, + "outputs": [], + "source": [ + "def plot_dataset(features, labels, W=None, b=None):\n", + " # prepare the plot\n", + " fig, ax = plt.subplots(1, 1)\n", + " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n", + " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n", + " colors = ['r' if l else 'b' for l in labels]\n", + " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n", + " if W is not None:\n", + " min_x = min(features[:,0])\n", + " max_x = max(features[:,1])\n", + " min_y = min(features[:,1])*(1-.1)\n", + " max_y = max(features[:,1])*(1+.1)\n", + " cx = np.array([min_x,max_x],dtype=np.float32)\n", + " cy = (0.5-W[0]*cx-b)/W[1]\n", + " ax.plot(cx,cy,'g')\n", + " ax.set_ylim(min_y,max_y)\n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "tq0vFchQwHl8", + "outputId": "919f1922-f789-4779-cbdc-4f9e742c358b", + "scrolled": false, + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_89704/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x, train_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SjPlpf2-wHl8" + }, + "source": [ + "## Treinando um Perceptron de Uma Camada\n", + "\n", + "Vamos usar a estrutura de cálculo de gradientes do PyTorch para treinar um perceptron de uma camada.\n", + "\n", + "Nossa rede neural terá 2 entradas e 1 saída. A matriz de pesos $W$ terá o tamanho $2\\times1$, e o vetor de viés $b$ -- $1$.\n", + "\n", + "Para tornar nosso código mais organizado, vamos agrupar todos os parâmetros em uma única classe:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "J1KaixW-cMWJ" + }, + "outputs": [], + "source": [ + "class Network():\n", + " def __init__(self):\n", + " self.W = torch.randn(size=(2,1),requires_grad=True)\n", + " self.b = torch.zeros(size=(1,),requires_grad=True)\n", + "\n", + " def forward(self,x):\n", + " return torch.matmul(x,self.W)+self.b\n", + "\n", + " def zero_grad(self):\n", + " self.W.data.zero_()\n", + " self.b.data.zero_()\n", + "\n", + " def update(self,lr=0.1):\n", + " self.W.data.sub_(lr*self.W.grad)\n", + " self.b.data.sub_(lr*self.b)\n", + "\n", + "net = Network()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rQ7W6TOacIAI" + }, + "source": [ + "> Note que usamos `W.data.zero_()` em vez de `W.zero_()`. Precisamos fazer isso porque não podemos modificar diretamente um tensor que está sendo rastreado pelo mecanismo *Autograd*.\n", + "\n", + "O modelo principal será o mesmo do exemplo anterior, mas a função de perda será uma perda logística. Para aplicar a perda logística, precisamos obter o valor da **probabilidade** como saída da nossa rede, ou seja, precisamos trazer a saída $z$ para o intervalo [0,1] usando a função de ativação `sigmoid`: $p=\\sigma(z)$.\n", + "\n", + "Se obtivermos a probabilidade $p_i$ para o i-ésimo valor de entrada correspondente à classe real $y_i\\in\\{0,1\\}$, calculamos a perda como $\\mathcal{L_i}=-(y_i\\log p_i + (1-y_i)log(1-p_i))$. \n", + "\n", + "No PyTorch, ambos esses passos (aplicar sigmoid e depois a perda logística) podem ser realizados com uma única chamada à função `binary_cross_entropy_with_logits`. Como estamos treinando nossa rede em minibatches, precisamos calcular a média da perda entre todos os elementos de um minibatch - e isso também é feito automaticamente pela função `binary_cross_entropy_with_logits`: \n", + "\n", + "> A chamada à função `binary_crossentropy_with_logits` é equivalente a uma chamada à função `sigmoid`, seguida por uma chamada à função `binary_crossentropy`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kdDxWeCqwHl8", + "trusted": true + }, + "outputs": [], + "source": [ + "def train_on_batch(net, x, y):\n", + " z = net.forward(x).flatten()\n", + " loss = torch.nn.functional.binary_cross_entropy_with_logits(input=z,target=y)\n", + " net.zero_grad()\n", + " loss.backward()\n", + " net.update()\n", + " return loss" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zAAgw0h6KzUd" + }, + "source": [ + "Para iterar pelos nossos dados, utilizaremos o mecanismo integrado do PyTorch para gerenciar conjuntos de dados. Ele é baseado em dois conceitos:\n", + "* **Dataset** é a fonte principal de dados, podendo ser **Iterable** ou **Map-style**.\n", + "* **Dataloader** é responsável por carregar os dados de um dataset e dividi-los em minibatches.\n", + "\n", + "No nosso caso, definiremos um dataset baseado em um tensor e o dividiremos em minibatches de 16 elementos. Cada minibatch contém dois tensores: dados de entrada (tamanho=16x2) e rótulos (um vetor de comprimento 16 do tipo inteiro - número da classe).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PfyqjVb2wHl8", + "outputId": "f9f5af23-005e-42e0-928b-9890b6c4e0cf", + "trusted": true + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[tensor([[ 1.5442, 2.5290],\n", + " [-1.6284, 0.0772],\n", + " [-1.7141, 2.4770],\n", + " [-1.4951, 0.7320],\n", + " [-1.6899, 0.9243],\n", + " [-0.9474, -0.7681],\n", + " [ 3.8597, -2.2951],\n", + " [-1.3944, 1.4300],\n", + " [ 4.3627, 3.1333],\n", + " [-1.0973, -1.7011],\n", + " [-2.5532, -0.0777],\n", + " [-1.2661, -0.3167],\n", + " [ 0.3921, 1.8406],\n", + " [ 2.2091, -1.6045],\n", + " [ 1.8383, -1.4861],\n", + " [ 0.7173, -0.9718]]),\n", + " tensor([1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 1., 1., 1.])]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "# Create a tf.data.Dataset object for easy batched iteration\n", + "dataset = torch.utils.data.TensorDataset(torch.tensor(train_x),torch.tensor(train_labels,dtype=torch.float32))\n", + "dataloader = torch.utils.data.DataLoader(dataset,batch_size=16)\n", + "\n", + "list(dataloader)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xrwgkbQjhkEp" + }, + "source": [ + "Agora podemos percorrer todo o conjunto de dados para treinar nossa rede por 15 épocas:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QGchp9D6gVJa", + "outputId": "b4c4751d-cb56-4104-d5b5-f1ae9d3d858d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.6491\n", + "Epoch 1: last batch loss = 0.6064\n", + "Epoch 2: last batch loss = 0.5822\n", + "Epoch 3: last batch loss = 0.5679\n", + "Epoch 4: last batch loss = 0.5592\n", + "Epoch 5: last batch loss = 0.5537\n", + "Epoch 6: last batch loss = 0.5501\n", + "Epoch 7: last batch loss = 0.5478\n", + "Epoch 8: last batch loss = 0.5463\n", + "Epoch 9: last batch loss = 0.5454\n", + "Epoch 10: last batch loss = 0.5447\n", + "Epoch 11: last batch loss = 0.5443\n", + "Epoch 12: last batch loss = 0.5441\n", + "Epoch 13: last batch loss = 0.5439\n", + "Epoch 14: last batch loss = 0.5438\n" + ] + } + ], + "source": [ + "for epoch in range(15):\n", + " for (x, y) in dataloader:\n", + " loss = train_on_batch(net,x,y)\n", + " print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nnyEjYAWToPd" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5QaDiCQUkFOT", + "outputId": "45b4a66b-1222-40f4-c758-d58f1c7daf8c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 0.1330],\n", + " [-0.2810]], requires_grad=True) tensor([0.], requires_grad=True)\n" + ] + } + ], + "source": [ + "print(net.W,net.b)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s4_Atvn5K4K9" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "PgRTHttLwHl9", + "outputId": "d9abf92f-cb70-4c56-ccd0-5e027239da58", + "trusted": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_89704/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_dataset(train_x,train_labels,net.W.detach().numpy(),net.b.detach().numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1W4TZfXOmIlS" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HUjdeIefsIsg", + "outputId": "a1a363d4-a307-4769-9ccf-fe8a857b62af" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.7333)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = torch.sigmoid(net.forward(torch.tensor(valid_x)))\n", + "torch.mean(((pred.view(-1)>0.5)==(torch.tensor(valid_labels)>0.5)).type(torch.float32))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fv7JxC3uToPe" + }, + "source": [ + "Vamos explicar o que está acontecendo aqui:\n", + "* `pred` é o vetor de probabilidades previstas para todo o conjunto de dados de validação. Nós o calculamos executando os dados originais de validação `valid_x` através da nossa rede e aplicando `sigmoid` para obter as probabilidades.\n", + "* `pred.view(-1)` cria uma visão achatada do tensor original. `view` é semelhante à função `reshape` do numpy.\n", + "* `pred.view(-1)>0.5` retorna um tensor booleano ou valor de verdade mostrando a classe prevista (False = classe 0, True = classe 1).\n", + "* Da mesma forma, `torch.tensor(valid_labels)>0.5)` cria o tensor booleano de valores de verdade para os rótulos de validação.\n", + "* Comparamos esses dois tensores elemento por elemento e obtemos outro tensor booleano, onde `True` corresponde a uma previsão correta e `False` a uma incorreta.\n", + "* Convertemos esse tensor para ponto flutuante e calculamos sua média usando `torch.mean` - essa é a precisão desejada.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_95qF9lY2kHp" + }, + "source": [ + "## Redes Neurais e Otimizadores\n", + "\n", + "No PyTorch, um módulo especial `torch.nn.Module` é definido para representar uma rede neural. Existem duas maneiras de definir sua própria rede neural:\n", + "* **Sequential**, onde você simplesmente especifica uma lista de camadas que compõem sua rede\n", + "* Como uma **classe** herdada de `torch.nn.Module`\n", + "\n", + "O primeiro método permite especificar redes padrão com composição sequencial de camadas, enquanto o segundo é mais flexível e oferece a oportunidade de expressar redes com arquiteturas arbitrariamente complexas.\n", + "\n", + "Dentro dos módulos, você pode usar **camadas** padrão, como:\n", + "* `Linear` - camada linear densa, equivalente a um perceptron de uma camada. Tem a mesma arquitetura que definimos acima para nossa rede\n", + "* `Softmax`, `Sigmoid`, `ReLU` - camadas que correspondem a funções de ativação\n", + "* Também existem outras camadas para tipos especiais de redes - convolucionais, recorrentes, etc. Vamos revisitar muitas delas mais tarde no curso.\n", + "\n", + "> A maioria das funções de ativação e funções de perda no PyTorch estão disponíveis em duas formas: como uma **função** (dentro do namespace `torch.nn.functional`) e **como uma camada** (dentro do namespace `torch.nn`). Para funções de ativação, muitas vezes é mais fácil usar elementos funcionais de `torch.nn.functional`, sem criar um objeto de camada separado.\n", + "\n", + "Se quisermos treinar um perceptron de uma camada, podemos simplesmente usar uma camada `Linear` integrada:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D77pXPR6oFRs", + "outputId": "efa49e5c-72d4-4781-89d4-4ab6597d2b0e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Parameter containing:\n", + "tensor([[-0.0422, 0.1821]], requires_grad=True), Parameter containing:\n", + "tensor([0.6582], requires_grad=True)]\n" + ] + } + ], + "source": [ + "net = torch.nn.Linear(2,1) # 2 inputs, 1 output\n", + "\n", + "print(list(net.parameters()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0tbe0Et_oiNo" + }, + "source": [ + "Como você pode ver, o método `parameters()` retorna todos os parâmetros que precisam ser ajustados durante o treinamento. Eles correspondem à matriz de pesos $W$ e ao viés $b$. Você pode notar que eles têm `requires_grad` definido como `True`, porque precisamos calcular os gradientes em relação aos parâmetros.\n", + "\n", + "O PyTorch também contém **otimizadores** integrados, que implementam métodos de otimização como o **gradiente descendente**. Aqui está como podemos definir um **otimizador de gradiente descendente estocástico**:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B4AxyrFMozh0" + }, + "outputs": [], + "source": [ + "optim = torch.optim.SGD(net.parameters(),lr=0.05)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6eB8v58eo9pp" + }, + "source": [ + "Usando o otimizador, nosso loop de treinamento ficará assim:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ups7nlV22ofp", + "outputId": "503d8ae9-35f3-4ecb-e2ff-4da2ec2914eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.7596041560173035, val acc = 0.5333333611488342\n", + "Epoch 1: last batch loss = 0.6602361798286438, val acc = 0.6000000238418579\n", + "Epoch 2: last batch loss = 0.5847358107566833, val acc = 0.6666666865348816\n", + "Epoch 3: last batch loss = 0.5263020992279053, val acc = 0.7333333492279053\n", + "Epoch 4: last batch loss = 0.48015740513801575, val acc = 0.800000011920929\n", + "Epoch 5: last batch loss = 0.4430023431777954, val acc = 0.8666666746139526\n", + "Epoch 6: last batch loss = 0.41254672408103943, val acc = 0.8666666746139526\n", + "Epoch 7: last batch loss = 0.3871781527996063, val acc = 0.800000011920929\n", + "Epoch 8: last batch loss = 0.3657420873641968, val acc = 0.800000011920929\n", + "Epoch 9: last batch loss = 0.34739670157432556, val acc = 0.800000011920929\n" + ] + } + ], + "source": [ + "val_x = torch.tensor(valid_x)\n", + "val_lab = torch.tensor(valid_labels)\n", + "\n", + "for ep in range(10):\n", + " for (x,y) in dataloader:\n", + " z = net(x).flatten()\n", + " loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y)\n", + " optim.zero_grad()\n", + " loss.backward()\n", + " optim.step()\n", + " acc = ((torch.sigmoid(net(val_x).flatten())>0.5).float()==val_lab).float().mean()\n", + " print(f\"Epoch {ep}: last batch loss = {loss}, val acc = {acc}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vRLXEQ4Qrcvx" + }, + "source": [ + "Você pode notar que, para aplicar nossa rede aos dados de entrada, podemos usar `net(x)` em vez de `net.forward(x)`, porque `nn.Module` implementa a função Python `__call__()`.\n", + "\n", + "Levando isso em consideração, podemos definir uma função genérica `train`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5c6WsBhlrlIs", + "outputId": "54de8404-4170-4a15-abba-039d06d5e946" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.48486900329589844, val acc = 0.7333333492279053\n", + "Epoch 1: last batch loss = 0.41338109970092773, val acc = 0.800000011920929\n", + "Epoch 2: last batch loss = 0.35756850242614746, val acc = 0.800000011920929\n", + "Epoch 3: last batch loss = 0.31495171785354614, val acc = 0.800000011920929\n", + "Epoch 4: last batch loss = 0.2824164032936096, val acc = 0.800000011920929\n", + "Epoch 5: last batch loss = 0.2572754919528961, val acc = 0.800000011920929\n", + "Epoch 6: last batch loss = 0.23751722276210785, val acc = 0.800000011920929\n", + "Epoch 7: last batch loss = 0.2217157930135727, val acc = 0.800000011920929\n", + "Epoch 8: last batch loss = 0.2088666558265686, val acc = 0.800000011920929\n", + "Epoch 9: last batch loss = 0.19824868440628052, val acc = 0.800000011920929\n" + ] + } + ], + "source": [ + "def train(net, dataloader, val_x, val_lab, epochs=10, lr=0.05):\n", + " optim = torch.optim.Adam(net.parameters(),lr=lr)\n", + " for ep in range(epochs):\n", + " for (x,y) in dataloader:\n", + " z = net(x).flatten()\n", + " loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y)\n", + " optim.zero_grad()\n", + " loss.backward()\n", + " optim.step()\n", + " acc = ((torch.sigmoid(net(val_x).flatten())>0.5).float()==val_lab).float().mean()\n", + " print(f\"Epoch {ep}: last batch loss = {loss}, val acc = {acc}\")\n", + "\n", + "net = torch.nn.Linear(2,1)\n", + "\n", + "train(net,dataloader,val_x,val_lab,lr=0.03)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KzuIDqJ8sFYm" + }, + "source": [ + "## Definindo a Rede como uma Sequência de Camadas\n", + "\n", + "Agora vamos treinar um perceptron multicamadas. Ele pode ser definido apenas especificando uma sequência de camadas. O objeto resultante herdará automaticamente de `Module`, por exemplo, também terá o método `parameters` que retornará todos os parâmetros de toda a rede.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tBtytmEAsq-O", + "outputId": "06ad840b-c2b7-409e-e01e-a9170548151d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (0): Linear(in_features=2, out_features=5, bias=True)\n", + " (1): Sigmoid()\n", + " (2): Linear(in_features=5, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "net = torch.nn.Sequential(torch.nn.Linear(2,5),torch.nn.Sigmoid(),torch.nn.Linear(5,1))\n", + "print(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5r5RbLB1s6YB" + }, + "source": [ + "Podemos treinar essa rede multicamadas usando a função `train` que definimos acima:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ogXKdcfIs_ND", + "outputId": "957ccd8d-0076-4e9b-89f1-edc1de75f18e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.5835739970207214, val acc = 0.800000011920929\n", + "Epoch 1: last batch loss = 0.4642275869846344, val acc = 0.800000011920929\n", + "Epoch 2: last batch loss = 0.35158076882362366, val acc = 0.800000011920929\n", + "Epoch 3: last batch loss = 0.26132312417030334, val acc = 0.800000011920929\n", + "Epoch 4: last batch loss = 0.19465585052967072, val acc = 0.800000011920929\n", + "Epoch 5: last batch loss = 0.14735405147075653, val acc = 0.800000011920929\n", + "Epoch 6: last batch loss = 0.11454981565475464, val acc = 0.800000011920929\n", + "Epoch 7: last batch loss = 0.09244414418935776, val acc = 0.800000011920929\n", + "Epoch 8: last batch loss = 0.07805468142032623, val acc = 0.800000011920929\n", + "Epoch 9: last batch loss = 0.06894762068986893, val acc = 0.800000011920929\n" + ] + } + ], + "source": [ + "train(net,dataloader,val_x,val_lab)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jY4R1XEGtEzJ" + }, + "source": [ + "## Definindo uma Rede como uma Classe\n", + "\n", + "Usar uma classe herdada de `torch.nn.Module` é um método mais flexível, pois podemos definir qualquer tipo de cálculo dentro dela. O `Module` automatiza muitas coisas, por exemplo, ele identifica automaticamente todas as variáveis internas que são camadas do PyTorch e reúne seus parâmetros para otimização. Você só precisa definir todas as camadas da rede como membros da classe:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SlsJmGu0tMsZ", + "outputId": "240d5c89-096c-4392-99cd-1ade5ff3e3e1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MyNet(\n", + " (fc1): Linear(in_features=2, out_features=10, bias=True)\n", + " (func): ReLU()\n", + " (fc2): Linear(in_features=10, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "class MyNet(torch.nn.Module):\n", + " def __init__(self,hidden_size=10,func=torch.nn.Sigmoid()):\n", + " super().__init__()\n", + " self.fc1 = torch.nn.Linear(2,hidden_size)\n", + " self.func = func\n", + " self.fc2 = torch.nn.Linear(hidden_size,1)\n", + "\n", + " def forward(self,x):\n", + " x = self.fc1(x)\n", + " x = self.func(x)\n", + " x = self.fc2(x)\n", + " return x\n", + " \n", + "net = MyNet(func=torch.nn.ReLU())\n", + "print(net)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HwdapRxft-7M", + "outputId": "6eb900cf-4902-4a04-c62b-497b68455406" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0: last batch loss = 0.7821246981620789, val acc = 0.46666666865348816\n", + "Epoch 1: last batch loss = 0.7457502484321594, val acc = 0.5333333611488342\n", + "Epoch 2: last batch loss = 0.7120334506034851, val acc = 0.5333333611488342\n", + "Epoch 3: last batch loss = 0.6811249256134033, val acc = 0.6666666865348816\n", + "Epoch 4: last batch loss = 0.6533011794090271, val acc = 0.7333333492279053\n", + "Epoch 5: last batch loss = 0.627849280834198, val acc = 0.7333333492279053\n", + "Epoch 6: last batch loss = 0.6030643582344055, val acc = 0.800000011920929\n", + "Epoch 7: last batch loss = 0.5775002837181091, val acc = 0.800000011920929\n", + "Epoch 8: last batch loss = 0.5522137880325317, val acc = 0.8666666746139526\n", + "Epoch 9: last batch loss = 0.5250465869903564, val acc = 0.8666666746139526\n" + ] + } + ], + "source": [ + "train(net,dataloader,val_x,val_lab,lr=0.005)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dvAiaj_JndyP" + }, + "source": [ + "**Tarefa 1**: Plote os gráficos da função de perda e da acurácia nos dados de treinamento e validação durante o treinamento\n", + "\n", + "**Tarefa 2**: Tente resolver o problema de classificação MNIST usando este código. Dica: use `crossentropy_with_logits` como função de perda.\n" + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "7THJ0lhITxDi" + } + }, + { + "cell_type": "markdown", + "source": [ + "Vamos encapsular o código do modelo escrito em PyTorch em um módulo do PyTorch Lightning. Isso permite trabalhar com seu modelo de forma mais conveniente e flexível, utilizando vários métodos do Lightning para treinamento e teste de precisão.\n" + ], + "metadata": { + "id": "bPpYZFQAXNMV" + } + }, + { + "cell_type": "markdown", + "source": [ + "Primeiro, precisamos instalar e importar o PyTorch Lightning. Isso pode ser feito com o comando\n", + "\n", + "```\n", + "pip install pytorch-lightning\n", + "```\n", + "ou\n", + "```\n", + "conda install -c conda-forge pytorch-lightning\n", + "```\n" + ], + "metadata": { + "id": "Crqwpx6gZUm3" + } + }, + { + "cell_type": "code", + "source": [ + "import pytorch_lightning as pl" + ], + "metadata": { + "id": "_bBSXSELVlRK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Para que nosso código funcione no Lightning, precisamos fazer o seguinte:\n", + "\n", + "1. Criar uma subclasse de `pl.LightningModule` e adicionar a arquitetura do modelo no método `__init__` e o método de passagem `forward`.\n", + "2. Mover o otimizador utilizado para o método `configure_optimizers()`.\n", + "3. Definir o processo de treinamento e validação nos métodos `training_step` e `validation_step`, respectivamente.\n", + "4. (Opcional) Implementar um processo de teste (método `test_step`) e de previsão (método `predict_step`).\n", + "\n", + "Também é importante entender que o PyTorch Lightning possui uma tradução integrada de modelos para diferentes dispositivos, dependendo de onde os dados provenientes dos `DataLoaders` estão localizados. Portanto, todas as chamadas `.cuda()` ou `.to(device)` devem ser removidas do código.\n" + ], + "metadata": { + "id": "_Aaz4FNpZjqL" + } + }, + { + "cell_type": "code", + "source": [ + "class MyNetPL(pl.LightningModule):\n", + " def __init__(self, hidden_size = 10, func = torch.nn.Sigmoid()):\n", + " super().__init__()\n", + " self.fc1 = torch.nn.Linear(2,hidden_size)\n", + " self.func = func\n", + " self.fc2 = torch.nn.Linear(hidden_size,1)\n", + "\n", + " self.val_epoch_num = 0 # for logging\n", + "\n", + " def forward(self, x):\n", + " x = self.fc1(x)\n", + " x = self.func(x)\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + " def training_step(self, batch, batch_nb):\n", + " x, y = batch\n", + " y_res = self(x).view(-1)\n", + " loss = torch.nn.functional.binary_cross_entropy_with_logits(y_res, y)\n", + " return loss\n", + "\n", + " def configure_optimizers(self):\n", + " optimizer = torch.optim.SGD(self.parameters(), lr = 0.005)\n", + " return optimizer\n", + " \n", + " def validation_step(self, batch, batch_nb):\n", + " x, y = batch\n", + " y_res = self(x).view(-1)\n", + " val_loss = torch.nn.functional.binary_cross_entropy_with_logits(y_res, y)\n", + " print(\"Epoch \", self.val_epoch_num, \": val loss = \", val_loss.item(), \" val acc = \",((torch.sigmoid(y_res.flatten())>0.5).float()==y).float().mean().item(), sep = \"\")\n", + " self.val_epoch_num += 1" + ], + "metadata": { + "id": "0vp2ROQ9UHeE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Vamos também adicionar validação `Dataset` e `DataLoader`:\n" + ], + "metadata": { + "id": "tuWOgQabncMG" + } + }, + { + "cell_type": "code", + "source": [ + "valid_dataset = torch.utils.data.TensorDataset(torch.tensor(valid_x),torch.tensor(valid_labels,dtype=torch.float32))\n", + "valid_dataloader = torch.utils.data.DataLoader(valid_dataset, batch_size = 16)" + ], + "metadata": { + "id": "h3bAMM8RVckT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Agora nosso modelo está pronto para treinamento. No Pytorch Lightning, esse processo é implementado através de um objeto da classe `Trainer`, que essencialmente \"mistura\" o modelo com quaisquer conjuntos de dados.\n" + ], + "metadata": { + "id": "yj0Cd6OOnoyy" + } + }, + { + "cell_type": "code", + "source": [ + "net = MyNetPL(func=torch.nn.ReLU())\n", + "trainer = pl.Trainer(max_epochs = 30, log_every_n_steps = 1, accelerator='gpu', devices=1)\n", + "trainer.fit(model = net, train_dataloaders = dataloader, val_dataloaders = valid_dataloader)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 864, + "referenced_widgets": [ + "1657a223e5524ca682ea081b9e4addea", + "4f111fbdbc3440f783adc3df3df46494", + "cc048e2c91ce48258934ef577cf229d1", + "d3c01678943341c5b814430145692a69", + "9b55455cc9b541ef9634be2d790bb831", + "15d7c09876074047afac1e41cb9ac121", + "90137ba0ff814f71ad964f45d704ee89", + "0919c9545ef64ed1a65f3f489d51cccc", + "12cad77b8027415b82937ad33f793a63", + "25ff491870eb4bf6ad4a320e5c99bd31", 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loss = 0.6169812679290771 val acc = 0.7333333492279053\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "40eaa223a6ef48628829d45f576c5293" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 30: val loss = 0.6142613887786865 val acc = 0.8000000715255737\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iC7doHoWToPg" + }, + "source": [ + "## Principais pontos\n", + "\n", + "* O PyTorch permite que você opere em tensores em um nível baixo, oferecendo maior flexibilidade.\n", + "* Existem ferramentas convenientes para trabalhar com dados, como Datasets e Dataloaders.\n", + "* Você pode definir arquiteturas de redes neurais usando a sintaxe `Sequential` ou herdando uma classe de `torch.nn.Module`.\n", + "* Para uma abordagem ainda mais simples de definição e treinamento de redes - explore o PyTorch Lightning.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "celltoolbar": "Slideshow", + "colab": { + "collapsed_sections": [], + "name": "IntroPyTorch.ipynb", + "provenance": [] + }, + "interpreter": { + "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5" + }, + "kernelspec": { + "display_name": "Python 3.8.12 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "livereveal": { + "start_slideshow_at": "selected" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "1657a223e5524ca682ea081b9e4addea": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + 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pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/9) + +Embora a biblioteca `numpy` possa realizar a primeira parte, precisamos de algum mecanismo para calcular os gradientes. No [nosso framework](../04-OwnFramework/OwnFramework.ipynb) que desenvolvemos na seção anterior, tivemos que programar manualmente todas as funções derivadas dentro do método `backward`, que realiza a retropropagação. Idealmente, um framework deveria nos permitir calcular os gradientes de *qualquer expressão* que possamos definir. + +Outro aspecto importante é a capacidade de realizar cálculos em GPU ou em outras unidades de computação especializadas, como [TPU](https://en.wikipedia.org/wiki/Tensor_Processing_Unit). O treinamento de redes neurais profundas exige *muitos* cálculos, e poder paralelizar esses cálculos em GPUs é fundamental. + +> ✅ O termo 'paralelizar' significa distribuir os cálculos entre vários dispositivos. + +Atualmente, os dois frameworks de redes neurais mais populares são: [TensorFlow](http://TensorFlow.org) e [PyTorch](https://pytorch.org/). Ambos oferecem uma API de baixo nível para operar com tensores tanto em CPU quanto em GPU. Além da API de baixo nível, há também uma API de alto nível, chamada [Keras](https://keras.io/) e [PyTorch Lightning](https://pytorchlightning.ai), respectivamente. + +API de Baixo Nível | [TensorFlow](http://TensorFlow.org) | [PyTorch](https://pytorch.org/) +--------------------|-------------------------------------|-------------------------------- +API de Alto Nível | [Keras](https://keras.io/) | [PyTorch Lightning](https://pytorchlightning.ai/) + +**APIs de baixo nível** em ambos os frameworks permitem construir os chamados **grafos computacionais**. Esse grafo define como calcular a saída (geralmente a função de perda) com os parâmetros de entrada fornecidos e pode ser enviado para computação em GPU, se disponível. Existem funções para diferenciar esse grafo computacional e calcular gradientes, que podem ser usados para otimizar os parâmetros do modelo. + +**APIs de alto nível** consideram redes neurais como uma **sequência de camadas**, facilitando muito a construção da maioria das redes neurais. Treinar o modelo geralmente exige preparar os dados e então chamar uma função `fit` para realizar o treinamento. + +A API de alto nível permite construir redes neurais típicas rapidamente, sem se preocupar com muitos detalhes. Por outro lado, a API de baixo nível oferece muito mais controle sobre o processo de treinamento e, por isso, é amplamente utilizada em pesquisas, quando se trabalha com novas arquiteturas de redes neurais. + +Também é importante entender que você pode usar ambas as APIs juntas. Por exemplo, você pode desenvolver sua própria arquitetura de camada de rede usando a API de baixo nível e, em seguida, utilizá-la dentro de uma rede maior construída e treinada com a API de alto nível. Ou você pode definir uma rede usando a API de alto nível como uma sequência de camadas e, então, usar seu próprio loop de treinamento de baixo nível para realizar a otimização. Ambas as APIs utilizam os mesmos conceitos básicos subjacentes e são projetadas para funcionar bem juntas. + +## Aprendizado + +Neste curso, oferecemos a maior parte do conteúdo tanto para PyTorch quanto para TensorFlow. Você pode escolher seu framework preferido e estudar apenas os notebooks correspondentes. Se não tiver certeza de qual framework escolher, leia algumas discussões na internet sobre **PyTorch vs. TensorFlow**. Você também pode explorar ambos os frameworks para obter uma melhor compreensão. + +Sempre que possível, utilizaremos APIs de alto nível para simplificar. No entanto, acreditamos que é importante entender como as redes neurais funcionam desde o início, por isso começamos trabalhando com a API de baixo nível e tensores. No entanto, se você quiser avançar rapidamente e não gastar muito tempo aprendendo esses detalhes, pode pular essa parte e ir direto para os notebooks de API de alto nível. + +## ✍️ Exercícios: Frameworks + +Continue seu aprendizado nos seguintes notebooks: + +API de Baixo Nível | [Notebook TensorFlow+Keras](IntroKerasTF.ipynb) | [PyTorch](IntroPyTorch.ipynb) +--------------------|-------------------------------------|-------------------------------- +API de Alto Nível | [Keras](IntroKeras.ipynb) | *PyTorch Lightning* + +Depois de dominar os frameworks, vamos revisar o conceito de overfitting. + +# Overfitting + +Overfitting é um conceito extremamente importante em aprendizado de máquina, e é essencial compreendê-lo corretamente! + +Considere o seguinte problema de aproximar 5 pontos (representados por `x` nos gráficos abaixo): + +![linear](../../../../../translated_images/pt-BR/overfit1.f24b71c6f652e59e.webp) | ![overfit](../../../../../translated_images/pt-BR/overfit2.131f5800ae10ca5e.webp) +-------------------------|-------------------------- +**Modelo linear, 2 parâmetros** | **Modelo não-linear, 7 parâmetros** +Erro de treinamento = 5.3 | Erro de treinamento = 0 +Erro de validação = 5.1 | Erro de validação = 20 + +* À esquerda, vemos uma boa aproximação com uma linha reta. Como o número de parâmetros é adequado, o modelo entende corretamente a distribuição dos pontos. +* À direita, o modelo é muito poderoso. Como temos apenas 5 pontos e o modelo possui 7 parâmetros, ele pode se ajustar de forma a passar por todos os pontos, fazendo com que o erro de treinamento seja 0. No entanto, isso impede o modelo de compreender o padrão correto dos dados, resultando em um erro de validação muito alto. + +É muito importante encontrar o equilíbrio correto entre a complexidade do modelo (número de parâmetros) e o número de amostras de treinamento. + +## Por que ocorre o overfitting + + * Poucos dados de treinamento + * Modelo muito poderoso + * Muito ruído nos dados de entrada + +## Como detectar o overfitting + +Como você pode ver no gráfico acima, o overfitting pode ser detectado por um erro de treinamento muito baixo e um erro de validação alto. Normalmente, durante o treinamento, veremos tanto o erro de treinamento quanto o de validação começarem a diminuir, e então, em algum momento, o erro de validação pode parar de diminuir e começar a aumentar. Isso será um sinal de overfitting e um indicador de que provavelmente devemos parar o treinamento nesse ponto (ou pelo menos salvar um snapshot do modelo). + +![overfitting](../../../../../translated_images/pt-BR/Overfitting.408ad91cd90b4371.webp) + +## Como prevenir o overfitting + +Se você perceber que o overfitting está ocorrendo, pode fazer o seguinte: + + * Aumentar a quantidade de dados de treinamento + * Reduzir a complexidade do modelo + * Usar alguma [técnica de regularização](../../4-ComputerVision/08-TransferLearning/TrainingTricks.md), como [Dropout](../../4-ComputerVision/08-TransferLearning/TrainingTricks.md#Dropout), que será abordada mais tarde. + +## Overfitting e o Tradeoff Viés-Variância + +O overfitting é, na verdade, um caso de um problema mais genérico em estatística chamado [Tradeoff Viés-Variância](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff). Se considerarmos as possíveis fontes de erro em nosso modelo, podemos identificar dois tipos de erros: + +* **Erros de viés** são causados pelo fato de nosso algoritmo não conseguir capturar corretamente a relação entre os dados de treinamento. Isso pode ocorrer porque nosso modelo não é poderoso o suficiente (**underfitting**). +* **Erros de variância**, que são causados pelo modelo ao aproximar o ruído nos dados de entrada em vez de uma relação significativa (**overfitting**). + +Durante o treinamento, o erro de viés diminui (à medida que nosso modelo aprende a aproximar os dados) e o erro de variância aumenta. É importante interromper o treinamento - seja manualmente (quando detectamos overfitting) ou automaticamente (introduzindo regularização) - para evitar o overfitting. + +## Conclusão + +Nesta lição, você aprendeu sobre as diferenças entre as várias APIs dos dois frameworks de IA mais populares, TensorFlow e PyTorch. Além disso, você aprendeu sobre um tópico muito importante: overfitting. + +## 🚀 Desafio + +Nos notebooks que acompanham esta lição, você encontrará 'tarefas' no final; trabalhe nos notebooks e complete as tarefas. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/10) + +## Revisão e Autoestudo + +Pesquise sobre os seguintes tópicos: + +- TensorFlow +- PyTorch +- Overfitting + +Pergunte a si mesmo as seguintes questões: + +- Qual é a diferença entre TensorFlow e PyTorch? +- Qual é a diferença entre overfitting e underfitting? + +## [Tarefa](lab/README.md) + +Neste laboratório, você será solicitado a resolver dois problemas de classificação usando redes totalmente conectadas de camada única e multicamadas, utilizando PyTorch ou TensorFlow. + +* [Instruções](lab/README.md) +* [Notebook](lab/LabFrameworks.ipynb) + +--- + diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/LabFrameworks.ipynb b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/LabFrameworks.ipynb new file mode 100644 index 00000000..22461bb8 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/LabFrameworks.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classificação com PyTorch/TensorFlow\n", + "\n", + "Tarefa prática do [Currículo de IA para Iniciantes](https://github.com/microsoft/ai-for-beginners).\n", + "\n", + "## Parte 1: Classificação de Íris\n", + "\n", + "O conjunto de dados Íris contém 150 registros de 3 classes diferentes de íris. Cada registro possui 4 parâmetros numéricos: comprimento/largura da sépala e comprimento/largura da pétala. É um exemplo de conjunto de dados simples, para o qual você não precisa de uma rede neural poderosa.\n", + "\n", + "### Obtendo o Conjunto de Dados\n", + "\n", + "O conjunto de dados Íris está integrado ao Scikit Learn, então podemos obtê-lo facilmente:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Features: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], Classes: ['setosa' 'versicolor' 'virginica']\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "iris = load_iris()\n", + "features = iris['data']\n", + "labels = iris['target']\n", + "class_names = iris['target_names']\n", + "feature_names = iris['feature_names']\n", + "\n", + "print(f\"Features: {feature_names}, Classes: {class_names}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizar os Dados\n", + "\n", + "Em muitos casos, faz sentido visualizar os dados para verificar se eles parecem separáveis - isso nos daria a certeza de que podemos construir um bom modelo de classificação. Como temos algumas características, podemos criar uma série de gráficos de dispersão 2D em pares, mostrando diferentes classes com cores de pontos diferentes. Isso pode ser feito automaticamente por um pacote chamado **seaborn**:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", + "0 5.1 3.5 1.4 0.2 \n", + "1 4.9 3.0 1.4 0.2 \n", + "2 4.7 3.2 1.3 0.2 \n", + "3 4.6 3.1 1.5 0.2 \n", + "4 5.0 3.6 1.4 0.2 \n", + ".. ... ... ... ... \n", + "145 6.7 3.0 5.2 2.3 \n", + "146 6.3 2.5 5.0 1.9 \n", + "147 6.5 3.0 5.2 2.0 \n", + "148 6.2 3.4 5.4 2.3 \n", + "149 5.9 3.0 5.1 1.8 \n", + "\n", + " Label \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "145 2 \n", + "146 2 \n", + "147 2 \n", + "148 2 \n", + "149 2 \n", + "\n", + "[150 rows x 5 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "df = pd.DataFrame(features,columns=feature_names).join(pd.DataFrame(labels,columns=['Label']))\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(df,hue='Label')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalizar e Codificar os Dados\n", + "\n", + "Para preparar os dados para o treinamento de redes neurais, é necessário normalizar as entradas no intervalo [0..1]. Isso pode ser feito utilizando operações simples do `numpy` ou [métodos do Scikit Learn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html).\n", + "\n", + "Além disso, você precisa decidir se deseja que o rótulo alvo seja codificado em one-hot ou não. O PyTorch e o TensorFlow permitem que você forneça o número da classe como um inteiro (de 0 a N-1) ou como um vetor codificado em one-hot. Ao criar a estrutura da rede neural, é necessário especificar a função de perda de acordo (por exemplo, *sparse categorical crossentropy* para representação numérica e *crossentropy loss* para codificação one-hot). A codificação one-hot também pode ser [realizada usando o Sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) ou utilizando este trecho de código:\n", + "\n", + "```python\n", + "n_values = np.max(labels) + 1\n", + "labels_onehot = np.eye(n_values)[labels]\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Code to normalize and encode the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dividir os Dados em Treinamento e Teste\n", + "\n", + "Como não temos conjuntos de dados separados para treinamento e teste, precisamos dividi-los em conjuntos de dados de treinamento e teste [usando Sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defina e Treine a Rede Neural\n", + "\n", + "Agora você está pronto para começar. Importe o framework de sua preferência, defina a rede neural e inicie o treinamento, observando o comportamento da precisão de treinamento e validação.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train the network" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize train/validation accuracy graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Experimento\n", + "\n", + "Agora você pode experimentar diferentes arquiteturas de rede para ver como isso afeta o resultado. Tente:\n", + "1. Rede de uma camada com 3 neurônios (igual ao número de classes)\n", + "1. Rede de duas camadas com camada oculta pequena/média/grande\n", + "1. Usar mais camadas\n", + "\n", + "Certifique-se de observar o overfitting ao usar um modelo robusto com muitos neurônios (parâmetros).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Experiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 2: Treinamento MNIST\n", + "\n", + "Tanto o Keras quanto o PyTorch possuem o MNIST como um conjunto de dados integrado, então você pode obtê-lo facilmente com algumas linhas de código ([Keras](https://keras.io/api/datasets/mnist/), [PyTorch](https://pytorch.org/vision/stable/datasets.html)). Você também poderá carregar os conjuntos de dados de treinamento e teste sem precisar dividi-los manualmente.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora você precisa realizar os passos acima para garantir que o conjunto de dados esteja normalizado (provavelmente já estará), definindo e treinando uma rede neural.\n", + "\n", + "## Conclusão\n", + "\n", + "1. Redes neurais podem ser usadas para tarefas tradicionais de aprendizado de máquina. No entanto, em muitos casos, elas são poderosas demais e podem causar overfitting.\n", + "1. É importante, nesta tarefa, que você observe o comportamento de overfitting e tente evitá-lo.\n", + "1. Com frameworks como Keras, às vezes treinar uma rede neural é bastante simples. Mas é necessário entender o que está acontecendo.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.7.4 64-bit (conda)", + "metadata": { + "interpreter": { + "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5" + } + }, + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "orig_nbformat": 2, + "coopTranslator": { + "original_hash": "7f1acf4b57027280a167406a1d109891", + "translation_date": "2025-08-28T14:01:14+00:00", + "source_file": "lessons/3-NeuralNetworks/05-Frameworks/lab/LabFrameworks.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md new file mode 100644 index 00000000..af044bc8 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/05-Frameworks/lab/README.md @@ -0,0 +1,19 @@ +# Classificação com PyTorch/TensorFlow + +Trabalho prático do [Currículo AI para Iniciantes](https://github.com/microsoft/ai-for-beginners). + +## Tarefa + +Resolva dois problemas de classificação usando redes totalmente conectadas de camada única e multicamadas com PyTorch ou TensorFlow: + +1. Problema de **[classificação de íris](https://en.wikipedia.org/wiki/Iris_flower_data_set)** - um exemplo de problema com dados de entrada tabulares, que podem ser tratados por aprendizado de máquina clássico. Seu objetivo será classificar íris em 3 classes, com base em 4 parâmetros numéricos. +1. Problema de classificação de dígitos manuscritos **MNIST**, que já vimos anteriormente. + +Experimente diferentes arquiteturas de rede para alcançar a melhor precisão possível. + +## Notebook Inicial + +Comece o trabalho prático abrindo [LabFrameworks.ipynb](../../../../../../lessons/3-NeuralNetworks/05-Frameworks/lab/LabFrameworks.ipynb) + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/3-NeuralNetworks/README.md b/translations/pt-BR/lessons/3-NeuralNetworks/README.md new file mode 100644 index 00000000..d4b42301 --- /dev/null +++ b/translations/pt-BR/lessons/3-NeuralNetworks/README.md @@ -0,0 +1,53 @@ +# Introdução às Redes Neurais + +![Resumo do conteúdo de Introdução às Redes Neurais em um desenho](../../../../translated_images/pt-BR/ai-neuralnetworks.1c687ae40bc86e83.webp) + +Como discutimos na introdução, uma das formas de alcançar inteligência é treinar um **modelo computacional** ou um **cérebro artificial**. Desde meados do século XX, pesquisadores experimentaram diferentes modelos matemáticos, até que, nos últimos anos, essa abordagem se mostrou extremamente bem-sucedida. Esses modelos matemáticos do cérebro são chamados de **redes neurais**. + +> Às vezes, redes neurais são chamadas de *Redes Neurais Artificiais*, ou ANNs, para indicar que estamos falando de modelos, e não de redes reais de neurônios. + +## Aprendizado de Máquina + +Redes Neurais fazem parte de uma disciplina maior chamada **Aprendizado de Máquina**, cujo objetivo é usar dados para treinar modelos computacionais capazes de resolver problemas. O Aprendizado de Máquina constitui uma grande parte da Inteligência Artificial, porém, não abordamos o aprendizado de máquina clássico neste currículo. + +> Visite nosso currículo separado **[Aprendizado de Máquina para Iniciantes](http://github.com/microsoft/ml-for-beginners)** para aprender mais sobre Aprendizado de Máquina clássico. + +No Aprendizado de Máquina, assumimos que temos algum conjunto de dados de exemplos **X** e valores de saída correspondentes **Y**. Os exemplos geralmente são vetores N-dimensionais que consistem em **características**, e as saídas são chamadas de **rótulos**. + +Consideraremos os dois problemas mais comuns de aprendizado de máquina: + +* **Classificação**, onde precisamos classificar um objeto de entrada em duas ou mais classes. +* **Regressão**, onde precisamos prever um número numérico para cada uma das amostras de entrada. + +> Ao representar entradas e saídas como tensores, o conjunto de dados de entrada é uma matriz de tamanho M×N, onde M é o número de amostras e N é o número de características. Os rótulos de saída Y são o vetor de tamanho M. + +Neste currículo, focaremos apenas em modelos de redes neurais. + +## Um Modelo de Neurônio + +Na biologia, sabemos que nosso cérebro é composto por células neurais (neurônios), cada uma delas tendo múltiplas "entradas" (dendritos) e uma única "saída" (axônio). Tanto os dendritos quanto os axônios podem conduzir sinais elétricos, e as conexões entre eles — conhecidas como sinapses — podem apresentar diferentes graus de condutividade, que são regulados por neurotransmissores. + +![Modelo de um Neurônio](../../../../translated_images/pt-BR/synapse-wikipedia.ed20a9e4726ea1c6.webp) | ![Modelo de um Neurônio](../../../../translated_images/pt-BR/artneuron.1a5daa88d20ebe6f.webp) +----|---- +Neurônio Real *([Imagem](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) da Wikipedia)* | Neurônio Artificial *(Imagem do Autor)* + +Assim, o modelo matemático mais simples de um neurônio contém várias entradas X1, ..., XN e uma saída Y, e uma série de pesos W1, ..., WN. A saída é calculada como: + +Y = f\left(\sum_{i=1}^N X_iW_i\right) + +onde f é alguma **função de ativação** não linear. + +> Os primeiros modelos de neurônio foram descritos no artigo clássico [A logical calculus of the ideas immanent in nervous activity](https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf) por Warren McCullock e Walter Pitts em 1943. Donald Hebb, em seu livro "[The Organization of Behavior: A Neuropsychological Theory](https://books.google.com/books?id=VNetYrB8EBoC)", propôs uma forma de treinar essas redes. + +## Nesta Seção + +Nesta seção, aprenderemos sobre: +* [Perceptron](03-Perceptron/README.md), um dos primeiros modelos de redes neurais para classificação de duas classes +* [Redes multicamadas](04-OwnFramework/README.md) com um notebook associado [como construir nosso próprio framework](04-OwnFramework/OwnFramework.ipynb) +* [Frameworks de Redes Neurais](05-Frameworks/README.md), com estes notebooks: [PyTorch](05-Frameworks/IntroPyTorch.ipynb) e [Keras/Tensorflow](05-Frameworks/IntroKerasTF.ipynb) +* [Overfitting](../../../../lessons/3-NeuralNetworks/05-Frameworks) + +--- + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb new file mode 100644 index 00000000..a8386e78 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visão Computacional e OpenCV\n", + "\n", + "Este notebook faz parte do [Currículo de IA para Iniciantes](http://aka.ms/ai-beginners).\n", + "\n", + "[OpenCV](https://opencv.org/) é considerado o padrão *de facto* para processamento de imagens. Ele contém muitos algoritmos úteis, implementados em C++. Você também pode usar o OpenCV com Python.\n", + "\n", + "Neste notebook, vamos mostrar alguns exemplos de como usar o OpenCV. Para mais detalhes, você pode visitar o curso online [Learn OpenCV](https://learnopencv.com/getting-started-with-opencv/).\n", + "\n", + "Primeiro, vamos `importar cv2`, além de algumas outras bibliotecas úteis:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def display_images(l,titles=None,fontsize=12):\n", + " n=len(l)\n", + " fig,ax = plt.subplots(1,n)\n", + " for i,im in enumerate(l):\n", + " ax[i].imshow(im)\n", + " ax[i].axis('off')\n", + " if titles is not None:\n", + " ax[i].set_title(titles[i],fontsize=fontsize)\n", + " fig.set_size_inches(fig.get_size_inches()*n)\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Carregando Imagens\n", + "\n", + "Imagens em Python podem ser representadas de forma prática por arrays do NumPy. Por exemplo, uma imagem em escala de cinza com tamanho de 320x200 pixels seria armazenada em um array de 200x320, e uma imagem colorida com as mesmas dimensões teria o formato 200x320x3 (para 3 canais de cor).\n", + "\n", + "Vamos começar carregando uma imagem:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(242, 531, 3)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "im = cv2.imread('data/braille.jpeg')\n", + "print(im.shape)\n", + "plt.imshow(im)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como você pode ver, é uma imagem de texto em braille. Como não estamos muito interessados na cor real, podemos convertê-la para preto e branco:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(242, 531)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bw_im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\n", + "print(bw_im.shape)\n", + "plt.imshow(bw_im, cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Processamento de Imagens em Braille\n", + "\n", + "Se quisermos aplicar classificação de imagens para reconhecer o texto, precisamos recortar os símbolos individuais para torná-los semelhantes às imagens MNIST que vimos anteriormente. Isso pode ser feito usando a técnica de [detecção de objetos](../11-ObjectDetection/README.md), que discutiremos mais tarde, mas também podemos tentar usar visão computacional pura para isso. Uma boa descrição de como a visão computacional pode ser usada para separação de caracteres pode ser encontrada [neste post do blog](https://learnopencv.com/image-alignment-feature-based-using-opencv-c-python/) - aqui vamos focar apenas em algumas técnicas de visão computacional.\n", + "\n", + "Primeiro, vamos tentar melhorar um pouco a imagem. Podemos usar a ideia de **limiarização** (bem descrita [neste artigo do OpenCV](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html)):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "im = cv2.blur(bw_im,(3,3))\n", + "im = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_MEAN_C,\n", + " cv2.THRESH_BINARY_INV, 5, 4)\n", + "im = cv2.medianBlur(im, 3)\n", + "_,im = cv2.threshold(im, 0, 255, cv2.THRESH_OTSU)\n", + "im = cv2.GaussianBlur(im, (3,3), 0)\n", + "_,im = cv2.threshold(im, 0, 255, cv2.THRESH_OTSU)\n", + "plt.imshow(im)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para trabalhar com imagens, precisamos \"extrair\" pontos individuais, ou seja, converter as imagens em um conjunto de coordenadas de pontos individuais. Podemos fazer isso usando técnicas de **extração de características**, como SIFT, SURF ou [ORB](https://docs.opencv.org/4.x/d1/d89/tutorial_py_orb.html):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "First 5 points: [(307.20001220703125, 40.80000305175781), (297.6000061035156, 114.00000762939453), (423.6000061035156, 133.20001220703125), (242.40000915527344, 144.0), (103.68000793457031, 57.60000228881836)]\n" + ] + } + ], + "source": [ + "orb = cv2.ORB_create(5000)\n", + "f,d = orb.detectAndCompute(im,None)\n", + "print(f\"First 5 points: { [f[i].pt for i in range(5)]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def plot_dots(dots):\n", + " img = np.zeros((250,500))\n", + " for x in dots:\n", + " cv2.circle(img,(int(x[0]),int(x[1])),3,(255,0,0))\n", + " plt.imshow(img)\n", + "\n", + "pts = [x.pt for x in f]\n", + "plot_dots(pts) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Para separar caracteres individuais, precisamos conhecer a caixa delimitadora de todo o texto. Para descobri-la, podemos simplesmente calcular as coordenadas mínimas e máximas:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "min_x, min_y, max_x, max_y = [int(f([z[i] for z in pts])) for f in (min,max) for i in (0,1)]\n", + "min_y+=13\n", + "plt.imshow(im[min_y:max_y,min_x:max_x])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Além disso, este texto pode ser parcialmente rotacionado, e para alinhá-lo perfeitamente, precisamos realizar a chamada **transformação de perspectiva**. Vamos pegar um retângulo definido pelos pontos $(x_{min},y_{min}), (x_{min},y_{max}), (x_{max},y_{min}), (x_{max},y_{max})$ e alinhá-lo com uma nova imagem de dimensões proporcionais:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "off = 5\n", + "src_pts = np.array([(min_x-off,min_y-off),(min_x-off,max_y+off),\n", + " (max_x+off,min_y-off),(max_x+off,max_y+off)])\n", + "w = int(max_x-min_x+off*2)\n", + "h = int(max_y-min_y+off*2)\n", + "dst_pts = np.array([(0,0),(0,h),(w,0),(w,h)])\n", + "ho,m = cv2.findHomography(src_pts,dst_pts)\n", + "trim = cv2.warpPerspective(im,ho,(w,h))\n", + "plt.imshow(trim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depois de obtermos esta imagem bem alinhada, deve ser relativamente fácil cortá-la em pedaços:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "char_h = 36\n", + "char_w = 24\n", + "def slice(img):\n", + " dy,dx = img.shape\n", + " y = 0\n", + " while y+char_h0:\n", + " yield img[y:y+char_h,x:x+char_w]\n", + " x+=char_w\n", + " y+=char_h\n", + "\n", + "sliced = list(slice(trim))\n", + "display_images(sliced)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detecção de Movimento usando Diferença de Quadros\n", + "\n", + "Detectar movimento em um fluxo de vídeo é uma tarefa muito comum. Por exemplo, isso nos permite receber alertas quando algo acontece em uma câmera de vigilância. Se quisermos entender o que está acontecendo na câmera, podemos usar uma rede neural - mas é muito mais barato usar a rede neural apenas quando sabemos que algo está acontecendo.\n", + "\n", + "A ideia principal da detecção de movimento é simples. Se a câmera estiver fixa, os quadros capturados pela câmera devem ser bastante semelhantes entre si. Como os quadros são representados como matrizes, ao subtrair essas matrizes de dois quadros consecutivos, obtemos a diferença de pixels, que deve ser baixa para quadros estáticos e se tornar maior quando há movimento significativo na imagem.\n", + "\n", + "Vamos começar aprendendo como abrir um vídeo e convertê-lo em uma sequência de quadros:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total frames: 876\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "vid = cv2.VideoCapture('data/motionvideo.mp4')\n", + "\n", + "c = 0\n", + "frames = []\n", + "while vid.isOpened():\n", + " ret, frame = vid.read()\n", + " if not ret:\n", + " break\n", + " frames.append(frame)\n", + " c+=1\n", + "vid.release()\n", + "print(f\"Total frames: {c}\")\n", + "display_images(frames[::150])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como a cor não é tão importante para a detecção de movimento, converteremos todos os quadros para tons de cinza. Em seguida, calcularemos as diferenças entre os quadros e plotaremos suas normas para visualizar a quantidade de atividade ocorrendo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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Pce77ZTiVk89qrUNDWxbhBFf8iFOx9necHpRqvHCFMyRKO6XUBLcS8RycN30zQitorVdpZ4jkfJzyTnTvHQlprQfgDFVyhpumkO9xGqda4ZRdJhB/SOh4+87DyQuvBCJ7so/OxwqBLDcoHhJfv2RlTCGE2JPEK08lKw8swhmucC2wA2cY4GoBGLWoF0x0z65N2axa2UJrXQT8BNyqlEpTSg3ByW9CeVzC8pRSahhO7xk7U6fWASePK8QZQvpKnIDAvhHLE5Uhk+17l/KpBHmzEEI0WcoZzvhV4EWtdSpllmT34keAW+PUWdWm3iwyjbHae5KV35K29yS4r3cATnfPpR3wCU6e4XOXJ6ob/BQ4TSk1QDkda9yG04lHhnsuiepWk53zApw2tbbAoThDKT8QY9sMnPz5hUTHEEKIxk5rfTlOHnEgzrDIFSTPS5LVbe1sLMbZ1Lyvxq0nU0p1xBma+bY4+6uTuIQ4bVihZZ1whm4Oj4SlnSGJX8IZnrnC/feSUEB6KvlUvOuXwjnHbbfbxfgTIWpNAgzFbqW1/sXt+r1Ca/0iTsXb0alur5S6EicjGh/RvW/k/gPaGdLjSKXUcbuY1lj7KgNWaK2fdZf/F1iN+xY1ToDhX+Ps8nLgN6315J1ITijS/T9uN+9bcApAsa7d2SQY+lFrvRgnGv/xyPluoe0jYIrWOrLr3mKcwmKkHKqGTSkDntdaL3ILv3fFSZcQQjRp7tuvhwMPJlinB86bwNdorX9w52Xi9L50VZzNQvf4e7Q7BCNOL7Px7qVlwHta62luV/b/APZTznBaaK1/0lqXaa1L3fv5dpyCJCS5p2tnGOKtbtfyn+JUlJ4Ulc473P3/hjM0WbV0um9u/R8wzs2vcPed6BkgWV4T2nfC54BE3HzuVGR4ZCGESFheiOa+BfwZTiBHGs7wHUcqpS53V7kQp9eL/ji9RJwFfOyWj6rRWm8FjscZHmwjcBTOG8prYqy7zU3nB6qWPQ1qZyiu14GblduTr3aGzVqunaHM5rjnc0pt9uvupwTnha2XlFKhYcSi87EcoFhrrVO4fnHLmLVNmxBCNGZJylPJygNP4NxDWwCZOI1kk6LWT7VeMO49mxTLZgnKFmfiBA+udtP8KlV5XNzylHJ6SHwcpxwZpPbKgADwT+0M9/U/nIDAsRHL45YhU9j3LudTsfJmIYRoqtz79ss4vQimOpRh3HuxUupYIFtr/UacbVOqN4tKY7z2nmTlt5Tae+Lc18uAH7UzfGQlTtBGC5wXAxLWDWpnOMu/4wQ/rgRWuOe3JoW61YTnrLXeoLWe55YFlwM3EbsseBKwDfhfouMIIURToJ3hh3/ECci7jOR5SaJyUuR+U47FUEodgPOS0tsR85LVkz2EU2aKDoYM2eW4hHhtWBHOxsnPlkdsczhOXjQGJ/88GHhGKTUo1XwK4l6/h0hwzkna7XYl/kSIWpMAQ9HQNDWHyIpJKXU+cDNwmNa6RgNUFA8RY97vosh9/UaMoY9dI3DegJqnlNoAPAyMUM6wJyZwGHCi+/cGYD/gfqXUo8kSoLUuwKmQjHdsANxo9XZEZNQpnBNKKT/wPs6b4JdErTsXZ0jl0LrdAD/O2+OQ+JoIIcSeZAxOz4Cr3Pv4DcDJSqmZEB7K5CvgTq31yxHb9XS3+8Hd7l2grZsfdMEZbqqS1O+l0ffd0P/j5aeReW2ye3qibX+LmBeTUuoo4GngWDd4I5FE6crEyafmRsyrzXNALKFKwu92YlshhNgj1KK8EKkbYGmtX3IrstZQPcB8IPCRW7Fna60/wxl+Zb9YO9Na/09rPVxr3RznjePewNQ4x/bgvJ0bXQGaKq+b/phJIcWyaAwGTk8a7d2/q+Vj7v9DeViy6yflKSHE3mIM8ctTycoDA4EXtNbb3BeN/oNT5xavF71E9YKJ7tmpls1ili201iu11sdorVtqrUfiBFWE8rhE5akcnN4z3nCvTaiX2zVKqQNjrB8t3jBakctrU4ZMtO2uSpQ3CyFEo+f25PQszqhTJ2utAylumuhefBgwLKLt6A/AtUqpD9x1alWfl6S9J1n5rbb3/cj7em23rVYm01o/prXuqbVuhRNo6AF+J3ndarJzTnjcCNHDYQohxJ4gVDZKVuZKVE5KtN9EzgHe1dV7501WT3YYcG9EnggwWSl1hvv/XSqfpNiGFevl7EE4QxhPd/PPaThDHR9OCvlUDJHXL9k5R4vMx3Y6/kSInaK1lkmm3TIBecCRONHoHpy3ekuA3u5yv7tsDc4btmmAcpedCWwA+sbYbx9gHE4XuF6cN64qgSFx0mG4+x6H8yZUGuBLZV9Ac6AAJ0M0cd5y2oYzfJcfJwo/NF2Dk7G0iTj/yOU/4/TckesuV25a+uFkDGmAPyLdd+BUMLYCmgE/4ASxRJ7bUzgFoOhzvhBo5f6/H84DwQPu316ct7rex+m6N3rb/jjDzxyI85b4K8B/I5afj9OdfzecRrY3gZcb+vsmk0wyyVTXk3uPi7yP34cToNESJ8BgKXBjjO08UdudBKxz/2+667yE0y18Ns4bZQuAC+Kk41A3Lxrk3sMfxBnqEpwhVvbHeYMqDWfox81AC3d5snv6KThd4Rs4eXERMCZi+fc4PXj4cd4+3oQT8BdK11bgoBhpziPxM0BLnC73T3bX+TfO28ah7eM+B7jLve52rwH/dP9vRq3zBc5bYA3+XZJJJplkqq/JvcemAXfj9KiRFvmMT/zyQqIyUg5Ob7hnuOu1ASYD/3KXn4PTsNUNp0xzBFAK9ImTxsHufTsH5w3dnyKWnYQTcGi4ecObwMyI5aabtkvdPCkN8LrLRgEHuHlgOs7wW0VAO3f5OKC1+/8+OA1Vf0/l2rnnNNg9fg7O0CTrgDR3+aXAfJzngXY45a1LU7x+ccuYyc5ZJplkkqkpTSQuTyUrDzyPE2iQ6+YhfwHWustqWy8Y957tLk9aNiNO2QKnjJRNVY9QW4CWEctjlqdw8s/IazMcp26wPVX5caJ8ygsswRkuy4NTJizCzYtJUIaM2D5meWpX8imS5M0yySSTTE1xwunNfAqQFWNZojauRPV52VH5wBvu8ubu8oT1eVFpSNbek7D8RoL2nmT3dZyyXClOsIUJXIdTX+ojed1gGrCPm6ZOOEH8d7nLEtatpnDOY9x9Kpyesr7F6f0qcp0OQBDo3tDfMZlkkkmmnZ1wYghOx2njMd37bgnOaCLJylyJ6rZqVeZyt0nHqQ87NGp+snqyVlH3fO3mP+nu8oRxCVS1jf0EXOT+33CXxW3Dith+P/eaZUfNPxinfDfI/Xuwu6+xKeRTyeJPkp1z3HY7ksSfyCRTXU8NngCZ9p7JzbimuTe97TiFsCMilq9wb5iRUxd32XKcoT6KI6aJ7rK+OIF8of1OA06M2O+BOF34hv4eE+M436Wyr4j9zXHTMB04MM75novTfW686/EdcGHE311ipGtFxHIvznAp23GCLB7BbdByl6e5yw6LcazncYYgK3Gv871UNYYd7B6rNOr6Hhix/RnAKnf7D3ALthHL/4ETwLIZp5KzWUN/32SSSSaZ6nsCbgdecf//d/deGnkfLY6z3RhgTdS8HJy3tIpwhpu6jaoKyAOj94XTpf1anIrJj4CO7vz+OG9wleAUbr4GhkVtG/eejhO8XohTafkrcHrUtu1xuq8vBpYBl0Qs+xanIi7yGkxylyV8BnDXORyn8a7MzSO7RCyL+xzgLn+BmnnouVHpDgI9Gvp7I5NMMslUn5ObN0XfD293lyUqL4yJsd13EcsPde/jhThlkaeBDHeZwnkZapV7n58P/DFi24lR9+zX3f0U4jSctYpYdpV7zy9xj/NfoHPE8nNjpPMFd9nBbt5VRNWwVgdFbHsfVWWiZW6avSleu1PdPKoYp8zzKTAgYluFMxTKNne6BzcfT3b93OVxy5iJzlkmmWSSqSlPRJSn3L8TlQda4AwDtcnNy34ERrjLalsvmOyeHbds5i6PW7YArnXziRI3jdFlsbjlqaj1urj3e0/U9YqZT7nL++M0zJUA86hZnxmzDOkueyHGvs+Nuoa1zqdIkjfLJJNMMjW1Cejs3ufKqV5Hdaa7fEWMe2KXiO3j3oujjvMCzrD3kfMS1eeFy1wkae8hSfnNXSdme08q93WcoIolOHWL3wH93fnJ2gfzqKrT3IATUG/GuT5jiKhbTeGc/+Re91KcvP0/1AwcuYWI4HuZZJJJpqY4uffa/7n32R04z/AXRSxPVOaKW06ilmUud94EnJeYVYx0Jqwni1pXE1X2ipdPucu+o2ZePMZdFrcNK2L7J4nTkRJwpZvHFeGU566Ps150PpU0/iTROZOk3S5q2++IiD+RSaa6nkI3BSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQogwo6ETIIQQQgghhBBCCCGEEEIIIYQQQgghhBBCiMZHAgyFEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRgwQYCiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQogYJMBRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUQNEmAohBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIGjyJFh5hnKp3V0KEEEKIeL6031KprCf5lhBCiMYg1XwLJO8SQgjROEiZSwghRFOyV+dbKoVT17U87VT2WRuh46e6XxW/LxRlqOgZMY5nR/xXg22ldtzaquvrlKrafp5NRV1ezz31GtVGvOsZ+ZuJ+K3EVd/XMtnnvod+lnt1viWEEKLJiZdvJQwwFEIIIYQQQgghhBBCCCGEEEI0AY0hOEep+ktHZIBUjGBDZaiUYqiE2GtE/yAif5sNFTQrhBBCiCZJhkgWQgghhBBCCCGEEEIIIYQQQtQNpRL2TJgqbetq/w9N7ozUemSrD3VwbkLUGQkUFEIIIcRuID0YCiGEEEIIIYQQQgghhBBCCLGnqM9eBFNVh8F/2tbO/tzAvsjAw/o6ZpIE1f8xlNFwAZRCNJTGcO8SQgghREwSYCiEEEIIIYQQQgghhBBCCCFEUxfZk1l0oE5oWRML3gkHEyoDZajYy4TYm2ldd70YSoCfEEIIIeKQPryFEEIIIXaBsU8fBs0CT8cODZ0UIYQQQgghhBBCCCGqKFU1xZrX0EOrRg01HB1AGOYOh6xtjbYsZwr1atiQQyWLndPQ3zshhBBCCFFrEmAohBBCCLEL7Cwf/249G53ma+ikCCGEEEIIIYQQQgjRZCXtkTAykDA6qHBPCzLc084nmtbSU15DSBZcLJ+JEEIIIeKQAEMhhBBCiDriadMaT5dODZ0MIYQQosGYPbpi9u2ZWn5omJi9e2CkpdV/woQQQgghhBCNVqyeC5WhwlPKJDiq0fK0b1c1Akxdfk6hfUmviEIIIYQQ9UoCDIUQQggh6sj8e9rT8511DZ0MIYQQosFc+9nHfPr1Wynlh57WLfngmzeoOGif3ZAyIYQQQgghmjilnCGFVYpNe6F1E02R6zaQWgUQNlbR1682n9NeYv0T2aiXreozUw0KTHQ9Q/uQ4NK60dCBmlo3juHbhRBCCFGDp6ETIIQQQgjRlBm/LeGoY89ErV5K39uCLPJ3AZY0dLKEEEKIRs/avIVjjzkb/6L57OGDfwkhhBBCCLHrtIZUnpxrE9iWbN3I5fU0ZG+8YZHD85viUMGpplkZzrqhoKqdsOjp4XizK+h6+m87tf3u0vaacmxzJ3uvT3Y9lZIAw6Yilc9JPkshhBCiUZIAQyGEqA9SoBVir2GXlsKMuWilsFesaujkCCGEEHVCeX0svmcI2pP8mbb9t5Dx7i8AXPPSRQSzNBnrFG34OfE+3fazPo+0gUVL6zT9QgghhBBCNDX2gYNZepoPgD7/2YIV8Yy86vb9aDHHIvPdqc4Mrdl+9mg2j7DBht5/nYddVOTWS8cIxgoFCmoblIEyFNrW4d4D4wX5hfZl+P0s+r9BaI+m2W8G+U9NrpsexiLTGgq2i0xrbTVUvXwordHHTnaNQtvtwrXMn+whmOZt9G0SweUrq8+IPOfQZy/qTyP+bgghhBCiaZAAQyGEEEKIuiCVNEIIIfYgKs3PL6fcT76ZmXTdHpWX0vPzTKwBPeh893R0oLLacrN3DzAN9Io1TDjkJy5uPplOnqzw8kM/vJD0knYEOrd01p+92AngF0IIIYQQYm+gFGa/Xiwfn8ayk58A4NCPLsS7iHDQ2HV/eJ9/5xxHj/eqArG29Ycrx3xJue3lR1/76vusUU8VEbylbbQdo+fCWIF9of14vfxyslM+GNBhAjxFzV73diUwMHK7eNun2jOjtpKvsztFBs9F9wgZur6JAgyTBB82f2HqLiawEZDgQiGEEEKIRq8W/aQLIYSoFaViT0IIIYQQQuxBtAJrQA8+evsZjM7tayzfcK+J54kd2GVlTBvs4doVJ2JpOzwBLLuwC1+8/SJfvP0iun/33X0KQgghhBBCNKiMiVtZdPYT4b91qBrZDUCzdVQgn1J0u3kKn++by/8GZmBtK3Drn2vX7BfquTDUk2ENbp22auh67ZSDCxswUE0ZKI/Hmbw+Z/J4nWurjFp/NrH2H5O2qwcr7o325nNPVUP/hoUQQgjR5EmAoRBC1DEjM5PLFi2m7PjhsQu2EmwoIpitW3H9krnYBw9u6KQIIYRo4kpOHsnFi5ZhZGQ0dFLEHsqK01j37Sn30euRBZx0wCnYK1az+LGR8HWHqu1sg+e6vcNfl87ir0tn8Xy3DzlozimMP+Ecglg88ORjfHr+PQmPIYQQQgghxB5L63AAYeglnAcnPsbiR0Zi9ujKX5fN5uyc5ShNVTBZKGAtOuhsJ56n4w6PHMEqLuGcg85g/AEn0OGSrbU+xi4J9QCYyrntahDfrojupTA6zeFAwIj5tWknkLKS2FlaSxCmEEIIIXaZBBgKIURdMwwOSNtIxtVrWf+n/cKFt2V3j2LzJaOqCnNSoNsrKb+fxS8NgVEDANBFxVz/1EX4lm1u4JQJIYRo6nJ+38rtT52FrqxMvrIQO8GM01jXyZPFZS2/Y9G/mmG0aA7ZAW7r+iFLXhmMkZ1N5uu5jPj2Kgb6yhjoK2PQl1fCc60wlqxh6CPXsKCyDbesOY5eL13m7FCek4UQQgghxF7G1ka1l20G+f3o7CCYJgelQYbh47RDf2bZqwNZ9upAPK3yqweqac26G0az7NWBLH5hMMrvrx64luwZO/JZP/q53w3wC65YRXD5SqyNm3bxbOtO3J4XG4i2rJgTthVuE9CjBzifkc/X0MkVQgghhBAiZRJgKIQQdS0Q4OpVx9AxczulbSMqbjqVUTDQovyYEeHJ7Nuz4dIpGoRSilHdlxPI8gJOpVOLuQGKB7bDGNAnvF7w0KHy/RD1ymzRPHwv8nTskHwDIUSjZy1cQrv7fkYHgw2dFLGXeb8ki9e3j2DxmBfYPL477Vptp41Zyv7dl7JjXH/yZm+hw7serlg1jnVBTdpKP5lry0HbtP/3z3y9vR8Lt7Si+e+aS9ccyNYB2Zi9ezT0aQkhhNiLmC2aUzF+OMrjaeikCCH2cqYyMJXBP7f0IW25H1VewUWr92d9sJg7W81m8ZgXWDzmBXR2Zo0X2b0HbmXxmBf46ZBHnPvZzr64E91TXkQQo7wMlJjZtyfl44c60zHD8XTtXOOaBbK9jOixAmWaDZRKIYQQQgghak8CDIUQoo7Z5eVs3b+AVaNK6X7j5PD8bmfMJn2th08mPhKeFp+X34ApFQ0h9P3wfjUDALNZHm9PfIhPJj7CgmuzUH4/yu/n2P98ndr3Q4ba3rvtwnDrZcO6he9F68d3rOOECSGE2Jtc//FZTL1iKIV2GdP++QT/2/dtHtx0GJsOLOWZex5g1QmtyJq8gm3Hmfxa0Z6fL7iPklt3oPJyUX4/G8uzeXvQM/zl7y+x5mCLC274kEUXyXOyEEKI3adseHc+ffIxpyde2KWylhBC7IxK2ySIFf77mxsOoNMdPxNcuZpVI0v4oLg3QSwK7TIK7bKYgX4By6TAKqVIq5QDAZWhnEC3ZMPv1ndgYaKhjROkLZXhnXenBZc055uJE/nkiUf45IlHWPGHdjXW8X0+nYIDCrDLYn+OQtQp+Y4JIYQQoo7IK5lCCFGfQpXRbiGu830zOW3ieOdvpehR+itJqm7EHi64YSNnDzoWgLSrfDy26GsM4KJzrqbHlFmJvx/S2CF2oYLI//VsTht8LGibVkUzkaomIYQQO2v6KQ9w75hRTBh0DDf98i1j0sFQNigDG4WynF6cdfM8ss0yAmgqgx7svDRu/PoT0lQAAxifUUz/hV/RwePnvoY+KSGEEHsdU8rYQoiGohQcX8ywS69hzrWPA6Bj3JL6vnslfW5fDIBVsKrG8g5nr+ZM3zEA2KXbUj9+suDCVEQPsVwX+9yZ/dXlcXfSszs68P4Bzkg1nUpmVtXvxstn3LYCIeqFSj3gWAghhBAiEQkwFEKI+hKj0GaXl0NFhRTo9mJGWhqb3upE83syMX6YBUphbSsAreny/nb+sOZGAFrNWYhVXl5jezW0P9vvrKD5GZuxthfu7uSLRiT6u2Q2a8a2V1vQ7C8+7Nnzku9gYG+2ud8lXVFR/wkWQsRU/Fm3GvNybk5L7XcsRCMx9tdz8b3UnKwtU7j5tosJpivSt9lk6hkYaMwK0KVl0CyHLp4CspQXW4OqCNDSLCFNWfxx/tmUvdsaZQMaus8ploYQIYQQu5UleY4QoqEoA6uoiI4fbWbYtstAQevfVxI03CF0bYuX7jiWnitKsbZWBQ6uu3E/cg7bQMAyyT9zU93VFe5MgKC2Uabp9Ci4M0F+ibbRdo0eDpWhGl3vhQA9Xy/lxZ+OI2vrlKqZe1vwYEQ5Tg3tT9G/ygDwPJqPtzjI1utKafOHFdjR9ZGRn3HkZ94IgkabJPcz8HRoz7an0zCUpvzd1uQ/NTnJhnuP6O9n2sdTGzhFQgghROMmAYZCCCHEbubzWGizZsWSPXseLWY7/7dqLHVUtMzgh4EvMeLMq2j7xQasxcvqLZ2i8fN5LIioe0vzBNHKn9rGSuEz433ThBC7y08D3q0x79BWF+JtgLQIsbMsW6Esp/Ei99Up6P0HUdg9nfI/DifT+I7CfQLkHNKHrHlbuHD+WTzY+01s20CVlxHQBretPJ6iz9vQ5unJoDUlJ49E2XqXetctO34EaZsrUD//mvI2wUOHYvsMfJ9N24Ujx2GYbD9zBPnfrSa4ek3d7180CcrvZ9vpQ8j/bCnWxk27tK+KccMpbe3BrNTkvDYl+QZCiNpTBmgpMwkhdh9rwRJazF8MShFUBspw6g+1VmS/8QsM34eCc0c7K2toP24ln/T+iCAWo864Bm+JBgXKgrxXp4Kd/B6mbR0+TkIpvPxTrwF/UUGG4WOFgs8SDbG8O035jayIRzP7gEEU9kivsZqyoNnr09DBoDMj8to2kYBEY0Aftg5pBkD+BwuwCgpqrFPRMoOfBrwKwOBOl9NifhC/NwhGZDChe+6Rp91YPs89RJrH+Z6VGdQYdatR2c1pK29V/fuZtluOKoQQQjRdEmAohBC7W2MsuIk6ZeblYpeVx+wVzi4vJ2fc0qoZ0d+HFArRXmUy66+PM1hfTquoAEPl9WFkZVbtvqzM6TlT7HGiv0tWQQH+sQUpB2Po6b+TfmT8YFbR+CmvDyMzXXozbeJWBYtrzFNBeVZoioy0NFR6zYajGrTdNH63ts3KoJdSXfM7CmBpMJXz77dDXuCVHr348N18UAaLLzSZfcQDpCkP4Gfu+Mc4q+94Ks70k3daIbe+fwIlZT4oKyBNWRTc25l2X83Edp9/jrjtB16ddDBdp1f9FoyMDJQ/RhB9nOvZ/Zb5fDe7L71+TnKehomZm4NVUMC6yyppkV2C77NUL1LqjPQ0nrzjIS699RqavbsNu7QUs1kzrMIdSRt+I889VqNdU2FkZoLW2KWlDZ2UBmM2y+P1O+7l4tXX4NnFAEP1p0283ftV5lTm88h/+6cUQCCEiE8FbVYGg9AIe8MSQuwFbKuqTtD9t1rQn9uj4LJTsvj69HsBCGiwUKxwy49v/PlevGhMBZstH397Zwx2aWlV3UHhjqqheKOCt6oFBkb2FpdKD3Lh4DBVfb3I+ZF/R6tNMF1D92S3E/X6S87x8r8jqz6z0JXfaKXzj3cPqgowjHecyOvTQO0KZl5ute9CqPy14oTmzL/UGdL78JXn45sNWBbWjh3x9/XtTHK/BTvUO6eO8d2rqyG2a9MjvlsuDCelshK7pGTX07A7xTnX4Jq1+I5w/t+SlbsxQTshxc8rXEbelfoVpVC2DtfLGVIXJ4QQQiQlAYZCCCFEHbtg6ixue+EsOtw9ufYVP6H1w5Usdq32sfHiYfxwywPhv/eddBW9LqqHXniEEA1u40XDuO/6J7mn5yAJKthZjWD41Yt7HlZjnqdyZgOkROyqxf8axIw/PIhJ4gayjVaQK/uObfRBVnZJCX/tc2BK6y66dxDXHfYZRlYWRmYGytQU2RZppgdLa2xsyi0venshurwC72kl9LCLISOduZVt+M+jj3D851fR55o52JUBpuzXjO7WLKfRyb2/LXi0HzOPeAQAC42N0zhXaOuY13P9gRX01jOTBt5XHjGY155+iAuGn0TnCfNBGbvUc2I8dkkJN/c5iPzPV7LshG50vXIzL057l9POuxrvVzMSbrvg0X7MGfsopdri3GEn7nLPdw1FfZzLiq3N6XjK7w2dlAYT3LCRK3odVif3ef/R67nYcPMQu+aLTUKI2vF+PZPreh+CrtjszNBaei8UQuxeUWXTWIFn3f8yjYtvrVmGDFERwWh2ufN8vPHiYTx6/aPc0WN41X0txpDD4fmR6Qmtn0rZOd7y3VXmDp1TbXqfDQVbRveCWId1LL0vm8XFZuzPTFekEMBWn9cvWXCnMjBzsnjo109paSgCaKaUt2Ti8OFYO6q/iPbOy48B8Ni2QfxvQHr42moDrGpBq+53KVaddygAdmeCC1VUQC6434nUAjQrjxjMR88+Gv77xIWnYhy2kwGGjbmHwD3Emle7MHn4c8nrVxJ9/lrj+2IGl/QZC0DLymn1UhcghBBC7EkkwFAIIYSoY9lGGXonRnHYesFo+l04F1sbbLugJdb8xc6CUMWLUqTPWMGBV14CQLsZqwnivLGX/lkmhf/oRLtP1jBu3TXhfXpHGLSe7Lx9aWuDOW/0o83DybryEUI0CQa0MBp3gFKj1wgqe2P1diuaJm1CrpG8B8NSXdJkhrxK9fvZ58ntvPfxEfjKf0P7vBimprnhw6+8BLDo/fbVZK4x2Pez+YCHNff0IOvbBeAxaWUWkaYs/jnmXSZ9sy+G8gHw6xsDafPw5PBLF72fqOCYSdehIn62G4cZ/HTmfbHTHqhM7SSVornp9A4Ys/eQOmKkpZH+RTaLP+yKzoAOHy4l38yEJMPRFX7aA98sL+Oucp7vMrfNqrc01rfAba3pWFF/17ipqKv7fsrfcSFEarSW5zIhRKOng0FI8MyqoUYwoDYg20jw3BBviOFG8ELebhPr3KFOzj/ZZ7Zo4gg8hSbd/jx5l4+VqrXv9sf8Ppc2j/ySdF27pIzzbvgT+90ylfk72rBtYmc6fLyYHdf1A5zgQRtNrpFOv5/Pos1TaXiNWU4PnMogc9Zqxlx1GdqAtr9uwPZ46+85Ntmwyom+00qRZVQNkHt/97e47X/HU3FkoTM6UPS20WX66B47Y60TuV49iPdd8nTsQLM3nIDQPaVtwGtaZBlpNetXEtW1xPrMtMYuK0u8TvQ+9pb7ohBCCBGDBBgKIYQQdeySb86l/Xyr1oXNkvaKlzp/j6VtxmeejTVmCJuGOhUbHZ6di7W9EJWRRmEXk/YTZxMMvZln28xZ056upUGCK1aRsWJVeJ8tPSOZv28bpg15E4AenfrUzUkKIRqcVmAojTJUg48SJITYu1W2zGTLvj70gGF0enUZmVPTGeK9AACtFZ0/DVLe3MNTnSdhYDCmWR+yDAVKkWeU4UVzdMZqTsvahIHCRnPYuBasN0ZXf2lDARo6PDkHXVZG+sZ92a/jlfQIzG2Q806V2as7q49vTcW6Yowcjd2rhCc7pNaAeEevD7j2p4vIeM9p/GvKTRnGD003OFIIIYQQosmIqo9stjjACd9dQU9mp76PUK9+u+PFqFCvdXWyr52rHHHqVSKH6t19T93+TSbeot37Alrpuizyt6c6dLCiqKNJrqeMZVta0OWz+cw8ris9y8ppPt9ixMzT+XnIq1haUbkyC+8XU1CeqqZna8s2Mt7fjDIUthsAqDwetLUbegmuxVDLaeuL6fPjH6vNCwY89NDbawxfHvtYDf8SYdzvUiDA9DUdAcgtSPy5l544ksJuJioIbR+bWq8v4TW4eJ9ZZLBodEBhjHuD2boVa87qQYenf084PLgQQgixJ5AAQyGEEKIuKUWvi3duSGJPOUwpt7BQqKDNqrF+Xjr9PwDc+e5JmKbJ9pHteeSKidzz8mFQWgpKYVdU0O3MX2MWcLPe+gVzVlemfOFU2pilDV/ZIYQQQuzJLG1jJus5YQ+z+gg/z01whpP6x+Tz6fDBGvjQqYgPrloLtkXF6aOw0FhYVVFySuFXFtmGYrttMy/gPKeUax/P9HmFzvv4MDCYUQGtzTLaeZyeBo//9lzMrUWYq7fS+y+aYGQPGLXobcRs0ZzKHJPJ5X6w668Rcce++fzr4hd4YuAgtr7Vntf3eR7Iqrfj7VWkBwkhhBBCiIR8n02j52dUBcvE4QTZGZEzqv4fCjZsCs9dO5nGcJDhbn6Ds/Ntu6/nwpCeV/4SETSXuOyqfD6uuehd2ni382zR/lg7iun9l23YGzaRNS9A7q9dmDzJj1cFnXpnwwTTDJevVJofs2ULsN3rGghiF2xHl5Xt/qA8ZQBuOqK+J/av8+l8Ws1Nwr2C1lka6u93FP4uRZWJgxs20vm0jSnto+S87fw27A02WSWc88zYRhtguKM4nSnlFpuslmAYdfMZ7eTnYndoyVNX/Ic73z0JJMBQCCHEHk4CDIUQQojGQCna3TuZv987zPlbz6PrbPj7X0N/r2Txi0M4pv8M7u4xEPRmd36Mgm9UJYK1ZDl/7+7sp4ve/ZVWQoj6ER4mdC8LZKoX7hCsNRoSmkLjiRCNQJe/TeGOW4eDtvnT4tc5It0ZYqhCBzl12LEEN2xEK8hSfmw02v3JURmgVHswCXLoV9fQ68IZ4V4mljw4kqWnTQTgjqNOZsFfc5l/+JNYWvP2B89iYGBjszpo86d+h2OX1n7I+AUPdKn5bFUPMt/5hcfe7Q26lGbHLOHkq25i9s2P19vxhBBCCCGESCYcUFcjkDBGHUNont4Nvc7FOvZuCvoLB1k2xHk2EGVUBWbpGC9d2SWlvD20G5gmfQJzMVq35PkfXue0i68l/ds52CvXcHePgRh+P928v6NyssLfF11RQdlBffjyqYkU2xVkGF5OXXIs1uHB3fq5hoQ/X3vv+Xz3VN3PmM3fldtuQnH9HSiFekE9Y67T9qJX1l86hBBCiEZCAgyFEEKIOrDmlv1odvAGMo9aRub3LRmSt5r/LhlK+5NSHLIvXmE1Yn6ff25nUVYv0DsxDKAEyQixR+rmgYNmFPLdpaNQP//a0MlpcoyMDPb5sYxmnmKe+X6M8xY/VH/zOfL/kUM3yX1VxLG39V4IgNYY+/Zk/1dm868bz+X+bW6PgjaYW+aw6PER3H3YG5jKwNYWZqVGl1egsrNpZ1biV77wfkKNeX0eXs8R75wHgLlyHn3+qTnmmYvDh/TfuYGPe02imzfAgVO2EghHLTp+unQ4anL1++KaW/ZjwoRvwn/flT2RF7fuv3t+z6FjaE37N5dyxK/ngQ1pvy5Cmrd2gdYUnjWKY276DoDvLx0p+aEQQgghRG24QV6RvRcqQ1UNYRsKBGuoMnBtg9CUQplmON3asqKGPDVqBFKGAusig+2q1wvsuUFpMc894m8jI4N9/7eDXLOMZ34YQ+/rf+W0i69lx2U76P53DxW2B0jHbwRp7ism1+O8bPbzyf1YekFrmg/YzFFnX8wjzz7Kia9eTff/FqKtzWBbLHllMHq7j55XTa3rk6rb/TVyobqtXE8Zz31/cFXdVgyRZeLJx/YkuHL17kpm3Yu8JzXEENVR9YPZP+QzKHfN7k+HEEIIsRtJgKEQQghRB/KW2mxJb00my7i03Xc8s+Eg9PTcOj2GtXhZaivGqfBbd+N+tJgbwP/pzg3hLIRofPzKy1/yF/JV1oF4GzoxTZAOBHnn5xHcO/Z1zjnwB16962BnQVS9ZM9HVxJcu676TAk0FKIaK9PP3/IX8PPv/ao9s2gg73cPtxincoupmT3uEbYfV4JtDib/g3ns/+H1aEPTakr1AMHg8pUYy1eG92EtXoaxuGr5uh29+W9RM275/hTmjnuMMb+eyeZVzcLLPSeYtG4/ksy3qxpXylvb/C1/QfjvU5YezfxJvejAz3V7MZIIbtiIscEZoiq6mTLh85pSrLp1NJ0+K4Kpc+o/oU1EeTMj/LkemnWA5IdCCCGEECGpDMea6AWppvryVMQQvTHFOa9wD37RwYV7iXCvllUz0JbFm1NGgNcmd4GJ8vlYd6CHUzrOZ0DGarZbGZRrL7Y2yDLLyTNLAHjttENpP3Qth7VeyGv7HUq2YZG5SoFts/LvI+ly51QyZqfjLannOpUYgaT1frxEAY71UYdkmvy55Y/km5m82WFwVHqq3wMiy8RH+/rVfVoaWri31TifQaKgxFhBiqkELrrrXNbuWw5L3zMDkYUQQogQCTAUQggh6kDWm1PIehNQivcLhrLojd50+M/ubawOM0yMfXqGC7cqYGHNW0Tv4xYxz9+Ljp82TLKEEHXLW6R5aUd70owAZmkQAOX1ofp1B8DYuI2gG8AiYtOBSnpe+Qu3vn0cf913En876S1MVbOyd+LUU8ha5gaNu/dWrRRGaQXWoqVNNsjQ06Y1Ojcba+GShk6K2MO1+2Q1bX7KwsrwETjKZu7+L3JY3kkwvY3TW4X7Gwrdw/SCZeiKioT7LNiczb3qCHo9Xc7z+3cn/Ylm9Pp4avg5SD28g0VmJ7q/XbWNr8Dgw5IMjst0hlOeNb0HvT4pgIF9w+sYRWUEl62o82uQiNmyJbpdPgCDT/qd6f59qj2vVeTbmD27YS9fxbvn38cZW26gVR138tGU+Yo0rxa1AKC0pYdmA/uGn3+FEEIIIfZqCcqq1XqucwPJjP69MbYUYG3ZijLNiIC7ehw2uC57HoscdQD3HHe1vL6n9YYXJ3hS2xplqOpBhtpGB4P0fq4M22NgVJZh9evCKxMeYWZZF34v68D2QAZllpcyy4uhNIay8SqbsSdNpUvaFtp5C3jonKfJN5xe60u65XDeSV/y3d0taHffzzXTFJm2WNdexwgCjbWtm/6416C+6nEa4vtiWby+ox/NPcUUbc6ibYJVfQVGuOykAsHdk77dqS6uf/R3I9F3LeL7+N62YWzIiV+/9sc2u540IYQQoqFJgKEQQghRV9zC5tLh5bTezT3hRPK0bsm7k17Gr5z+Wz4pTeORHn0oOnALHdnSYOkSQtStZi9O5s0Xndopg9nOv9068emk1wHo8fqldL9eAgxT0fGU33mJjnGX/3H+R1ycu67G/GvXD2P+0Ki3n5tQ7wYLbu7KtUdM4uP+zZKvLEQSyrKx4gydtv4/Gcwc9rr7VyYA3+3zPrM/ruDmnvuHh18L3cOOOu4smP57wuP1umA6OyaM4oW37+P8PmNJK3Ui7jytW/L+pFc58rxL6P7FlGrbdPrHzzw26WTGv/cipjJY+oeJ8Ifq+z1m0TgYU/vz3xXLL+/J/EseB+Co486i4/Tqz5FL/zCRaw8axoKRuzddTUWzFybz0oudADhwxi/c22ZW+PlXCCGEEEK4ooLvwkwTZZoo0+CGD97iqhcuodPdW0EZKBPnWb0+hwdOtQydauCQ1rF7L3TL69FDAYeOHx4SOjpNDTk8dH2LvKaq+hDZIUZGBi+9O5FCWzO7oh0/F/Xg2c0HkWVWkOMpZ5/MNeybthoTTbn2UqJ9lNh+TDRpRiVeLAKYBLDQJmR+9hvfftIMHayo2+DSWN+jxlA/kyzYsQ7YpaVuvU4zepF45KJO//iZl/4Rqv9aVW9parTqayhlrVk8vILFieoW97BYZSGEEHsnCTAUQggh9jDBjZs5cexZaLfAXNEui0sXvQXA3185k053NFzwoxCibqx9tz+37fMx88vaM3loBjpY/a3j/a+5hN4/LCcYq+JsT60Yr0fvHz+a93zVB91cc6fB98Oe4ctFVe+GmzSta9v77mVMemwosCzpukIksur2/Xj4rKcxlRGusLfGDOHsiR/y3wMG0vaKEsblno6d4eXVdybSzEjnsLknkXGNFx2sGvfYXrqCcWNPh8WL4x0qNrNqeOXgxs0cd+SZ+JfNI1b9vfp1EccceUb1mR6DRz94imOevYn0TZqbF8V+Vnr6gpMwfphVu7QloxRdH1/MmGkX8eXTE7n2v29x3UsX0OXD7Zz35iccnF4KeLG1QgcDXH/0ubRdN7fG0MoidUseGkXbPpvIPErufUIIIYQQK28Zyt8mvEGaCvCvy86jy69LsIkILFQKDNPp2S7YyHs825ngoUSBX/UZWNnYaNvppDI60NCyeKOoDwXBTIqsNPxGkPb+7bx111hylpUSzPTyao4H2wNmpcYst7G9BmaljVlhYZYGURUBen70LBOv+w/nHXQunU6d4x4gUc9wSaKxooe61Xb8gMLQvnY24DBecG5K20pUWb1L5bNJpefLVEm9qhBCiL2YBBgKIYQQdaWxFC5tC2vuwvCfaeubc+sbTkN622mBhkqVEKIOpX2aw93mOF4c+AKTOaBqwZZt9H72Mnr8uFyGR65D1qKlNeblvjKSUb9fH3P9U/9a3ymqG9bGTbBxU0MnQ+wBWk8NcEmb81h+3FMs+kcOVvFw8loX8Yfs9fzXHMK64zuxfUAANOz/wg0oDc0WaPzzq/cwqINB9O8LUjrmpiv3o7CPxZgXb6TyfqehM2+2l1aP/4ydYB+6oqLmMZTi2KduovMXRaiyALe2d56bbjrlPS7I3RBe7Z/Xl1N05ghwh1Lv98/1BFevSSm9cSkDa8sWMpbmYmNzVEYFV+ba2GkeTssqBLwcNOdEij5oSyt+rvdhf5Xfz8KHBoKZ/Lm23VcmWW86n+HS+0Zh5Vikr/bS8c6qAE01fF8WnpdO72tmowOV9Zbuqufwmg1GRaePYv2hVY3DZhGUfNiGzIjg6t2WTiGEEEKIWEJBL3VZt5jKELJA62kB7jROQxvQfeYirG3b3bTYEemy0XY99QaXKAAr3jC5MfdTFUCkLavmdloDNmZ+K+bd6fR+3fYrk+w3f6laXh+fQ2OT6NwiPu/Nl46isLfGLFNsCmzFr4L8b30Pin5oRWnHIG00VDT3Y3sVlk+h3F4jtaFQlkYrUEGNUVIB6zdz/PM3Yvk0OcvBSEtjwYMDwNTk/eql1RO/OENyWxbl44ey6lgNMR7v+/51BdbmzQnOKSLIMLLnyYjfwvJ/jSDQoipQtu9flmFt2VqLC5iCeN9bZTjn00gCV2OV4XxvNqP3r5dhBBWdK6Y2cArrUG0CC6PvA6neD+qrd0QhhBCiEZEAQyGEEGIPZ23dRpdbJzd0MoQQdajFM5PZmL4fDAQdMfRP6PfeyPsU2CNkvvMLme/EWdhEAgyFqCv+T6fRa9sAOA6WjHmBT0rTmFzck9s2DYdgEDV2K8uHvcEWq4RTL7qW9GlLsbZu26VjFvaxICtIt5vm0/uHSnI9ZbxcfgCtdmZnWtPxXz+jcdqxurijM/+r89GsGug0OFra4JjOcxnRfxnjMwoxUIz65Qpyl1c/on/JptoHHbqNYJbWseLj2PJ9Wzo+vnt6oFY+H9OOeZB8MzPpuj2KLiXrTef/j5/wLAFMbpx9Sni52b83K47K5pdj7+WcG8c2WODexlGw/Jinw393/eBiWk8tRgP2gYPRHsW6A9IaPJ1CCCGE2MspA2L1wb2zwW4p9tbm/3Q6nT911rcSBfPVV09siXqH25ngQ61xuuKLfSydm83y8c6zYY8dl5L9hq6+7V7C060LwVY5MOW3avOVobBGDcR/3Cae6Pkhk0t6EtAmvxW3Z/PCfHpN2s62fXMJZEFltgdlgzbAU66wPRrDD0ZQY3sU2lRgp+PZ6qXd9xVoA3zbK1A+HyeNmE6Wp4KXKw6gtaFQpoE9rC+rjtUsGf8kNhoDxXflXr4v7oOlDWaldU58UpHlKWVUfQ9C3zFt86fjP+TSvLXhTY6+9yRIFmCYauBY5G8uxu9PGapaHV5De/yEZxmbEeDa9cOYf6czL/eVKeS6yxtPSmMz81sQ6N8J44ff6u3+5Gnbhoo+7QDwzViCtWOHsyDyO1GtN81GMCy3EEIIUY8kwFAIIYQQQogmSGmwdR28HatU06lEb0ppFWJvY+twg+SNz59Px3+FAuK2YdmtCWiLXCONr559koOuuzzc893O6nmlE/hnA/OHKcBHT+q2h4WeZ89kCtWHR3//1rMZf+l/sNH8eOcjABgYeJUzTHO/xy+n47/W1thXqvcuS9vVWnIsbYc6TGzUbAxueu58Ot5VFQi54W6YN+xxNjWODjrClh//FNeOGMaCkR4eefkx+voyAFgf1JLHCCGEEKJh1abHvmQSDRkbfczQ+gn3t4c8J2lNQFsYqMYfQVWPFlzdhj+N/YQP++dH9PRnoNLTufWlF9gczOGnkl6sKmvOkOxVTHrpADqtDLJhvzy0B9K22vh32HhKLIxKC09RBaoyCJYNpoGd5kPZNqqsEiyLW59+nsH+Ev6w6BTsI8uZNyYTAj56WbNAGWitGfjYb3zSejpBbDw45atL3ruI7jeEyo7JX+RShlNPliiQL1RutanD539lxDx2aJ6oewVH9OTdf9/HBfscjVVc4syss/un8xmuO7kb0295FIAjzrkI71czYq4nvRcKIYTYW0iAoRBCCCGEEE2QVmDURdRJU2okaEppFWIvo2bO57hRx/Hn/31cY1n7CzdxQvrx4d9w9uZZddaWZ2Rmcs2v02lpFnHaT5fQ46xZdbTn2Do/8CsnPH9ctXklA9vx3dNOLyifXXwPmy/wYUV0RXjZ72eSf2zyoY1NpTDDw2Y5AZvjTjufTrN/jdWXTaPynwMPodOOxp9OIYQQQogmZXeUgeurZ8JU7Wxgzk6m21qyghNGOs/zvXbMo5G9C7NbnZezlOHLlvGPQ08huHwlwTGDuPXp59kczGFqSTdKgn7ap23nk7MOIL27TUkbD2nbbbJXlOHZtANVUoYOBMCysUtLncA6Q2Gkp6ECQXQwSMWB+3DnZ28BsCRg8q+u77F6XnOeHDQQrTXKNDBat+TP337EX268hBOmd3C+E0ELPCa9ts9P/TOq1qtc7O+HoTQ9PryUfu5LYda6VanvM4lYQY0SbFh/8t6bzYU//AFrR8QLfnUc6Nfmudkc9/6xAPg2zqmqx6jWa6F8rkIIIfYe0levEEIIIYQQTcyKO0cz/KxfGzoZQggRpoNBgqvXUG47Pf4ZA/uy6q19MXNysLZsJbh6DcE1awmuWYuuqKi27dL7R7Hpyv127sCGwTD/Nob6fWRmle/qaSRll5SEzyM0ZUxdwcB7LmduZRnjZ1zMqR9ezQi/lxF+L2f8fDEls1qw/PWBKL8/vB8zL5eVb+6LMaB31alghHswNJdvYMi9V7L8Cigat4+7gsmSVwZTedTwej/P2gqu34BdUhJ/hQYMEO/2XgU9X7qs2rwzmk9mzZu9aeepagwylZLGISGEEEI0rLoM9pOhOmOzrfBzfHi405DQ8+Be8lw4uSKdix6+Br21AHCGOm5tFrOssiVllpdvV/XgywcOwE7zoBV4S2yyVlfgXVcAhcXosjJ0WTk6GET5fKg0P4bfD6YJWrP+sqEsPxMue/RKyrWXzVY2m61s2nkKwDBQSoHXCxWVXP3Q5awdb7HumI4EV60luG4DwVVrsQp3JDmL2rG1wiw1wt8B7DoKMY3z240MKmyw4ZH30O+0XV7ufIZ1LaLsapeWVtVjBCrr/lhCCCFEEyM9GAohhBBCCNHEVLaw6J9VD5VoQohdZmnb6YVuL3XL/BPJWqOx/R76tt5AuWmiRw+ktH0ayoaM96eDbeHp2pmiAa1J/2AqdstKKrem7dwBAwHOXXoKWd4Kyhfk1em5pMravJk2D23m3MPPIf2DXNrNK+b0YYcC0PYdH94dlaQduLl6736mSZ/WmyjztQJAlVcwYdmRPND5fXxdiikb2oU2D/3Mto97sWlIOh0LhuL5djadWm8jmNEG3+4/zZ1m1UdwoWFSesIwtAEZa8tRk52g+3d+H8zqsmYs3pZPPk6vkZ6CMtI3p1fbPM+oZEDrdXjdodeEEEIIIUQDifWsGAqEil6WSoBUnOfEnU5LI2W2bMmOg7qR9eEs7GF9KW2XhmFp0j+cETdozsjIoOjofZ0/NNw8/2Sy11joYBAAZcNmO4OCQCbpZoCyYj+dP5xH6eheGBZ4Siy8W0vQJWUQDKItG9xeCPG6JRRtO8Mk2zbFQ8v405BveP3zcVjaYFMwmyI7nR2eNJRpUD56H5Sl8c9eTpvHpzLmF/iAQbTCCcwrHzuYQKZBaPCO7E/nYJeW1jgvT/t27BjZkcyPZqUUpOvvUkTJKSMT7nNnKUNVCyRssKBCUZ3W1e8rie4lSjWpe4EQQgixO0mAoRBCCCGEEE1Mr8un8uLlRzPkhhUNnRQhRJS9ObgQqDYUcMlBzr8b/9yG6cOfo8Au55zPx2KXlLD2mPa8dP0D3PjR/vQ8e+ZOH88uL8ces54CoCuTdzH1ccRp4FR+P1hVDXLNj6k694L9nX8z+AUA66vqu7S2bqPsYICNAARXrqboQHh9zmCmj3qOp/r2YtJneeSfsoqCZzPZ9545zB9q4TtiJT5W1vkpNjVGZgbvPfwA+WYmA6ZOoO0JzvyeZ8+kQCny9TbA+YwWnZ/Hr6c8AFQFsT6+5WC2HVzEyiVB+vqcxlBLa2lIEkIIIcSeQ9u168VQGQ0/XHLIzjyTuc/sZlYmHz78IM3MjGrPiXsUw6R0WBfeevB+Lpw+gY03V/DD0CdZZ1lc88URcQPmVIe2fPTggxhKMfSN68j7vwzefP1Bzpx9JmrlWmyfga0NDKXp7t9A61aF2F07oD0K744gvsJKVFkF+H1o20IphVYK/H5Umt/5DCwbXVqK8vnQWjEhZx6n3DOXxcEslla0ZmtlFqs8LVCZmbT6+zKWFuTT8lyFkZWJrUvBBmWaKK+HU+/7jHNyFlOuLUwUZ84+E5Ysr3FeBQd24oV/38+1X42rGqo5jiI7jZmjXsA/2ul5/+hfT4LFy3b9M3F/a0kDChvLb2xPUp8BgcmCmvfy+h8hhBB7H8n5hBBCCCGEaIJaPzWV/xt0UN0N5yKE2C2aXBDTrg6l5G5rYze9c09i9NQilv6zDocrVorv9mvDPh9chYlznSbMXsJvBz9Zd8fYy4yeWoSy4Pizr0i6rgyRLIQQQgixBzD2guc528L/+UwuGHoif/rmU74Z8hwAlk587tbiZZzZ/ygm9DsSbcIBj03lzP5HYS1bxcInBvDyUw/S2bODXE8pXXybaZ1RjJXpxai08ZQGURUBZ0dah1+00pUB7O2FBFetYdGl7bn7h7e5edo3/N9vX5C2MI2zBxzDBYOP4/viPgRskwrbQ5nl5ZLvvuPGdp+xoyQNXVEJ+c1Zekpb+t68HKNnFx6f/wUX5i5jwDvXcnb/cU46l66IeV45b0/n2n3HYRUVAdWHJY72zeh29PkgedlANDGpluXquj5CAkaFEELsZSTAUAghhBBCiCZm8cOj0J+3YeOrbSQYQogmpskFMe1qUKC7rdGUqx/iXIO2vu3cfPx7LHl5cHjemlv2Y9GTOxl0qDV2UREqUPX9aOMpJMPwcUmL7+HrDvB1B8qOH7Fz+28gu/07H/FZtfVtB8D/63JG3nwZv1WWAzjX84vWdPbIwB5CCCGEaARCvWDVZW9Ysfal7cYXEBN6VgxNhllzXkjouTxR+aQpDUmrFIsfHsXKO0bXWOTp0B77646YPbvF3ta2sLcX0s4sIsNweuPLM2zMT3Ph6w6svGM0ZrNmBL/qhBra39lGa6wdO7B27KDny0V8/fcDsHbsANui53MBDnvxRvIMgwyjkiI7ncLKNMySAN4dATzbyzB2lEJ5BbqoGEznczJbt2Tz251Z925fxh8xDVsr0lSAPCOIYYFVuAMCQTr4tpHvLSbHU0aOp5w0FSBDBTFNG+XxgGFgby1g87G92PB/itP/diMH3Xw1yoaFj3d30hnnc9fBoLO82rWN/Vta/GR3jHKDkX++jFE3XYpe5/Qor4btU1XeOiGivJVqWbix/a5S8I9bzmfkny/j69dHNNmyZpgyqk+R94rIzzD6nhLzPqmr/z/pZDfOe6sQQghRD6QmVQghhBBCiCZm0OClvNvjS36rLOdGtT9o6cVQ7EaxhouNM4Ts3szSdrXhkq9dP4ySoJ9/tfuiAVNVf8z8Fmw8uRetXvsd2+05IsRQNb8XuSuCnDb1Ijrr3wFQg/tTsE8OeS/v/DDHZr9ebB7ZgubPTw7vc/OInJjrtnrtdwJDe1LQ219jmbKgxXNTk/YQa2tFsZ2BXV5VtWJWgpkVZMvFo2n54kx0RQWerp3ZeFi7uPuMPve8eYoHMsfSi2lc/stZNM8rrrZ+4TCTrDajU05nfThm0Tjy5quEn3uDU4q7fz6aFgsU1rYC8l6ewklHXsZtQz/i7JwtfNbnE8DX0KkUQgghhACcXteSDq9a1yIDYhIFx9RnOS9WgE9onrZrfWzdxMqkZoXCqIz9Mk5heRr5Ohhzmad9O9Yd34U845vwvDRl8HGvSQAMKJoAwPayNFoFbaKvip4xl/QZhMvyavJvtMsZhv88DyY2Jbaf8qCHaqUlpSDNz/oTu9Hmx22otZvQ2Rk8v+9L2Frx3NYD+OPs8/AYNh7TJmuNjZGZAT4vd04fz+huy+mTtcFJqxHAVNUDuQqO60/J+CLu6vsJT7zSkx2njwTALtvJpuwYQ+feNPgL7vn0uHDZK/Str8hP59u+rwAwuN3lpDellwJ3UtZbvwCQcdRwPv/TxwAMbn856Q2ZqN0pXkB3fQ65LIQQQjRxEmAohBBCiEbNbNHceXs5UIm1vbChkyNEo7CpNJtFgRIWV7Zt6KSIvVGsitY9uPLVzG+BXViEDlRWm2+kpaEyM7C2bnP+LlcsCpSEl1taVWsw+e7FEfh2aBbf9kO9XC8zvwUoA11evtsDvYzMTCoGduH1v9zHdV+emdLx0z6aSuePqv5efVQul571CR+/3KzWxw+d+6pj83njsvu5adIJaMtm+fG5fHrePXT1ZrEqWEy2MmhmZgBw1Mw/suoSi0UHP1Fjf5usEs55fSx2SUmNZZGWV7TkvUmj6fWXqqDItvf/TODwoTz0zIPc/N6x6PJyNh7ejk9vuy/mPs28XJYfn8vlp1ade/5Tk8l/ylne/cxZGJmZKK8Ha3shZn4LzJdtvj7vdWZVevjnawdgl5bW+prFZdssCaSxzU587uX/aEvLX37F7tct7udeUuZnfbDY6cGwrtk2CwPpbLNLKC+LEySoNb0umF5tVvczZ/H3J09k1FEPheeFhpLbbGdi5DdHZWQ4v6PiYuc5NKrhydq6rUGCOoUQQgixl6irsoK2qwfqRT7TVFtWdTwzv4XTM11lAKugoOF6Xt+VnsBsm4UBP80TPSc2FlrT7c9Tas5XiuCatTQbDxZgZGSgMjMBsLZsAa0p69uWt/98D0VasTlgEdAGYDDIqHrRzSooIP/YAmJezejPVhlo5fQ+HtAeSmw/lm1gp3sIZniAdIx0L4HcNH76y0Mccss15BcUY/ud3hPbeYJ8NGsQvR8vRVlgFFeCtRZyc9CBAL0uXshPj/Zj5MjlbAtm0tIoxUQ7PRjmZkPQYuA1v3JT6y/5rKQvKIMDbvyFDyeNost104CIOuII9o4dYGuM3GysbdurB+mGvttKOd9322JFeX7MgE4VtMPleTNQy178wy9cxvne1mXPpLWlNRgmZssWzp9FxTXKjzXOvSmKvvaxggS1rppvmJg5WeDxYG3ZWn27+kiPEA2gIesJa8Ns2RJ7e2GNeteYJABYiAanEr3Nc4RxqvxChRBCNLgv7bdSKtlJvrVnumLxIg5P38516w5mxYiyhk6OEI2C8njCFZQ6GJCCdSOTar4Fknc1dkZmJo/P+5yzbryBrDerN/ys/tt+3H72qzzbq6u7sokyzRh7cWx8uxuts4vgyM2pVZrVJp3Z2Tw5dxL5ho8RU8+j/Ulz63T/CSnFkgdGcsURX/DlsFbYlYHqwVdKseG9Pkwd/hIbrQou6TM2dkCcUijTRAdj99ART+S5e5WJV5kU284wuPt+cSV97y/i0y/fYOzJ57BkQhrLTnkSgAodwINZrZfJkE1WCef0SR5gqDwepwErVq+EXh86UMn69/sybfhLFNqVMffZe7qXj+YMoPfFc9DBgHMNova56PmhjO0/j5X7Bbhj8WQG+wxOXXI0FUcXJ03jzlDe5A2x42dv5OFPj6b7jZPD51qDYbL9zBF8dNd9KV3PnU2ntqzaBfzF+a0aOVlMnPUh+YaP4b+cR5crtvDa9PfxUn3dk044Hz39911Ke2MkZS4hhBBNyR6Xb+2OHuFjBJQ5x6zqJbBO6+CihzZOtE70M3msZ7tQkFh4n9EBRQbKqL6/Wj8nNoR4AU0R12zJA6OYc9ojAJwy5g9YS5aDUhjp6ehAVfnJyMnipdkfkW9mMnDqBNqcMD/xcaOue8VRQ/jumad5qrAdeWYJjy4/lLQ7cglkeTACmtVjfXxzxr0ENJx89420nrwdK8PHsMdn0cxbwvD05fxS2p3vRrYE2wbTee7WWqOUYuHj3blq8LdM2rAPV3f+in19W8hQipkVeTw07lhUWQW6vBxdWoZdXoHyesDWzueoba5YtJDD07djY2PgpP2Af16Dv1Dz2v/dx+X7jMMuKU3Y86Xy+kDbzj4jr7VSKI8XDIWurNyj6tnMXt15+5vXARjw+jV0vzFq1AClUD63bBUINv7fTB0JlbNj3uti/S4j75mx5ofE+f7tcfmWaNQi68qG/3IeHU7ejfWEtRBK54Qbata7CiEaVrx8qwFfmxBCCCGESO7hSydwxO+nk24GGjopQjQaOhhEByqdgI49qNJTiMbGLivn/EuuI/fbpTWWaQMyjYqIla3w7zLW1Pp2E31z8zoPLgzJUIoMw4dp2G5jUS3fvE+0TZJ99X5qK59eOQa7ogK0zYo7R9Nzmp9Ov2Q6vUwAAW1RrhPsR+taBxcC2CWlnHPxdfx900i8ygkEyzLSyDLSeP7g5xjy6jwA9p84jcePfiG83YF/vZrDLrqEfR6+vNbHDCc5GL/xRVsW6f9rzfMDX8SvvDQz0uj9fQWBscOqrTf/2v70ub8k/L2Itc8+95ew/Lre6GCQmy+5jCMuvJRlH3an5VcmRlraTqc/7nkl+B6Hpg+uOIxeT28Krx+TbWFY9ZdHhfPB2jaAxfutVgbCv6Pnh7xAv083kWukk2H4qk2jn53J+j/tVz8nJYQQQoi9UyMp16epABmGr0YdXOVRw2n5c97OP3uGyho1pngBO/HLDdWCCCMnQNvamXb2ObEhRKS/WqBSxDXQJuFn0chgVLusrMbzbLnWWLr6kMgr3hjAyn/sF7PMpwwVvqYZU5dx8CUX81txRwAMpVEBi9H/N5WK5h4y1il+LOvIuVf+iawNFsGcNLTPoHvaJkZmLOWK2RN45/7DoVcXjPwWGDnZkO58Z7RlYRhOqgrK01lY3o7xj97EqDevJ80IgGGAzwumia6sdIYMDwSrBQKGvp9ZRlr4emhD0fzb5Zx36XXosrKk3yUdqHQDFnXV5PaIpQOV6IqK6j0f7gncMk6G4QNDY/bvTc9pfjwdO4RX0ZWVbmClveecdwyR597nQaecHVP0/aXabzPOfJe9/0C6TE3HbFb70RmEqEvhekKz8faoaZeUcvYl15H7v2UNnRQhRIpkiGQhhBBCNGqer2dQ3mM0kzq2pguTk28ghBB7CU+XTqyY0IFOD87ELi9v6OTsmWwL32fTiNUslT/H4qpJ59CTX1LalZ5Vf28L68pKhn91NYbHRu/wEbxlNABdXl1NcOXqFHeSoFEzSYOntWAJ5vyqdQKdK3i0/S+U2pWc6j0utePvJGUoNg/00iVtS41lY9JtxqTPAeCDlftS0dHDURm/AVDQB0raeSntvOsvMHjatGbF+d2rzdMGfNftXlqZzlBmAW3xycJ96La9otp6xk+/Yie5vvbvCwg18Xi/cIb8bVswgB/696K39dsup39nmN/NjPm7iJazrJSRX15D78qGSWdt6MpKhn95DYa36szenTso5rotNzbeCnohhBBCNFENFWQYcdxLPzsflVuJZ2VatTo43/YKflrQo+6ePUNBXdXmxR62ucamdpxlTXFIUvcaKEOh7aggw1QCveJcJxuN1gojM5NVVw+ksjBA83Xa2XeC62Rt3UbGlyVwG/iUha0VKmDzzqJBdCgI4im1+csXp9H39w2U9G2FNhVKa/ZNW835s87FmJJL9uoKKlplkFZagaqoRFUG0MCaKwYytPMCAIKWQantI2eFTe5SzTnNLqSvvc3p9TDW5+t+Ny793Pl+Rl6WTosqCG7YiP+zTcS8Gqn8riLX2QuG31Sl5Uxa0I/elWsbOim7XeS5279VlbPrkqeogi8X9KWPtaQe9i5EaiLrCbNmpDd0cuKzLfyTYte7CiEaJwkwFEIIIUTjFPFGbv6Tk8lv2NQIIUSjU96tJfdc8ByPPb0/SIBhvfF064K9YVO1YX09HdqTPWcTfeYAPbpir1xbsxe33dgwoSsq6HXeDAA2XrUfs295HIBDp1+IN9UAw0i7mvYdXr4uMymx89BBp5rQGcKq7qsMVXo63115L/lmJpusEhYEMjkoRscq2U/n8sZh+3HC8TMY4fey+Own6uT4ZovmlAztxCMXPMlBaZUsD5ZTZHsZ6vcBmeH1Cu1KelyyFLu4OMZJxLneiYaVm/IbvaYQuxGtMWkq6cT9HZ0/vaGTIYQQQghR91LskaznlXFenqqPZzqtgXoIClQG6D0gVCFe745J2ICNTVZaBbpfN+6/4Fnuv+AMjO+nO8NIm2Y4SFMZCkzTCerTzotXyuejpW8LBrYTYGjbdL/NCRYkEKTvr1Depy3aUBgVFhiKgT7IeiebjI0VVOZ4ULYG06gq59g2Ey99lDyjgm9K+qC1wqssyporWv+8nX53lhFom4cRsFG5mRi5WVhLV+Dp3BFdXIK1bTsAPa+a7qQ5ote4cA+Hu9DroNmyJcrvI7gmKuCuqQYaJhlyPbh8JT3+uNIpne/BvRXGUu3cE4n1u4vRW2GsbezfFtDznPqo/RAidZH1hEIIUZdkiGQhhBBCCCGEaII838zgkR59sLZsbeik7LGM7Gxe/N+rFB47oNr8fh+u49Pv3wtPDOzVQClsJJRRrWGi55W/cE+PATzWsxfW5s1oIFDP1euWtjl8xoX8e/+jCMRoUPxm4kRUEG6dcEGdHnfhrb04+Z4vuKfHAL4tS+OEp2/k+suvwNI2VlSjhApdI6XAMKs35oSGiAsNW2aYKfXgIoQQQgghRJMRHlY0aqjeyKFGk/TcFzmkr4hiO+UPS2u+G/Bf/u/NZ3ioz74YP/xWbRhmIz0NIzMD5fejPJ7wfGWa4POS7y0ioD1UWia234Od4UdvL0RXVBBs34LNg/wYARvPliLMonIMDL7690OsONZL9o/LyJ65DnvJSqz1G7Hd7cq1l4A2KNceApbJAZkL2dFDU9wjl8oOzXn09cd55O2JvPjxM1zx2SQAjv10Botu6el8J0ITgKGcCVCm6ZxD9HcmxnDQ4XlR5asF93Wk+/ubnD+k7CVCogMKI8r31e5Bkd/NGsMpq9jfRSGEEKKJkh4MhRBCCLFbGBkZ7D9lG7lmWY1lj3x0NN1ujhr+WCp0hBBCNDC7uJizTrqUvMXzq4XH/X52b47M2jf8t/H7oibRQ1u9cHseMQb25ehXfwLgucfG0+rxn8OrmEqTpjxAZex91AFTGbw9+Bme/uQAxp9yPkc8/SM3Nl9abflrJ/+Hxce2wdI2Y668jJLztjNz2BsEtMXY8y/BV+ikT1kaXfp70mOqb9qjZsHrd4zjzLmfcEBaCWhQGoJYHHX+ZfgKKlh7aDbfXHEvOvLZJlFPJFo7va5II4QQQgghhKiFRU8O508HfMG2YCZTRuZgJ+rp3n0eXX3rflx2+icAfHb0AII70wN6qlLpFUwZYKf4cpK2a9YfNqXeC91zjzfsc7Whk1NhGGQohV+Fmn7d6x26noaJSvOHX3zShoFSCq0rqqUnTQXwqqCzSaWF7TNZ/nQnyren0efRYtK2aDrevojhOSsptNIpsMtZF/Rw77jXeHbAAXCWjZGZjg4EqRzZh/GPfMu+vh1styHbKMfnCdLSLMPKC1KZ6SVzZSX3bzycEdnL+efkY+j9WBmGfynvnzmG3lvWEIwYUltbFlgpfMap1Cu7wYZ9b9/KkoxuwMJUrnLjF3Xu9rJVHHnS2QD0XruGYC223WuFyuQQs1we/s2G7kHhwFW7avsQKdcLIYTYg0iAoRBCCCF2Cx0I8sIXY9Axnj5a/aYx81s4b6UC7b63Sf9g6m5OoRBCNG3GoH4sOT2X7n+bhg4mrDIWqdIaPW1Ojb737N8XVP873vaJhrjdwxjbi3nwq3EoDZ2XVA8kND9uxnB9Nh8Mfrrejm9pm17eTG5s+SOjTx3J0i+PYKI6Ajs7yPJxzwAwwu9lhH8rloY142xO6bDISTuKlceZGOUZAKiAovssM+nv6KS2s7hnVkeyl5dwRd5qwEenw1ayuEVH+r1xFb1+nINdUkKHQH/2a3EDPSpnukO2xWlY1Tar/zKa/N+DznNQREOaEEIIIYQQyTRru4Ormq1kk1XCFHNsStuU59tc1WwlAJN8Q+suMfIsu+uUgTJs5w2mWjCVwb+39uSprw5D/Z8zr9V0yH7jF7DtqpfjDAN8XqgwUVQNNZxpVJBjlGMojaoIoJSiZ6ut9O2xgbcuGEn3d8qxtcG+aatZG2zGfm9ezz+OeYsPNg9iyZTO9PSsAY8HLJtgpskVzRYCPqCSlp4iWmaWkK00ymejTVABi+kbO5JuBvBu8KJnzUKlp6F/XcCO44ex7axOdPi/yeH0Vb9GKv7/UyyDB5evrNX1bWp0oBKm/AaQOLhQxBb6Hhlm8nWiyT1QCCHEHkYCDIUQQgixW+hAJd1vmBJ3ueramZuO/hCAh7eeQIcPdlfKhBBiz1DaMYvzjv6GH25vJgGG9cDMb4Hu0Bp79rway4x9+mAU7CC4dp3z96B+aK+JUVKBNW/R7k5qrRmZmei+XZ3/r1i/U8NuB1etpee1q2NWrLf6dgOr89piDtnlpFZj5uUS7N+VZ7cPIsssp49/HYelZ7L09IkMvf0yWs4soqRjJoyL2k4ZLD/m6Wp/LzvxyfDfm6wSzrlzbNLf0YcbBwJQ2CszPO/WLh9xbenpND9mUTjwVM+aS/dZoMPDILu9skQ3NmhNtyOWs1x3Te05qBaNZkIIIYQQYs9XsCGHpwrbsSWQXb2Xt3jBfsrAv9XgqcJ22FqhKgNAVPlg+Tqsrdt2R/JTFq/HvyYnclhVQj0WVu/1XNsa/xYz/BmV9mxBWm6GU9acvzjurr/Y2JfOk4J8/NzjeJXJ4G5nk7ugLyxYhtbaGVpYKZTXi/J4qo5ra7LNMrKNckzDRpVbKNOk0jI5u9lkxo3/jTs+Pp+lhS2YnduJ1eXN6fnX2bwwaD9W/9SBns+uAdMEd/8AAW3xdnEntlsZFAQz6ZRVwAfFvdFlJoEsRXGPXAq2BlmV2wyzUjlDNbvlnHUHK6464lMm3Z2HMagfxoatBDdsTPkSm/16oUrKYvfMuaeUpZRCDemHsXQN1vbC2m+/p1yHehb+fWq7Zs+rNVaWwEIhhBB7plr0rS2EEEIIUU+UIrhiFe/0a807fVvR4a6fk28jhBCimrSPpvK/AemJh8ESO23dhN78+/3nUJ6a7+md+Nb3LPxTJ6cSWSlueOcNPvvgZXq9vKz6io2xklkpyg7qx2cfvsJnH77C+tN6134fWlcNu6WU82Z/xNv9Gx/0MvvaR7HquN2i4Oi+vPLfx/huVCs+7NeC6x+6JLxsxu1P8NmHr/DDY0/G3NbSdo2ptgJj1qNNmHLPxPC8i569kubHxAkq1W5jRFRDIhC+foFDNlR/DorVGBz6WxqChBBCCCFEhF4XT+Odvq1ilwtr9PzmPIt2unMy7/RtxXv9WoaDsMoPjCgf/KHPziUm1bJPxHO4MhTKUOFyVY1dGtHPxU2oiTPOOQHhMkKN4ELLAm3T6Y6fw5/RXx55gc8+fIVeryxPeLiv+33INy88Ex4u+deRL3PN2+9gV1SgKyudAMCMdEjzY2RnYWSmo3xeMBReLNKUhak0BC10moc0M0iR7WOTlc3TTz/EqR1nkaYCZHkqULk5eP9QSvcn3DQFglBW7rywpcFC89aYwUzapxnf/OMAhmav4MPjRtDmfyZFo8t47+EH0BUGpUGfc6l8XuxyZ9hmrcB0+1y87u23WH5B96hrp2OXi9x5/V9ZzLzbWyW8Vk2dkZXFK+8/xfYj+yZesTHWRzQhkb/P8L1IyuRCCCH2Mk3o6VsIIYQQe6x4lUFCCCHC1OD+XL1kAZ6unRs6KXudTR/04dXr76ev18u1C+ZgHzy4oZPUeLgNZWpof65ePB9Pl06gbYKHDOLiRcsw81tg2wYRA3HVi00f9KF0/2LGnnIOFTqQdH1TGdWmlHzdgSUPjUq4yjsX3Ufwq07Jj9+6FVcsnI918OCqZyClaDc5i+uWzKflz3nxN9aa0hNHMmHBOoyMjNTSLoQQQgghRGMR6tU7irZ1wt4JayzfiZeEGhWt2fBeHxY9N6T6y0h1eF6mMjBwgqEOSiviikULUYP6Ob0LRvQUiGmCYaDLK2jjKaKX10ebzB1gKMwtO1i4qRVfFfdndklnSrSHkRlLGJK+gvE5sznk66WM+98Slp/fDWvDJnS5E8SoA0GU1mQoH3/66Su2XjAKy6fo6d8AXg+584tI/zWde7fsj5kVZEiz1VTm2SifL3wd+ty5mLf/fCTXLZnPIelO0Kx1yBAuW7wEs3VE4GB03bL7wtu8EzvQ9+YYvRdGihf8uacF5IVeXtvTzmt3iXhRMBT8W3Md93sYeX+Lc78TQgghmiIZIlkIIYQQjUdTDDKU4QmFEHVsxZ2jCXSshEIvPa+ZwtJ7R9Nypqb5t8u58YXz6VIwt6GTuNdQfj8LH9uXh/u9xqsFI3n/3QNAQ9eVa4gcPPfxx0+g08LK5Dusp/wilE7l0VBs0e+JywHouqh6OuOlKXPBpvA2nX4pREfss/eTFehpcxJuD2Cu28qNL5xP2V8r6fhRa4xKzTGZW3nWrOrJ0Kzjdoxms7dxyOM30rFiKl2bbeWoXnO576oj8GAm3zhCot4Lg4cNZfkfnf97p/joMMWqtrzTpCD9tjjXzm23I2ODpgWrau4sNETYYUNYNEHx55fPpdvKtVWfkdaMzl3KURkVLM1byoe0CM83+/dm/vXZ9LlmIXZREdkLC7j3xVPoWDm1xmFW3Dma7JXQ4pnJqV4CQfXfUc5MP60fkR61hdirjRrAogv89L78V3QghTxeCCEam+j6qlhBRdpumDqt6OfveEOORqc5std0qF3aQ9soo+p49XD+O84YxYYjnBee+t60CmvLlqrjAuCcu+H3s+DBAfy73xvcOXe8s6hGL5MKlMGO04ez4VALlKan90cgy1ns8bDw0SF0e9PCUxpg0ZUm2YYvvHmonBN62curTI7MKORxT8S1tuxwoKGyNbqigglP/YlghiZzLbS1VjH/xo70yXfKN93TNnHCB9eCR4MGVanwbzW4cMJnBLM1RnYWKjsTXVrKpvMG4z9uEwFtcVBaJZZfQZHGqyxURYDlZ+bj67+d6Vs70bpFISOylvF6xshqw3tbW7aSNcPLn168AIAO35XiXVfAX144m85Fs2teL/daK0OhMQmuWlN1XaN7h49Tp7vh2v0IpkOHu5t2eSC6bgmoeb5Sp506rSk9YRhrD4Ge10xNfO0iRy1o6oHQQgghRAQJMBRCCCGE2FWRlVNSMSOE2EWqdzEndp/L7IIOADTvt4VNZguyVrel4z9/xkqyvag7yudj2pEPk29m8re5/el4p9PAEB201/o/bsNDbXoCqMMA9ch0Dpg6gbZXzo+ZzniCy1fS8c6VAOF+BkP7PO6r68melnwfdnEJradV8u8LnuDc5dfQekYAA4PCg7rStdmycK8Zdcmat4gO8xdTedgQ+mfO4uLcdVx88IvszGANkb0YelGUHtofo9Jm4zAfZwz6gX+2msOh516I94vp1bbzfT6djp/X4kBaU9bSy+CeSym9pIBgMPanZOuq9Jj9e7PhwOaM23cWK3xOj4XWvEV0mLcoZr+Qx42fwtu/DqHFM7VIl6j+O8qbAI80dIqEEA1pR7cMfjnqfs7xjZUAQyFE05WofNIIg15Cw45qO8bzvB1REt6ZcpQbdOZwXkjSdVm4dq/1puGwaOxTGCiOvesU2LKlZm9m2uml7+NxD9PZo3ghbzsVhw3F883MGuemDMWm4TDzqIcByDLSq16QMk0+PeohzphzA/7tXpYeOhHwhrc1lUFAW9jE+axt25k8HpTH45QtKiro9PCvqDQ/KIU2TS485FuKLT8FgQwWlbSi0xcWls9AaY2n1CJ9zhoWHt8a26uhVQsCeel4thVQuH858wb8l3JtgYbizprKHINS209Jn5Z0Gb2aoc1X8cnK/oxqt4Ivtu+Db6PHGVo54roGN2yk450bsMYMwbt2G7qwiDbTWjpDPSf6SAzlfMbJyt5RgYfmoVvpmLODwN0Jd994WBa3rBuLf3v1suVx46dwb5tZfFKaxiPX7ORQ56Ka0lYGXfutTX4PCn2nQr9VaS8QjdHOBOsLIfZ6EmAohBBCiL1bdGVrbQtUEcMaCiFEbSlPVOU50Pm0OfwOeNwe0JqNX8zWB1swbuL3fL5PTuwdGaZTeW7r6g0vYpeVa02FDmDHauTaFXVcgVdv6YwQ6/saYvfvyrfPP0Nkg5ZXmfz08JNY2q63IZKNrCzefP4R8s3M8LwKHcCvvDX+DqXDq6r3cBg9RHIzM4P/PfkUAW0xZOofmXVMJ4qnTEMbqnrjlGHWrtcTd72c16dQ8nrEfKVQET09BrRFua6qrpl/QxZj+89m6fByUBXh4b7kt173dsfvSAghhBCi3kQ+J0b31lZLFdrphU/Vd+BBqLe5BEMj181hVPVAv7oOsAz3kuc8z5tKgWGEn/OrnZ8ywA12tNC81+sDFj5rcVOPA5zylls+CG2jTcgy/BTbFeF9B20TpRQV2v3MVVWPhZHlm8jgQgMj/LlqywI3QE+l+QETZRjgccshHg/YGsrKWV6Wz6DsVTw272BaPZvO9p5ePKUa2wu2x0ObzS1o51+F3SJAcc88ylqYtFqejg4abLMqSFMGFQSZPOE+NloGP5V1p9OtC9lUls3/NvbA57EYmzeXxy87la7fz0BrG+XxOml0Lh4ANz3zMtc+fxGtZwR4/5lHmDD0eKyNm6p/Bm5QVzhINVEPcpHf7Yj/W7ZBsAmVB+zSUlaNBB/VX4YLaJMKHSCgsxooZXue/Ccnw5Mprhx5D5Z2A9EYSWChEGInNJ0nJCFE06SUPDwLIfYOWjfNQpnco4VoUA8u+Z7Sk0YmXa/X3+bw1cFd4i5f/lp/HlzyPbcsnoWRkVGHKdy72UVFXDz8JE4cMp5O565u6OTEVR/pVFFDR3natOb/Fv+EdciQWu/LVEa99GAYy9zKMk4acTx/39wfgC9KvZw4ZDwv7GhFz/cvY+z5l6S0H0vbjL7tSozv8zj/mx/4w8iT6HL7AhY9Njy8zqAZFsv/NWqX07zm5tE8uOR77l/yA+fkrKT3+5fz7cGdw8v7XL2I1UemOX9ozfLX+lP4cdddPq6orqn83oUQQggh4qmr50T/V7M4cch4ThwynlbPzayDlCUWDqSzdVVQWV0fw7KcfWu7XntvVDZU6CDlOsi9n7/M/Ut+IOObZjHXDWgDW2ssrbF1VZDl2j+P5qhft4TTqdygRQBTKfZ98WqWHZOL1pq/jDiGNi/8CjhDIQex3KAyC0vbWO7+Ab4uy+C0oceh5ix26jADQWdSCm0aYBrg9TjBjxUVFBzRnatn/kK6Wcl//3o0XW8qxre9kvZvLaPNWSu4+brXOPuSzyi8q5w3lwwm7xcfWUsKKW+u0M1yUB4bCzDcsuWhD97IxTddx7zSdvzwW2+KKv0MbrGWIzvM57mxY2j/z8UsemgwOhhEmZE9Tlbn/2qWE1y4aXP8D0LbTqBtrJeyktSFtj93HcbJpQnXaQoWjc3hxCHjeWp08jonUU9C7QVNsc1AiMZE2rCEaDQkwFAIIYQQe7fIgv7eWNjfG89ZiEbkjHtuIGfWhqTr2SUlWFu3xV3e7iU/Z9xzA9ffcwl2eUVdJnGvZ23c5Ew7djR0UhKq03TG6t1XKfr7PNje1KsRAtpi3wcv58q1ToOGVQ9Zji4r48h/3sDgf15O96/Pw0IRXL+RL+86kH2mnEkAE2vjJsptL0a5wleQ/PdRaJcx5J4raf3NOtp/sZU7HjuLdY/ncF2bL/nbIR+y+EUnyLJ72iZs/y6elFJ0/HIHJz9zA/196Qx89mp6vFLu/N7dz8EuKsLu0o7NH/Zm84e9UUsy8T7dYteOK2JqKr93IYQQQohY2r3kx/dU813ejw4Gw89FumI3li8b4bDNtaYV5drG1poeXg+9vD46ZGyvuV5Ej4amUhgqogc9H/Txrw//3e3tcgY/fw1Zhp8hj15Dt3d2YG3e6qy7eTO6MoBWioC2MDDwYFZ7wcvGCTTs59vKhqeboTq1B9tGaw2mCVqjbF3V45ppgq2xTUVv71aaeUupuHAbK/7QDu/GQhbc25ZxrX4nxyinuaeYka1WMqr9SgJZClVWAQbY6V5Mj4VPKSq0TQDN8NN+g/M3saUiC19eBePbzWVJUT6fPnkA9tYCFv+nH13fDaI8HjDccmdUj/EnnfoDi+8b5vRc6M63DxzMmnf6o/z++HW80R1hJOgYw9peiFVQkPBjbkyU38/ad/uj9x9Ubb61dZvzO96ytWESJoRoeHtKYJ60YQnRaMgQyUKIemd278KOga3IfOeXhk6KEKKRMvv1omBQVQVo3rwd2LPnNWCKhBBi92j1+M/EHnC2JrNZMwrG9Sbv/d+wS6u/Te//dBqt6j55YiddN+c0OuVtZ+6S9vRyhymyDxxMcUc/yoLst6Y17uFttUZrjZlir4NqcH82DsvimEXjeLn7OwD4t5Zz7MLj6Pj0XH4+sAu0m4xZD/WaOhh0hikCtLkfeQcHKTxjOMqG8nIvbcwd7DhjFF18z5PRvZC1hzi9h5yx/BBOazWNEzKLa+yzQtu0e3o2Zfv1xSy36PDmMt6/6RO8Ko1lgWIysp1G1vtmH0H2sl1/b1NP/52uG9pzzOHj6PbKJuzMNMqOHUHaR1Or0tQ6g2lDn+QPy8aycUYeGe9PTbBHIYQQQgixN/J/Oq36jFCDfKwAg0RDx+5OsdKg7fDQyc6fdRNYEB4mWduUjh9CMM3AsDRagXYLKzpOmcWwIpYp5//NvlqKtXkzRmYmhcfsi2FpPJ2d8kW5tjF0kDRV1QwbGgra07Edm8d0wKs+Tyndxs9z6L61GyceeAzpWzRGWQALp9d5raoHEhp4wkMkh4ZMNty+ZlqbfqYNfZ2j885BrdROz4KhXgK1doIelYFTDAyitMZUkGuWcXefd7nLP57N69uTnrGDJxcdgG0bBAImVtAEpcnbrtFpPtCAYWAY2r0Wzr/3tf+COa0yuGvFeJrnlPD1pt6smtGeXp+vZ/NJ+9Dyi+UEN24GQ8XsyfKG30+hS7MC7KygOxSyG2DoM2iZXQJA4PChmOUW5rT5FJ40mGbfLIvd0+EeFKyilKJ1ThG2Lx9fj64UjGgNQLMvne+nEGIvtgfd64QQjYMEGAoh6pfWrD+yLY9e/yh3vDe8cTekCiEazMoT8vn2snvDf+/35vX0XJpdfSXLqhFQE6YiKsN2I+X1odL8gNO7UJMXUTknhGh87B4d+ODf93Pu1LNgyfKGTo5IoO0J8wkAvajqdWLrjaXMHPY8m6wSzvl4LHZJScMlMFKCe79FannCkjNy+OPY7/h5oI9PF3Z0Gt+mzYFDwXn6bwPU3xAKRmYmGAbagAylmHLPRAAqdIAiO8Dk+yZSYJXy1dBnYChssqDgstbc8OdTOeGQ52vuD1DZWbT6x3IWbW1Jmz+ls9Eqo62ZwXU//oFe580AoOuEX2uXUPdaK48H5fc7zzXu33bBduwxzndiyUOjuPaISXz8UdVQasrWrLVKKTs7k8zVM5zeRoQQQgghhNhZuyO4MJUgxljPtfXc45K2LE7411eckvMb220PmSpIc8PArzx4VfXe/0LWWqV4gTRl4FUGBgbHnH853i+3oNq34Z1772Oj5SXbCGAqxXYbLNvCNBReZWFmZWFXVIBVScHo9nz5rwcosi2SlZK0rVGGwl68HGtMkOsWTuWu/D/QcdEyZwVVtb1XmQSxsEOBd9iYEdfSwMBGoyIvuccDtu0EF2qNMo1wWUMrRZpSZBgVlNh+HunxBtbtij/3GYMOBsPHVz4vuqICs0M7ynq0xAiAqgxiBf1UuvsKaCjCZl2gGRuLsmmdXUTwztZ0/2EauktHvvnXg4zfejUZX23HrgyAVens3udDmSZ2aSltT1xABdCLjdWukefb2fimZoDPF1GGa8f39z3G2PMvwffF5uT1nU24TtQuL8dz+CpgFeuu2o/ZtzwOwKHnXoj3CwkwFELsIRqoDVAIUZ0EGAohdguvstw39Bo6JUKIxqjjPVM55+Gxzh9ao++Cl+d9Vm2dG9cexbpRcXbQQG9dL7l7CFP+cD82cO4hZ2E19YAfKZwJ0ajp6b9zTu8jsEub+L1G7HF6/GUGU27NAcp5ZXBvOlROTTE0cdcZmZk8MPdLWpqag6d24pyDz+T9H97Bq0z6fn0JvR8oZ9Knr3HahMvxzFzkbKQU582axPGZWwBvjX3mm5m8Ov19sgw/dhebyZ/7uXifoxn0v+27nmCl2HjJCB7800Tu7jkYtMWqP4/g5j++yat9OsRcH63xfjmDSweM568z3+Oqe66g5cTJ1ZZHsrWK3/WKEEIIIYTYe1QbEraR9FgYKV49lKqvV5NqMtEY4PTml0AoRRY6XILQhkKZJnhMMgyTbB3Ar5x1TTTbbA+FtuaS/B/wztFcOfJkghs2xjlCbMpwhywGCAZ5ZXBvOgamu4mq6uWxZnoVBiZ2vJKZ7fRar2x3WGQDCDrDJivDQJum+wKXieWevYWiXJtOAKBtOS9OpflRSoHfj85Mx0o38ZZoVNBGKY1PKSq1xqucIMNKbeL3BslPK2FNuonHNFElZWy2grzzxIMMn3QtvS6eFv7uLrlrMOce8R0/DEirGsI5StFpw/nw3vsByDXSoAsUf1uBV2VEXMimG0AohBBCCNFYSIChEKLetZu0juvXX0mGJcN4CSFi08Fg1ZuvQK/nCzlu8vXV1kkrCOJ1h5mMtvg/wxi0r/Pmrq0Nyq9viZ7+e/0l2KU90MxIB6D1K1uY+8Romr04ud6PK4TYS2kdvydX0TgkaLDI/3c6+3W4FMPSZJbFzs92lpGWhv1JPpneCn7/sQdd/1KLvChZI4u72NpWwJjrriB35mKi+yTXgUp0wPn/7v6O2mXlXHTDddimwu5jkPvyunCPI9pSqDKn54ueDy5gQ1lV78hHpK/HH9ngFKWZGVpmMsxfSsYnPi5o/jOvM3zXEqw17Sat49b1F5GhnfKR9kALT9VQzb2eL+SdL8biZ1q17eziEpob5WhDETh8KOrmzfiO2YRdXl7tEIbSVO+WRAghhBBi77X4sZF4Cg163L8Qz7s+PKrmCDvL3+xJq0d/boDU7UYNEVwYecxEwzXH2k4ZdTI0stmiOZ53fVRen4+eOR9lVi3zAhnKIk1BmvLQ96tL8C9L4/eLH01p37ZXYWRkoLYVMu7667jg9vc5NmspJopsA8otTYn2YGpNZ0+AzLeDVFqtGZpT9V0zlGLC0qPZ/FA3MuxfAOj61mbu+vUc0iPKA8o00Uphl5U5QY2RQZhukGFoKGQDhakMLG1joMJBhjY2Hsxq22BbzmdjGE6LcWUAPAaYBtp0ekXs419Hufay3U4jU1XCl21RN+Sif12ACgTA58No2YKKlplUZhl4SjVUVKJ1Os9vH8QH/zqMif/3EEW2j43BXPLSysj3F7PaVE5womFQqk3y0Px7zJv86/2jaXPCfAC06QzRjEp30hlVfl3+f6OZMO578s3MavNzjLTqH5a73Yo3BpDxQ1bN3/seEnzY/pN17LfpUgDyYpTdhRBCCCF2hQQYCiHqXXD5SjKWr9xjCmlCiLq1Y8IoyvKrv5ncamYp2W9MSXkf3u0Gi7e2RLmN6e0rg7ut5yTTrdB7vtMP7HN8JzwVo8j+b+ppF0IIsXdQP80mO/lqO8fr5dVeb5BvZjJgW34KiYkxrEiSoUaUUpS2Msjz+2sssw8YxI5u6eS9lDiwsV6aNG2LzLedhrjSq/bjsU6fMGTaBbw48IVqqy0sbMXm4qpGp4O3XcA1vb/lgtwNWNpm9Ow/UB7wMKDVep7t/CWjZ5xF0K7+fHLi1ovInVnz/GsruGwFGctWxG1cDWb7Kcv3EHkkT4f2rD2pMydP70Or5QHMcotVW/PoqmvXA0o85ceMwPYpMt79pU72J4QQe5tU80Ih6oryeNh48QjafbaO4LIVDZ2cRs1TaOAtUmBrlm1twSuDn2OQ38/6YDFHzrwIAH/JHlhvXc/DDO92O9sDnWWxfFtzOgSsGnWFplKYaLxKYWCQtjiNVjMDcHHsXTnrV11X26PA50UHAuS8M50tf8vG6y73K4MMZRHQBhaKUq15rssn+JXT/2GFVvjc/S3a0or2Ec/hdqafshYm6bhDJEcERYYDCw3lDG0cJRRcGN5XjBpSraJ6bLQ1mE6gnzKN8H6z1gYY+su5lJf5OHffyRyQuYitdiarC/LorG2n98KMdGjRjLLOeZTlewimKTLXB1lzfFuChUGe/vwwen40h83/ymR1oAUry/JJM4N0SdvCdJ9zTIA0ZXHKwjPYuCObykqTjVftR7v/Lq6R9sDhQ9nWt6qkdNTh0/hHy7k11iuwyzhsxoW021DilEPd709lQRo5e+Lv3RVctoJsN0+Q4EIhhBBC1DUJMBRCCCFEg+p4xWL+1uGTavNOffU6uvyU+j66/LV6I87ueifbLFf8VlnVa9BrQ57lthbHU/Zfd3leLirdecO2tkOgCNEUmXm5YJpYW7c1dFKaFLNFc7AsrO2FDZ0U0QQorw8zv3n1melp/F6ZTXOzlNLSFALgYjXMRc+zbX6vzMYMOPNVbg7PXP8Qf557KebaddVWXXZKGtce8Skfv9QMs3UrdFExOhDEbNGM4MZN6Ijheo02rdBr16MrKsLzzPwWUBnA2rGjar3sbFRaGtbmzTFPIfJ+42nTGqtgO2aF5suytnS4cgdvfzzMaTmzLH6rLMd3QxZtZ8+r2r51K/798FguOOglgli0+LOJsbWA+cf1pfBvn9B6whrskpLk17I2lAG6ZjOPWQ7zytuHG71WHpPOUYdPZ+FLVeuU927DU9c9zO0jjg5fky4/EvOFiuUlLVCltavuWX9mBXk5pWS866Yp6r4U/RmZLVuiS0vr/hrtwYy0NIxmeQBYm7dU6707HuX1hX9HZm4OKAOroCD1Y2ZnY2RlJl9RCFGDEdDMqcxJOZgl1r1biPqk/H4evH4it264yHlxQcQV6t3bAtqfVMBdPx7N3zp8wufFA2nr9pK2R4ocEjnW8MjhIDS77joGqE0QYNKe1CPSvgus7YW0O2mHG2TmBNCZ+a3xG1swcHox9CoDrzIJZmoqc5xhhY3IQMKop+5QT4HleQbZeTlQWAymyery5my2Na1NgwzlA7MSrwpQqhUltkG2svEqO7y9F4VXmaT7ApitW2Ft3ATAmsNyOOePn/PV8znOdbA1KDvmtdC2Dr9wHZnmyHRH9mLozHCDCUNBhm5AofZ6UEEvurISLJu0H+fT/vMSlMfDxx/uw+F95rKosg2dJixCZaajWjTHbp5NUc8cdnR2hlT2Fms8ZRZPXPEkFz17JZ3vn4nq2pENwTxmFndmSVE+GZ5KvMpCG6AyM7Fb5pFvmuj/a0nHyQuwBvXki7ce58hf/ohZrlhV0RxPu7Zg2yy/MMCig55N+rlvtAw6XLkDLAujXVuwLIIbNtLrUhllSwghmiSt97yXJ4RogpRO8BB/hHHqnvsahxBCiCbjS/utlJ4aJd9qopSqWUGm67Bysz4lSfumD/owddirbLTKuKjPWBnaVOy53ML9pvd70zq7CH3o2gZOUMNKNd8CJ+/y/68Ny7c1p92J85JvIPZ6lUcN54tnJwLOEFcAG60KLh0wHru4xBlGzK6jvgoMs/q+ov92LXloFNceMYmP+zfj6iULuOm582k1M8D7zzzCGcNOYN2TzZgy7GUsrfEqk4P/dEW1nor7zvDwwexB9LqgaujoJQ+O4oojvuDzfXJiJm3TB31onV2EOn4Hz82dxCk33EDWm1PwdGjPG1PeIV356PH5xfQ6f3rMdF+9ZAFHpZeGe/cIuIF/Boqtdhnn9Bm7+4LnQs8ToTRG/x0S5/rH3B/U7lkqapvo+1Lv6V4++nVg+DM6bt5WHvx0PN1vkF6bU7Xpyv2Yest/ADjynIvxfjUj6TaVRw3n/WceYcLQ41n7ZAva5uyoVR675MFRLDjtsZTW9bddJmUuIaKlet+F+PduIepTbb6je5hdqis0TJQ7PGwqAf9NlmHWnBcRtBe+BrZOXg8Xq0G/jurtlKfmizG7/LkYZs1zcs/B7NWdt79+DRsn0M9UCkvr8L82Nl5V/dqF5kfzKy99vrmQ3levAJ8XgkHm39mD5Sc8FbW9TZmuBJwhhw2M8PFDZlXa3Np9lPObjlU+iHG9Q9dO25rCCcP54Z7HagYTUhV4GAqcPPrEczB+W+z0QJiWBn4f2usBpVCBILrYLQdpDYEgdlk5q17vxeODXyPbKOe2A0+kvFcbylp6qcxWBDMUZrkmY7NNRa7BB3fcS3PTz6Cnr6HlrCAvPvIAzxaM5rfC9hhoBuatYdqhrcGyWH1hf6Zf9zB+5eXgiy9m0zAPP19wHwE0Z/8/e2cdHsW1/vHPObMWDyG4S4K2UKAUqBt1l3ur1JUqba9L7237qwttqbv7rbe01Iu7u3sgLisz5/z+mN3NRgkQCHI+z5Mnu6PvyM4c+Z7ve9a16OkL0EMP4Mv3X4qfs+oujdVxtMImVr9zl/u6PI0xvXqjHWf7790dqV8ZDNUwfVwGQyOxo47GBoNhu6jrvWUcDA2GvRlTsTEYDPsCWtfq5rNXsI3YW/8DTmh5DUKBp2L6bgzMYGgEtqOyLiyLrJ/TeKDta/x91ZlEdnFo+xoe4fBG/1e4//eTKDi8aL/tJDQ0jKRJSznh8mtBaYR2TfqEBk/xzMYX6Fe/Fxtwbz5++QWUXxzh3Iu/qZI+DIh21vmoNpkF1/fCd4YXxrWHY9cC0OPpTXzzweEIZlVdWFo0/zWdtV9ksaEii8N/mMYF195G0dUlbBw2hJw7pnLGiBsB8B1TdZvr/jSM5CPyaHbKEh6//AL++5cSJvb/EKBGJ+JupXp5oq7yRUOfDTtyD1RbxyOcuBMKwKIbeuI7w0v/Ge73P6Qt5DFxyvbvZz+m7ScrOWGhm28vMHVpg1KWJU1czNkjRuLdOgtLNsMj1fa9Y0UT39sGw97O9pTJ9uZ6rWHvxdQbGsSSVwciCr10vzU6MEI5Vc38aqv71uWQs7e3w1cTZWlVTYBXr8gwYd3qboiJ24AmP09CCrSSxN0ZE0SlWK5bYUS7KY8lktgshMKLm8I4JiiMzY9oqogCY/O1FmjbRlgSHY4gVO33Tmw9R2ucaEnQI1yh3KmLTyL47zZYKtp2WFv9oBbiQkwhyBq7jOMLr+PDZx+nmUyKxugKI1XC/i0h0ZZ0UxML6YoL/b74tdN+HyIUZv7dHThj4AyWlrQgv7wlXZO3Mra4L50DW1BFxfg2p2FV+NEeiXAUnoJylozI5h9nfkC2lcRhfxlJcJjNgH9O552igUzY0oV2KYVMWt2Z8r+2wRNZzqIx3fi/wW+xyg5zzQ03kDp7PSnLkzlhxSh+vW80Q1+azvpQBpnemUS0ExVnVp5fR6u40LDAKees625h4H+m8Uib6aDdY+330A00nxPCCimkml3fbVOF0MkH0+Pfc1l9hEbF3Pcb6pwVu167UJhrMBgM+xTbU34wz1GDoUkxAkODYW/G2AEbmoD1dw7DToGkjZoWz07Y9goGw/5I9NmsZi+MNksaDHs5DRAbTpyZy8Rmi1HalE12hIkVXZk8I4ccbdL1GOrHKSjA+920mm4ctfxG864fSkUrgacU2j48frfEJ3+dgbh6ADdmrqG0Wr+jU2tCX2DyHOSJw7i50/eMpqe77NIViKW1LKsVE2fmYjXThDPgy1kH4B/iJbQxBWFp1tw5mPb3TwCtaZU6mMUt2hD4V0e6PreMtNWKrXOyacYS5K8z2Hz1gMY9+FoovnAItl+Q9creVW5WupojSPQaPdBqZnSCSbu7vdjr1uONphdvqBxENMtk/WF+OHwwoVUOhTKTwN+60PGByQ1y9mk5CbqkX9Wgfa26rIFBGQwGg8Gwl9G/62qW5mfXPnNfblvXtafUrZy9nR30NVIs71nnrvjCIRTmSoQNnR6dCeFwXGQoZGUGElFUSu7X1/LhsWN4ZcthfDnrgJobE5BYdREezeRjRxOoNnAjoh20IyASAUu6/xt4Wst1hL7fX492BJkzfLT8aSfqa1rj5OWR9EuQSB1tN3fn9eetqYeAhF5bC9Cx6yclKIWI2NhtmrHs3GQCeS3pk7uCIanLaOMroryZjyWlLYloi4AII1s0R4fCeMIRd91QBCIRMpZmc/eU07jHo8j5YTV5B7end/J6vt/aiwxfBQenr2JOoC3WjNVopejXcS2bIpmcOPaP5H4/E2VZkLeFFsVl9Bx7HdiSow5YyE3NfyOkZVQQavFr0MPlP18BChDQqcMW3u/1Fkk/zeebD4fwcfdoPU9Ar2+34CxYUuVcufPqF7P4twQZO/0Aejgzt/967GG/DYPBYNjjMaJBg2GvwQgMDYa9HfPSNexmHrn2BYYnR7h1wyAWPNvU0RgMeyjRZ7OnXVt0ego4CmfxsiYOymDYTupzcKglzVDOyMk8+MqJJKUFac/63RDgvsWYxUeQM3JSU4dh2Fuo/vus4/d67NUTeaj1DL4sDzD64Z71b7OR3Eas7l3QEcnvQUU/X2zT2k0DpjWOVg3udEvcJlsLcQoKQGtybpxE3mc9aJtejHNaKW/M/4bT7xhFJEVw7m3f8cMD6Xg6tSNt0ip6LUjl8x8/4JQvLyX9nYmkv7NTh1c3dYg8Q38soG16MZFXdtF+dxHL8ptTmp9Mm4Rp3lL4rCwZgOOSCpskrv2NUOfmPHDxqwD8/dnLEDZ8M+pBrnj8hAYJDNPfmUj6uw3s4Lxsx+M0GAwGg2GvxbStNxwh63Yu3AMI/bGABYPeY4tTxqXPnIZ2XEfuuBFg1KbQ2byF3KvWM35eDt/8fBC5d0br4YnHV+1YrbQ0yudqAoIqqZJD2gZbuuWysER0bIdOrb2MluiICFCiND1uWIAqL2/EsxDbl447/TnRe/z9hQfR6/6tOMtWopOSwIqKJZUi0q4ZVtCmoEcK3533EG8VDWJNsBlfbO0HQJavjExfBV7hsDjYhmDn5vg3FCNKK9DhMDoUQiQl0fLzZbT8n4NIS0UHfFgVkl8KctkaTMFv2US0hRAa4ffjFBWzeEtLZizuRO7ISWhpYbVrg07yo22HHtfNRYfDTPzHUMZfPJujktYT1DYBAWOL+9PrgUKcxcvwtG3D2nM7Qy/3cDq/uRqUwl6/AYR0B/bEhLa1ub5Wq8dZud1g8xacyXPInRytuhrBoMFgMOz91NfuadIuGwwNxggMDYZ9mW11EtYlFDAYdgRTADMYarDhmTSmD3qPzU4ZI3oOR5WVNXVIBsOOUd/zXWu047iin8um7b6YDAbDHsmt33zBra9czd8/uJZvXhoDltuJpFB1Oxg2cJsd7q3f1SPr5Qn88HIK4ND74zV8OGsAve4viqfNMjSctmfNrzGtzaPjefrRXADWz8/bbqGoYfuxfpzO0znuObc+zWfqoLfZGku7Z+pfBoPBYDAYGkgsPfB2uxbu8A4bKMgSEq10pcNgYwkYE/YvLCthskBLd75X2GhZdT7U8dmq6lwYI6gVONEUyR4P53z6O1dmbKw3tMQ0y7uKxDTCsf3NO/wVxn6TwujcPvF52rbRW/L58w9fkeMtYkkkg09L+xJUXlr4SumatIX2vnw6eLfSQpZz3vSr6XR7CfYBFk7XTDzlaXiKQ8iictcRUQiE7aALikj9XNJPl5BXkcqxLRfx29Ht+D7Snta+La4gUzm0O3ueu47H7aru+9FK7ms5nc/L03kmNwfh89H5kVk8/8s5nPzmGNbYipBWXNRsErd+P4HLBp3F+jHpTB/0FJsdDVrjfyvE0vxs2py1ocp9pdW2s4EJj4dHxr7BhQ/eQcsx442w0GAwGHYnu6KNozYdhHm2Gww7hREYGgx7K9XFg7WJCbf1IjadEYYd4LEzz+FRr4UsDwEJeevM/WQw1KDNLUFOzLwIoTWqfEFTh2Mw7DrMO2Cn+fSgF3h2xmHMHGjVPqp+L0YdeRAXPfdllWl5dho/DGqBCgabKKp9C5mczPGTN5LlKa0y/YyUlfSdeDXZLyZz2cJPcbTkkTfPrl2ot5O/40szFpK8oAv33XQZnacvpeKgTju1vUQeO/McOm9aUmtaWVVayiWnXU3m8vlV5s+7oBu+i310fXMNJ55yEcxfUmW9zpOTeLLVk5hUvw2j9JuuXN35N34tzGXtkFKk0LAft8mu/ucw/nThh1WmjVl2JM1OWVLHGjtG4vPzyKTfsERq5cyG/mbNO9pgMBgMBsOeilaVAr9YSufGEhkqXdWRENBau9MbiWwriS9OfIIpCzshUZydupau391A7lMR0Jo/v/c2g/1B7skbxLRr+3Hfey9x8Wu34i2Bd256pNHiSMQSgoMeHUmomWb+5U/Hp/ebMIJO/7aBJfG+JKdvV/7w8rd09hRRqDxYaDp48+nq20y6DHLvylOQf8kkkunH8Utal9pobwXeEptwhgfHJ7Es6Q4qC0fQ5RU4HVrS5u2tOFrQwlfKr8Xd+GXEIKRa7w5OLQ2jtUZ4PKx+tycVW5PIvXYKSIs5f+xOzo2HMPfsJ9k6fwMfXHwseu5SfDOXcc7ZVyNDNkvuCvDesOdYHvFx0g+LeOHlUzjl35egAh5OnvIzrz95Eh2+XY8du+aOU/89VUdZueKMwZx27zh+GNyq0mXSiFIMBoNh17Er2i5q26ZpIzEYdgojMDQY9la2lRbNYGgIOzAiRM1dCFBrB6+hgRi3kf0Ge8UqwBj8GAyGbfNO0UA+/XwonfTEpg6l0bGTPFyWvrnKtM3OCn6whjdRRPsglsWlGXPItqqK5br/eCN6k5/V50fi1+D/sqp1rjRSuSRV+LksfT0fLcvH2bQZiAoMlULrah0xtfTLtBkf4k59BR2oKX5cdmEz2v2ciu/bvPg065MsFvZphn6kEzl/mo6KhAGQaWksurc3PZ/aQrufw3wTGIC6QtHrgWzstevi6//yxUEknRHh8TZTaz+g/bystvHWYXjLNM1fmADAsJYruCx9My08xYymJ0+8fibt5mw7Re++hNWiBQv/2ZVeD6ylxSybe1PPqTI/eb0AGldgWPX56YoLU4WXhQ/3QdiCrNkyfo0MBoPBYDDUzop3cwiUNaBstw+Lh7bpXNgQYd/uSI8cExnu8v1oHCRC13FuqsfhVLaEx9wHXWdAQR9fEn18sfJagKN7LObHS/uAEmTKCkq1TZ/kdbw7YijJwqb5kI2Uh7277NAk4D1qC80DoWiqZIlXWFQUB1DzpiE8XpDuMXjyirn/s7Poed7TdPCUkyKKaespAeDMKdcS+D6N1quWISItcFJ9CFuB14MMOSA8OEmSiONDht2sFluHd2PzoQ4npq2h3PHz4sxDyfrVj1yzmIX/7oH2KtKXeGgzZhoISW6LPJbKbMB12Vx1dkuEo+j39i1oAT02r8GO2DiRUpg6Hy0FLT8bxLmhG/jimCc5JmUhLx8xlE3BLFq/NJ0xn51Et98KsFeswsrMYOF/e8brnp5iSZe/1l9u1o7DmW+OotPMMqzyCC98MZyuzvRddq0MBoPBUAvbys5oaBDq8INYdUqALn+ZaM6loVExAkODwWDY36mt8awBhQ2ZloZzQFcAPEvW4+TlbWMNAwBCoIceiGfBavB4sHPaAmDNWY4qKWni4AwGg8HQVLy7fACd/mlEIoYdxHF4fOsQMjzlWAmy9qzvAuQNdphz3NM8mt87Pl0P6+d+iJYDrdIQataOO+0GNkseLchxNxmxKx0xtKbsoA60TltXc6Vqwkbv99Po8H3t2x905EKm2z3psqEnarY72CXrlQl4LhzCpw88woh/DEdHBYYiEODao39g3LvDSFqwgVbNOvD7E89x4msXQ4LAsNNXxfwyqBvUIjB0GrHhrWB9BsUlyXRjQ6Ntc3fQ5axlLMtvDi/UPr/DN0VYW4rYnySGIjWZkUd/x/fPHkzSp5Pp+mnTxOEVFrccMRalJWOyjiRrnvt7NvUJg8FgMBhqoof1o807C3AKCpo6lN1PfUK9eoR8nq6dibTOAA1i0txd7rAfT5HcqButdC90t6+IpTwWPi/q4F5kWl+gssPoIX23OegqlOSpkdQ4JjScGQoxOdiFazLWA/B8h18Itf+BCA7lyqFMwbDAKmac8ThbHMWXfd/Ci8UqWxM5pCcy5ODZUoqzeFmjHf60ge/jaEVIu9funZJWeDZ7EZaFsCQiWl/TGzbT7W9reGToCQzKXI0lFF7hcGXmPPw/pNPq9ZmIrGY46T7CaV48FQ6eIrAqIqD9OD4BqRbeYovyLtlsOSHIM0PeZkJZdxaVtqL5T36y352FSE/j8zMeo7vXw1mLz0A/46aqtrV0T3s0nhPPncjHswbQ/cppCMvCjgk7Y9fG6yfj05kk5fXhnQGDOS5tHq/1e5V/Zp5B+fQedPnbZESv7uhh/ahI93HVkT8RkBEAJhZ2oWhbJ05rOv9zMkIKNNDlr9qtXe/D4mODwWBoUoRADzkQz8LV+2dZbRdS0snP0cfMZMWwflizl5n2IkOjYQSGBoPBsD+jde0V5Ni02OjVWhpYgof25MeX3R7Hg+65gZbPbDGjIBqATE3ljffGcPodo6hoIZn6l6cAOH7E1Xi/n9bE0RkMBoPBYNgbUeXlTOlvAWlVph85fSIPtZ7Bl+WZfNs3HQD9OIz98LUqy926YRALBiZM2E5Xw/b/N4Hv/y89us4qt4FQuEKocS88BxAXotk4rrXvdmx/66EFhF8LkfPyMhYNqn9ZJy+PHw5IQTCL5f8cxoLrxlQeUwK3v/sew5MjNdffGWeWWs5b7nWTd3x7TYjaRv7j0976hce+OoVud6zdTRE1PfaKVdHf0aImjaNABfl2cDtUWRmpNyUx9kP3Hj/msqvwjo0KZndwEJnBYDAYDPsaYz98jWG3XUfae/ueU3xDiAn34i59dZV1E6Yv+HcWy497mXIV5tyBp0bdyakUJDa0vNwAJ8JGFxYCCtDhSDQ1rnvcWot4amTZtSNff/AKIW1z7nHPIY+TUTfC+uONaB+Kmsd+1o830uuBQq758cMa85xoPIXKx5RQC5SW9PZvoIUMkyLh+7deBuDAyRfQ5sydOeqqhHQkPmjKEoJXbjmTbuOmuKmMfT5ICiA8HgSgbZvgqUF+1S3BcdARmxOWgLZw63XBIFZpGK8UWBU2orgMAXizkhApFkJprLIwl9w3jv6BVcwPtaOdr4CpF3en+frpIAROQSFbVRLtdQVSaByvF8JhVNTpXnhcR8eQstwTJiTC43Zfa6VBuPeKDoXQ0sL7+1ymH53N0Ck+cOCm9t+T9naQfw8YTuCZfN7r9g2zww5/7TbUFcgKAeTXdMWSVuVJi/aBCCmqOnruDudOg8Fg2E+Rqam88f4YTr9zFGnvRstqu6PtQlR/1u9j7SVCkPHWJNaOa8X/pjzPqZddb/qfDY3GbvAaNxgMBsMejdZ1/yln3ytYNTGqpIQRR1xIxhdzaPPmPE4//GxOP+wsfL/OberQDDvAynuH8rflM6v8iR/aNXVYhn0YmZLC9UuWUnHm4KYOpSZmRLdhO3BiZQ0DVquWjFo6D3XkQU0dyo5Ty7VM+nEOZx7mlnNOP+wsht5zM14ht9lxB9B5chKr/jO0yrSeo1ax7LTm2x3a2HIvpxx6Bj2fXVBjm7VhbaMjdP2dwxgyq6YwEdin7mnnDxrxWyZ/WTabu5bNYcJ9gznl0DMYc/RxAHw1/EBy79lx10uDwWAwGAyG3cEph55BxqczmzqMJqXO9MhaVf5V4++bD+C8w87F2bxrM9ZopWvG11iCrupl89ixRvcXwcHRGomokvY4JiJM/Byjel3msDtuoNedy6tM6/7ltZx7zAVccNSFLIpk0N7j5+38Ibx2wlG08BTT2nJwgHzHS0Q7OFrx/cAX4+2KzlEDdvrQPVh4hfvnwXLbayzLFe15Pe7/WBtOKMQhv27h2ukzuGD6Yi6Zu4ws6SAj7nkS6WmgQAYdhK3QyQF0wIcVckheU4o/L8hJ70+kwE5hSkVXWniKuThtJSolicWP9Oeq2fO5ZPZSOntKOfSpUejLvAi/D2FZSFFV+CqF4ofjH2fYjAq01nQa7+UvS2bQdnxy/NiWjB6E/qoFqqSEp08/nVHzzyVFhJkfasefp/3I6te7c8aR5/LPo891V6jLXKGO6VpptB0TqKrKfpKdxbSBGAwGQ508e//jLHlt599/QMP6KBL7vxOfzfW8H/Yqosdlb8rjvMPONf3PhkbFOBgaDAaDYZsUXjqUrSdWVJnmlEHf0TcA0GF8YS3jNw11Ya9Y5X4QAgq3mZzBsAfT/ocwN5TcUGVa0iZNFrWkgjQYGgEdCvGP5y+lw/xNJCYpWnH/UFTHhOe0FiBqNlzKlUl0+esuSsNrGkp3itt7juPuF08n9+pp+9y5TF6wMV5mAECDtKFteO90dttZhN/P4hf6ID1u6cnvj3B0UpD7/dYOjwCsvs0Yqz5P5udB3fn4gFdY/nZ/N73TRqpeD2LvroRnw3beg4WXDmXLQZrut01k8bOD8aS76YqVEuRevxRVUoK/sA3eaEfXtpjycn/aLwrh6dqZRfdkknvb+krnlNqQtZ+5Dt+Xcau6mvC95TyW/TZfpGzD/jCKVVeDItDm9zI+qziSloxv0LbW3zEMJwk6/Ldhyzc1sXspZ4xN2x+KGOm5DoBOk1ZTNLgdG88JAdkAZH/VgYw39003oPAJg1h5Hg1+JuddP5Tiwyvfwx1fthpldHiN5ycgbGgXqvn8tEdtZfVl/Wt4T6pNAbrftm9eJ4PBYDAYtkW8DW4/pU5xYeKgmmruPR3fs/j2x8PIWjnBLRPLbZff69j5tpdpgMthQ0l+PZO+429wy0rhaSAE4eEDWTPCrrpccggbB6U1Jy84n7wf27rNJ9ptRintbLP4jGfiy0d0ZeuLlVBHkEiStto4BUV4vF56j7kBBHSeHEYtX4V2HEY9fi12MiRt1mStnMioJ67liEun8PdWPyGFJqIdLCFoJgN09ZRywjN30XnFGmwh8LRuxaJHWgOQ/VWg3nL3+juGUTGgHCk0adKHIurWiKgcQBW7F6RVKaCIOhZ2D2yit28TjhY0tzTJwkO8UKk1wnEAD1oKtM8LErwbCtlyaBu2nlTBFjuVUtuPPxBhclk37nrpSMpvC3Nav+l08+aRISNkSQ+ectD5hSDd/dtKcnXP33niuePoNWoJXlFCe08SByWvZHykFz2SC7h7+WkUfdSWltZ0cBw6fq3In9aBLLUBtWwVttOVz4oO4quXD2P0bWNIylc4y1bG763l/zeEtJXQ4tla2sISrm2lu2Ht2Zz2tXYag8Fg2JM448cbafdFI8mWtvd5nZiNpK5199Z3gHKwV65u6igM+xhGYGgwGAyGbbJlgGbZUa9WmXbrhkEsuNptoFGw943g2BPYWwulhjiecdNoN66pozDsT2jbpu3DE1xxYcJzV3es4OhuS+LflRZIoWkbKOTuFvPi0x8v6Mwn44dX2aZQGv/XU80zqYnp6dtAr27rcYSs2si9D2CvWkO7+9fUmL6/3nHC52Py0U+SbaUkTLXYfJCP5r6q7qQpywpwFixhW9S+TTjm1asoXpvNk20PY0m0LNf9netod38tYrfq6aK2gy0DNLce/zVf0IznjnuF4ckRVkRK+c+Gk9jk8yIG9SW/d82yovD6qDihPym/L8EpKIhPb/HsBKxeOWw6ug1Hd5vNnOMPoPn4ZHRRMWVDu5P0zXS0bRPYajNy1engVNTYNoAYP4vOW7vz1Y8fAlXPzT3LTsXX/VOOStq+YTJi/Cxa1qYVrOO8eY/YSqf0YiL/3a7dNBlCCIZ0W8HcYb1o+3MR7R5wD9YGtl7ekWlHPMNd649Bacnk7H5kNG24jYu0CJ04gORpqyjI9fH98Q9y+em3k/pL1fuzOpHhg0g+YxMf9Xqd+zcdD8DcZgfibYSQqj8/Pa1bUTagY61igV8O+KTWbXxUms7ocX+scx/+rSHEhFk7H6zBYDAYDIY9nwYI+pLWluItDlSmD0yksdPFaoWwdlDAWI2UDydVlvij6XULcn0sOvL5KsspNKFonXvtrDZ0f2gy2nEIDx+I8kq0rOw2dbSOuxjW5sSe199Hu609UYtX0+mJOe7ESAQVLau1esoVtVnZ2ZSdcjCtn5vGz8d05+5WP2OhCWkbLxKvgC2Olw4PT8W2Xbd03SydpbE63IbryHiz7mMvb6s4ptsSbC25ed0R3NjyR3p5vVToMHetP4rCrl5a5eXA4tUIKyY4VGil0LZNmfLjaIElNH4h8QoLbQmEZaE9FsJWWOVhV2wYscGSlOdks3mow1OD3+XDLQfTxl/E1KLOjF/alR5PzeHiqfPp41vPvHBbPto0gLZJxfiKNHg94DiUnNiXHsnz6OzbQq+u68HrQwrNuIpknl59DMGT25JhfcbaGW3p9tI0hM+LDofxfzkFP7hugxGb0hUZvLP5EHq9NpdZ13WisKtF6oE9UXMWI6RAdC6joiK1qoCkFqysZpQN607SNzOhXy7Ka5kyssFgMOwm2nzrJeXjSbt/x6Zf22DYbozA0GAwGAw7hIVCBgJordER27WTNhgMBkOT0PXCmdQ2Fm314AGUflzpoHRVxkKuenZhlWU2OTYjew1HlZfv4igNdaG05NrZF9PmvGXmfbofM/eWMTWmdX/nOrqN2rbAsC6UR5D9+nTmjO9B6beu44Vwak/1If1+d51wZIfuQykUwu/HEoqIdnho83FsODKMjpSz/sXWTBz0CCENlq7cv9W8Gf975nHOufymuNub8PvRoRCLrm7ONcd/zw8HpPCfpS9xy+tX03J6S8Y+9zRnDTgFZ9NmfN9OpejbbQSmNaUqSKoMoKUratSRMEknrOCK1y9j+XEvb/exbg+2kkScxuk03R2oYJD8oxTdxy1hyeEtaHuWO114fWgLfgs2Y+XgCoTXR2unCRp/dyEyKcDrzz7GBXfcgVDQyvLw1dOjOf2yG+t1I/zzmNcYnhzh1g1HsXKwK3ZNZdecm4KjuvDWAw9z44DT0WXl6Dpa9UpVMP75hOQgJzw7utblHDTnLjofceyuiNZgMBgMhj0DERWbadvexpL7MQnCq0Wjkji9z2wWDNSAqkVkGF22MTvlhWx88WIdxFIiS6FBaoTHTRn89zGvcFQggkLHXQsVCgeN0jrqOAhWfH2YfsuT9Oh7NT1HCvf+EgJdi4itfFBnPnrmMUYMPieeFthBEEEh0UitcRDRtLx6u89tzl1TWYN7jwu/n3vGncI7Xb5jbtjL8qE2h06YxleH9KHnrR4I+CFio8MRiITddn0gTSosoEQ5JFs+lAdESjJOehKyJIhYv8ld1udD2zadX97AA61+YHm4JTnJmxmQvJKbv72Cng8vhtYtSJMVtPfYPLqxD2VH5bO8eRYtPCvA5wOfl48ff5QSpTnht5vodvFMrMxMQsrD9b9cQs/R5fzw5bNsdsq5HxBeD8Lng4ogJOZR0orut09CeLyIltkUOUn845q3+HPfc+lxnTvcp+slC0FHPR1j5zV2vyVcq4qDu/H1M09x3tCzWf8Pm5aphWDKyAaDwbBrUYpyrRGKukXg2xCIb5PE9au/X2Pv3OqDrnd2nwbDPowRGBoMBoNhh7iv9STWLwoBcN49d9L8hV2UctNgMBgMNWloBXfqfM7vd/I2F1Pl+TsZkGFn+X3QK/wwP4une/QyIkNDJTvZlvXms49RrgXflfWMPwu6l82getdd5NgBvPTSE8COl+uuyljOyYsX0NGTTO9XRtJyuuLZRY9z58Gn0/7iVQw/fxRf/+fhKuvYGzdxUf9T8RbMBMDK6crT37/O9WdeS+5fZ/Lzf9oCRQCMv/phglqB61fRYJwlyzm/38mc//tcpp/3GPcdNZSZB2334e0w7S5ZixCCveVXLVNSuHvuT/T1ag7Zell8evbPybzT4WHGh7IAGDA5yIffHEqXv+w7dQBVVsZ1B51OWtFU0oTk/Hfd30zs/twTSP9wKiN//yP/nvoJLWSIFDmO6u6c35T7GX3IMTVXrsOl1BMurPFMMBgMBoNhXyL4VXvWb82gywX7mRuZrkUcuN3b0CBASNfNUEf2klKtkG6+Y8ASEkerylTBuCJBpQXjz3+EsnM1Cmjv8WNT6VrooIloFS8nhVXlZ4krc0sWgl+OGs2HE/ryTb/sBOGaqiJSCHw/i0v7n4ZTkAdkVglVaY0SKrpVdshZfuXbvWieXkb6OZv4+5xfOchnM2TGxbQYsQWtitzBYFK7zo5ao4NBN2WyZSFTkvnf4b14cPRwTu0xl8UnZ/HQpE+REXC25COKS9BaE/y0BWNy3mF6qANvHdKXLG8Jvb0OOZ51lCStYcQ1t5GzYC2kpaJ9Xp474ghuf6Q1J+YsYEuzDITHQpeVU3Bqb97+v4cBgQQsj+MOdrMkSVYE4VFoS1ChwwSjqasB8HgQUtRM+KA1JWcN4M2HHiYgYFKoNf8c8jlrJ2fx+2D3XMfdv7fxe/AKi2d+f5fTH74L69X1OEZgYjAYDLuUxHaYOp+2O/sc3tb6MZFh4nLm2W8w1IkRGBoM+xrbqvTsROozgyERv/DSxeuOBDz6+omMP6crFWEvLc9biQ6Fmjg6g8Fg2IfZnne4cnC27lrxoBjYh5J7q6YnNe+D7UMKRaoM0NlrhJ6GajTQuEKVlXPSP+9AVzPKu/mOD7g0fQtLfHk4W5vXub72SLp4UwFwvNvvRNL93XIOnXszAC///TGsCkH6hFVc9p/baV48HR0K4S13n13VU4tVeUbJaByWQAWDWB3bUfpuc+56YhieCk1Rd5h7Se1ObMseHoK3WNLxP9XyF2uNszWfsPZw3MwR+F7PIpWJLH+7P/8d+Ol2H+v2okpK9qqUK6oiyI3/uRltQYuVYaxmzch/qznrPm3J8Vt74S3XpDORn+4fRpcVpU0dbqOTeD86W/P3uFHr2rZRhUV09YTJtlJrXcZBbP+7fy+6Rw0Gg8Fg2F6yAmXkJyU1dRh7J7urjJDoXrizoshasIQk97XrSV8GweaCGTc/iUTTTHppaVk4WqFwhYXVxYVOtCwY0a6o0IlW0iw05WgCQnBy6jze+/xSmt0u2XB0CwoHhcm9Ymr8/Gk7Ulm2jMW0rdFkUVGco9U2B561fdGP8gaATXTwlOMXyYRtCye/AGFZTL9/AB1LHITPC45i8ehOpE1Ios2b8xDJSait+ahIJ2xlQUUQiea4KyfwSech5PxjBqJbJ9ZOy+KUhbeRttwi470NjGj+LX7hZW4YbvrvSFos2+Q6OHos2LSFBY904rZ+41hU3hqkhSotY/ldfTn2xBl09CSzwSlHAUJohM+H8Hj49f4hdNkSQTiaoHZcQacg6jJpVRGOJoo4M6ds4Lx77kRZcPAVM7k0+3e8wuH9dw+i3b8FetaCuFi26r1W2VeWPGcdQ/890j2fv2zGKS42ZWSDwWDYDeySvou62nISXYIbIig0mgqDoQZGYGgwGAyGncYvbQIeG0c1fgOQwWAwGPZwhMBnVR1C7njM+2B7mDWtG//KWM85GXWn4DTsQ0QinDb3UgKebadoS1/SUIWhQ9YrrpOclduNLcNaAnDvrJN5rXkh6/Iz6CTmAiAG9SW/TypoaP7hbFRZ2Y4dRyITZ9PczcDM+cOvJnOl29kjtzMLnSir4Oh5Z5BUVI4DOFkp/H7g65x809k4S5aTduLBcEm1laRF4UWD0a2D6LK6O44fmHoC5Plp40SdTDwOXmHS5NUg4V4CoFkzAh6bMgfXxcQrKLhsKNmfLsQpKGi6OPcgbpnxR1pnFrNyWStymbzL9mMfM5CSjj6UBWfOa4HXqt1BaH1+Bp2ZDUR/733TdllMBoPBYDAYDI1OY6VKjm6nVAU5Y+H5dH2/GD1jHp727Tj+2HN4vsdbdPFUFRfGxIRKx8R9Ou5EroCwligEEe22eSRLG0drAgK+6vsGf0y7Fm2B9DlV0vEKGRUaOg4Vs5pxR6vh3NF6bP3xy4aL27xjp7ofUlJwNNyyfiih2ZnIpCS2/KEfLb9fgy4qhpRkdCSCiIr0dMRGpqbGRZ0e6YDfj0JwX6upLBrcCjslGZXso+V0TSRJ4gQ0D+Z8yIPrT2RTRRrrCjLo+OY0RHYW+H1ov4/8k3N58tBXmFzWjS9nHEgvvRxd4Q5M9QgnmoY6enoE4HW7qtP/N8MVVvboSiR6DbTU8fl1Ya9YRfMXVyOTkhiX04+KYV7+0GIyr/d7lQvOvI0OyQcixs+q+94SAnv9BrJf2YxW2hV1ghGUGAwGw75IosgwkeqiRCMyNxhqxQgMDYZ9jYZY/RoMjcyXzx9Oy2cmkKH1zmbyMxgMBsNehp46F9/xVaf52OnMrvsVuX+fw8c3HMk5NxqB4f6ACgZJP2lZg5Ztwart27gQrD+hFTP/MgaAoy+/Cs/YNXRiTXyRxRensPy8ZwA4efzZsGT59u1jG3Q6fw4AFcMH8cZ/H+bWb89Cl5ZhJ7kNc6qeZKz22nXu8yQ9HRkIoB3NarsU4bjrCKVZZYfjbh4AMinAc/95nOv+cQvNPp5Z59a7XzKD5Q8O5Yi/j2faR5JO58/hz6+dw/nHv9wox70vYWVmuCnwQiGcggL8wws4cfoqHmo9g5COsMkJcc2ca2HafiAw1Ik9n7VMBzqe597zudv7e91OCm4tZcLAN5kVhn/1ORJVXl7rcp0TPi+5OJVl5z/TwD3cvrMhGgwGg8Fg2EMRUlSmid0etAYUWknA2Ws627UTlQUKWG6DZ/g6tHKiQrKN+Ic7/DA/l6sz1kTFbu7yMfdCJ6FFI54iOSouDGqLoPZQopJoTTEOgoi2yJAhnGQv7T5cDp9Y6KxmoDQ6HAbHAa8X6fPSdfRi5iw/AOvub3G0xruNc2ptp6OjJWD6IwfR5bNZiM7teevfD3PNpltJ/q0YXVGBKi2j+7XRmJQCSyIzM0AL8sPJCJ+XEuVjrV1Bhe3Fm52KFpA+v5D1x2cx4spvGLPpaDaP6oRn1hI6J5VBRhpICRGbYLtUXv7Po3iF4s4PrqDXs4sQSUlIrelyz3QmLxyIfHASCvAKsCzlOh86Cpmc7J5zv0VQu0mjtQXC60XbDjI9FV0RRAUTsmUkCjmTAuQ+uJyZF/XljpvHst7O4NcrH+LgDreQO56qZft4OmuNTElBeD2oiiDCcoWXwmMhU5JwCou26/wbDAaDYfuwMjNQpWVouxEHAdflVJg4HxpWrjG6CoOhCkZgaDDsi9T20tzDUjsZDAbDXoexQzcYDLsI/Xkmk3IfZ6kxUzPs7STUObzfTePm3GPQka2s/6gHvwx6hAiVKcbq47jxa3nmqxPoetcErsk5Fh1aWXWbobz4sqqsjD/3PILsb1ex/MyudDh3bp3b7fbXKUz/RxKwC9K37yPlBJmSwtOzviTLshg68eoa5/PHilSe6Hs4OjiviSLci2mEe+QPS08lOLwQHapdXGgwGAwGg8EQJ0GYtkPiwr0YV1BZTZgXE5VtJxJXZOgTiqC2CAiHLBkmzVuBhSCCJqjD5Dk+3n7jSbwIglqRpzy0kDbHPn8XnR6bhbAkd03+iX6+UiJotjqiMt1vbezINZMSL6AlbLiyP4/f+iy3Djyd5PCi6DmQyLQ0hN8HgHYUKm8rI2Ys4D9v5pB3k0b4ghSqZK55+iY6fLwOJ8vPsx88Q1BLQtrCLxxuabaUY5v1IAnQwRA6HEE7hSy/fzDj/vgQASG49Mxr6LxkLmVH9uTLZ0bjoDnmv7fjK9Hx85olPST7wwivFye/gK6/Cf7TehwKyHMssqSD9mrweNBlxVw7fQZ/ffVSOj4wGW1XPT8yKcCT0z8j27LwYqGANbZgue2DkARpgUoQySa4GS58shfD+85j1TAnPq/4nIN59YFHuKXX8XUO7DEYDAbDzhFrg7r4jlGkfjBp1+ykofqI6svs5e17BsOuwggMDYZ9kfrU+AbDLuCKG79kyiWdKA4nRTu8dkGnrcHQxCx9oz/+hUl0uGf8PiMiMBgMewYByyZZ+oBgU4di2Nup9l464sEJrPh3cxbmt6LZKUsA6PFCIYf/fC0Aqetc57WkaSs5/EZ3WtGZQQoPGEzu9TuQ6lVEu9+0Bq3jZUKv5ZAsveQ7IWjA4OBUK4i2oimxQiGKvuqO80ELsl6ZUGs5U4dChP7Rmg6h+lW6G24aTOTQ4npFiDtMzAGjNvam8oKUpElBhkzC46magvew2WfjeaI5/uAU1n7UB88vGbR+YnwTBboLqW8Ee/Rabrh9GP3Pq/s+Wv5wL1I+qqNxfAcH/7W4z0+Zpw0ytCk+rfS8Q+h2+4I617mm+dvxzwP/fT3JearO3+DvH253SAaDwWAw7BOs+7gP1s8ZtB49YYe3sfTNgyrbjPZ2NChds8AgpEA7tSy/p1JL2VxIV2SoteL9W0/iyWsrmDnkdSwhcLRGRuWECoGDxhICC3eQVLIUnPrSKFpOswmlS76+/1EOfe4OgrlBZhzzNEEdwS8kJcqhSFlEtCRNerjxws95uNtwelwzh7/95Ro63LKY0R0/j8cUQSPRdPXYtPgtjS23dGBLv1SGXTe19uOSFqv/cQihbkFyLp1eZZYqK+cPN99O1pTVrDunM1lWOcLvRzvKdSx0HERaatwlKtKjHe0fXsaj//dHOk/eQqRfN1o8sIpOngKUB7QlERGHTClx0ER0hDWOnyF/v5GWs1ez8ZJ+eM/II+MU16FfeTUTg+146q4/kLpsESoUInXqak66+RbQ0HrGOvKHtXWXBSQSKTRIiY7YpFiKZjKJkLbZiCJNetBeBV4POhgiICJoDwiPx3WpjKafDh5/EFl/Wcmld4xCRquE4VTJmP88wVYnhRuOGMe7nw4k+/Ql0XujqnNVjycrWJbZC8uZEZ/X7OeVXHXzbSQFTaYJg8Fg2GVE26C03AUOyVVca+toh9mb2usMhj0EIzA0GAwGw05zU7NV0GwVm50yRniGG4GhYZ9EbAjgz3ct29dc1QeAVlODWD9Nr39Fg8FgMBiaiLtbuC5zX7YIMJqeAKi5C0meF0tXDAiBk5dH8ieuK2Dm5T1o26mYyI7uNFFkuJ1YmRmsvbIP908J0mJWZePiprXN8PQCeclQMt+oveNX/jpjm9v3F2hK1qcAkHf9UIZ0X7jNdWT/3qw7NhOADu+twm7fnC39Ush+rloceseOeUcoP+sQirpaCBvaPD25UdPI6HCYIb/eiMfj4J2ShkxOZt11/Vk8K0TmFD9tJsxj7ahhVJQGaVGw/zbElnZSvN7plzrnH9SqLynVJ+7s/TFxNtU9d7ylislrOlWZlpYcYsqA92us3urHTTixlOh7SXpDg8FgMBh2Bzf3+pEHV5y+U9sQGwIEWznkXTeUFs/uuFCxyUgcSLszxYT6Bt00AZlLIpz5043k6Gptd0LiHTuV4MlDkEMEIEFE49ZR0ZsG0CjAEoKAsAi2iVCQ40V74JDfr6Pr10Ws9GYQEB4yZRilNSVKstFJxdGSQquA89IWMqlnFzY5DhljF7DoojaEO2giWICKl++SpZdXOo2jxyU3kNk5n9Ftp9R6TJuvO4SDhi8g21/KIqgimhBSUNTZQ0H3zoSyNWf9ej09PVtACoiJRoVACIHWGitoM2F1Z7p9u4LSgR1Zc5LgrY5fuDJLH6iMZCKZAcq0IiAEH5f24pGpx9Prh7VsOrEjnLqVv+Z8xdM6F4Cs2YI/e/5IzqeTUR4vAE7eFpI/2QRaYwNp7bI4YMKlfDDoBc5efBbFv7ekuVoev2+eLOzKa0uH8Fq/Vzlh7oWkz/fiZKez/ux2jJzcG9IVa28egLCh/WsLUaVlBJtbjO78CVd/dQKqrAyA9DataXFvmEKVxOHJi0nrFuQT3aLqydSavOuH0mJaKdaP00EINo8cRutf8rFnLyTp043bf9MZDAaDocHE2qC6rq5o6lAMBkMDMQJDg2FvxKQ7NuxmrHLBxGDdQ1S7eoM0l0luyjtzb+7xCI8Hq10b7DXr3dQQhgbR7a6J0Q+deeGGJwEY8dZIOv/UdDEZDIZ9g3UlGfweVMwLdW3qUAz7AJ6grlFum1HeOf7ZysxAZGbUXDFahguGvRQGk8jo0B57zdpa9yHT0pBZmVUn2g72hk1ux5CQyCQ/skVzEIJQxMOCsKKVtY3gW7Vg9qgxHHXV1aTMWwudOgCQe910Fj81kA7XLKfilw7Yq9eC1shAANkiu844q9Ps1Qk0e9X9fO71P3BM6nx+DwosqpZfNzst4ucj7+AMnr/Rfe//fdrVbDo4wLkX/8T459y0YlZmBiIpCXtD7Z1PVnZzsCycTZvj9TiZkoJslom9dl2D4q7OhvNCvDnkJTY7aTzzQv/GFRiGQnS7cGb8u2jVkqdGjuE/V1yB9dN46N6F5298krvPvgQ1cz8ZZFF91DvgKau/fuQJ7lydyNO+HaqoGFVSUvv8zh1Rm7fg/3oKnb6uOs/q3oWJY2vGJuyEaabOZjAYDIZGYG9pX1pbkklZSaDO+bNLO2CVb197u9WqJSLgj3/v8dRall3RgbNv+IFfnw3gadMaXVGBU1hU9zaymyNSkkHpWsuzntatwO9zy9nr1jc4tjjRcnn8c4yo61vMAQ4hEV73WqIU/qSIK7CD2gcl1HWehKi6nwaeT6206yxI46Vytjq2B6XwzN9Ir78r7GgsWmmEFXVj1AKrXDAlpHEQOLiCOKUlYW2hqPzvFTbHJZWw8NQxcCosCCv+OvwChKOwk9ORSJKFRVA7hLSHEpVEWFt8UdqSNCvI6pIs/JRBm5Yk+8MARLSMOiS6fzGWn/tclWNxouc09nu78IZvGZa8hHtXnQqsrzLASyQlMW7UQ+Q5gtPG30Du1UugWaZ7bSzLTROstfvZtpFL19Dp/CJsabH2D22Ye/RzrHUgz0lyY8wMUNLex3rHTwsZYsyCI+h5+yoIBDhx5G/8q8VMvi5Pi8ea9epEsl6TVYSmWmkQEk/7NqjCItT4WXSamUzZXA+lT7en05fTIaF+Omb2kXR6WqDeEKT8N53MlSspHtyBibc9zmkjrmfdtWWMO/9Z5oTTeeSLM7GSkwlluPe56NQOuXItqsIVqiwMNwOgWAfYEMnE06kDzroNVepP9496kVtfvZoOk0FYFiNv/JjRnrNpt6mlW4cz7Dds65lsMBgan+ptULtmJ9XKNKY9xGDYKYSu50d0vDzP/MIMDaeuEfDb86Cu/nCvvk3z0DcYmo56XC62fJbDtIHvuw6GPYfHRwo2Ft+pDxo0dta8txrI4AP46pPXOO3Ic3CWrmjqaPZOzPvKYDDUQ0PfWxB9dyU+Q8zzw9AY1NMRuPSxISw+fwwKjazFnmTQvSMJFCo+uP9hru45HFVeXrnN6DbW/GMYs657ssr6TxZ25Zt+2W5HJRA6cRDjXnwOhWbwfTcRyFd8+uAjNJMBjrz9RtLen1Tjfrd6dOerHz+Md6LFiJVZQicdzFcvPM25A0/F2bSZ4guG8PEDD3NFYpw7c44SSXjHC8tVRlbp8Ix2oi99bAg3Df+Gr/tk1rqZzZ/2pE16Mc7R6+P73DRyKC/d/jh/7TZ0xzrjhajsLN4dnfnVB7jt6wPe6ro3RHX/wHrYSUfL25Yu4PZXrqTDvTXTLMq0NF6b/w1n3jGKtPcmJsRXGXeVezb2e9pGPKbOZTAYDIbtpgnbl7brvbWtsssOlG38P7fmo+5fxr9LBN3HXk3uFdNBa06dV8ATX51MtzvrdjPc/GlPpg56m81OOVf0OQlVWlpl/oHT4P5W0/iyPJWnc3K3K7449QkEY/OERA/py9cfvIJC4xUWt24YxIKBdmW5cze6de8sMi2Nl+d9TZblCkDnhnXd5e6EcrWQok6Bo5WeykuzvyTL8uNoTQSHiFYEtetwCBDUAichtbQlNLeefAWs2xg/h/+c/gM9vCHKtGKL46W7192fxBUbxj6Dm6g5Es1JfdDbt9H1w1K++ORVzjj6fJylKytFooBMSnLrLT4fr8/+gtPvHEXG/2YiUlMQSUlgRcuxTlT0V1EBto0OR1wxnpAsfvEgphz/BCVKc+NJVyDKKkBpiERI+sChW+oW3p9yML3/uwF7zVraT0zl0fbf8VuwGaNzekXdL2v/WQqfj0cW/cTFD46i5ZjxyJQU/j7nV+760/WkfzMfmZGOvW59/J4PaZsZYQ//vPpqAgs3UHZgO95//nFShZd8FeauNadReKYH4bHIezGNH/q9AUCS8HH4qBtIe39K/PyEvu3EdZ1+RgrFqcl5nH3Wlegpc+KxjVo6j1tfubqy3C0t8v7XndZpJW4dzrDnk1g/jtV9Er838NlV5Zm8I20M28DUtwwGg8GwN1HXe2s7WkcNhlqIjeBvrPQ61d3PYt+NK5rB0PRU/z0m/GndSM8Aw+5BCiwhTWq0nSF676/9yzDKvula+Y7SGnVoPw6fHYz/LXl9QNPGajAY9nxMedfQ2NRWZoshwBISiVseqP73r9te55Z/vYevnnqeFuAVVnyd3B+v5JvLDo92nGlW/N8QLnvsUxSaY2+4nrYfLUMo3M447SBqudU33ziMkz5204DFtnnihe5f9htbOXx2kEse/RxFpfiw2djFXHrhyLhDxU6fozp+i1rpys5Grao4s/QcvcE99joQQlc6wMT2C/TywpEzS9HD+sUW3K7YQycNoO8U18Vxl1P92bS/PauqdE5F/5RT/99OnqPHLv4DXV5bVed8Cdz8n/c4fHaQ9hNTK91oYvUz23adWWKx7G/XzGAwGAy7h72lfWlb78EdeE/K6GCd2J8lJO8d9SyHz6rg8NlB3r7nJHo8Xb9btRA6Wp52U9bG3ARjfzI+f9enHPbMX8mJF17JtJD7/Y4WPzFkVgQrO3uX73tX4BUCv/DiF168Cedv843DqrTZrbtraLz8Fi871fKnHYWDW58BsHBTJqdJizQhSROSFlLQ2oKAUJRrD0sizTn47bls/GNvwv26cNgvGznQ5+AXHgJCkCYj0VituLgwEYnEiTqtP3n2y5z6yi9uXUi7A0i04yAsi/YTUig4+0B02N2eIpoNWcbKsAnCP6XAtt1pSqP6duPwWRV4OrYDRxDRmizLQnstdFk5RYPb0ffrTVzaejzfvDaMnmPK3G1Ii3U3dqL/F7e4bvDbeA7oiM1tF11P24+XETrlYI6auJkWVgXKIxCWRKclM3C6ww/PDqH759cR0jYWmkiqBVLiCToEtWaLCnPkx3eQf2t7hN+HjkSwHUlEKzY5NsddcQ2ZPy6PD7YRlkVWoAyvcFgfacYGJ1xz4FR1lEPrv8D6TzrTbUpg99S3DI1H7Bm6I6smPJP3+PeawWAwGAxNhEmRbNg5TCO1wWAA9DfN6bL+GkREkBue0dThGHaWJnKlsZpnsXRUD3KeXFFnmsE9jcxlii1JrUhhefycebeW8dLvRwBwx5Ff07vjBiJNGaTBYDAYDLVg1dHofmaK65yyOWbw0YAUImKTHz1lBsLrpg3OWAR3/3QmdwPeIRabBndFdy13U4AJQW1jU4LZcGrqPLp8djufnjgascmP/Nl1Z7u4xTyGJ0dwtCKkK+N2tuYjf8vfvgNvAMLrY+U/BtLl/QLU3IVAtU5dISHqJlLapxX5PT20nVL7tvQ3zVmd1Jw2bKhyDpOlj79mL+L71MOjydjYrjJYYFMFn4w/mFzHlL33SSbOZluJr/+YVgBpBWx2yjjsvjvIeX4D9vKV8TI1QNvfbPxf1XFzGgwGg8Fg2G5i5cQ7W35SY95gv5fB/oUA/LrsYOxVa+rdVmJ7ao/wrBrzP/9oGO+3H4yn0KIrdTsh1h1sXa7MNaer0jLkzzMoVgEgwqJIBq//fii9Qguj61Sm4d3T0cEQgz+9He3R9Mhdx+s577PivsGgoMPgtfw9e2F82emndGBuxlCEptY6SnybHkiTY/EKKyryk1UGPjlUOhlaAgLCwYvDZZmTGHtGT1Z1bckL44/kBY7khsPGcXH6LILackWECefUEqLKdmOpk48IlHB0UqnrbigrUxBrpflpYl86bbbRB/Vg6dmpHPJFDj2WRrMLaQ2OA57oYBSl4uJCrTXK7+Hv2Qs52dsbpObXYDv+9MMf6FWWB7ZNJFlyU/PfOOKTO+g+pZxgq2RWX5qOFepE91c20/HLFG4quoJucgpaVaZFTsTq3oWlV7QCQKiu6G5l3J61kE2ORnlcYa0oreDd8UPpMbGAipZZ5CtFsoygvILNx3Vgy6ERSpRFmnTwb5XIpWvd9OHhSBXjg8BvC1C2DVJAtD7b3F9OplXGmkgW65xUFl2ZTKdWB+P/0i0jKyQtj1zPpuJhtHrSdTF05i0iqd8Q7m39A4PvHkXO6/k48xZt484z7E4KLx2K44fsV6ex4p8D6fpeAWreotrFhQ2sZ5s+LoPBYDAYto0RGBr2PBo6MmQvqMwaDPsLLceMp2X0s/ll7uU04eg8kZHOv855nzffPR72EoFh6vsTSX2/6jRnwRJyr3c/P/zSCfjTQnRiw+4PzmAwGAyGnUXu2Mj/rFcnkvWq6xhxyLQKLs2cRBvLR6mOpvyqo7gxP5JN7nWTmbiga63zLSHjnWwNET7uKMLr4cZzvuLDyScQmEu9Lhdrj5PcevyXfPFws2obcZdrOWZ8le+eLp0IttA1l413hDSs41ZPnUvOVFP2bjQaIy3y7sJxeKe4N+emzSOoYXqoLYsvfYajf7wK34pVkJ3F4sueAaC7/zq6fV3t2ExbisFgMBgM20+0rCACfn67/GGayaR6F9eWRCQMSqmNxPZUVaU86NLhnvE7FfL2oJXrchdzS/y88CByb5iMI63dFkNjoSNhckZOAiD/sqH8+tc2LL70mfh8J0EE90G3b6Hbt6ioI2V1YoOyHK34qKw1Sks6eLcy2K+JaIhomwiasHa/K1yn6YBwaG6VUaS8vNfnNZ5oeQTzL3fTXH/auR9/6O0KDGPERIUx03SZkHwu8bNXWGDJaPpfQCty/jQDmZxM/sk9uO7Ub/lucGtUMITwucOYtFIIJ+Z8qNGOckWHkQgiISW0VeDhocUn0OOWWZCejo7Y+MoU7xT3o+c/5oPXw/qrejLr/Ef5tKwdL393JklfT6fr5zZ4PJXiwoSypqdDezYd1YrFI9zzH9EOa+0KXinOpVz58IQ0olkGTlYqOSMng9+PcLLYqvy0tkIoj2DrkWGmHDuaRZEkfq9oj4yASE5Cl1egbRulBVIIvALUAd2wVmxEl5SiHQetNAsKWrEyvQUBEWFlOJs5p42mf/mt5IwLoHt1I1nO4Ke+/+PuVr2Z+POBqLlL4um002WA/575Ls/8eh6Bedu+9wy7j8i5+RzUch1r5/TktnM+451JpxCYq2u2NcRSvCeKDKsLDmN192cmuM9krU0922AwGAyGOjACQ8PeiWkQNxgMhl3D7ni+JnbgJuzPXr6S13t0ABbWXGdPpbq4oVrndO6VU3dzQAaDwWAw7ALqKB84WlU6IYpoeiu7srFe2zYT+3n57NOrmDjoDRylqzhyNBSFJKKdqp1+u7DMosrL+aJPMwJMbryNRjs2/K+WMa/b0zjVNYbS7VTW9q5PgWfYCUTtziy7k9j9+eMvZ7E0P5sO1xdw+tQvmzQmg8FgMBj2eXak7LkTZQYhxc4XORJT425jX1C3FtKNRe99fTJC0OzVCbz87TBOn/qlK86LYuMgqepCqHBTICemK7ajJ2WTE+LVwUfjFJeyaeQFzPzzGCSCCA5SaywgBIS1pFx7CGoPDpI1keaUKT89kjdy+EeL6erdQmvLwS8skkUERcMEnF5hIRGEdMStd3k8CJ/rHo9lgVY0+2we376dAVS4KYKljLoXeuJOhjqaHlnbNtq2XeFhlG53THTvF5/PTaPsOKR8No1xnzdH+Bx0eTneUpgeDvBmvxx8zEMLCTERai33x4J7WrLkOLfuYwmJV1jctOI8IsdsRlgW6YF5rL72AN4a+Sh39TgSbdtIB4LaS0ePF6GAMg/zIykoLfn4wqPptGGZu7tw2HVkxHV6bGX5+eqjVzn0TzeQ+UGl+1zKyat4fNTZ3H/ty0wu60YkZTVagDOgB2M/fC2+3L9azKf0q+n84eAz3cw6GjY75bx20GEEyhuxXmhoNF7q+BubP/iWET2HEyjbxjVq4PPQYDAYDAZD/RiBoWHPY2+rqBoMBoOhUfB07cyIb37klXNPQs3eS0SG1d9ZtYx+rDhzMH+472u+GNAOFQzuxuAMBoPBYNh+ej17A74S+O72h7a5bExcqAVRcZxda32u7c1lDD7rFr4b9ZDrwKFpUL1P9u/N5e9/yRGBErzC7UCzE3o+d8c7Nv/yodz5l7cBeOHKs5G/zawyv8f/LefrpwcCy2vfQDU3mtCIZPpcNpKFV48BKlOxaaUBx9SHm4p6XCqrLrfnCEBDI5LpYJdgb8rjjOEXElgxH6U1atlKThr+RwBy85ZXSbtXncWvDOSBQz/cXSEbDAaDYV9mF7pL7wksfnkQgbQQna9YCVSWg+vi9jfe5fbXrqTDfxvoQlhte1rVcx5rcTuMrlR1eztQbnG0JJ5bNrrNulLf7tHEnRcVTt4Wzhh+IYe9M5M/NV+AinqTKRSOrjkAqrZTnyYkp4xfTkRbdPa9zga7tMYyyQLSpCZTh4kQJqgFmTLIRjuNQpVMWFsUqgAQJEXYrtNhLfrC2twLS1WICBq/kBCxUeFI5QpaITxeSs84iDP+/T0/HtYeHY4gPB5XXAhg21HnQte9UDvuNZYzF3PS8D+iV69IOGegKyoAEH6/K2TUilWvdeHx/i8QEBHwekEpBMS3hXSFjonPgF5/28SpD11Y9fhKy6P3k4UqK6fDq4v489cj0JGlhL9tz72dX+XHkt7ce+EgMorz2TgsC6UlfX0lqGQvMhJBV7h1v5iDoYOm0Alx1dnXkbVsESp2EZUD0kJ5oIOnkAlassaRyIiIP7Mcrejx7g3kvFKI0BqVtyx+LgrVHuhmbgCgzY1lnJTxR/eaVSytnFHfs6qu91ND64IGg8FgMBiMwNBgMBgMBkMTUa1Sr70ezk8t4l//8JL1wRBS35/YRIE1ElqzeeQwUk7dyHlpC/lCdmjqiAwGg8Fg2CaBLdD6l3yOTL+TTsHtcOKNdj4ue3gITrpD0hovHe6dxJLHB9H14wjJmxR+IWtNPVYXclM+/3jvQvpf+jC50ldjfsrSYp567zQ62bvGUWL9XcPodPIKzk8tAuDZJIvqXUzOps2waXODt2mvWEVSXjvW2qUc+/qddF+yATs2c2/ruN0f0MrtoN8DnAurY69YFf/szFsU/6xtGz23YYN1mjUvjd/fBoPBYDDsFPu4O1SrcR7sgLfBy1//3Qg6T4lse8HtpRZxYdyBsLoYqoHlF610fBt177f+dM97GnHXRdyykTNvEVsjKfH5MWGhExMbbkMY66A5O3VBXHrpABZgCRGvH3iFxELg+hJqJJos6XDJzHMpWZfON6c+ioXGK2IxNOxYIjgEtSKowREOi/6ZjiobULmAFiA02e0K6BdYzY+ioysu9HkR3mia5EgEHOUOCHOcuIpShSOIhUvRSrP18sGUtRN0emCaO10Kt+bm87LgP534V99P+aqoH2P/N5hOzKlyrivPuQQqUyXba9fB2qrHE7sjte3+Ppyt+ZBfCECvzE309OUxrbwLnlWb0ZEIMtyc6RWdueaDE+i+JQ8djqDDYYTPx+JHB3Jt9x/i108sWoWKiiNlWhoL7u9OryeK0BZkSpsMTzn5TjLSToxHM+CQJcwp6kHnh2ey5OFBdP4sgpZQrk0X+p6KvWYtrEmYsA+/fwwGg8Fg2JMwpSPDjiFE3aM9DAaDwWCoj228P27o/QvPdz6F1N0Uzq6kqKfD6W0W8Z+Nx8TTdhgMBoPBsKej5i6k41zXaLBBadVaheCQA2DSHMac+RIRLO6ceS5CCr48/TEuXHQHgXyFt54UYEmbNa9tPBTn6E78b+NGtAC7U0s6/2MCyy/IItcbYq1dypNbD3NTdlWPcxeQc+oS/t3xc/6VNxAAT8nOdxLL/r0p7ajZ4njp/M/J2MqJ1q9NOWGPoQECCat7F5zsNJg4ezcFtWsoXJ3Jvzr1AeC/rZs4GIPBYDDs/ezD/QUZb0UHwaal1bnM5FCEL4v7A9DrqaKqAwAO7Y939RZXFLMLqNXxcDvKl7H1LaH4sjzA2BU96cDcxgpv92NZCJyo+2KlMFLhCgsVikjC+XHqqVHElvIKEa/NSNy0vC8WHcDGUAYA/qhq7dC0xQzz5+OgCQiL4g1ppC236OZJIqRtIjhEtCIY3WeiY2Estir71xonGkcEmHPUc/E0zhJJSEf4sLQjmyIZ5DvR1lSvxxUXejxue2SCuDDuOCgkwgKkQA3tjThrK8e2WsXSB9zZWmlwXJfCkwbMYWZZRz7/ZRC5D013HQxjJAhehdwOx8vY8yKh3F3heIloSbIMQ8CP3aUVdvMI60KZdH9kqVsPjFTWyT4+bTSdPA5LbC9vbh0GSlX+FnxeLhg8iamZB4FwXSizrDK2Oqmug2HC8+qdLt9x1xkFLHgyg/fPGM2Vy24lUKBwtmNwnKGJqe39syOiw334PWYwGAwGQ2NgBIaGHaN64b8+C2lTIDMYDAZDInWlzdGaiHb4csThtJ3awBQyezg5IycxES8QaupQDAaDwWDYJUS0w4KjXuTp/t34uk+mKy585Qo63DcBfD5UQqdMuY7gFRYIagxaa/HMBEp/yuH7cS9z4ukXw8Vw6ks/80WfZoCbuurBzUezaFAEyN9tx3fxzMtpc+YCAASz3In1ubdUrw8LWcVBxftEPvO7v8G8cMIytXSuGfYAEt2BqnWSLhrZipuO/4av+2Tu3pgamcqyKmA0rgaDwWAw7DAR7fCHH68n98pp0cEKiytnSos/vf4GN718LR3ubVyBYdx5MOa6vBPbiRVx73z1CjrcO6HqAnvjYJi4yLDqZEdrHNw/Ff9OnUOhLCHwIvAKiVdYSFxXdktIvrrjGPzfz3D3JXxorXn3wcuYfc7jrHcEmxzBDyc/SqaUhKJueMnCBwKStRMXCsYIRQcnSyGIRg9UFtMCQrj1qSgS9/voJ84haavm/+5/FjwehCUr0yODe/0cBx2xQQp3fsJ5+vOrrzPIX8qta4cj/QItBFprhBBQEWT5MAXSIteajQj4QWk3Mind+z3mFqn09t0rCY6cQgoiyiIgHLI9JWA7DHp6Bq81H8/Hpb1YaLXAKShx61cx4WT0XP1t+dlYJ25CO8H4/p3NeUw7SLp1uGOH4RWSFp5i1kSaI6N1sYh2UChk7Opr131SRKtnaWIXuJAadh+xgWOmj9pgMBgMhkbDCAwNBoPBYDDsPuoSFwLO0pWcecjpsLFhKd32OEyDhcFgMBj2NWLv7ervuIR+sOOvuo6UuRuiThKbGHP4UVTcG+a+5ZMA6On1V9mkQiEUVUV1WrPuT8N4+/pHgcAuO5xdSl3iQK3cztpox5l9rkP/q27inWsf3X2xGbYfrYG6O+p7/HM+393fA9i0W8MyGAwGg8GwZ3Lk7Tfi7y05clY5Px+YFC/rikF9ufeDl+nr27MHkmil4+Wesdc8yA3Hn0voyI2xme7/va3dS1WtbwBRYaAgoiFS7VgShxBZCd8jWlOmNWucZCwUXuEQEA4pwuZfT79EWFsEZIQUEeZv519Jz/8u4ZTvb2bss08T1DZKS6QQhLTNBWdfiyyuYN2JLZl81xN4sLCje5JIImjXz1C7bosAfuHBL+D5oly+O7EvOslfeVyACIZoXbaYvNNyyfGUugJAACnAdlDFJRCJoLWOigsthN8fracoV6yH65Qo0ZDk3r8i6nSobRsdCbPxf7149ID3WRNpznsH93DFhUohhEA3xFNe1iLh1KqGONYrIEWG0MkBmnnLGPbl7fR6bCsEt6AdB+GpXNYvHA598g46vroUHRVNFn3Wmc1b0+l+8Yz4cp0en8NZP17Hn197A6UlaJBTFnDmkDO45cdvOTopiIMEpZFCgwahwBJ70f2+v1KjraLa973pmWUwGAwGw16AERgado7aCmemwGYwGAx7NNayDfR78AbabWqCVCf1vSOUg7123e6LpbEx7z+DwWAw7AV0/SREv5U31Dm/7S/5VY3MRM0UVx2/LqFf0Q0goN3UJdh5efF58+9pjyfPy3mf38Sic8Zw4FMj6fhrPkW9M2vuLNoptvTRIWivwyWP3w5Au5WLgBxOSJ3Psx9dQ2/fbyiSuDBrIld+dGmVTUTCHrpethAd2vVuwcseGYK/cwnlBUnkXjW16syGuBBqjZOXh7c0h04ezboPe6I1+H7MoOVT401ZoolY+5dhMKiISNhDt8sXoWq7lxJ/B1qTd24ftg5U5NxoBIYGg8Fg2H9p0valJkJXVHDcg3eiq2ml2v++irQVWbxdcSzqw2K63FWKvWIV1to8rnjiVgA6/l6CTiwrVhHBxER8CYMb6ipXqkopnNYJA3mru2zXtX718n1CGeeuB67BThYk5Sky2Fh7rHsDTu2O4wqNRGIhCAgZdzFEayKAoyvdAsNaohA4CCJaYqGIaA8R7SEoFEERIcsqJ03YpElBmvQRuq+Y0rdzabawHL/wuumPo5dBCcXWf4UI2V5K8sMMeuIWJtz8aBVHwjKlSZGCcq0oU5IWlqb/67fQeuBGnurxDi8+cShSuhGWrcgg586prujOspA2lY6IOnogjlMpLowLLiuvvUi4R2TCZ2FJdCydcnS9DpmFfFowgJ/fPJh2Ym5lqmV35crtx/afiBB1u2xGHd+xLGRCbChFqRPAKpOQX+i6LyagHcWFD95Bh9/ycfK2xrfRLrWIkV1/4t4PT4ruWuP9KYO2YzfTwiqjXPlof9xqFrceQM5tU1BRR0qlBSoUYsSDt1N4oA2W5vyH76RVcFLNmA17DNLvZ9mruXQeLRDjZ9XpWrh4zGCyp1hkvRpNd7+3PdMMBoPBYNhDMAJDg8FgMBj2M5y8PFo/nkc9yf0MBoPBYDDso8ifZ9D657rnbzOhlRAweQ6tJ7ufHUAe2JNg2zR830zhuSNf49ZXrqbrR+VccNAJpK1RyJIKINNN86U1ef0FKWv6ISbOBuDcoyfy/syBtH7c7bxxyyg55HpTmD3kDRRJABzsF8wb+laVcDY7ZYzwDN8tAkPVIsztvcfR2lvIaHru1La8WBzYaj0KwZz0jEaK0LAj+IduZfqg99x7yXsihKOp0GrreI8SThc071RA2bmHkPbVHFR5+W6MuBGox1XcYDAYDIaGsj+2L2nbptWT42tMtwHWrqNjUXc+v+l9Tmk+AlaADoVJWxt1qCsqd89VXGSlar6LE0VYtYmxqqefre9dHhPabMc2sp+fgBjUl0iGv9b5ewsx0Z2bsteN33G9AeNphd3PmohW8XtYQVxQGNGSMBKlJWEsfDgEEThaorQXJSTIEF4UAa0Iapsf+3xKn1MvIpTllu8TxYOOFkwZ8D4Apy4+CeemYv548ln8veMXHOhzkAKCWuKNiguLlJ8WVpCkzYKC8iT6eH3MHVJZF7q1wyAW2K7oTg3uQ3kbwWVLz0PYNlgWaI3WGu0oVwAIrmuhFq7mUUjAAa34y8Kzub379657X3Re5JCeaCHw/j6X8rMOYfOmChZM70SPNxa64sJa7gshBVrJmmLX+IXZdvpkCUihEMFoHuNY6LbtxpxwLC3HjEdFnRFj1xzgj6l5nDrkBa5ddQr/af85JxTfTNuxkCVtgsrLlR1+ZV52eyZZKciEGrAOh2n5zARK7x+Ck6po/cxktNqfnnB7J7mt86gItK0qeKgmMkxqWU4kLb3qfNgrn28Gg8FgMDQlted6MRgMBoPBYDAYDAaDwbB/U71Tsw4XlMWXZXL8g7/Ev2sPiFmLKT26kFfueZT1J7UDKl0xFox4mo1/CseXV7rmdmuZ1CS4nW8ChCDn0uk8/M7ZtSwk3JRf8ZTSssafiHaECb8fLWFq2MfWw4spODSf9ve5ndQyEEAGAvE0ZYYmQCnXFSjWkRhL2xYj2gHV+vHxNL/bz+ePPYZo22q3hxm/V7y+3b5vg8FgMBgM9aA1BarCLVMAds+O/Db6OX4b/Rwrz3PLDLFyoftlFxd6d0A8s+iGAJ3vXbTD6+8puOLCmoK2mIthTPznigqr4lDzuoSxCGov5dpPUHsp0z58KAJC4U24jvOGvsXsUWNqrG8l1K08QoHj4JxcxH1rTolPD2mLkIYIrqhRAT+NephfBr1IqQ7haEWBU06BU06F43XLhMnJbLwrTPbx63CO2YCuqKix75hTYdx1MPbfstDhCM1OXcpffj0nIVhJ/m1l8Nc8ZFKAt554hMwvU+hx3xKEzxffVg0Xw1i9J1Y3iv3FqCGoda+RVhodsSm3fZRrgdISHQ6TYVW4bqGOQofDSJ+3iusi0kJI4f55vKA05baPYhVkaiiVwqNKeSrvKFdrFvCSKT1EtEWhk0KRnYTweihRSYS0jRQameQObOv6pwnkjJzs3kOGPRoVDBI5agOeH6ZVTqwlu0CHc+fSanRNcbjBYDAYDIbtw7RaGwwGg8FgMBgMBoPBYGg41dwAcv86k1//1RIoAWDi1Y9w/fCTyT+ypMaqCoWiakdN3CkjcRd7al9OA/uAXfcOHXfRiHVuRr5shV1UxP39jwBdeX6snK48P+51LOCIt++k658nNHLghp2mWoeonjqXi/qciFOycreGIVNSeGDeOFpIm6Mm3EDnP8zerfs3GAwGg8FQN86S5Vza5yR06UJ3QmLZURAvGwopXJO3Ol3gGrEwXJeTYR30vGEu6y1r2wvuZVi1eLUrrXG0xqKqk7uFRkUvXkxsqLQkqHw4CCw04HE/C/AKidKaiHaquBbWxXvdvyBvcQgLN62xwkLioUx78Wsn6qBoEdSaiy4aiXfWMkKDchj7xgtceNpViOVrKT2mJ+8tfhQHOPWhuwi8vNo9BiErBYTR/9pRcXdMIQRaa0R8nhN1BgSvdFMi61CY1iM2o20bDXgBx4srSMzK4KmJH3LVNbcS+HU+WJa7LaXQCfW8SgfJWhwzE+uTCem9lRac8O2t5L4c4snJz3D95TfTY8YiVzQZc2OM70AiZC2OiGeVcZHntOjyRYSUj9+PeYLg0eAVfpJlmHLlY2MwHe2U8dLgg/jTmC7MO/IF1i4IcdOgs3Dy8qK7EA0xXTTsiezF4miDwWAwGPZkjIOhwWAwGAwGg8FgMBgMhoZR3dVQa1Qw6Lq4jWvPnx++ivMXncff2n2F820bLvnvKNqM3VD7tqKN/rU5GMawaksNtxvREO300vEJh/i3wrj2WLndEhasmU437k6T4FKT4avAshROcXGVTg+9MY9z/nYnZ/7tTrq9V7yLjqZ+7GMGUvZNV4Tfv+2F92OWvXUQjGvP0jf617iOVdhVjkRS0tZyaONJxe+P7Jp9GAwGg8Fg2DG0dssHcTfkWpapNfVxZXmz0cSFO1gWUcEgqqyscWLYw5FCYNVznpSWCcsqLKGw0FhCkSxCUaEhWAj8wsNBT97EwGnnb3O/fuGlvSeVbCsJLwIHHR+EFUHGRY2OBqs0zIZL+9LxvsUoNKK0Aqe4GCuoaeNJpY2VjLR15TXTqvJ+0hphSYQlXSGgFCClKwbU2hUXxsSIsRFe0bqMKi1DlZXHHQOlA4RCiIoQHT1JaI/rTihiLoWyavriekkoP5efdQibP+0J0kJpiYhIrJKQu8+IK4zUfbrBuPbIsdnkXzTQ3Y+s/bqpiiCqpBRdVu5+15KAkLSyfPiFl2TL3XbQdh0PnaJilC2j18QPUrDynqEsfewQ4yy/P5DotFndcdNgMBgMBkMNTOnIYDAYDAaDwWAwGAwGQ02iLhc1ptWC0yyZb3t9yMnjzmZtckf+e84pfNHzY8764hScTZthUCuU1lhCIGuxAezTZT0brh0KQKsPFzX6oTQm2VYK3/b6ghPTL3YnVBeYVUnbtY0OiuiyqqSEzDdc18Km8lqQjqI46Ce1ifa/x1GH289dA76lXPkYk3cEW64dSsu356JKarp17jLXjEiEY6ddhd9rU7IpFW/0d9P6sxXYGzY2bBvR2MInHkxxJ9M0aDAYDAbD7sJ1uK46TXg8bLn8YFp9tw575WriXnp1CRG3h/1ZLFOPyE1VczOsniK5LrzCRiLxCXcNKXQVF5d2P5SwpE0WDGzY9iLawUEjEUS0A3ji7oVunLDqtAyyhm3klY6/Eqnl8ld3h0dIdG3OmEKgY/eU1vE03olIdKXLIWB17ciG4W04eUZbmq0No7q1Z8NhGRw68480ywsmrJjgmpgQh5AKrRLOkK55pkvbWjx34Bv80zoUj3TQlkZbgoiWKK8rjLTT/Xzb8zMA+nYaSXPLqnRerAUREzxSOZhtiwpz/uwLObz1Mlr6igk6nnicKbMDDG5+Ho4StAptIuXAfNpnFGGG8hgMBoPBYDBUxbQiGgwGg8FgMBgMBoPBYNg2tQmuoumthK1YHClDZSTTbsx0Ns0/AF5yF5FpaUSS6+jcFBJQvNHtI/L+6naE3Tz5mvjsSLVOKEcrLCHZYJdSogV5ThIyOwsdjqAj4cY6Uqzs5gQsu+q0zAycQAM6dbWOp0eOOX7s6QmaZFoa1vy1tL9K4YRCTR3OnkPiPS8trOZZrAtHeOfLI+j6cQmjP3qY2767qHaB4S5CBYO0PnMBAOGbhjHtX2MAOGbFVXi3JTCslt686IZipg96L/rt9l0RrsFgMBgMhm0gkpL45p8Pc5JzB9kfFFSWK0SCIzY74Gy4namR9ymErHTyawDVUyQn4kQlhBFtYaHxigg+4RDUXgLCISAEEumKFqUA2fDrFNI2CjdVsxIKB0lQe4hoD46WhLVk9rVP4hUWzjZcAYXXh8zMcD9H0yCjtJsOOXofCCsqyEu8L4QEHYGQxeZQqnsMXg+EBAWDW/Hxnx7kxgNORoVCbLpqIH+78S1e7p3juvvFhH5VApGA424nZo4YS/tdrSwaI6IthCUpt32gQfvdc6s90bht1+ExpKOSP8uqKmjUCaJcpdHSzU5uNc/CI0spVIqvynqRceoKpnzTieNaLyRoewngpnJu+8gEeNiNyxECaO1u1jH5kQ0Gg8FgMBgSMSmSDQaDwWAwGAwGg8FgMOwYsVRuU+dyc/ejueGdj1l924Aqi6x7oz0//ucxAJyo1E5AvGNLCs3AH0Zyc/ejubn70egZ82rsJuZ6GEuZfNJDd3Fz96O59+DjeO7Xdyg5a0CNdXYUmZbGs9M/5Y3O46pM7/59OXNGjN5m597eyLo32vPhjC95etqnyOTkpg5nj8TK6cKHM75k4rUD6fL3yfF73l6+sqlDMxgMBoPBsKchhCt2qk3fp5UrtqomtPrtv6NZ/WrH3ROfAXCFfU616+DoqhfNQhHRFo52ZYQ+4USdDBUBofEKiTfRlnI7dKAR3P1H0ES0QmmJgySsLSLEXAxVg+ofxecM4PGp/4Nm6e79pzTatisHPGld6WyodTSlcaWjYY+bZlIyvAJdmpAeO3YsloXw+Wj96ixe7tMjPs09YdE0y1FHxJizoPB43L+ouFBYFsKyahmwBmkyDF4v+pRCev1pISiFg8CqcNBl5ciQjcQVciKIH1MNEs6TbNGcl6Z9wn/bfM8x39/KFwd3RPq8+C0bR0uCtqdyHSGrxCWERqIrBZkGg8FgMBgMBsAIDA0Gg8FgMBgMBoPBYNin8XTuSM4UP1bvXACsPj3ImeLH075d5ULSIunnVpSeP6TqyonOH/U4oFh9etB1vOT//nkpnd9dV2Ve27slB79Y6Y7W97mRtLlbRlNzOcwY2Z8eD5WjI+E6XQgtIePiQgDhgI6EcQqKuOSa28j4eXm952DL57lsuH1YvcskkiwEfV8YSdu7K/fplzZ+4QWov5NvD3WKqfW6R2l7t+Tka27kimtvQ1VUNEF0TUfLf3k46qqrOfem22g7TpAzxc+yR6K/g2rXMln6GPbcVDbceghAnffr0seGkDPFT4vxmchAYJfF3u5/qznqqqs56qqrKbm5mJwpfkJjO9e9QrUO9JZ3e+LrGwwGg8Fg2AVUF5vVU4b0Cy+Wpaosqx0HrXSle+EeWs5sFIRo3OOrJhpLxNGaoHbdyr3b2GfMwdASCksopFAEhE26DOIFvFhIhFtPUBoSBIqD/3o9h84+G0fXFAk6WhFOKJtF0EjhihlVfJ/ufIXGjtoB9v9gGVuvGkrSpKUcec01rLbdsrsWkCkBKyoc1AoiEbRtx534qgjzlHKFgVphf9+RLZcNRAWjTuYRG5Qm68cVXHHtbWjbBsdxt2VHXPdCqJwWEywmumxGHQadQw+g80QfVqsWrihRSGRyMi3GZxIZPoi2n67m1mtGosrK0eEwm8/vQ5unV1OofMiQG7uOCv0iOKCiIsbovrTjVP5F49PhMIQjWEK4Z1JqhNeD8Pnwe2ySrRBCaHc7MaFkzPFTSJSS2Lr2VNMGg8FgMBgM+zMmRbLBYDAYDAaDwWAw7OdsvGUYraaUIcbP2qX7KT3vEBy/IOPNibt0P4YEhIBgiK8X9qZnRb47qTzI1wt70yO0tnIxKfi/Tp9wYfYdpMbWq4taOlqcFB9Pth3PaVPbYq9YBT1aVi4+Yx7NeriCLaU1WQucKi6F4veZdaYkk7XavkDw6BKKioeQ+e5U8vp56bgiHTZtrjPkosVZZG/aPufBUJcgpd1SSZlRdXpM6Ljk4lQCx1eKFjs9Mw+nsKjKsrujU6rwkqGUthdYYWjz6IQ6U8E5aX6eajeJk5POrjFPz5iHf0YtK+0IQrB+1FDajSuq1Y1yTyN27DItjQee/JZsK4U7jw7zzR3DaPvopLjIVhSV0G3c5Xxx+NO80e6IereZ0a2Ap9pNYrNTxghr+C6L3V6zFv8a93d88D2Ch1rP4Jtmfh4TvaMHV//9p6fNw78vCxUMBoPBYGhKEgRlfSdeRPZcOzq5geXDxkhxXF9ZYD8rA8SEggpFBIdI9Po0pIbgaElAuul5fTh4hYqmSxZYQrj1g+j2WkwVdMu63F22oyC0qCVHcC6/H/hxwvYUCo0CrPg07ToYaum699VhhXhfq9l8d2ZPNuueZL82hTLtdvOmryjnkO9vppdT6C4YdSx03QMTNlBdZAisWNSGdltUlfUA7E2b8Y3dAkkB8i7oh69Uk/b+pLjzodauSE9YFihF8Lh+5B3kDsjq9MISdEkJVrnN94t60tNeHxVgKlCK8Yu7kVMcxl63Ad+Gje7RWhbJWxx+Xdadv7f5OiFmV2QZ0QqhqHJfl5x/MEVdE7x0orOcJI0EAsJCWBo8HrBtAlYEC02n9AJm/+UQury0jOKhnanIkjR/ubKdwlayahpmw/5DHam8DQaDwWAwGIGhwWAw4OnaGS0br0FFKG3SZBkMhr0X04hi2M+IlwNKynDqESft65x5xc/8Tx5J6/E7tx2reRYiORl7zdpa5288PUyL5iV4xnfGXr4ST5vWaKXi597Tvh064Kt/J5u34hQXV+6zVUtISzFlMKhxPgGszEwI+Ol+yQzs6DR7xSq6X7KK6t0lP5T3wFORkDYLKp1Eans3aI2nUwd0aRmOFFUcBqvE1boVFVkSB83PFW3wBKPbkhZW146oNevRoVC1bbsdb5aQtboFLjj0DS5udxRbZ3bn7ivf5P+2XETLejqAcp/ZBFsLaxxzffzvyDGcqW8k5yOBp0snMjwLq8x/6/SnKdOV9+uj754KMYGhVmgHRLTLMNaRvLIwi9LCxk1BHD63gNF9P2B+sD1fPJYNuvajFCGHcRUWwt72WfC0aY1OSYp/r36NPJ06oItLcQoKau7Hsrjuis95Tp1G+5Iue89vUyl+qGhLc6uUEzNm0/HyfL58vAXajnawbtxE90s28eik4/GU1p8QpKgomXEVFnl2+91WrppX1IZxGbP5pSSatq6h+zXlPoPBYDAYGp+oOFBGHMaWe2nzmA/vnEWVZdHE8m1CGaTWcmLisoll9J19h+/JZYCYCG5HY0w8ZwmCOjfdcDQdMcTTI0cAp5ZdOQiUllhCERAxgaEiIJyoCDCatjeBzDcmkPmG+/ngmQ4ffXI49lutcA5QVepLCsWvFZ3o719LlnSIAEHtxYkOsJKoGiJDieCXINzf6yOeTDuO8G+dsfjVnTlxNj2mehDt27rCQaXjDoWVMsbEc1S57ZyRk91TFRUKxk+dZbkOf1rT9fLFLNzSkrT3id9/Mj0dkZqMs34jWmtWnySZcfYjTAhm8uS7J6FLShDzl5NzeQjl8biOgVqjQiFyr57jxmZZbipi20Z2bEfqogI6vZRO+6P8lLdPJn1NFo4lkQhXDKpxY4z250Quzue3fq8yK5yKFIocTylp0kNEK34NtiAgIuiyaFe40gSsCFIoBmas4o0bvuP0zy9iw7lh/thnKjN+7IazZj0q6kKpd5HAsMH9UVsLa61vGXaC6s+U/UxobTAYdh6ZloZolQ3UbCsDt22cZhkAOMtXgzJidcO+hREYGgx7E0b00ejItDRe+/ktsq2URtvmZqeMET2OR5WXN9o2DQaDYbdh3jOG/YjEcsCBky+gzZn7r8BwYj8vrdlJdSGw6O+53DT8G77uk1nr/O6XzKD4wiG88vMjjOhxPP73HJbmZ8fPfe/P1vNQ66o2ajFxWUxodsCYkXS4tzLWDc9nMXXQ22xVFft9Gaz6+QRYcVMvHr70ZUZ371nvutq2+ax3c7KYUG2Grv1zlNO+ns7DX59Gtw9qnvdYh92G5zP5dcCjTA2l8eJBBxAoczuwPK1a8PHP73PS5dfjHTu16sqi0imwLuHim51/YvPXZYzoOZyOXy/l4/9+V+8xdn/nOrqNarh75nV/upWc9ybiadOaj3/9AL/wxkWPAP/+w2XoKXMS1lhVtYNCKzdXWaKjyemLaJG4k0Z477Y8YyEPckD0W90Nl3rGPB7sdoAb5zbwv+fwcfdP3C1qxfEjrsb7/bT4/Ph1r+V8xu4l69N8Ph/10V7z21RlZbyU26XaVLvGcqsPKaPTNp6X3S+ZkXBNds9xO0evT9inKc8ZDAaDwdDkaI2eOpdHcvpy55I3ufXVq+lwX6U7cozEMkgO02psowqJZc1oOtfa970NX749qe1HVC0vIxPEcLHDrXbOdgSvcPBg4WCjgKDWRLQrIgxqi0jUOVBpSVB7kUJhofAKmwAaHwq/cAgIhVeAT4JXWFHRohuo0KBj10hIpgywCL0aZPw1L7qOhQkDgfKcEG8dMZC33j2E57u9T4my2OqkYqHItMppaZXGpYsSgVe4zoD3n3A2zrJVhE7qyE8/vgAkoYVAeH3IjDSwXRFfLH1x/Pxhuc6DUsZFhNXd1rXSrnN3TMCnJEIItOMQdjxoLVzRIa74bsXIHB656GWe7NPPTb8s4c3iXD4/sBWw1hUPOk48pbIM+EGpeFzacdyTJgXatmn35iYebPsdUgiWRuD7J5+i52c30ukLN05Ha6QDKhxB+rzx+EcsP5vwsVuQmRmo9wM83/095oeb8+zAQWjbpqe1wE0dDWT5yvEKhw3hTJZGQghHozXc3WIWFb9M4Q8Hnxk9N7umL06mpfHKz2/Rchv9UY5W9HjnBrrdYbI/NIiGCAXrup7Vn6Pben4aDIb9i2rajA2XHcDMv4wB4JjLrqrRnrnoH7ksO/9ZHK047ahzcZYs363hGgy7mvqHXBsMBoNhxzAjnwwGg8Fg2ONRpaVcfPZ1nHD2pbS/I9jU4TQqK949kMXPDN7t++356Fq+vXBovcs0+3oRl559HaqiguC1mds895aQVYRmz145hlPnFcT/Puz/Yp0CtP2N2s5n1xeW8+R55+yyfX56wRH0eLzSsfL8z39j8w1u2mCrtjJxtFGu8NKhnPHDbPzRjrJt0fWjaznonhvi33u8fD0nnH2pey+VlxO8NpPc167fiSOpG3tTHqefdQUP5XfDEpJfgnDC2Zci5iypd72448dehExO5vDZQVa8m8MJZ1/KUVdejUJz3ZgPWX5/5W9bino626RF32mS9/q/VPnbNPUjg8FgMBgM+yuxTmmN62Kzs6IlISv/at1fVIxX398OECsnhk4+eCeCr4VEcWEMraq5EMqdLk8qLVAJgzCcqLgwJg4EsNBIoeLiwviyCJKlTYpUBAT4hEgQ/8kqddZ4vBC93vUEFQrhKElEQ4nykW+nEtYWEuWmYRauqM7GIaQjOFpF61uHkDx+KcPPGcHwc0bQ5qOlCEu6Qj7HQTuKTRf0ocdUryvYFK5QECHc/1DpcpiAkNH53mgdTSu04y4XviGTDn+q6tIUrxIoN61yz8c38/klR1SmAVca7VQKClGKklP6MXRaOTIpEN8HSoO0WHttJ86/8EZOGnUbDoKIdhCOQEY0W1WFu7gE6XPj0xGbFrc72NenIzwenPxCHC1JE5IWVgl4PehwGF1RgaoIoh0Hv7SxUISUhyLlj/4m3BTMEukKg+u5ZI1BQ1ovTBvHrqXF+Ew23+i2W9T6vDEYDIadxDzHDfsqxsHQYDAYDAaDwWAw7J9oHXcf29eSFfRtu4Glgezdvl97zVpYU/8yTkEBYmYZy+8fQve3ClALKkVaX783lCUnt+R/Od/Wuf4RATgikOjAlrqTUe87OAtqCt7sDRthw8Zdtk81awEKt3Gh2/vX8f1ZD/Ngs6rLyM+zOCRyJaP7vxufFsoQnJ26hK4fjKLnis3b/A3m9lnLkkgHur1/HQDdvq6AibPjnT/OgiUkr29R9wZ2BuXA5Dm89sYJPNtG4S0WdJo4gfp8DYTlduShNPTLZfl5aXT725TKzrU9FcviumbTeO/4ASzrlo6TpJAI/jHjDLJna6zs5iz+Sw4DA6Pr3ISQgqua/0qut/Fc4g0Gg8FgMBj2Zm565yraTQk3dRg7hY7YvDr2KHLWFdVbDt4pGqEzXnh9LLtnIN3fLkTNXlhjfixNciIWGoSKp8YNiAhKu7E4CBwkXnQVYVhieuREl/PqtBzro3fgMuYe9kp8/xJJipAs+ncvbmr9DUEtKVTJlDgBkmUouoS7P4Vy9XdIpNBckraRB5uBKi1DTl0A0XTPwuerdFmybcJpgiua/8afGOa6EcbcC6UAJ8G9UEiERaUoUEZdC2MHEBVeqUXLKg9Kx9Ivg6MT0j6vWR9vD9BKI6SKX9PY/iLJkmuaTWaSPMXdtxQISyJTkmDDFnwbBTLUMnru3XW0gLDW+IRAW7gCyIibstpZshzRvzdL7uuPFRbc1Porni4YwKvfHk1OeC7rbh1M2lpF+odTwePBQuEgsZVFGCsuuP26PI3bPr+UHuXzEcBZbWbwyP2n0+3ve0Edbn9nB8THU8b1ot2SvfuZbDAYmoYWM8rjbZM9amnPbPuLpqu4DqGhR/7i3R+gwbCLMQJDg8FgMBgMBoPBYNjHmLehDeGCAG2aOpBasDIziPTtwjWnjOXLX47BP7tyXsfPt7AgrStvtW7ORWlbebekGTm+TQz0+wB4q6Q5ZcpPa08hp6fUknbUuKQ1GfaatXS/dS33DDkRf6E7LdZx12rcBtamtsXqr3EOysUzx00Pst6xyLltCo6qXV6YmB77m55fcmvGIBYMrL1zx8rtRkXrRvaaEFS5p9o9kJAWtx53xrijjBTgOBT2SOX3Cx9mxH+G7/mdU47DS4X9GTvwedocEhPvStq95MM7diL06M7SC54FfE0ZpcFgMBgMBsNeRed/TNh9OxOuLK2x07vqSJhud0xsfHGhVjWFhbWJ9Rp4PCLgZ8KFD3N03p10KOuMs2wlUNWB2xICC43SGkRUYKglltBuOmIggsZBgJY4gFVHVdMVDFq1xygEGW9NJGnrwcjDKh0UFYpk6WXOuaMpUmE2OV7KlJ9Sx0+Wp9IB3QIiWiGFiK4liPvfSTddsXYcd1BTtH6io+I/f4Hm1fxDiVsoxp0LE2KMubZZFqKaQ3miyNAVCyacAKXcaQpULB7lpjoWQkAsrsR9RJfxlzi8VDgIlHYHZUXFjyIpCZIC7kBQr4XSroMhArQlsKJHriUIjwcdiSAO6om2JOuOSmPW+Y+Sr2wmBtvx6O/D6XXfQuwDunLuJT/x6tRhpH/ohuCVblwh5SGirfj5+KbwQLrfPhHldes5l6av4IwLH+KKpqzDmeaN7UPrBrcJxZ/JtdbrTYpkg8FQN/K3mXT/zf1cW2tm8seTyPnEfbZUH9BgMOwLGG9Og2FvI2plX+X7tuZXn2YwGAwGg8Fg2Kfp/IfZ5F43uanDqJWCk3vx1jtP88MhrfF/OaXKvD5vLiHcLsxbpx8NwKsXncL5n94cn//WqUfyUa+WPHbTRXHxmWHPYu2QUlo+7QrxVLT7ccPjfiaMepxWViljP3iVwpN6IzRxh5DGIPBiMYsuf6bRttdgElPUCRF34dhbUeXl/HBACjeuPKupQzEYDAaDwWAw7C4S+xDq+tsdYUhRpSxd/XtDUcCc28ew9uFAlbTQEoFXuAI+L+CNigstNAHh4BUKb7QOE5suhSJFhHG0u92dyX4gqynGIjjx7cWcEqXQyAQZZ1ArIgl1X4lwNYNRAZ8QAuH1ILxRP5louuLs16e5g7NUVIDoOK4wsJY02dW3oW3bTW0cFd8J6dZztNJox4kL7uLVOSncPxKcEWMxVxOLJn02jV8PDKDKowMGHQdVUYEqKkYXFKG3FuApKKdQJaEALTTaErS0kgkIC225QkatNQ9+9BKfffIyv938CJscm6AWPPavC+h1xyJ0u1Z8/sGL3Jg1BbQrShReD6lWEK9wUFoQ1F43bgEyJqdMuN8s06+2d7Cjqd/NtTUYDAaDYbsxDoYGw96GrjbirHrBeVvfDQaDwWAwGAyGJiTz0zmMmH81I6Z/TabldirMCbbn+wMzmX9WW3qVr0YVFHDyUefAisX0WJnGyWPOAYi7TwR+msNpR51btUFYa1TZ8t19OI2GGteBUZ3GVpl225tX0vHf4+tYY89h5X+HcvCxC8gbVlhleqzzLoYl6q6bbBg1jJMuHs/Mg6DtxDSebfMwlkitIiS9u9WvTFiaCcA/77mCZq/uRieYGA3thIi6d2hA6Oio5X2gbqaWrXR/m0Du1sX7XHp5g8FgMBgMhj0arUHXUgKTVs1pjU0s/W6jb7dSgFbdKa+Gc14DCWtNRDt8NeAF3p97IN8e2AwSRHt+IYmgsAAvGic61/3sCgsdBArhuhhug3EVfh4/+XRYtaTe5VRl4mEcrYlE0xs7CJSWWCj3Tyh8QhHUEBCV6Zg9WJTqEMoLslkz0ApdWlZtJ040fXFM4BcV/1mWmyIZQKmqToY1AlVufYYEJ0IchBRoJRPSKbvppOPbitWBEu9RKRDCg3bceIQU6Kjj46YPu3J378+YXdGR3w72ocvLXbGjEAREhEIFVplFyvhFnHbcHwDoam+i8Lhc/n3vS6QJmyLl4EWQbVkord0UylIgNuZx1tF/ZMD7i0HGnB2rDlIMKjettBDgiTob4lTGvq/U8mdSWwABAABJREFU4Qx1sB2OhwaDwbBdmHeHYR/GCAwNhr0V83IyGAwGg8FgMOyFqLIyrOVrOS55LdlWCgCD/LN55pWb6PlAKc6qNQA4i5e5/7dshS1bq24jGIQle6+YsDYOzFzHickhFoTLOfvlOxAa2kwKN3VYDaLtbxHmbepNS8az5IkhkBGhZcuCGqlALOquw7SaXME3ahitGc+Mtw7glnNT+CznG6yEDscMmcSJySEA/pIsYPABLL4qQI8bZ+2aA6tOrPOhtpRtVZaTbmqwKI1o1LjLEX4/i546kCfavMWfNvXnk2+GMu/Spyi9pQjVcSjNX5yAs3gZyx4aSovpLUh/Z2Kt21Emn5fBYDAYDAaDoSlQrmhPoWhjJXNc6ny+5dD4bInEiooHXWs7VzhpEXUnFESFR+5smVCHkUBtUs6g9rr116ijeUOwhMDSour2hcYSCq9w8FYrTv8STOP2V69EKGg120akpUAojCoqYf2NA4ikQpenF1U6FSYICIWIpiEWwnUg1NoVJ8bSFFdHykqHRCmqbkuKuOiw/Y8V3FV8JW2cSbVvJ7bvGFHRowz4WTi6N4/1fpefi3sy9p0htGNa9MRYqICHVlYF+cqHFQZVVAxFxSAkMiuT1DVp3PzW1bx16eNkSpsXiw7glXdOAAGd5hSgwxEIhlD5hXz9zGG036zcOCIRIsoTv4gR7X5o/YmPX7MOJpsJ7vmJhmsGU+2nCFm7kNtgMBgMBoMRGBoMBoPBYDAYDAaDYfeiqwnPsq0Ulh//Mse8dRXe+U0UVBPz5bI+OEjmFLSl438n7FUDipKW5wNZhE8YxLunP8lgvxeAUuV2IBWuaMbz3XI5LXVufB0t3A60GPLXGbT+1f3c6snxrEgaxgOX5PCn5ku4O683x6XNpYOnnCfyjnD3uVVR3D2FSSc9wgjv8QBMC4V5O39InXEmr29YZx+Ow1/WD8dfuP91KgifjyknPs6HJbl8MGcAbWZouBQu7DyFZzucTPPocned+ikPytNJf6fubb1enE1Q+zgztX4nF4PBYDAYDAZD4yI8XsJHHoC2BIGNZahZC+pZeM8aGKKrOetV/x7D6tMDOz2AmOAONpL9e6M9EhaswImuotCo6Gifz5f2ZUjqMk5PKUBGUxGjo1qz2CmIZ48CFRMZEk2XXMdpktRex3CFeNs+XovKgViWUEgUPhyCWnDP+pOwhOaEZnNZFc6mwz3jQQg87duhMlKR0fpWaUeFTo+Abbspkh1VNUWxTIixRgYsBdWPQcTc2HVUMJlwIAnf5c8zaPMzroOmVu6RaO1eM63jYstEQSOA9HmZdMITvFfcm//9PJjuj05yHRYBYVloBfdvOp6Tms0h3MImfFQ/tEeiLUhaV4Zn2Qa6/F8xZZf48Av4Ma8HHe6bgExKQvi8kJwEloXH66X1V2vAktCmFbq4hFKnnDQriBTaTZHsKFL/N42UaNrn2PlZHNE8uvFkUHvHoL/9mm09w4Ss/D0YDAaDwWDYKYzA0GDYV6krhbLBYDAYDAaDoWFUS79rMOxKOp0/hwWAh9UIj1tV146zV9x7i25oycLznyaiHZKlDwBHq3hnW85tU3j//BM544G5hHQEod20wUFtRR0xqHT7UG5nVdsHx/PDT0O49ZP5TDqnB6/9fSgn9pnHsoODAKQxkeILhhDUGuHxIIXmilkjaHNu3WK2Nk7DUiqr8nJWHwI+MbXSgSSxQ0Krms4k0WmxFG4xEa1WGqH2LveLoNa8/p/TSGsl+f3xMYDFZ7cdR6exDU/XLdE8Nvp8AoWKMx94ZNcFazAYDAaDwWCogpACmZrCR688STMrmQMnX0CbM7e5kvs/scybUN6Nu9jtQa5eC+9MjdYP3O/r/61pk14Ap7npjh2tQSiC2q1bdTp/Dn//58Wcee1T8W3IaJ3fW5nhFy9uedgvXIFiTKxYm4zQiq5vieh50xpw6wV1CSNjdaSIdnCi6ZljaZglmoCMkCwjzAy1ZdPQYgD+9ugfuG74dyAthNeDDvjQfgtKQfi85Nw2xd19SnI0jmh9RVA15bRTVSgopIqGrWv3H5cCqqeprnKPVFurnrTL8bpttN631RG8968TyfnfVERSAFURBOWgvR7k2s2sOtTh81/78+HxT9PqxDApQuIXHgY+fyudH14JUpIuQoDAUe45lVnNcFplEskIEM70UNHMFSUqj0BoTfP5QZaWuTEnWWHKlB8cx71WCW71ltD8ecXZ6OM2xuunhj2c2p5hMYy40GAw7EkY/YZhL8cIDA2GfQ3zYjIYDAaDwWAwGPZarJyuPPLdmygEZ791O53/1jBRXFPzfFFnvjy6F3eN/56jkqo24K94uw+fDnmU9U4yI488k7R100m3JP/+4BS0vRmAdX8aylWXfMXXfTIrV5w6n7MGnIKzeSU9btrCSk8SEIzPzvhwOtdPvIi/zfyEg3w2qotizdK6Ow/OeHsUXf66A+dTJ3YYUrMjLT7fde0A3M41XekcUnvCsD0PVVLCtYPPIeWtdXyU8w6Q2tQhGQwGg8FgMBgMNeh58xJW+lKI1Q/aXboOpIint7WE4LAZF9Hi8gJQefH1LCGjzoUiYZrAwXU0BPCJqEAR4i6GsVqGRe1iQ6DSya8OQZOsts+I0kS0KzB0olv1CYeAcEiRoej+K9cRUiD9fvB50ZYE23FTH8fSE0fTIycuX1vK5rg7YQyl0TjudF0tbXKtgi1ds04UXTYurKzHVU6VlDDqkDNJK5qFUhpCoQTHRYHwWFjZWWR6NxPRFpscH508EbzC4turHiTvch9B7SFNRghpSYo3RKhje0oPbENpG4twhsBOBuVxXSi1dAd9yXCAQ1I3k+H5f/bOO86N4vz/75nVSjqd7869926qMc02kEAIvQYICb2EnlBCC6n8vikkJEBCc4AEQg2BUEKHEGqCbWxsDLjbuPdyZ1+VtDszvz92pZPudMXG9p3tefull0+rLc/Oqszs85nPU0ulX0StjiKyAkoHoxTCjRBxNM8Nf56PFxRzx94Ho2trmzyX7YpN8bWO5sSFW7qPr7ofi8ViaYmmfkMtlp0EKzC0bBMWPrEfZWW1bF7UiaHXTQZg+c/GEz9oIykvQr+zF2JSqTaOchdGiPpklxUW7hD2vO8qopugaohm4VkPtHU4FoulIbnfixaLxbK12O8RS1uwdgNn//5GAAZ8WtPoZTl6D9b+Mt9Foc3HXAKOKp7Lw38Zzwi3klxRmhCCDokUAyIR1qclet0GjJem7piDWH1ukkFnrQdj6Pv2Zv6+5jg6EQgAN146Dn1SOQC9LhP4a9Y2Oqzx0qjlK7nm9u+jm6pZlsPAGY3bs+lzKpyMq5/QlVNyLKfkktEmcAIJt13+s3HUDUpz/O9vRv+9gh5/iCE+mtH6ONoAf81akvcdzKGnXc0HX7+Xk26/md5fLMLPWeeR35zM4EV1Bbc3SnHer29g04EeuIbjfn0jXeum7JjgtwJ778JisVgsFstOj1bZfqpRoKtr+Oavb0A7gs6Lvea3zbju5T0nz63QmHaQCJeh+3nohLfikr2oHqAZdu1GAFRVFQiJjLqcedtNaEdQtthDrZ+f3cWA1zazh76KL668Dx2qt3R47tIYtNAoDK4JzPswJjtRqNaEwsJwOSJwIZQY9nA3sP7lEQhhEC91oft/17P2Domvgq0HdVqGxjDmT1fz9TOncXfvYMxTZQTKCLSpH3coI/GMxDP16dvBLyR5bO0xOM9V0OvXDhiDMGCSyUBUCKjxe7L2h0liEYU2UDe5K/1++zEyHsmWH85vTwFaZgWRQkQCsaKudzVEm6xgME+s2IQwIisubOD6JxyHRU/vTe/HY8TenA6Og95YHlxLIYP4whLFJpVCbzaIqEv3aBVx4VNpYiQE7Pvnq4lWAgaEAuOAnwC3BroMSlLT3cE44FYbYpsgUmtw6wyrxwvOOeq/fLapL5+U92fPstUcUTaHfu5G3ouMrm+fsC0TD3Rk7+Ov4YMT7yp4npZ2xlcRBGbv+2l7D9Bisew47PeNZSfGCgwt24Q/j3uSoxMe1/U5gDnhsvhBG5l+wDOsUzVcEDna3qS37BIs86u5atG3KVpvcGsMdTXt4OaKxWJpEllczKZT9gag4+xK9IzZbRyRxWKxWCzNoyor6T6h6VK0yZ7FTN3/Qc5afBRX9XqPw4t0uxhzDXeLmX7AM5y75ETO6PYJpxZX5zkuaDROTuJSRwU9O1Wx+eyD6fzvL6HOI7a5/vWKPQ1fHvAMAMeXnAZrCh/X+D5dH9zOLo9ZoWEziYtCYkTALzaIqgg9H/uCJ370Jif3vYGSbR/hNifx4sf0FgdzdvfziG3SGD8/SVj25OSmNzaGLn+dhD55BL1LK/EuXr2do/1q5N27cI6yRiEWi8VisVh2TnKS1cZLb1kfuaVEdztIhGcEbsIJJrSoOJhiP3eFUICo6fbItFCIqPMqPplpsxhYPRTnqvoJQhInEBsKDSYoYOwJFYxlRDACkBCWSg5cDqUInAwVgTizhxNl6v5P4wjJkAVX0Hl2MdP2f4xancYRgnme4qR5p9PvkblMPGwghALDWh3BFTpbIhkyboYCFYoO604+kMTKWnr/r5ZXrnmcY7pfRXRTOogDAnGcUlT3ifGf/e+ju1MMwD7qrLBdGuQvwud5LoZNjGUKCrdkU86FDVzkGhy3S1kNKlZUf2whQQTugUEs4eZKYZIppNbEpIdCsEklgDQDn1+PWbYK4UaC650oQncpxeucINnFBQFuNbi1mkitZv1+LpE6iA/ezHWdp7KgdAbnTr6EqKPYu3gFP/7ydOJ1KYQQyB7dKD+kD51enU389Wn06nAg6oTCzbLDsKmnHUc7+I6zWCwWi2VnwAoMLduE9aqUdWoZ5ekEUAkELhrrVA3rlbSds+2Nbd8dxktVe+IdvprbFv6HoxMtzP60WCxtR/i9KPr2YtIdgcvo0KevYMiMNozJYrFYLIhYDBGNIosTrFcCTQ0J4dBBxts6tJ2KlPGpPt3h+xPOYuLBf23rcIDAvaNCJyk/vxPX/+JMTv3mI2g0xhiMCUqOqZxiYsUvT0N+3o+XPrifY9ZfzuqxUe44/xHueXEkAMITVKhaOjmJgscTsRhCCHQyWfD1HUaOk2Geq4cUoGDI//s0eO66bRPfV6DhNYq+W4EoCpKCurq6xXFoyotQ40WJ7ohgtxW2TI3FYrFYLBbLjsXopgVuzdD3tgKTsoQMnfnCMr8mdDxsgmqTolYrMmuUSIe4iIBx0EIjMWhjUBicLZmGYkAZjSMEEslD6w/FfGMlSgiE6IojJL5RKAQyLI+sQpfIjJuhK3wQgrN/+xq/f+9EBr6k2KzT+EUSmY4gNLjFCYTrYkIThBptWEfg2u559elfkXG3bBhnOGbJklteOSMCROF0KEanUqALtEGm1GPmOgqJcET9tQCM51N6whJkfDWirBQciamqRsicFHVYXlpEo8iiOLq6hpR22aiKWeV1ojy2DuM6iKI4Ih5HdyxBF7kku8bxShxUFIrXKGLlKSIbqkFrHp/wDN2cNADrtWCV3wmzPMFc0Z0Haw+j7OTl6HgMpKR6n168/4d7Oe2j05BK4ccErh0aWCwWi8ViseSx5b12i6UAT+43ggtGHs36I9LZZf3OXsgFI4/mxj2ORNfWtmF0FovFYrFYLBaLpT2w4umhPDH7TX4/+V/ccOApXDDyaMbfdX1bh7XT0u/shRz10+tJG9Pmk44e3tyfC/Y4FvXlkuwyz2iEEAgRxJbUblbAtfayg3jqvadwRYFyXcDQn0zjzHO/3+TxVjw9lPSr3bfdCTQkJ8kppMgXD27BPlY8PZjkKz0QQuDsZBYUDa/Rsh8dxBOz3+SJ2W8ixuzR4vb9zl5I7MQmrCctFovFYrFYLJYtIbcv3ly/vKEIruG6xqDCSUJjnvshF486hkvDx/4fXEVMuDhC4IpAbBgTEeLCyXMZBFBNjL+EAdHgNSnqJyUF22q8sAS1Dksk65x0rYMhKsLSx0hmnnIvx9z5ARfudzIAdd1d0h0j6JJidJeOiOIERsBsrysXHvAtLhh5NAPPWxCMYQjKHecJLWWDtsy4EkoZPETgBGlUIC780xdvUHXyaIzvZcsIN3UN6p0mZSBSzEwCl4IVTw3k0c9f5XefvIYsLUU4Tlb8mIl12Y37c87HMzHaoIyko1NLt0gVS/0E973yVxZdPQKTiJPq3QGvJAoSnLQmXq5ILK/CXbwW1qxHL1/Fz/Y8nAc2jqd/pAMDIlGGuevxO/p0Lq2lR6IaWVbCuZ/MZvk1oxG+yV7TFU8O4O3f3EVUCDsByWKxWCwWiyUH62Bo2SYUEhCaVMqWRd4e5Fj6W3Yse398NpG3OzLowwUcGNsMJLh/Uz+e/NUJPHP7HfSPdGjrEC0WSwPMitWMv/4KMDBszqbmChtaLBaLZQcQjfj8YcMhfHDHWDo/u4wiJ8F5nd9q67B2WkwqhZM2FJbo7VgUEl1VBcCw+zxGfXkVn192L2XPp7m+53NoU58kAzAOTboTQlDWTaaadhuJRnyKIh47zFM8dO8A6l06miKnbFg04nPH0H/y85dO5bhbrqfL/5bhN71lu2HB/Qdz5dffyrtGxoGuYcm1wQ98yaTHxtP9vqbLedv7ARaLxWKxWCyWbUZrnQ5DB8M8hARTYGyhQdeFjuhaMeRun1Hzr2LO5RNQRqMxwe4QOA168U7OeMBpODbIOb7MCBNl/qgt1xVRIYLJWHmvCzAGbSSHTDufxFMdKXMWIZVB1IFbE5yPMIY5Px3AiWOnscbriKmuCfrhoZtgEE94R9SYPJfCvPZycsR0eWJEQTcpMJkJV2FpY4xuOkclA2FhMNksKMVstKHnnVFO7XMDwkCZNwe0xqggnqCktWHgC+t5cObpFOkpVKsYVTpOjY6yXpUyIFIbHtugXUG02gMRQfgC6RlE0sP4KiumNJ7PxJ8czPiy8dR1ljx5850goCSWokdRJctVnBJZh8k0Exq0QQpTPznM5uHaPy2Nzy0Wi2Vbsy20GkLY3xjLTokVGFosOyv2h2ebYNJpDpt8BVE3uEFgAAE8tu+jjI7F8tYtK0qyrp/hhaFvc+jn53LRgImUyDpqu0t2voJnFsvuga6poeQfk4O/2zgWi8XSTrF9qh1KanIX/vX5OAY9N51Fpwzngf2fYp0qYZ8pZwHQZ0015oC9WDOuNLtN9+m1iI9mtFHEhak8ayx1XfOTWzsyTkcIll04lP6vdgJRIKG1o9HQz93I+itPpOdTs1BTvqBn5wORlwkWburKtWu/y+AuG/lRv9ezm3Rc5DF2xhlMHv1c4V0eOppVhyXYZ8pZmFM64aR6klivs7/rbYU4YC827t2Bzn9rOo6sU0fojLJ/LMqjQ//JBf86Gr+mZkeF+pUYvfcibur8Zfb50mMjFA+uyD6f0GcyQweNZjt6SFosFovFYrFYLAgpMA0Fg2bb3eXL7l8ICMcxXB68JhHoVpRGVsYQCYdkPfZcx+JTewTiRCS3rN2f1z7cn2Hyk+z6Oiy97AqNZyTKSFToYBiXHhKDZ4L0rRSaysUd6fnGLESHYmIVHsI3eCUuS07vjFBw8rgpXN/tfaaneoYOhIErYcYdMM+xMMQo1ViIGd4byYxlnKEDWXlCD772SR96rkwGLoMNyyq3hAwllkYjP/qMEkA4DiYWyxszYQyVZ40lsc6jw3tzWf2D8fSKvkaNjuGZCDKQ/2X3qWIS40gwID2Dk/QRqTTG98EYZFGclRfsiTDQeXaKjp9upuQWhYgpiiIertBZIaURgABXOCw5tz8H9fyCZCFBqsVisVgsYHMJlt0aKzBsr7QmQdQevrxam8hqD7HuStj23GaYVIr+3/4i770c6dmDu144itv7vE6vHFfC/+3zAuwT/F16Nfzm58ez6JuPcP6PJgDBetU6yRfpUnuNLBaLxWKxWAowYMIsRFERdOrIgDO/4Kdvnsra8lKGnD0DANG1CwtvHM6C8ydktxn69BUM+aiNAm6CQ2/6mD/0/DRv2XaLs8Cs2AgOM6+dwH5VVxHfpPPXawOEEoyNr+fNn9zBhf85DyorAfBRdPteJf6atWw8cyzu7fVJmqJ3vqBoxSC81wsnbhadEee6o17j1T07ccPCWRyd8Lhu9QHM+ccOOSWgvkxXFqNZenwJd5z3CPf8bVT9Nclt+5xtjFIYI7JjBNmjG2alv1M4+62tLWGFX03fcDz05XcfaOOILBaLxWKxWCy7JC24Ehpt8vvluc7ijVcG3QqXw0LbAU5pKakyJ+temKGpcsgNUUbz3B6Ps3aEy+dpwV5RwWvPj2PobZMgGs2up9EoDK4w1JgIXuhJ7wpFR1lLVGjSxiHSpzcrU5pIjUDX1CJiUaJLN2JiUSpGdGPypXeyWSuSRuAZ2CO6FuHIwERBCHAchBM6J2YdCEUgwtNB2eRMSeMgsHzh5ubR3bjrBw9y536HBFXMmrpW4fLcfQkhAmGj4wQPY+oFjUJi0vVe9BkB6fgbp/DSvH0YXDeIKT++lykpwXKvCwAdnaCKmhFAxMErEkQSDsKATGtknR+4N6bTGKWQJZ2ZcvPdSCTD37qcUXdIejgx4ok0UelTp1yMMSxM9cTxABGMs5+9/E4AksbYEskWi8Vi2X5YHYFlJ2UretqWdsPO0rG1X5DbFtue254G4sJnpv6L5f9vON945OYt3tXZX57KH0bsV7BsuMVisVgslnaI7VvtUFY/1ot/ffIaD055HllcTIfjlzLknM+yrw9/azNzz7u/DSNsHdq07VjMaU1psB2IMJBu8FnKNlFu6bCcJN2aS8bw8utP4or2UOC5lRhD9hQKfHcYHSTNTCZ5ZgxKS85ccBp37n8of33/SapO2W/HxryVdDhuMSfeseXjIYvFYrFYLBaLZZsTjn+MasFVrjXjJCFwhMQRMqi2m9mnMcy9Zxgf3HV/4DAY/vOMwsv6C7bMoc/dyI+GjOcnQ8bxXl2HbFzZcsFGBPs0Bs9IPOPgmQjaSBJOirjwcAUck1jD3z9+numX7cvAn03Oxgiw6FdFfPDLu9mgFFXawTMSV0CJMBjPB6ODMUnm3DLCv1znRykC8aHj5E2SCpooWL/khU/4w8j983MdjcpPN56UVT8mCl0Cc90SjQ7iUwrje42u6ZyvP8y/nv0LG1QdNTqGKxQlTh0ANVpiIqBKYqiowK3yiFakcDclcTbXYFJpCEsz40gkGeGjwUjJWpWiripGVTqORmDq6vj36M70uWsKEEyQu/mQ0zlj6mVoYJPG3jOyWCwWi8ViyaF9ZSUsFoulLTAmtOA/mIPeXEYHGeeMP77F7ec8usW7umPg8wyY6CLj8W0fp8VisVgsFstWsvy5vVjyq3FtHQY9fw7HXHAZ517xQ4Z9kGb/aR4L/3hw9nVXqK0Wz8lEgoFTikgfc8C2CrfpYwnDgdPPZOzNV2z3Y2X6qhmKPl7IERddwmKvmh9d+zSX/+KF7R9DC+iIoZsTo5Ms3AdecO/BfPfWN1DkuPuFJaia46zS2ez/qWZ8vAqAG7u9z+hPYfSn8Og+j227E8iJc/4jBd4/mfdkmDxTJnD+oJCjRZhQQwo6fdSZR/Z9jDsHPceY98vp7iRoUZvaSpeM1PEHMnBK0XYbdyx4dD9+8oOn+NKr5oiLLuFPFQN33HveYrFYLBaLxdKmmENGs9c0idOl8/Y7iBDIkhKGfRwhdeyY1m3S0GG8IbkCuqb+zmHCKQ+z/3TF6OmGfaYLHv3aw/WiQqNIGp+U8fGMxqPlProjJPef/DdGTRWMmioYF9+EcUC4kUDIBwgRlE5WwEZdlC2FHJMeHZ1aEtIjLgQOgoSIcuTfJrLXJ4KKVwYjEgl0xw70eTDKgfdfRw8nQjdH01kqSoTEybgGQr2gL9NmYRuYcGwZOAzmjM9yyiObnPFns6LOAuMWk3FGbPCacGS+w2HWUbHx+F+j2aQlHg6u8CmVSaIoHGHQUYOKR3DrDJHNSWRtGlGXhto6RCRs57ANMkWVjQGhFEv8DuBLUioSOBhqg/F9jFIkPl7EcRdcgVq7DoBblp/EFd+7Bl0XiBsrzx5Lp486g9zCCXLSoctHnaj6ztgt285isVgsFoulHWJLJLdXmpsV096dC+2MHsvORviZSqz1eHTaeB5lPE8e/hCHxPMHt9etPoCXZowGYNSmpXT+sAcHdj6Tyfv9g+HvfQ+dchjQbwPPjnqKC5yjd/RZWCwWi8Vi2RqE2C36r3pWKaVL888zfeyBVAx36XHPxCa3c/YcwZJTu9D/9ilBGaVmWHPteDrPTRN965PGLwrBilvGoWNhPK7h9Z4fkZBRXh245xafTyGMUvx7+r6M2FjL9rqiMpFgyU2jmT/FRyYlkRGCQW9cAkCvKRDp1ZNFlw9m0J9moTZt3vYBCIGqqCD2zgySRvLdkgoA1rVg5LG96T4VRna8EuFoRlWtBCCxdDMjX7+KUTXz6DhgE5d1XMg8rz7QLrNSDH3vIuYd/nDhfX4MRwy4lC8O/jsQiOj6Rjpwe48Z4RrbXlhXusDBL2o5YeSITFlq2WSyEuAnfV5nlOsCDr/u/gWtmuPZwr2AzOfIH17Lq73fY/+f/ZAhT21AzVnQ8r63gKK5cf4+cCwjB7xE9O1PeeC540l31Ij9g9f3mnwO3ads00PW09w9j4bts5t8h1ssFovFYrFsK/TX92PNgUX0vmNik2O4SEUtL0zfn5Hpuds3GKV4bfo+DN+QLPhyRlCYLeW7jTk64XF04nOU0fgolDF4Jihf7JmMPK11aAwOcGRRLYfG/wdAB1lUv4KUWUfBQMQISe0GL6GJCp+48CgWPn9YfxjPTTsAhOGlb97HA3WHs+mLrnRNf4moTRFfW06XDv2DWI2h1kCyoQhS5ogNId95MBQiCp0vOjSqwRkLiZA5Y59C5AoGM2TGSLnHlzJ/fznbyA7FLL52T64sfTwUdipqjUtceHg5aeySUGBopCCS1JD2gv1mRJEN3iPKGCJCIKQBIVjjl4EfrBOVgdPjqhvH03NqHeKjL4i+/xnG95FSs7auhNj/ZqHDtkms8Zjy6TCGmSn1bdiKcYiQgp/3eY2zu4yipMW1LRaLxWKxWNo3VmC4M9Kebp63p1h2B2x7bx/CQXDk3ekMfydo4//3zilc3f+dvNXeevkghv9fkIBXQtDlr+tRX47h+Qc7MeK6ZagNG6n91sG8ctsQe60sFovFYrG0Kwbc2lhEuHp8hNFHzqXinqa3qxrekRvOfYHn/ji4RYHh3t+ZzbS39qD/W41fE47D9855kxHxVdll/6nrGByjvJhe4bIZFX15uWRe3rZuZeNEhozHEQP6ouZ/CcYgi4sRfXoy/Iop201cCCBKOvDrc57kge+dztoDi/jGOVOYs399u6j99uT2cx7lwUe/CdtDYJjTx3yjei/mxQJ3hY1+z+3W/4wM6Ae+wl+5qsl1Sp+eTOkzDjIeQ3l+IIScPZ/hl4ICNpUP49WaLqzyOmXjjH2ygEHpwejDg+duNby7eQ8I03ilT0+mdPE+UMCg8c3aGGnjsKS8M31YvVXn5QwdBBs3oSoqssuaE9tmcGvCODMiuEKJNqOBfKGiMvorlbaW8ThiyABevOIPDHeLw6Vx5l38Z/ZbcxW9K2uavUZbysAnlzEvOgQuCp4PuHUiy34xnjlXTACg720CufRLxOCB+IuWtHq/Tmkp9OkBgFm8HJ2sTyZHBg/ExNyWdxImRzOf/6/KpopiXq5JsH47fo4slvZOoe9Ei8VisexCSAdn2CDM0hWs3r+Io8+azMw7Bft+dyZT39yr0Rgu05ffEoHdFmMMuraW4ZdPDV28w76yqRe0GW0CEZrRGEW4Tguzq6RAjBiEWL4WXVXV7IQgCPrpwf9BWWSFIRUK91TeepJAv2fQ2fUNiPppRO/XSXo4tQyI1KdehaFe3Dd8IB2LalEYkkaicyYguULhCkVCwEvz92bU7RtQCxdz20fH89lbIxn0q0mYRALTrzsiESPZ0WG9NnjGYaMuwkHjigZtI0TjksYQiPJ0g3bRJq/t6/fR1Hin8Wsmdx95+9bBMaUA3Xh/oqSEzy+7F4Bao0gaQ9JEKBZpagDPRJBCMzXVHacuzOXUaoSvMNFQ4CgEJlOGGrL9+skpoNIFIVjrdUT4AkdqSiMp1hrD7654hJviF9P/IwL3Q99HSoMrVZ5AMvLuNIa9mwlY1P9vTDCOqaxCbdiYd14yHoehA4mK90l1BmfEUExRlNdrBtDRqaUlCt0L2RpEJIIcNgi9cCmyYxl07Zh13WfNBkxdXfb+SqR3L0xpMSgdjLd2FgpNRMssb++mPRaLpX2zHe4TZe5j71TfsxZLiBUYWiwWi258U0IeuZz7GZ63rD85CcewQ+G8N52Hhw8CgsFj4sWPefbFnkDt1g9cbFLLYrFYLG1F7m9X5vcoc6Pf6F3zN2pXPKdWMuAXk6j4RfPrJF78mGf/1QtMyze/14/flN9fysH4Pm/tVcpboixnYdD2w6l3y2ixDxaSPHxv3nx4At8acwJq7To2nbI3L91+JxeMPBpdU9NirFuLWruOh4YPRvIpvf4Lc+7Kf918Oov7hw0Hlm23GKC+PaE0Z2nL12hriD2RZGF5V3qd2oJ4TSt0beEYhl04jYcYHDyRKRCCVRfsxbRb7iPM0tHrronMvIsWnSBqdZp7DjkatXYdfZi1FWcUcN2br3Ld3y6l328m5icfoMl+vIi49L57CrPuluHXYn2cQoqgxFaYVBOAi0YigsRjo5018X3bMIbwefLwvfnPww8C9S4omWTo5B/fzQFHXkDv09c2KAW39d9vsadSzBk6gWmpppNa8346hOuOeoNX9+zU8g7Dc1t1wV7M+HEgUvzGhZfg/rv+83/Ka1O4rKx1Isl1qmabfd6HXTA953tn+3yOLJb2zjVvvs71f/te8J1osVgsll2OSI9u/OvdZzjmosuDfvefHIQjWfe1JAPMFExr7mNvz7GzMQQKwtxlwfNs91aI+ieFYtEKo0EWFfHEaw9z8k03UPLMx80eVuX0nR0hwEgyssq4kCSNptZAlXZZ4nehs1NNXHg4GBSCEuFRIhUlElwcbjv3PBaeFWfB6X+u378G43sI7XLHy39juBulWhtqTQSZPZYm6ihc4VMiI8z/2uO89mace4aOpOKQcvqLSVkx3/X/eIZhbgVL/VKmJvujjEAj6R2pYHRsU73ArkA7ZUsfZ8SFoYBOaI3B5AsGZQFxotGFSybnCkDD+0dG1d9DypRLFkKETdxwv4G4s1Z7VBnNJh2hShdRIusoFmmkTBJF88BxxzDMWY/XswRZ7aNLikCDSKYwyWQgSlUqiFEbXOHwsysvY8R/v8AMG8Cc2l4goSjiUeSkQTpUqSJEA02kFEF8uq4OpJPvzigkwgnayaigPU55bQq/f/1khtyQLzDMjOEc0YHZV01AXalZp2q5eNQxrRrHNHV/ZUuRQwby+jv/5NhTzmPuucUs+vYD2Qlwe9x/FT2medn7K6v/XML0A57ZpuOtNiH3vd/COL/F7S0Wi6U5tqQKR8iOuo9tsWwPrMDQYrFYthd2EGKxWCyWnQCZSHDUlDW8dMs3ib9aoN5loRv9lt2Lbdmn2Ub7iv93NqeecAF6feB22OnV2Vww+1J07Zxtsn9LPcnvldDPq6F5/8qtoyVHPzljPseecA4/f/4JDonLcJtt4z7wx1NPZ+DaBfn+Jw32nXVMyVsnN+amvxuN73P9Kd/Dv6uaN0e+9JXjNc2ctmxN6eUtJPm9EkacfyWfX3wPZ81eDsAesbuBZhwGGwo1816TCEfQ+9mFHDvpXDCG2Nw56Jxt/nX6obzQGgdDQBhjP+8Wyzbk7lO/1eg7cf5fDsQtSTHou5+3WVwWi8Vi2UpaWb61VeTuZ1vud2viaMVYQFdXc95Jl+L9upz5RxwQOCTmvr5kOceecA4A86+LseioR/KEhhmqjKZGS1LGIWkiuMJHh66DUeHjCk1caOJC4OIgaSI2QXYMIQmEdDVGU6Xj2VUcoRs5EB4Rr2T93GDyze+fPIN+t32MiMe469tnsuhHDs+NfQhX+Cgk5aoDcekFG0qBiETCksQiuwxtAoGfMdnqBJnn9W2c0w5Zx8j610whN0Son5Sai9FZt0KDaap12Hje/lx98z/D0shBm1fpKApBsfBwQ/WfJOf6axDaYKIRhK8D50HHCcSASgWCxY3lnHTi+cTnz0HX1REpr+Kzjb0xrmb13wei/1cCej5SaIwIXP5wHEw6jSM1UhiUMTQK3GhM7uCsBYfMgmyPz5AQiIgbxOf7eZ8VvWQ5x550DmLuIkbd0Yljnzg3+9rA5QsxtXWccuKFUL6AXtclOLbTOYEwtnb2to+zLbFuhhaLxWKxfGWswNBisVgsFovFYtmNMZ7PA68cw5AlFfiH7ceXZ0YLrjfq9hX4K1bu4OgslsLomhqYUX+zW1VW5j23bDvUgkXbdH9CCoyC7tNrGfbklQAMW7SmXtSSk2zRySR8OosaHQOChJlEMudXAxj6eG/k/2ZsdRx65tzmgsx/nuvekSs4VPXnVPAYn81hY82I+tVzk0+5JeAyidoWEk2OkCij88ot6zBJaYzA6VDM3F+PYuR9G7a6zIpwoyy4fQzDH69kwBu1jOr4A+adNgFXOFyzajznvHsARoA+X3PkwV/kb1woYZNxK5GBG67eWI5Ztz5YvcF6avb8AgE12F/YRqYtk9utYMPl46jYSyM8wbAff4pJpdo6JIulWQp9J3bqUUnv0srw29disVgsuxRGA84WbSLjceb/bjTDH61Et9XYqzX9P2PQM2bjvz6ebjWN15elpcy9uAMAR46ciTIajUEiUSgUhrQxeIasuFA1UJkpBAnhUywFN608lv+9vxdIGL5mFYie2X57ps9eaLygEDgNrfNCrll1IK98sh+LT34IgF920zhlpdCjK7ImSYcPunPqumsRvuDF0/5EXHhEMwWthSwoLgyaxjQujZxtmLB8cZ4jegviufB1EXHz56U2LHdNE/NWjcYrFpzRYRUeBo+gzWt0DM9EQhGnyrb/nB93pvNkl85zko1jj0RCh0QBQmB8H/PpLIwQpI85gAUnSJzyFCKmcGsc1NxgvBQXHrnztb78/Vh+MOQNOjvV/N8fz2D4Tz/HpNPNNoNubjbYDkI4OZ/nbAnnUODp+fDpXJACtXoNZuXqrAN/9rJ8FvQF/SXLYUm4rB2Pt7Yru+t5WyyWrWMrvjM6ztrMoU/cyODU1JZXtljaGVZgaLFYLBaLxWKx7MYYL82gH09CA5XnjeMHR7xVcL3/PHAgrNixsVksll0X8dEMBn8U/L0lHqmucFh84l/Yb8ZVdP/ftg5KNngqGosNG7yeKYsspMg+z3LQ3vQs2djYqTFXNFfI7aPQsRrcr8wkQvPWKU4w7/QJHPPq5bgFtHqFdxwI9ZzhQyDiYJas4DtHTGTifw6mePoy+r/ZH+9biqerevDm2wcw+MeTQDp8a+Yarui4knsrBuTvr6HDTe55QtOuJzmxFNy+uWXtkPL9FHd84x8cVrSaC2491goMLTslFavKqKxKMITVbR2KxWKxWLaULUh2N9s/yyAEIhrl3CP+ywf/Hk9sxtaHtqPoPqFweVlRXMSVR/yHS8q+oFTWuwjqzD9j0EDSOKSRpHOEmDIUBDoY4sIQEw7vzxzB8FuCPrJyI8TW9+HRyu6cV7ImPCA5x6h/oowMnQuD9s8VMX6wYii93xGokzRPVPUkViERHYpJde+AkYKuM2rp8VEK4Ske+/p4jin7giFuBarhpZT145CsuNCYbMniRkgBqvF7Ik8gWaDcrHAaj2kav69Uo/GV0QZhwEOhjSFpBDXGJWkCV3OJwRUgw2uy8JiHGJq6gs5zwuP7OWWYI5FMsI1Oa+3+Lv848W6+89b3iXWuQ0Vj2XP4+7qxRDcF7SOM4aVv38VwN8oyv46zjviI6ZFOjQSGWefHPDFm4+bMnvnWuBxuKULWi0TDwWNTE+Eg//o0td6OCNtisVh2R/Rncxj0WbM/HRZLu8UKDC0Wi8VisVgsFgsAHZ+YxFtPlDbx6rwdGovFYtk1MbqAU187c6PLJlgySZqGZBxAmknGikiEO599iD2jRdsjxGbZGgONNXdEAqeyw2uZtp8kxlRWXj2eGT+egGccHv3BKQz6z6TwWmkUsnWJsgZl1fKWteiIUsARsR29T5pj+BVTuOOssxl/+x1BIre58tEWSztl+BVT2joEi8VisWxDNAX6Xk05bDdaz6Cqqpi8r0uMndttx1+yjP/sVULJ7GFcWrY8u9wzgXuhInAv9IwMSiIbSdo4FIs0URSuUKGzXlAaGUlQmlcKUIr+v5rE028ewzkvPhq0pQmFdFKSNA6eUXgGak0MF58SGbjxKeMQDadefXbQ03AQgOQf5x3DwC+mQ5fO6KhExSTaFZioxKn1mXNEB9569EzePfChoKRv7vhFa4zWIGXo7idBqXrhodb1YjnVYNpXw757QxoKDRutp8LFoulxU9g+GcpVnE06QdK4dHGqcUKhnCvAM4F7e2Z94whkXU7MEQeUDs4/c34yEIc6HsxO9UHWSroMrKG2qCR7fhWHbaan8wlIgVGKKh2l1qS5YuFZyCOXg6gCIfPHiIQiQyEBhaLpSWm5aMwWTa7bIoxGiAgmCClcZBqJB1sjKDbagN5ukVosFovFYtmJaV2vx2KxWCxbxk7irGGxWCw7BfY71WKxWHZddqC4cMlvxtF3cofmw8l11ZACIUTTDh8QJsV0fqIm3F62NBc53HZLyHUtlAgkgggOIky+aTR/fOB+frpoRvbx4y8/Z+kvxzURQ7Bdz++VY75bH8v6l0fw1A13MitdxylfP4Pof2ey6qbx/HjhZ9y88HOe++ExDH398pYDbs05ZspD72Liu44vzuCSr50dlHTfBc/Psuuz8Mn9WPfSyLYOw2KxWCzbk4aisEL9NiF2qfsyztBB/PjLz7mobEnWaTxlPDyjqdWKKm2oMRGSJpJ1L3SExhU+MnQcjAtFQjgcedM1jLp5cXbfRhsQEvn5Ak499DTeT7qN7IkUgRufMpKkjuIZB22COOLC55jrr+OEQ07hm+dcDMBvnn2Yhb/cD1MUI9mpgGdM1MV1FJcsOj3oe1bXNHnu2XGN1hilMMYE4sKc+LPjGiELOrxHevXkhwvn8OMvP+eni2YwfkYqEDBGXYQbQTgS4dbHaZTKHxNI0WgSlzaGNaGerVik6ShrcTDU6gg1WpIy4ORuYgzCUwitEVoHE8Ay40rpIITA6daVH86fifravrhVhg82jSC+XrJX59WkOgVlnXNjNOl0Y5Fl5rzdCOsuOZBTP1+NcCPoVAqdTGK8NBjD60fvw/Bfz2my3Rs52m8PHAejdKNzyFzTvGsLWQf+zDq5CLlrfeZbjR2zWSwWi8XSItbB0GKxWLYHdiBisVgs2w77nWqxWCy7DEIKlv58PG419Lpr0hZv7xnFPn+5moEfVRTyYGmWvu+mmbFsb7rSiuMKWVhYmEm+NCGcy5ZNVorv3n0jKpp5Ifiv/6drUZn1ttAUoinHQI3hupHvctd9RyKRjI65jdbwi3PcAAv8rqr164MwYzHm/2VP3C9inJG6jIljH2T+L8sQlKE2KH7w0BUIA9XfSXPOmMmNg8kVjOY6EOaKMFsSHO4iySydTKKXLGvrMCyWraastDZwNm3rQCwWi8WyYyjkZtjU6zszUnJY3McRLsro0FXO4GHwgKSReKHgz0EH/fjsc4MrNK4AVzjEy31U+ab6fYdtJHv3ZN6vOjEwsjlTrbYRCpEVFkIgOHSFpmh9Gn/xUqLRoE+/fyyKX6ICAVm4ujCAJtvv1kZwRo9P+H//dzIjflABnhe4+OX0q01zpZF1fUndwmKzendLXV3DTQ99L3BNFxCphV7MyBtjmFyRW0NnTF3vsrjs1nHseeR8aoymysSQQhPHQwpNrY4BddndZFtKgIlI/JIYrqcQnqp3DBc54kUhGBffRM2PNuOnoizY1A0dhb2KV/Fe8ehgPSFBK9ZfOY7Kw+oQwIDI+7hEG00WU0WCwxILeYkBjcZT/oqVhdu1ARKRU3B7G5MjDBVSZ90Lm3IsbOhuGFQa0AgniFA4Dsb3t1e0FovFYrFYdlKswNBisVgsFovF0v7JvQlqBYcWi8WyU5Psl8bbFNnq7/OO8zWyvGqLBYaRd6bR9Z0t3EjKIGGVS2uSq8bQ848TGy1WQjRO2BqDTCSo/cZeJD6Yg66qKrhLR0h0jiox181wZGwVh/ZbxBXLv87Per3JILeAU2Mr2ltEo0w54l5OvukGateW0OmQBHO+/jCucBj01vfoc/vHAJw8eyPf77iceysG5GzctDAwk7CKDOhH9d49g2USipdUwYKl1H5jLwCKv6xAzVnQYpwWi2X7s3lhJyqKSxnO6rYOxWKxWCzbg9w+aW6529a4GrZnpEPq2DEYKShaXQOfLyB59L6YsK9a0yMQUGXEhZnS0WljwjK8AoVACh0KCzU6pxicDEso/2DF4biVDWT4YTv6XUuYd/jDpEy0/rVwTKGNIR2WXlZIlJE4QmX3vW5MnE7FB2XjBAKRoyOzk5bQBqENqHrHtZOKl3HsN+7lwsiJGM+rj0eI4NhS1rsVSonQuv55c9e4wftB19TS+/c54xzpQDTHDTBTbtno+nLChSZXGc2obyzg0UGvslaBZxziwgehcUxwolKYbJnkqBDcsuZA4qsjGKlQcUlksxMIDDPOcw3uHWpjeH2vJ7i/YgyPzhyLKDb8a/W+FK0LXeqlwGjYNDbFgq8/AsC1q46kTrnMX9Cb4azIG0MpROOxYStoaqLYNkM61B07mg4zVqHWrGv8esNy1+G1yRMfGg3GFCyrvEvTsNS3xWKxWCyWZrECQ4vFYrFYLBZL+6EZdyWLxWKx7BoMv3TaVn/Pu8Lhnd/fwyG/u44eD64LylJtLeFvjnCc+kRKa8pXiRzrkEyyxmiMbt22BZ1B+vXmg4ce4tiTz4VPZja5uUTkJUIBIjhc+fk59PrWXEQ0ygOTD+X2HjPyN2xtc2tNrTEYCSYnp1mtk6ACVxAZiwUJ0YaJsjCpJ2IxTCqVn6TRQUZx5Ul9mfHjCdnFg9++mBF/6M8HDz0EwNCnr2DIDbuIwFAIRMT9au9Ri6UNGXbzJ0Drvz4sFovF0r6RFBAONuVUKGROP3fn+iWQRXEef+CPdJYRxk29mP7X9eC5B/5EmYwjQ4Ve7kQdZQwpo/MmL2kjs2K3NIEQMJclfhnLv+Yj1azsnozOEbiFAi1HCEymS2wMCkHSaFLGJWlcXJE7eSg4xuQf/omEjIax6Wx5XSMlRoDQBqkMQmlkygPPz8Ygs4cyeaVyheNkRYbBwXRjkWEDGo2NmhLJGR043TV0KmywHxGRIEUQixBoz8c3DkmjSIftHRcqLE/tkpApioVPTIADxIRkzncHMkiuI9WnLDiNeAThqaAdfJU//nAkrpCsUorPK/sgVhThd/GIXyroteITTMMyzRg26ySLT+6Ev3oNw5myzURnGpN9720PZHGC5yf8kRNvuYGOz67DKBWM2dJe/uc4o/LMlspWQUnpsKS1SaVAq51OU7xN2Mm+5ywWi8ViaStacffbYrFYLBaLxWJpYzKzke0NH4vFYtmpaapEU0EKJHRSxuOMI8/GP3IT1a/03WZxNUqgbY1rQ6ZsclMZGSGybhBb6wqhC0h9NAZjBJEe3fnjvPf4dfdpBY7dyv3X1HDFfidT+r0VvHP9H4BA1HncNdcy8to5OEMHMWHef7ii46JssjMX76j9+eO893C6dWvVb/anR97PhNf+Wr9gF/qZr/72wfx83mRkSUlbh2KxbBVL/j6Kqlf7t3UYFovFYtkSMmVit2QTWWCbnVhhlOnPnrnv8cgPOnL5f97h/AO+xa3r9mu8bo6sMGlC50IMrlC4QgcOemEHNft/uCxTSrZFROO+fzp0LgSICoUrfOLCIyo0TqHrJwzCGCIpg5PUOHU+sjYNno+uS6KURGGCs3FkUC5XqeChTSAoVCoonUwoQMx14hMyz20wEAGGff1syeH61wu+zwqNg4zOL5esDZtO2YefffFfZFEcbTJCTENHmabGRKjVMZSRdJRJJIG4MB4eW3coQneIoaMSoQw65mDiEUzMDc7b9yGVYvW5e3Hr+y9QJKLMSPVmYXlXIrWCPn3KMYl4cE6541IBP1u3P+ePPglVXhG4MoZunsJxsqWlHUyj0tOtYXuKCzMkjUGqoDx1pHcv7pr7Lv4Ro4MXc8dlubELwfwHxnD//Hf447z3iPTtg4hEgvPf3bAOhhaLxWKxtArrYGixWCztFBGJBANwK6axWCy7E/Y7r02pO+UgNp5fQ98zZjP//gMpWRih112NS3xadg3WXzGOolPXZp/rx7pT+vTkNozIsluQLc8kst/58yccRMmXEXrdOTFIcjwyht69KrIJp5vvHs89py/mWz0+5c93fIuuS6bT7c97o91iYNFXiyeTTJMimzhqysUjr7SUFODnJNAaJm0KJgbrj4U2gQOiVmw+ZyxdL126RWFnHGgyySohAseUwa7Lno//gNIvM8cM/hvyWXXr9huPs/bRrtw/6Gl+tPIYPvnHPky/6T4G3DiPzwftS5+HvuDbv7opu36s0tCB4Htj1U3j2e+0mQx343j/iJP+40HEX52St//e/1nPAXVXNnn8IZ+3Ls6dgY5TV3Pdr75Pl7qpbR2KxbJVJOJpOsXr8Fpeddcl57fKYrFYdgaW3ToOP2EYfPOkvOW6YhPjbv0BPb5YjB8uM0rVO3JnS+Vu4++8NqpSoTaWA9DnrfXc6l1I7G/r+G6nl4Fo/nrG4KFQxgACZQQakRUT6lB06IRCRM84KOPjCh8yAsNM+0kn6/zofLmag2/9Pg/+9O6gPy4kaI02kloDSROUFI4Lj7jwkELjmQiuyHeazE7okWAcQaRWE6nxkLVpRNJD+IovH9uDW/Z4k9+uO4wPJhxMt7rPw/FGvQulMaZ+rOMF7wCTOUye0K6BqFCb/NfJODXKrEN5q5EClKLzx2u45vbvwz828su+L+OFbb9JR0NXRz/rXlgiBQ4CKQRxEQkcC1XQ3m6lF7g4bq5F1KUCgWEkglGanh+Wc2nsWj664S6eXD2OilVlxKThlL6f8U50bHBOofhx0d9H86v9X2Jq9SDUxnKEG2XprQejigyDbwnGMkZper9dzrnpGyh+YQ0lt8TRM2a3eMqJz5Zz8K3fr59EJUAo6Jyc0ux2W4quqeVbt95E98kr8Y1GV2zivF/fQI/Z4ee90Ng0fG8M+bviO1NuAgPdyj/LF4TujGypULCB4BKwfT+LxWKxWJrBCgwtFoulvSIksJMP6CwWi8WyU2GkIOKE5ZAalMe07HpsHmmYvs8L2ed7DL6K0jaMx7KbYHJKh4XCjTPGTuW5ojH0KSlh4+l78eBhf+XohIcyGo3hlKvPYEliEL8e1I/hD0/CAMI3W+0CmD12jgOfyEksCCEaiwxzxYWQl2hzOndiw4kjAOgyrQI9c27OOTZw+SsQ8+YhksnD39jiU5AIHFFfqtik0xw180wGP1uJ+XRW5mTC+FuZJHFd3hr9N7o6xVy2ph/9nlmEvsnw5MD3GbTXEHRVFV3+OqngpjV7J3l8wIeA5O1Rr7Bf/6uIN1hHzVlAlzm7SAnkljAGx9t+yanUcQcSSSqc96Zvt2NYLBaLxWLZudCOwTiN3cp0KkWXhyc3Fhvligu3luYEPdm+cFPldbezkEcbjIT/7vsMrohnxxe5zoXaGBSgTP55KAJxoQ6VeCpHeJgNv5A7u5CojeV0+eskzjn6e5QtDdYx2RLJDkkdCAwdoZFC42CQQjddck4GznlOSiPrPETKR3g++Io7D3yWMbF1/H3lQXR9bCpEwrRvRmRIONbJlEdWodOgbqHAXcNzM7pJN/rM8rzxWabMdvgeKP/umOAekwker+77CK4QJE3Q9jUmioPGFYoS4RET4AqJi8NS3+eqBWcQS/sgJZGkIlKZZMUxXYht6kAkaVBRQbe3FoPnYeYtom9NHcuv1cxf2w2nysEvNny9eC7vRMajDxhFxcgEAP8c9ydGx2JMrR4Ujk81xWM20KNDdTDOEsG10rMX0LuiBy/85CWO634lbvOtB4C/eg1d/rqmFWt+RbSi898m4Wc+73V19Z/3FnA++JQuH4S72X4R7jgKlX3PsBM7s1osFovF0l6wAkOLxWJpr2QH4NvgRo/FYrFYLK0g8eLHJF4M/h550xxQKrjBaGfxWiyWHYDo2Y3nfhmU5V3mgwt0d4LET5/b891UV12ZpktJDdE3t/ZgOSW+MsicZERrnBvCpJka1pdJt92PIyQj/3olg5aWoKtDJz7ToPRYhpzl0oPVfjW9Ih1yXg9LsHUsAyHxihsnSjQGbRQbVB3JuihqYzlFx5Tnpz3t93abUT6uNy/edgcXv3Qcpqpqm++/84+XsLC8K73e2+a7tlh2a5yOZeiaOoyXtt+hFotlp2PgzwtPBNmm7ESlRNcc0Y1Xb/o9rqjvZ+eJC9EoDCkDOiyPLAmEgCp0LwTwTAQpNG5YxjjYOPw/M4EqvJefGV8YHAac+UXgbOg4oA0KGQgMQwfDTJlkB5N1SSyIACMEQhlE2kekgvLImRgcQIrwN0tKRCY2R2SXZa+bFIGnQVPjFKgXF+asY3KXCdm4jK7RGN1gjBW2h3Akj/3fnXRzDLXhb6srBBrCR+ASWSw8YkIRF5qoEKxVGgfFExXjiR23EqdnD3TnEhxjMELwx+8/yOxkX5Yku1DkeEx/pRsiGkXEYujSBBPrBpMqL8IBTPcUe7kG7UoWnhVn0el/Dq9BjlTQZMSgInDTb6IfYATtz+V4J/pctim5Ex4tFovFYrFsMVZgaLFYLO2Und6O3mKxWCw7NYPe9Xhjxt4MvzQs7diebpxaLJZdDiFAfbmUy0ceDVoHDh8H7cHbz/ytYAJgwFlzQEi2+pspx00DKYKkX9bVQ+W7fEC9A0fOdkIKjCKbgFNGM/3iu7n66G+wYlwTx9Sm/njaByHo94cpXPzaJbzx5j8abfK9KZ9yTGIdkrfIlHTLJEUz5dNOu+VGBv9z+ta3hcVisTRBVqywmyAiEX46/T2uuf37dH1wB4h0LBaLxbJd6fbQFC5/82z+9dGLSAS6mR5zRkwoMTihyDCzXCHy3AVdofLHCrkiQ3KEd9IJxHahUE8bSZIISePiGQdX+ETRSGFwjKGpIhIiotEJFxWTmGgElEYYg6n1SBoXRwgcoRGRSH3p5rwdFJ40Wu9IqII4m5gQ1YjcsVQTy0V2zGMwSlNlXM6+/Vp6/nU6oqSE3019hY7SJ2kEnpEUC48SGTgXRoUgIRzO/e7lOJ8vRLgRZMJgimLouMuacSW8dsPvmbBxPO+uHs6GihJUyoHboXffcsZ2W8I3yv7LX1Z+HbfCwS/VjB6wgpiIZNtCGZ0tQa2MDgSFYZsoLTFGBNfPkRiVcbMXSCTCNG5Lyw7GToS2WCwWi6VNaMED22KxWCxthjF2gGSxWCyWHUMB8U6xk4KILR9isVi2P9NvHMPwu5OgFbquDp1KYVIppBdMuBn895WUX5Sv2DO+j/G9LTrOmh+OZ/lzezVanlseOeMCklciWTa4dRIKDY1SjfrrCRmlxE3mrJvTp2+iXJPxfUSqcAGrEllHBxknIaONXtPhP+mD8dI4XTrT6aPOiAP3Lrgvy46l8/tL+M7V16Nraq1LhmWnpNttMep+2rOtw9ihGN/nJ9dcQc83V7R1KBaLxWLZFmgFqXTBl5QxKEzWNzBT/liHwsLccsjKSKKooJRx7msFRHi55YIzTn76oD3p/t8EvSNV1OgYtTqGZyJkiiJnSiS7OeI3lbNvJ6LxiyP4CQdd5GKKopioi3BktoSzFKZ+DJPpe+a6LCpV/zwkE2PWcdDoYDJURmzYxPhFOE7etnmu8PU7DyZXhfssFj4XXPk6Ze8k6PCiJi4USRM4RbpCUyY9SqQgLurFnHvcN4uVl++L8XxEogi/awl1vYqIbTKceMfNTPzpwZRXJjh6+Bw6d6sk0amOVUu78MLn+/Hz2afw2fz+CB+cTim6xGo5/AdXEpm9lBF/2cyYP/wgaFshcYTk2m7vUfa/LjhdOiOF4VeDX8R5pwciHsu5uDZX027IG2eL+kdL2HGZxWKxWCxfCetgaLFYLO0dO3C1WCwWi8ViseyqCEHk3Wn16buckkWRdZWM/N95AHStLCB43sJ+cqzCULWqOOc44QsZAaHeclG1OmIMS4+L1buhNOf00QxicxUj/3ceQzZW4gNOj+6sOHco/SITgUR2PV2gdNqqoxT90gfR4f15TF8+gCF16fblZtjeyoftIPzVayh6aU1bh2GxbD2TP98tZ6bHX51CYcm3xWKx7IQ0FJztyH1m+ttb2T/enihjgqk6xoRCw8ClMCMclMJkHe0UMk8I6AhwRc45jd2H9aOL6fbApGyb5IoMk8fsx7LjJK/1f4+1yuDhkNSBg2GmRDJAFE1cOJw072SWlXeiKJZm2v7PcvjMU3FnJTDSw0jQEYmIRhCewvg+aeNwyaLTWfThQAaJ8sInHDrE52K0KSwMhPoSycFJNC6n3MDp0GjDxkvGkioTxDYZujw8GVHASPH7Hb/kkrK5bNI+VVpmxZwAroBYuF9tDK5w+GR9f6KVBuFGwHXxiyN4xcE6neemKZq/DvezvkxODOCkATOZvGEQ8zcVIWoiVKTKiGxyUAlDz86VHFY2j48H78umIaMAqOuR3x5D3A48OvANvh05GW9iZ37d8STuHfwsVzsnZ6+lSSbZ+6MLGbSmGt3w/d/W4x1b+jegudLfFovFYrFYvjJWYGixWCwWi8VisezutPWNUIvFsvtQKOnRUIAW/u0vWsKAMyHStw84m6BnD/w1a7f60J0enUSnR5uIocGxhRD5boZSgMpZLUy6LT4lypdn/hmv4ddow+9VYxBOxknENEq0+mvWMuDMtVlRixrUk89vmECuuDDjYqJy9u0IwcLjH2RMt3Mo+tdmBn338wISREtbIONxZLeu+MutE5rFYrFYLJYdSG5fd0eP9TOCnnYoKtxSpDBoHTgEZssnC0O5cpmWHBCsJCTLjunAHec9wj0PjAzbO7988PLv+iz4xsOkTDCYSOooSeMGgsZQqCgxSGH4LB0leUdvBk5bijeyDzwN7i87MnDjepJ9SpF+KGB0Arc2U1uHRrLq8UEMfPwTiMcQQuRPNsqIC3NEg5mxjNH57w8hRb64MCNAVKHATjf9fhp72XTO7TKRv204lCV/NYHbu5DIeAzRuwcAKeNRFYoLU8ZBivz9uWGR6Fo8Pkk5FP+imNK5s6AoDhEHv9jBjwlUDGp6ukSqO9H/lXLWVnTmhB/NCI6hIqzY0BGzLk4kKUj1TNO3ZBNDouv48/fv44CYIibc7DGnpdJ4xqFEphnqRlA9u9Dvvs9Yv2xv1O9E3thNb65k4DnVGKMRDUpRF3K336HszuLCLRVXNhTNWiwWi8ViaTVWvm+xWCwWi8VisVgsFotlx5BxvMh9tMDV7/+Hlye+xJBXm3Dk2Fa0lBAKY80m1owBQ375sta4JIQuH9l9bmUyyBECiWzdMb8ChVwTt4jdVMS+6bTRPPjRP5AlJbt3ws9i2RnYktJ6FovFYtklcdG4QiMxeNmywzpwNwyfx4XizI8v5dk9emGSqcYiJSHyxzdCYozAR5EyPp6BpHGpVbG80sZRoZmb7sGvhh9E/J3PWXTVUF79+0MAmIhEJNNEK5LEyj3cNZuJLF0Hq9ZhlEIZiXFAhCWTcZxA/CYExhiM0vWiQaODh1ZNlnc2KpxVJUUw6Sr39BqWQ84Z1yw4KM2tQw5gycHJ8LVAmFd3+J688sHz9HZM4FxoBB6BaNMzMucRbOYIwQd1vfjVqHE4X65EJIoQRXG8nh2p7e7gFwuEhqINPpGKWmRtkl7/Xs2tp57PFZ0+5jdDX+D44bMwndJEaqFj52o+f3Mkv9736/xqxFgeqxyQd04/P+Ysbh1yAN+/8hpiwuWVVx9nzUX7ggDPSIzvB+3S0NXRYrFYLBaLZTfEOhhaLBaLxWKxWCyWRnxx6R6MqqzINeyyWCzbAJlIsNf/6pj60wOIvTG1rcNpW4zOERlqIgP6Me7l+Uw8a1/UrHnZ1SSaoS9dwYi/1ACzgoWZZNdWitdExG3mRQGOU+9mmFtSTEiE1I3cOyQCjckTGzbeb4NEVM66kUEDOOhfC3CE5h9PfYM+H1QDMP6HV1C8JsW60UVMv/k+IEi6NWR7SvgkktG/vYpRb67Zut+E3bREMkDUipUslp2H3fR7ymKxbBmZvnxZpC5v+YdXHIyY+FkbRdWALfk+C/vh2+U7UIhAyJb5ux3hCEEhMz4VlkV2cnrXQVlknS2h3KpTyfTzjQgnJBkUBg9BjY5Rq6PEpYdE42CQ4XFEJMKaZwbypz0f5oO6BLdfdj7xxevQZcX4JTFkWkHFZnRNLf4BIxn2x7kMjK4PSlHLcKzhZFwkCzgR6tC5LdM/NwpkgVrGANpgctrB5Lmw51q7m+z+Vvx4PB2/voYOxy6i+MNujOm4nF7uK9SZNFVGU6MlHhJtBEkTwRUq29augPG3X8fmvTwePPJRnO7dMIk4BjBuhGT3GMnOAr/YILRAuy51XbsGp+cbolWa71x9PbFyj/JRcdwjawAY3Gkjn3XoDJ6H8dIFrlVw/MSk+Rx11kWgofeiJWw8vD+u0I1Ell8Vez/AYrFYLBbLzowVGFosFovFYrFYLJaAsfuw/KgO9Pv1JNYcUkaHVR0oXrDIJlwtAGy4bBxuraHsycltHcpOjfF8np94EMPXVG9XUVh7RLhRFv9ifwY/U4HOERBmMHVJHp74NfaoXNXotaKVEcyns3JW/gqtV2jbQssaCuNySoYFJcIab1LQTTArhmxafGiiLj/rOhNHSGac2JdpQwcB0HH6OtSCRXQuOrDJbQFOH/QZ//jF4fT/1aT8mFsjxGxwng75STSNpvKAJB1Wdwt+Eyytomx+NWP/dT3Dk5/a31HLTsmm88ah4tDlL5PaOhSLxWJpPzgOP+r2P7o6xXmLv9HhUJqZvtK2tJW4L7f/sz37Qg36ssKNsuTn+zP4mfK8SUtOtiyqzDp0K0xWKucIkxUYZkr3ZlwGcwWHUprAITAU7fX4xOO6oosYTOPfy0CUJ9ANPMEdoQNxXVgi2RHgCh+Ab/abxxFF1bxU0xX3o5nQuRPC10SqUjjl1Sy5bCQ6CslePr/p+l+SJuedl1FNhu6DJvy7cUyG5IkHUT4yQu+7Ps5vTinqJ4IFG9RPssrRFa65Zhx1PQxulaDv7yaBEHT8UrOxqAcdxGIu6/0hRxbV4hlFrVEoAzqn1LRjTLZdHQwO0HGhR+lywZU132NESTk65oIEHY2QLpFoF7QLGIOKCapLBX7CgBQ4yQhlX2pim8A40Lm0hvXdihlTtpzpiSGIslIW/2I/Rsfv4aqVh/Hu4mHMPfSJ8BwNatNm5AefAuBLB2H617dlA9fGr8J2ux+wpWWCW2JXniRmyyNbLBaLxbLVWIGhxWKxWCwWi8ViAaBiZDGnnf5fpv4mSofj17BmZneGPNfWUVm2hsjggaAU/tLlTa4TrZA8VdWl/nll8/usO7qK8vIiyp5sXQzOHsMxroOoTaGsKCmL8dIM+8HHu524EEDEY3x04R2cPPMGSmZmWqDeMUWtXcfwK9bhN9ju5YoxLb4/mzymG0XsMQQAubYcf83a4IVM0kw2kYTJExbKfBdDyHMw1OHVlAimpdJ8vG4ApWIRcs8RiJVrURUVQYkypZospyU8n6equuMIw//1e4U9hxS1eG7KGJ6r6Q7A0Phazjz9Ayb/Og5G4XTrBt075yVVm0LuMxKxYi14PmrkAP5eNYKOTi1V6ztg/A08WdmP/3fwy/xy3beb/U0QG6K8WRvj2EQq/4VdNTHVAuaTmQz7ZOvdJSN9+2AScdT8Lwu+PnddD+oqiui19SFaLM2y7nCPaIc0Xf7S1pHsALbie8rp1g26dULNng9ApGcPTFkJat7CbR2dxWJpB0QG9EN37IBKRHGF5P06yUq/U/Z1pza/B+uMGIrYXFXf92wnOMOHIGqT+MtXgBA4I4cgKirxV69p9T6aH2tmnPvarv8n4jE+vOgOTp11AyWzwPiKp6p6cUrxEjo5CbRRSCQ0483tYEgaJyiPTL2roQSEMHl9+thrUxn8WvMxqbBHqIzAIRAXxqSHi0KG+40KhVEazziojJO6EBCPga9wylPo1WvZ84Rqzu0xiT6RTWxUxcSl13RbhGWSs5Okoi5i2FAwhuVnKP7f2Bd4+j9HY2YtxCiVL6QLJ1cZbRChyaHTpTOmZzf0rHkcfcEkLu3yP+5c+02W/C54vcM/P6b01SLM6D2Ii8/wTFAaOmlMvsASk3UvzIg5n6naCyelKfpgDsPfcTBD+mFiDkYKVFEEvygUJ3ogfEGkzpAuE3idNSbhIyOaNb1dEsuKqOupObTjesr7F7NZFeHUSkRxgltPf5YF6Z68//IYBr2yiaee6YLw8j+7cq+RiNXrtptFvPE9Rjy0GYRA7Duq2XXzxq+t2vk2CnpLhYq5k9q+YqWBrabh8YQoLCTcTcemuxS7svjVYrFYdgKswNBiac/kduRth8lisVgsFst2ptOjk5j6qAMoio9dxBCsKGxnJfZoDQvLu9Lr1KbX6f9/E3n8//pln/dgYrP77HfGzC2K4bqXX+TohMd1qw9gzv5btKlld6IV45wFB6ZafH82dZNZDu7P6288DcDQp69gyA1Bgsb4fiA+dMJsmc5JPsgcEWChcsR55cHq0RjO/8t19L1tIiIS4a5XH+Hs399I9wkTczduHLsx+IuW8NTIvgD8/O5vs+jbDxQ8zQhOVtC42SR5csxIdE1NzhpBonTxlcO44/xHuGfoyIL7yZ5eJMIdLz3CebdfT2yz4aXb7+SCkUeja2oYzlQU8OyongAMKeDKksvQ6ydz+7/P59hHdgc10vZnzm09OHnPz5v8/tzS72SLZUsZ/r1P2jqEdk3D79m5twziuqPe4NU9O7WwpcVi2RlZfW+C6Qc8HT4r4ic/uYySZ+qd3SUzQDoIx8EoxckvTuKO109iyI3bQWAoRCBwM82I+ZoQCO359Jc899kYhl+0AoTk1Of/x+9fP5khN6xpWVQUHqfZsWZLrtmtXXdLEBLhiKzoT0QiKGMIdWuo9et5emRvZk3vwx96fpq3qQYa9s51jpu3ZyIoI1EIpNC4mZeMDsoN555X5vhSYLTM6/drY0gbQ8pECOSNgcCuZ6SKuKhfzygVlmQOyjEjJek+ncARyDofWZqg+qIUN/36DN4fN4FNuogusg4jAKUgEgnfGyoQCGYmShGMX0T/3rz++t9RObGd89qTnHT4GaiFizFa1osMlcpOqjJKgdGsOmsEj99wFzcP+xpSpDnm3WsYftG0vGur9xnKq8//LSsurDGazdrBC9vRwYAAV2hcNB2lZq1yeXNMD6LR+YjiYkQijqlLIyIS4wbjNSPBSUJiNSQ2+CRW1KDjLsmuUZIdo9R1E1SP8EjuWcfIPms5u9vHVHsxPr1qX4Z8Mg1lNI+PCO5/9GMiGsLny+rfSjljuPgmTUcJebW0w2tslMpe6y1FdujAE6893MgFVRndyA0/d/y6Q8kIXCMRRCSQERil60tMN/FdISKRnIl426n0emvZAgf/ZtdrtKzBhMFMGfimSo1bt8RtixUX7t5YzYTF0i6wAkOLZXvzVTo89gfSYrFYLBaLxbIVpC5I0M+vauQEZ2m/pP49kBVf9GTIDbt+Cerf/fYBLjjqEoZf8kmrxkujpkV4/7GD6HFPMyLDAvtYff14fnvVI3nL5Og9uOjZwGLkNxPOodf9nyDcBrdGhAgSdLKw22CGTh915uU+fwSiALjCwWQMG5TihuMvpNeqWc14oxSOu1EsBZCZxGeBGFP/Hkhyqcf9J50ELGh298b3uem48+m5ejZGaS6ccRG6tvltmiP+4SyOO/q7APRa0cK5W5pl1I/XsCDWF1jS1qFYLJYCDJowj/ufOREIHAtH/HYRb9y/P9gJOhbLLk2FquWs0y6nbO4sNIGb6Tn/m86jl52M/O/ngZjEaBTN9yO/MlspWpl15kD2qFuNH/Yx/3XKOIZvXtSqPpssLuaUqUs4reQj0gMND392EBP3KyL1Zr/WjWO2U66jkMirpfPRaJJGkTYGlREiGpEVFypE4CSIQCNzSvkGJZJxnEB4lhF6GgPoxuIjgpcU9Q5+cZEm4aSICy+MJXgoBMJxmH3BYE4q2RvjCPQ4BxWTuFU+MukhUj5UVpOu6cx6HaGjrCMuFEKDSacDQZgjwZEYIUDpYCKVFKAlZvFyjjv6u5z7/Nt8t8N6nqjqybOnH4FesrjpxmpCyPrFtwexR83K4J5DbolqZdisk3jG4EG2fYP2CxwL4yK4Qi6GEz+7mB63CIRYGsQfdTGxKDoRw+8QxU84pEsdkl0EsXJD8VpFYkU1cv0mjnxrHr3dTXjGoUoV8dwtx+AXxZh7SH/c/j7n9PyYP8fPCD6NQuaNPcV+e3LxM6/y2NFfx18SiAyN73PD8RdSe2s1keIkFx57Mbq2sZv41ggLcyn06W0oLmwPGN/HKEX5RWM58doPmLR/Is/JP39l3fRr7Q2bd915sdfOYrFY2hwrMLRYLBaLxWKxWCyWXQx/8dK2DoHr/3opXrEhsVrQvSX3OQubX+pNr9W7hxzrko8voOe74e2IVtwg/s8/D6J2iCb98/H0+1Ur30tC4JXACYlk3mK5tpyfP3M2r5//B7wOgRBQODIQ6mkduHyEzjN58WXcPyQIIzAKDu80j+FuFI0Oy6zBPsfP5XMZxJkpm5m3n1YIKkULTSJzXFXynBdDNr/Um76rFWpO64SCeevNrmrVNk2ha2th5tyvtA9LgL9yVVuHYLFYmkFt2AgbNtY/X7sO1q5rw4gsFsv2JPpsJ0Z8diXSFwz4dAraD6ZymdpafvXMmQxZvrp+ctf2FkDkliHdQtTCHCGZADW/sXiqycP6Pnc/cwrud19gWboL/3z26/Qzk9p2HJNxc8xFioICrlyUMXg5ZXszrnqZsriZssjKBPuW6KyzoMh01psReQYCtHBMoYN4lIGkieCZCDrcr0vgiugCxSJwh1t6SmcS6ww9/r2C6r17Ea3yiGyuQ26uAV8x7+bBHDxiPutVMcPczXzzqZtQexhSNxxE/z/PrG8Xx0E4EqMd8H0wGp1WMHMutz/4HX7ZyRAvF/ScNbGwa7tScOBezL+oiBHXzMB4aXp8XMV3/3I9/dTHLD6nFyVLDZ0eDcprL75tHF4XH7csRdIYUiZoR2VEttS0g8k+YgKiQlCXiqIXLkB2KEa4LkRdTNRFxyP4RUGJZOkbhAK3BtxqH1GTxKRS/O0fx3D0t6Zwbdf3Wa9j3Hv24cSL0hzQdR2bVDF7RNegnczErEDAacLPrrOugp8/czZDNuePXdTs+XR6ZSx+rBg1a1LgTJoRFG4jN7r2JyVsns6za3jmn4fTT3/cdBu0Q4HkDmErv4stFovF0r5w9hjOnGs6MurmuajKyrYOp11jBYYWy/bGzqiwWCwWi8ViseyG9PldE0IwW9KkIN3v331EmF3/VZRXUg4g0q8vXr8uiImfNVq/z+8msuDxMRx96OfM+VXrjxNfD3eVD+baTguZsGkQRWsl/uo1DPz5Gm742unEN+SWuwqScFkKuRdKGTgbAhgTJAcbpC/P6zGRm0f3an2QLbEVn5Xd6b1ksVgsFovFsiMoe3IyZeHfub0zXVPDgF9MCsSFQiBiCfzD9qGL8yJZa+uviIjF8A7Zi+jHc9E1NQVWaOY4uZNcMv9vQUnlvF2lUvT/v4m8duTeLCzvSr/fBH3ONu97SlFfxraVznIajSIQ/TUUGSpT72KYISqC0sVNSqhy2i/j4iakQB+yLyVdakiHYsY0Dp4JHgBxoXGBuJBZR8P0nrX4JUV0j8eCuCrTyIpq8H1qxvTj/078JyWyjtnJvry5eR+G3b2INX8po99+m0lNMEHUUiKMwThO4GLYgF535VyzBiWec8tvVw4t5uMT7+SCG48OyuNO+YJ+U4JtTjxlEs99NobOT8fwx+/Jr8/4O6cUbyBpfDYoSGYcIHMEhq7QyPATFBeCuHBwHI1wnEBcGIti3AhEJCYiMY5AaIOTDMSF0RpNpDoNUZeasUPoNSnFnCN64naDEuHx1vj76ew4rPDh3ZqRTKwaSqTWD09TYDIuhgSTeQb+fFVBt8uyJ7egqkBWWLcLThbMvDemzAqu+xZtG5bq3tXZXYWVFsuOwop4LTuIuv5lzDrxPr79fyeDFRg2ixUYWixtTWZQn/mRtMlWi8VisVgsFsuuQO5NINvHteRS4P7g4gv787vzH+X+YcMLrB8mgbQDGW+YQuOnBjceu0+YyH/+sx/XvreQ1y78Gr2nhok06VB3xAa6mXVBQkDKoEyyNhil0KkUwnHqx2paZ8WHQoggJSYEUmhUeHwpQBnNTX+7mH63TWoc35bcFDXBvjTNf26UMfazZbFYLBaLxbIjaKpv16AvJvr35j9PPUKtTvPzbdRNkwP68uYTD3HSqRfC1C+++g7D2IUUGJwtdmXTRmK2kXhye+EWWKaNQIXnqsJ+dsMzzy2T7JkIHsEYQAqNFAYHgRSmfkzQsO1ynhstueHRpzk4VsEmHQjuAJLGJaldPOME7oVCIIXAFQoRdRly7hdE+vUmNbgb0tPItI9Jp/GH9uathybwScphudeFxxcfTNfvrEQ4dUhRRlJFMDW1iERRcAZCBO57ws+OKoQUIBunhY02wWu5IsOGtuqhW6SQIhRRBucqB/fnjSf/gkZTqz2qjG4kLsy7DgiSxqGrAFc4yPA4xvcR8Vj2/amjkuhmL/t3rEIS35jGKa+mct8evHrvn8LrpPFMINYskYLNWrHS78TUygFsOAZE9cxQeNrQ6dIB3ToBXLZ98tpjNxKWmQblv1tz7tvI7dFisVi26r6axbIVaILKNtYcoXl2ox6QxWKxWCwWi8VisVh2FN/8opLbFn1M7P0ebR2Kpb1R4B7NgLs+48Gvfa3g6t/8opLYwjiLTizLLlv01L5seHlYy8cSAkfIBiWKw2RHmCDDcbIPkXlE6hNvJiPkMwZjDHLIQG5b9DHnls7PO9Sx37mYgfcGZclExOX8ucvY+L1xLcfYgBG3zubkg0/i1LGnoBcva/S6xrQoPrRYLBaLxWKxbDta3feErIhtW6EWLuHUg0+GT+cEC75qgj13kopWW5xATZ+h6XfJmq8Ww3bGFaKRgeSc47sy8onvN7udIwwSgzbBZKLscjTFwicmgjGCaarNhKx/APd+/UgO+CA4Zo2JUKXjWfdCVyjKdYQqbajVigERj1u/+JDq0w9AdSvDTwQTnsSmKubfNJjfPvkQM9OGNX5HADrEUsjSEkRpCd2vqGHjk/25ct48RFE8iCXjyB6WBi4oCgtjzYjnjFLZR574Lsfd3WiTHU9NPPJufvX6U9SaNJt1mk1aU6MlNSaCDi+AKzSu0JQIDxdNXCi6ORoXh/G/vIa+V1YE47BYDBMJHeV9jXYlkc11OMlgglm0RuNUpyGVRvomvC4CB0FcSDpKSdIYypXLjTPPYP0VfVBVVXniOON7YAyLbxvLkI8LyVAz7SLqP2c6aAvj+/WPnHbKtteujDHhd4Wu/zv3sauKCa24xGJpe+zn0LKDiL3zGd8Zezr+qtX2fdcCVmBosbQXrPuExWKxWCwWi2UXYt/4MvaPRRncYUP9DXo729TSBLqmBn914UTlP+45mgFvVKHWrssu6/VMlPjjnVp/gIbvP6Oz4kIRunsgRfAQonHSUOsgcaQUOJLR0QgOIq9Esru2MkhiCYlRijvu/g7dPq7Y4hhVZSX+ipX4K1bu+skqi8VisVgslp2ARn3Phn3F0N2NdRsZ8/sf8GGyZFtVSAatgn6h7+ccr0FqL1fYluc0VmAMFi7LlPLdUtT69aiN5Vu17XYj049vBrV2HW5Nff8904tXiEaJUikM2kgcDC4KVygksOcrP6Ds0ZJg/NCEi5uQIivY81euQtdG8BAkjUuNjpEK3QshcFXMxBEXDvvFNH5col0HHRE4KcWc3/Ti8mPfZpQLm3QRrvDp6NRS7KaD92HEwVRWEUka9oiuzb/mmTYRAuHIxjEbDUZjtGH19w/iy98fmBWNrfjxePpdtYAyGWXVE31Z+fwerLz54KzIzDMOnZ0Ye0UFnql3dveQaCORwoSujwYXjZPjiCgJ3AujlQZV3mC8JEVQIlmCqEsjPAUG3CqFrPMwqRRCGbQxWSfKwAFSEheCzo5H3PUxUiKLirIln3PHd6rIcF33d1jx/J5Eem75hEjhOHmOho3cDXcBRCzGkn/sjRm3T9YN0+YvLRaLxbKrYrw0/oqV9reuFdgSyRaLxWKxWCwWi8Vi2eb8dO63eKTTBqYsGMQwM62tw7HsxHR9aFIjv76il6bkL8hNnIZJJKD5G0NCBm4emYSkyUkU6gIuDNqEjh2Bg2A2odVgn8FxNd0nTGxUdq1ZMuWW9xlJsncJ0TenNg5hezsXSofaUw/ASEisTCImfbZ9j2exWCwWi8WyE5Dte4Z9Je1A8Yokcvpcqk8cTemHi1Aby9GbK+l590QWXNazcYnZbUlTjmHb00ksLBenDh+DTKn22U/U9c52za/W+NooRF6J5NzSvg4GR0DvdwWJV6dBZAtSqwY8E4juktpFmWC84IRllwOxncAVgejQSDARiXEETp3PL8a+iSsUN67+Gqd3nkqxTFEs0iQiaaqVRBgDjoPJDEpyjCyypZxDB0IBGEWDUs7Buuky0N3S2eXpTobRpSuQSPbuvhrfSKZ3LM3uOyg5bdBoPGNQgNegLLJD4AgZFZnS1MFr2esjghjDYLP/G0cgNIi6FEQcZDqKUAY8n/TeA1g3xqXGaK5fegpJ5QbHcHzuG/AyCSEojqZRHTrgRiIIGZ5TOI5LHXcgGPjFipPYu+dqqpx4669lLkIi5NZ/3v5v/R6URWq5rtOSrd7H9kQIwbAe66kr6k2EsKT6LmpU2CR2guyOxd6LsLSEMbZ0rcXSDrACQ4ulrcn8IGb+tlgsFovFYrFYdgE6nzifCiEYZtqZu4WlfbKFNwmFGwWCGabBggIuLuj8sVYoKDS+F7weuhVmE4RCgjRBAk7r4H+lAmGhpD4Rpw2eUWhjkA2TDjkluPLOKXfc1wLzL+7IdUe9watv5js0ZsSFGo3cTgUpnA7FPHTXH+npwCEfX0a/M7bLYSwWi8VisVh2SmRxgufvvovuTjFjPvkOva7tyXv3TODoiy8n+nb9uCe3vO4OJdP3bE2/M9O3zesvt7xd6S+Xs7C8K71O3boQtynGBP313LFAK50ZlakXFSJAGRG67wnSSJSRBd0NC5JbZjgsISwcBxGNg2wgVgzfGxJNifAploK4cIgJF2U0OgIqJlGuwNlcx3q/hIdeP5rBL9TQ8ekPUQg6yjRlbpJqHYNUGqRAO+Ex/KCcr5AyO9YRQoMKpkcJwGTsNbXJiuQG3DYliF0GQsfBt0zhlRlHcMZvp7PxKA9dW8sgJuc0s0SjUQTiwpSBpHFII0kal2LjgQBJIKIs9IkwgsBRPhIBJ4jXSIlxJE5SYerqEEIQiUYQfjA+W/X9NFPGTmBmuojq0x30psABMRWNsvELQRcnuP7GqW9zk3OeZ9/1Gr9//WQqDvmK9ym+otrujT98jbpukutumvDV4thO6GQSffhqIqxGRCJBCetcF1VLQRq6WRptC0m2Flmc4MW776KrU8w+U85qH78xlvaH1VFYLG2OFRhaLO0B+4NosVgsFovFYrFYdldyXStaOTZa+vcRdCutpuiYxY1KHxcsVybCUmUNy1dJCY4T/h26GEKQPAmTg0apQJioVNYRRKORInDfcIRA0ooS4FsgMmwrVGUlN+xzLAAD/EVb5sBosVgsFovFshuQKQX7vzFPUP5BGkkCCIUlQrbv/l6uEE6pvOUYtVP0V/MoVKrYKTwWyIwzlDF4GLxQXOgIk3UuzHXfa4hs6ngNDyUFEIwvvvXpMg5PvBMI74yDRuKKoORyVChcAS4CiQwmMKExEUAKIimNWbEGT0cwAnREUiI9ksbBxaCNCCZCJZMAdH19IT/87zkYrxZSKYyQiFgUUVSEqasLxje51zxXiBk6wIvMuCgcA5X9czrXvXEiJl0TjIe0AYISyRkRbcYNMmkcPCPxjENceMGlwOAIgyugSsvgbwweQYlkI0UwHssdo8ng/IUyHPreSv72xjcY9ru5iA7FEHFI1pSy0BPZ4y98ZA9KS2rpef4aynWcbk4ddZ5Ll2Xl+FVV2d2anFNvkRbGpIFg8at9Tt747V3hX4mvtJ8dwbpLD+S2Gx7hj8P3CkpoWywWi8Vi2W2xAkNLk9R+62BqL66g68kLrABuW9BUssza+VosFovFYrFY2pgVz+9J346bmb+wF8MvD8qyLvnNOAaOXU6NF6XDKSuZ//v9iG4WDP7jXFL/LCEiNCv/3Z8BTy0l/ZjEvcJl9TE96f6tZQBEztf4q1a35WlZdiZyyxq3gj73u6hYFxBLGjiG5O/HrFrLwT+6ki4L52bFcpnEmRAieEgJxmByx2VKZd1HMCbr1lBxwTgGXjo/L5asm2DWrTBX5Kgbj/cKONhH+vbBf0zgXBFl2BNV/PP9YyhiSnZdI0Aitn+JZAKRocVisVgsFoulMbqmlm/9+MY8dzSArp8vxtcGCNz0/vGz4xm6tKrpnluYE/C/sT+pmysoOX0tura29XmCXJFbbh86dJ8LypmanNXrn2f+zwjG6vvRrTt0u0OKwAEv40KuGpxI3mSkwA08EInWL48KTdI4eZvpBt6FGoJxQUakV6DBMhOTcByM59M9UklcGKq0RCNJmwgx6eEKRYlTR1QIXCFxcmI0AoRvSBdL3Bc7cUrpv3lEHoHIuZ4aglLORkMsFpRCVhpTXhFMngrd2o3SoFL1bSJlvrA0cw4NJ2kJiYgEz3V1TSgAdBDCYPyMsFDiGY2HIWXqyx9nUAhiGOLCEBWCuNDERVAOOmkM+/+/K+nx8bpQtCkwsWhW4KojEqENA2Ib8Dv70LMr1CbBD2IvkR4JASISoSiRolOiLnSZjzD+qRvp856HiW2m7s2BJH6SwEyfnf1sPfGTkxi6pJnPZoh/5P6kbgo/m3XJplfcwnGsrqnlpB/dUO84WWiXxmCEYOjMyiDOlnKI26JCWoNjyHicyn/1puh3Hen5n7X8suIiSp3pwftKm3yhoTFbqODciSl0rXM+O6aVDqqWejL3HpPa4fif34hQ0GNZM585ACFwRg1D3Vsb3HtcuWrHBGvZbiy4eyxupWDgzye1dSgWi6UFrMDQUpC6Uw5i3f4SUReja1sHsyuQ68gBVlBosVgsFovFYmlXpJJRzukzGd1Hcu9lp9PtsenIlKAiWUTSi9ABcJICmRaIeIzXRz3PeYuPZU0aTCLO26Oe59iyc6kcqvl01KsAHJ84rW1PyrJzsYUZTef96YEvSHMOK8YghMBPEJbckkHyLetY2MAtQ5vsWC0jLnRGDWPtYV2yq9QdVcXTg96mOlyvUaniMClW7wzTzEk0KKFckUzQ1fiYabMomtb0ZturPLLFYrFYLBaLpQW0ouzvHzdyscstHCqkIPGvTzBGN99XFQKhDVXJGCXBhhScnNJgm0YOek046jV0WMsr4Rtulykb63TtwppTBtPj6VmoHNe3do8xQZlfN0h15rWcoGD7N+xLZ1wMc9FheeSGiKYujdFkXAsRMhBvptOkjYNnwDMOKnRIBHCFIi48XASucLIxRcJ9SC+4Lk8O/zsApcMqWHZM/ZgkW9qZQGQXLNQYzw+izrwnTCi4VA0GJTkiqKzINHc8JurHSSJX/IhBFhWx7tx9Obn4VRSGtDEoBJ6RaCNxwqvgYCiWmkTo+l4sA7fGianO/Ojz0+n/7GyM0mF5ZCc7NjIRiY46OHV+ttH9zsWsPqkb0oNBfVYwLdWH3809lp7pcoQwjO60gvfOORj4H92naopWVLH8hG78e9TvOafTtUTH7MH6A0oB6PHcPNTGFsoji8BBsTIZo0TK/M/SFgoKG6EVpX+f3PJ6UC8uzP2/Ob6q+2gDkWFVMkbC16gFiyhZuBgibhjYzqpGtrRHivcu561Rr7JO1XDBP49G19S0aju/YxH/HvWsvfe4izB6vy/5stwqUiyWnQErMLQ0wunahdg1qzHrOjPwzC/aOpxdg9ybArmdfOteaLFYLBaLxWJpBww551NuffBb/Pe4P3LWrffwrf+ezaA/L4A/A0ajUimG/WZW4PBWnGCepyj/+QB6vjcRhg1mvleDXxJFxxvcaLZ9XUsuWjPPK0L6Dd4XrSjFJouLEW4EtWlz4xeNbiTkczp2hEgEPbAnT//kDq755DL4vAqkRGhdXxpZSIzW2XLImTgxGqeslOUndmXGtffln0Y2aRbE7BnFCuWhyxLIeBwdlikr6F7QxLn6K1fR6QRopEcMP0OZZGYjF8PtUMLO6doFvbkK46W3+b4tFovFYrFYdnW2xMHK+eBTen5QX2Z2W8bQVAnX7PIckZA3pBfTbv0zx797GrQgMNxQ14FkXXSbxfqVybjvKRU8Ig3SnlmxnQJBnlugRiAxWfe9zCONgzISR+jggUHlXiIpgo575rqF+xSOg8iUaBYSz0RIh2JFZeqFjQ4aVyhiIkIEB0dIUsZjmZ9CeuAkfdzaKJu0JGUcXhv9MGVjoqwIBwvKCLQJHRuFCM5bK0w6KE0cCC7rHRmNMYFDu1L14kKjs5OqsmOpjDO80aDzJ2PJjmWBM2NZBybeeg+eUdSawE3RayDIdIWiRHqUCIkUgqW+YEDE4CC4Z+k36XPaLEw8Dq4bOFBmnBWlxLgOKi6J1BqS2kUkHdKlghnX3sdGXccqP8Iti0+j++mLET264QjDtd0+5Noff8gqvwi/SLD6iC784YqHSZpgHLX0hDLmXDEBgGOnnQstCQyByKRZ9J3ZAZ1M5b+wm4jrdCpFr1PnBO8vmVM62/eb33B3o4WS6ZaWqamLMd+rYb0qQnbtjEgkMMkkemcSu1u+GtvhvpbFYtl+WIGhJQ9ZUsID01/iOz+6kUHPTG3rcHZdWnIytMJDi8VisVgsFssOZviV0znn+Ov54KGHePE/fw/LRsFaleaqUUcjXy5m8cbO9D19FjeP+DpO+lMA1IJFXDP0CE6Z8Q5XdFxEbiLDYslF19TwqxFjKfanNH6xhfHP3PtGcvSes1lyUOuONfTtan7X8yPeqO3KNUOPwHiz6t09QnFh4JYhIe0FiUgVJkyUCuJ5sZhPht9N5j2dMkEyRaPRxuAIgSMEf940jDdHd+eq2S/w40cupO9vJzafWG6FoLIlZAFHla9E6IbjlHbggekvcdaNN9Lh2dY5a1gsFotlJ8Heb7RY2ifb6XPZnMhwqxGCxAnLGdReyoCGfVhTV4dOe6AVTiyWv05GEBb2v5PGx4OsA6BGEEXjokGAMg7aBCWNHXS9I58Iyxe30KbGmMA8MXRN9wiEhoXQaDQGbRSfpeHWvY6he591mKhLpE7TUQbFkBOyfoztGYmHpNZ3MX4NJJP1Akujs8fN4kjwg/LNjZwMm0OKwP094xL5D8k/hr6AwgARPBOUmlYGJIYS4YEI2tMzkhIpSEiXn6w5mNlfK+K3M99lVFQSi/h4JSWBENNxgnFY1MXEY5h4BL/YRcUlxpG4wkfWSeLra/goJVnv96JbpJLSaJLq0g785MNXGBCpxTOQDEWOf/v1XXyW6sOjaw/h9/1ehlBk6BmFK1p5r8IYNp+2H3//3R18f98TgjLRbUFbiG4yfYUG30vZMut5JcfbyfdAW2HFhduEgWfN4hrnCGRpBx769Gk6yygHfnwRfU+f1fRGxuSVjbfs/MgmLYItFkt7wwoMLY1ICMHZP3uDedf3ZFu8RcrTCSqOrMu6OOzWtPZG3u7eMbdYLBaLxWKx7Hi0yjrLxYRLRr9UbDwQAu+WbvRLBekEk0qx+B/74MztQP//NxHjpXnp+0cy4ftfY84hT7TdOVjaPVvriiccQ5HjtfIghrnX7snxZaNxUpqIN60+EaI1uC4iFs2WtzLpNAKy4kKjgiRkVKpsmbKU8dHUu1VIIVAYZKakspcmLjxuvegpfrbPqQw6Z2YzJ9NEoqip8aIQGAmOkCijG4sLt4VgxBgwClVZzQWX/ZCyaV82dlO0WCwWy85NK38r0sceSJ9fLGDjN5L2fq7FshOzTcWFOWVh25uDmXBk4FLuRkAJdG0tp116LZ1mLM4rXR30d4M/XSAqNGnTtEAobRxkKDoE6gsrZ0ogNxTr5bZRKPYLnAYFaeOQNG7e6lEUtaF1YFAiWWDSHmLNemRpCXEJ37n6eqRnWHV+iumHPkRCGJDBmCju+NQJEUyccl2IukjHCUSEmZLDEJRI1o1d9zLuhdnfhlDEmBWmKoXWBlmcoOfbcHPP54mJGBqdnYwIoYMhhdtRGUNKu5hUCikMERwkBrRGlJYEsQoRjHdcB+EpnFqfKODUpKnSRfzg+Dd496CR/LtybzpHarjlxXMY9HIdsnoOy70uDIjUUiIFrtHUGp8v0r14Yf0Yps4dRKK/4Ig/fcSg2PqswHDcw9N5/rHD6XXXxBbHX8UtfYa2RmAmHdCtHGltg4lpW0VuuxhD5XcPpNsVS0gdsb4+LkuOeNkKDb8SWmFCB9a4ECRkFMfZPZxCLfVoI7DfLBbLzoEVGFryMOk0B759LdLddqkEnXYYoT7fZvvbpWjJydBisVgsFovFYtlOeN/cn7UHBu4OA/48C7VpM/EVVQx556K89bTnMCL9OZuGFVPdT8Bx4wH4/f6P88KAMXxWMZ6ed0/EeX866VPH7ujTsOwkOKOGsezkbvS9cwrG9xH778mqw8voddekRqXFMAbvm/uzaViUbn+eBECnj2K8sexABjKpfqeFki1CAhrx0QxiTawTOBeG7hU6SEhidJDgGtqfZSd1Rhi4outrgaNI+E8RlPUKEoDh85z9X/HWxYiyNJGl8fpkQ8Nza4km1itaWc3Q9y9k6mETKJXx/DLJ23I8qRXRN6dacaHFYrHsxkQ3pfho7lB7P9di2VHY0oBfCWMMIrcNVdCf9UN3w4yYa+Ml4ygeuyFvWyd0TMqVBzk5/WxHaKTQuEITDYVwQP7/uX3xUGjk9OzO4nP70cf9FIVAhy6GKjxSZr/KGJQw9ceXgUBSAlIpihcuA6Bj9305rOhC3t//b3ihsHFcx0X8+boT0DFD3/dSRL9YGpQ/1ga0ny1vbFQ4zslx+8qIIyM9e7DkwsHB/YDNldlzWPnDg1BRKFlm6PzPT3l/zp58uGAo+w1cztOD3wIRTnoy9W2WWyI5JvJHE0YbTnnramSxT3RBEYOcmRBxwI0EYj5HggSRUjg1KaSnkJW11Ooo4xMLKOmeZHLlEBIyTfFKQWRDNcuu259V3npWRdfRRabYpKP8t3Y4UzYNYuaaXsjqCK6QvLZyT/bqvIZvFa8G4NZus5l04iCWx4P7GgMmBPdCCpEQDouv25NBz25Az18UXN9QfBmeWINrvx1EUXnvr7b5rija6PPF3H4MZ/3uk8u038s7HJNOc+B/rkFGNB2mFbV1OJYdye7yvWKx7CJYgaElD5NKMfziT7b9frf5HnciClmGN2EzbrFYLBaLxWKx7CiWnujy5ZkTADj+udNg02b0zLkMPa/xus7ggezx/Znc3udNvkiXZpff2vt1llzzEXe+djIAukMDNwtbis8SUjWqMz+7+Gkeu3cUxvdZM76MZ66+gxteOQ+zbCU6lcpbf+mJLtcd9Tqv/rkTAF0fnETXBvuUiQSiV3fUomXZ5E5QukkGTiMZR45sAjAswxV168t9EbqeaIPs1oWVh3fmi6vuy7oF+mg8o8JSYAGOEGgDDoJFnsf0yv4gqhj2g5zSzwWdMMLUodmCxFMYv/5iPkMvivDMp8M4pcM8ujpFgQhwG36+ZCKB7Nkdf9GSbbZPi8ViseyETP6c4ZN38/u5FktztKVbVUvHzutntrIc7M6MMYHYKxIBbQL3PcchMnggeu16dG0tSIfIwH6cdPUH3NL1M5JNfLk5wqCNyZvI42Bw0EgMLgIjyAr3yLgFNhAiCSFI9+/KZ1fdy2pVxxoVCxwMdeBg6AoVOCNC3qQehyB2nU6jqmsQqRRCCERxgq4vzMKfN4hPnyymX6SSmIDvlM7mrCtmUyKj7Je6lkGLEuj1G4Od5TgWGmMwXoNxujE4XbtQOW4As66ewDcnXUxsWXnwUsTh+R/8geFuMecuOZyKj3oy/OLpYAxLLhnHlJ/8m7ExB/BxhEAagys00gh0KDKMC83UVDB6m1fZHckqhl8+FRGJIKJRKC4GKTFFsfx7Br5CJtOgNWZzFZPKB+MKxQavhBo/iit9VBQq9+rCC1f9gUfLxzE31YuOTi1L0914YcV+rFjTCTa7IIPJYfE/deJ/B/fAufz97On/ecgzrB8U46CYy/H/DO6FFKKDjDPz0vs4bM5VlMz9EhkNJqcZpQIHSOqdQoP/6z9z2dcdJ+9zaXbCsq6xSfPYY2EX/IbjWGug0urfg21ern4XxKRSDL9oWluHYWlD7KfEYtk5sAJDi2V70w5mGFksFovFYrFYLFuLLCnhsQ+eoqtTzD5TLqHXqXOyry27dTxzLp/AkR++2IYRWnYGEi98zN9eGADUZpeNiiZ4/f3n+caFl+C+nXMjOXfc1EzSYtPJe/PC7Xdw8cijg+QhBUq25YoLpQjcCnPJKWO2YUKcaaPvC1wLjcoriazD4zsNxnQn/fMGhtw0KVtSLHef2xqTTvPiHt146OVDmbr/09t8/5tO3YeXbr+TC0Yeja6p2eb7t1gsFovFYtkdCCa8bIXgZnsKF3P33bCsZ26Z3PZIcyKmsERwZgwgpECWduCxD57i5JtuoOTZqUR69+SF//6TmHCp1QbPaBSgEHgm4yioUEagCB5S6DwnQwCZiUM3No4QjlMvPAzxjKJGS6p0nFodo0rHcTC4wqdYpikRXlaO5giBgw7LBcv640QD4SSOgzNzEb8beQAXz5rH8Ym1uKJ+/PHZVfdy+tEnoI5WkBG/FbquOc6L8388jKln3kWtjvCPJ+7FFRInlHdIXDyjeGzAu6x+v5bL9jkBXV1D10enctt/TuWZ/z1LygQOjMVSIIEqrak1QQnkzo7D/cefgp6/GMesDlpSiGCCWKIIXBeMwUgZlEbWmXMX4PmY2lp0dQ2139D89cbjOfz0aWxMFjOs+1q0C07aMDXZH884fFI9iCU1XZi5tDdscomvczARSPZNUySiwek2aIajnruRIf+s5a0XHm/8nmqAs5Wfy4aCsq0WFm5pTrHQZ72p9VozbhWC1RftzaM//CO3DDukcWnw3ZWc9stc69xrXGiZxWKxWCy7AlZgaLG0QPmrwzl/0MdM2jSYjYdUfLWdbeOB+qbzx3Huj14H4JXLjkB8NGOLtp//4IE4JR5Dzt6y7SwWi8VisVgsOz8j71zBMf84H7RBLJnX4vp7//EqdAROnBX0iV+57Agw8GESfnP2+Y3WF0vmtu9EVTtg03njOPeW1/OWfbRpCBWHlCPe7cPytwfQ97cTW9yPTCQ4ZHI5ZU4dd314DMOvnNLiNm1Bbpx3z6jjmNOC901s1rxAypfzfhl55wpe/9t4YC4AX/5hHKUjyul2cv17tdOb87jwyyuhbmbhA+YkY4QUCMdBODIooeYEiSyjTbZsmNICjaHWpHHDdF+uyFCKoDyyRBITMigL1jBbFSbyRCTCnh9rPrrrIMqemhw6EdY7WxRMzDR0vK8Pvv5/o+h5g8e+p/+Ad7//h206ia3TG/M4f8EVmNom2tNisVgsFovFsnNjdI7zWjjRRpuWLUu30wSaVlFIJFdA4GdUOntuTsZtMNxeki8ScwAXg8JkXfc8JDoUGepQeBgIDiUeMhgdtKLrbZRGKI2HotZESBqXpHGzr7tC4YpAEBkXkriIcPyc03F/XIYwXyKj4bqOUz85Sin0qIGc/Oj73H/9d/hrRRoTEeiIRITXL7I5hYjWBO7sueJRIan4e2fO7v9J9pwcDAcn7uXtul5M+OGZxDamWH2Lx9QDHycm6mP93rJDWXn1IEzl7KDUtBDgK7zw/eAIQUxIEiIKJIkbhQJcQmGfIzFGgPYDcWFpCSYeDWJOpRFKBfWphUAkfUTaq3dfDI+hI9AtWsWySCfSxuHa8//FynQn3t80kjkVPahMxqiuLEKui6JdMBK0a3AS9RPPjIRJyRi/Pefc4Pl34fzHXmvmIho6vz6Pb668mOefvL/gdS/oSNeMaC93/e36cWrtzltaT4jwc6Xo/cxCfjTlUtCzvnp8uwqm/n1qdGMRakNhoRUaWizNIASy4b01yxYx/8EDuf7Qf1PuFzP54FJ0MtnWIVl2YazA0NJqvKMPYNkx9W+Z3h9qSqatZN4P+zH8tvmojeV562/83jhUXND9/pYTYu2Zw3sv4OpOSxkcW8s9jGzrcPJIlQmu7rQUgBeLI7gtrN+QTr0q6V1aibftQ7NYLBaLxWKxtHP85Stg+Qqg+XySSaYY+8wNDHt7E6sP68hZpbM56IUbGLluHT2nuFwWuZIBkyfVbxAKnowVF7ZI6eI6/vT2cXnL3ErBACax7L0BdJ3jN7FlAxyHKzpNo6tTzF96VW2HSLcRuXEWJ2Fy4IaZSW043box/5YhQDDeLHppSvb9pHuk+FqfL5mTsztVUQFTmpgEFjoKZhM5QmYdThAiKLuldZAY0DooHWYEGh2URhP1CReFqXcsyewewbB3LkFIWPGT8fT7fSDqNDm6wUu6/I93y8ZueTs1/OyEicFMEkMtWETJsu5bvt8cKs8aS00vSa+76sfrqqIC8Xkti343lqFPVaA/n/uVjmGxWCwWi8WyS5LpmzX58ld3L9ymLog5/cim9umuKmfIs1cwomJBK47RWODXpmgDInNNVPA889Ih+zD3WzFGPPd9HjjhYQ6NpyFYi1x5lQoVZBlxYeZ5xsUw+D9nPKB1vZNhwwk/RiN0UG7ZMw6eqc+pSaGJS4+oUDjC4AoHiWR9dTG9PpmJiMVYft0YilcaOv/z0+z+jTE4FTXc9e8TGDF1EWZzJSIaRSSKgslSRoPSOW6P+eKxI3ou4KqOi7PlnyWCq1cdxtvv7MeQVZXIJauIvjKS/fWFfHbwEzhCoozm4LJF/OHbe8PpB2FCw0RdpImJCAd+eBlDe67nzsHPcdIrVyF8gQjHQsaBkZuWYWQwuUtEo8gOxUFJ5IiD8RXCjWBiLjoetI+sTmfPV7huUEpZa4wDCSdFRCiS2mVIdC3FMsVbK0dRUZVAKYnxJUaCKfbR1S7aBdfNn9BVY6Iw+fPgyXfHMjK6miHPXtPke15VVOBO87NiSgjucRTUmGbc8oM3Uf7ynRURClWFQa1bj5P2mP/7A0FAt0+g9OnJ4Xq7aNW2r1qZrqFTrMViaRZtRItzHSxN06lXJVd3Wso6VcNk5+i2Dseyi2MFhpZWUz4yys3H/yv7/O6Np1K8pJQbj3uFV+4ZAw0Fhgf7uCVpuH8HB7qN+e+aITxUtJZJm4cAOyBZtwUD9Gil4aHNvQGI1LQy+ZhDxZpSqmviDGL1Fm9rsVgslvaBM3wIwlf4i5a0dSgWi2UXxXhphtw4GQ1E9x3Hb9cdxrAfTkVpRXTBIga82XADe0uoWYRAjNkDhMBJ+gx7srrxOgfsxYA/TG/9jFOleHjTaDpFaqhaU0KvbRhua35nIn37oDuWoGfORe47CrFmI6a6BjNqEMyYi1EKp2NHvL0G4grJm7UxqtaU0DsSgdEjEXMWByWOyzpw8/EvA/Cn8lPp91J4AGOQ66J80bs37gGlmOlzAgeNXBrc9M8TFkoRJLYy4kJj8hNxEJQSMwIVvn8VJlsiLNsWOc8dIRl+T5plx8UZcNQS1O8aOxQ8WjGeaJWpH+MZjRw+DDwftWBRK1q//vzzlIuAW6P5a8UY2MoSVevHCMpGboC7wgVh+4lolIuOe5d/f/g1Yp9v1a4tFotll8OOuSwWSyNa7RLWxNioYdlfkb9Po5spX9pKwYrRBmfkEPAVetHSenFhVmwoEfuOJFJeiV6/kWFPlqE3VzVdinhraSLf4XQsQw/pi5k+u/VjyJwSv1mMDlzyaOwqt3lInFO//jGzDpa8fdheHBr/GE1jcWFD4wZlJI7QSDRRFDJXYJg5XqHrkyNuVJjAATF0QwycCwP3wrjwcKh3VuzWoQYxeg/M7IUcd+Yknv98DJ3/CSLqQiSC8H3M8lUMvX4p2o0gIhFwI+C6iIgJxjKOhnQYn8qNTeMZB41BE5Q1jokIb0zdhz0eXEVqQBdiHYrp8doiqtcO4C8j+5GQKZImSs/IJj49+4/cU74v3ymbRtI4vFW9J49XDmLQnwWr9x/AXd89ihE/+gLj+XltovYbhRN1MRWbEEVFmNIOmFiQkhaAcSOo4ig6KsGArPPqr63rIoqLQSsiNTCpfDC9iyp5feM+DC1ej2cc1m8swWiBjBiEozFRQ7QkjV8VQcc0XYrreLiyL5HaxuOl2EbJz5ecytDrJqMypZkLvAdFofe9LnDd9x2O3FSDWbEarU3wPsz9nI4eiZECpyqJXrC48fbNsSWfjVznSsDZYziipg5/6fItO2YGKQLBpJCIWJRrj3uDmPT4g38KpU9vYXw7M82do2jiuyC7rRUabi9k0ueBTX0C51PLzk34GevWoQax356YT61b6pZSsaaUhzb3ZoNXstX3CS2W1mIFhpZW0+OeiTx/T71LQl8mooEX9+gGNO6gDr9s6o4LbjtSdvxCnqc7yNrABSMsebXdaKmzmkOnxyfz/GPBNRHMyF+nFTHuKtfIQoObKrvBoM5isWSJ/7WSheVd6XVqW0di2a3Ygv6GZdei02OTmPlYW0excyM7dOCJfz1EV6cYAFXgZrQjJMeefC580rpStbq2lnf3LgaKGc62LY/cmt+ZuTf247qj3uDVPTvxw+ef47q/XUqPaR5vPjyB08afht5QzoZTRjLpN/cDMX7504sY/sxknD69eeGlv3HsxVcSfftT1JdLeH5UdxCCfuLjvD7ukBsnkz72QF56aQLfGnMCau26wsEImZ9YNBohIjnlxTRG6ECg6PkY38ek0xjfx9eSlAkmbmljsg4fEAoOhYMjRFAaGTBS0O/Xk1C/zhwr5ztRK2bsBx3Jcfc0hvgDFV/tdztMfhW9NIUPXk4A/5+9s46Xo7rb+Pecmdnda9EbdydIsJCQAAWKS4CgRYp7g3uhpYXCixaXUgoUSlvcXQotJEQgRogrcU+u7e7MnPP+MbN2716J3pvkfPPZT+6OnPnNGdk5c57z/BIbVUyvG7/LnRDGrsrK+HpAAVFMW9FgMBhSRP5azpw1rUyby2AwBGyOdnD1MqoPntG1dA4LkX7Wrc2NMO1+qPysZ08vvX5mG4oTXv6KBz4aRq/XWvLJmy9y9IEn5h8IU93Nqz6TBFFNYJXan6x3CWuO7s879z7IOTsdjqqoqL2segj2Ndi/7HaA0NDyhVH8+AKASqc9BvB1kJnXEimnwUx65JR7YaFIUCQTOMInIhQSO2P9r3Sw3Tzuhdr3Eb4mrjUVKkpcOyS1TZFM4GtJMxmnUCYCYaOwcLXPh/3fYOqbipv77MdatxCtQbZoTnyXzthlSeyVZbBmHVZxUeDIHh57nUwGIjghEFJCcVG476Ho0HXRiSROaC0Y1x5KB86JWBptW0QWrQXXRWtN8bj5vHfIbmBZ6ESSFcN6s+8fH+Cb/dsx7v3hzF5dSvsTZwBgWVPoMEaw6CnSqXRlQQwRi4Lrcd2//8lv/nEJPR+vRLVugS5w8KMWQoNVnsAriaKiFsLXWJUu2rGCwV+uG+xjixIAurw0i7LvOvGHf73NWcddzGsn96PX0PmoKptWHdZRlYgQ9yOgoaggQVk7SafW6zik/XTeGtgDKzEZfjko5/zr+n9jUP8HwrazrqM8IrHUtSaCfRRCBO3HrGtPSMEJL/6H+z4bRr9blqXPSRmR6TTX//fa3+jrCIZPPxEO2zLv0WRBAdr3g+OugjbxLi/N4PWJe9H3/J+DwXYQLFOdfKmdlY9OZJb1li7j/V1aAtAzu43bFJBWvfdFoOZ9dnPQ0N+D2u7pho1GTfipVn3CDkVTcxXeBL7Y+V0mvRXnhp77bZnrdVsm+7kqT930vXgsb4h2wRcdz6yTYjs5RwxNAyMwNBgaSkNHJW5pGvIjYH4oDAaDwWAwGAyGJs2ed19Oh0+X1Xx2T70Amt+A9GRbEFlUxFk/TOOh+4fQ5f051OWX3u9PM3n9iyO4buY/2D+Wp3NQ63RnnCUkj93zKKccdRl9zp/A8IN/RWzBj6jsF2Rak7eDB4gKh+tGfsEtd15MyxeyU3NXcwTI6niDoPMonbbK84KOl/CDkBz6YxknljyDFBYqPCbZaZFl2AFoY/FV3OHBo05ALpgZRLkB7a/EGRG6+GvqrM8GY9p9BoPBsNX4ZK+/8q8fB/DJgJZNs8PLDAAyGHYYGpI62WpWzJmjf+TowpEcuPqCYGJ1EYIQvHf03vQtn4kqK+fog07Cnz2vgUFs4L1mK6VRrV43qy4Ywp9ueQ4fwdDoasBGAo6ARLioIxRxbZFEhumRg3ZFJHQcjAkfR4AlBIhgG0KGbZVUCuMskWNqeuCUmGmjpBwMC2WCIuGxwi/gquPPovWTi3m+2xdI4SOiURaf1pqd5SooiKEtgbZk4MCeSIBlZdo1QiCEjdY648ruupm/s5h4Uk/6Xz6UCb96mHXax9EuIikRZRVorfFXrw1SFguBqopjNStm5pM96NB6KWedejlnjfmAXxbO48iVl2B3aMclX31FRPgktcXn63Zhxr4+MuIE4rsePbjon+9yz/ln02v2fLAkuB5CKYRro63gXJBekNJZKI3wFcJTgSujFKA0IumiHRtRECPy8yrOGn4Jct4iZLI5vpIUtKqiQ0kZa5wCym0fVRKnIOKyc/dljBzfj1G/LeTSCe8QEy6X/WdPnjz8SGB+plKyRXXV+/7C81vnqcvaEBrUgN5c849XeOzwo/DmZrb12yPPBCmwK+Obpx1YDVlUxOnfT+fZm0+k4J2x6bax0oIvf/kI02e2BuCRk09CT5xas4Cm0ve5CWxUSnmDYXtge3/2344ElFsdU2+GLYQRGBoM2xvmB8NgMBgMKUwDzGAwGJocuqqKA5+8gW5frMCfMbuxw6kVnUhw9wun0e3bFXhLl9W57Ooj+lB1yjqOKSwHbAYPm8zU/cKRs0qn00itV3GGPH8d0hV0mB64CNZaB9V+v1ZePITVe/ns/NTlAHSdvI7qv3A1UlGlBIUp98IwXTKA9gJXD9G/F9OvjfF8s0dpLiMoFAjSIkPIpEa2sThvwUFMfHVX2s8YGTjcZzHz0cHQzCOyIEK33+d3dfAWLsq/vw1lc/2um+cDg8Fg2CAeXrU/H/5rKB11E3Ptycbc2w0GA4HQRseDZ/myX7+TNaPaPUJrvHkL0l8b3DbZmHtNjpth0DZoOWE1Bz95A10SG+nAHpaZdmzMwicY4OQVCo4sTJDQLpIobugiZkEo/QvSGCstiOsgUbLSEoVECpcIipjwiWvBHn+9ih6TVuITipm0yhJ3q3Q8684cTMVJ60OXxGBfI2FaZICYcHGEwkIj5ixkvVvIzUv34ZNX96ULE/EWLEIWxJClrRCexqpyIekGzogq8FgXVuCOlxbAaRU4tYcDqbIFndrz8BcsxFnfEQA3q6q0r0AK5ty5N24LHywNQtP/1gW0bF5BaUE58Znruecfp/J/FrSYoUCs5ZcFq9ntgysoWODgVEDHyAREQQzh+4hVZfzuxbPoMWMOOp5AFBWCbaGd4BMcNI2Me4GQUGlE6JSnHTtoeSkPXRkPUkT7PlprrCWrUYkEaIjaHq2KK2kRqcIWijYFFXQsXEffwqX0iS5lZElPxIwF/PaFs0FCl0leIPirdh5q30+LRFPn1Iwn96bnqz72l99nnWsE7UiZOeeyefrp4+kyy8Veto5rXrqAHuunB46BYftTzVkQbG8LieB0IsG9fz+VbtNXED9kL+aeGezSzS2ep7NdQA8ncL1/KGLVU9I2Stb1VyfmPbnBsO1hrtmGY+rKsJUwAkODwWAwGAwGg8FgMBi2Etrz6Px/I2mC3kc5bEicK/aB2YP+Raqb7vmu/4OuUJntIKihQit63DspnQJN2DbJX+5BbMxM/LXr6txGeVdwWsTpcumkIL50wUHnTlpcWK2zJ5UyDEg7faAD0aPWmninImYe8hcSOpJ3uylxoURiCcm01e1oMz6e14Wlzy6L2LXFYt6L7VrnvhgMBoNh22PKug60HZdouh03TTUug8Gw9dEKlUjQ+f9Gcm/fI5FrncaOKC/+TzPo/NOMGoOGaqV62uWQ6ulqU6zpJ0m2yDizqfS/AEsAGnzAR+DqmuIrKTQSSGpB9/sm4McTqY3WGubyfWD6oJdYGFrVWSh8BDGZxNU2ERQOOkhbHMb7yfz+dLpvFBQU5G7fV+CpQDSoNBqNkH4gMqxeJ1qlXdozO63RnpteRiKJV9tP4TjEdlrLVX2/4ciiqTSXgnPu/BUr57ZiZXEz+iVm0PX+7/H36Y/b3GHNfl3w0fR4TeF8PjKIobAQ4ThoaaFXraHrPYvQJSUQcdLtJu1YKFsGboVaI9zQK1JrhBtWltaZtlo8Dm4S1aMzibYFFP60lOSQ/sTbe7SOVlBoJ2kVqaCFU8XqZCETV3Wk3Iswy26HWBVBV1XR5a6R4U5mtRlJVVfNM09IwYdHPsyw8mvpvX5XmJ5xIRRZIsTqx7/d46MRloWyJN3uWoQSgQBUCAGOg04mQUmEVPlTFG8i2vdpNzYBq9dRtUdrjtl1PLb0eX31PrwpVDpFtoy7QWzbgWOhwWAwGAyGxsEIDA2GDaUxX9htzm2b0ToGg8GwzSNsGynMvdzQCJhnCIPBUAuu9tPddm4oT9RhimQLEKm0XoDVoT2fPP8XjjjvEpxPx9VeqLTo/rvv6r/35BMXWlbgHpGapwPnwlQnHYDK6taUSHx8pBBYCGQomrTCDqUxe77Gx89GeajPzkHHTHZMhy1hipT0UFPqjtNgMBgM2xxv9Xmf8X9T/K7Xvk0zRbLBYNi2kVbgxJdyVKuLlDgojytg5ntQRt/zvmeL0ET7FlKiMQvBpEseAzKOfa72ccN6St3FFeBrgY/AD50Lg/l5UjrnS/OcdQxSTopCp9pEmRTJEeHjBK0MpNA4IkjNjNJ4SqJUKBRUChlxEIWF6Fg0CNAOxGpplAq+i6zpSma+Z7VzUu0VYQm0BY6wWKUitNMVoEHYFrpZER1Pmc0Djx3FkUdNDQZYWRb9rp+I1jpwxPRcon9axlu938LVioTWmcFbBA56urAwmBZxELogp/0lqhJQGAnSInsKkfTA8zNO80oFKZHjCXTSDVItxxMoz2X2mSWce/hXjDy4I5c89TpFMsFP8U4klIMjfErtMh6ccihdT5/ByoIYK3ybPt744FqqIzV3IDa0MkLD8NxIaIvppz/J8H2OxjvBxq1+nksR1HfWOhCI/LTvp0WMQoSOh66LEAItw+OUOvtEVvt0E5HFxbz83COceNP1NHtlLDP/nXK29DLb0hoRnRveZzZ5k02PfC6GtdwXDQZD08PTFr4RPxsM2wRGYGgw7KiYh2mDwWDYppFFRdwy+VsGRpLsu+qcxg7HYDAYDDsyGnytsITk2OHnYs0PUioLKVHl69JuD62sKA9O+ji9miU0jiiqt/jjflzO088Po+P9I3NnZDt2WHnSPUmZ6bSCoKNNK3SYPmz6k3vy5REP4eogNXJaTBh2KDoifwopH5GbSisrzl8WTud3Px9H2QEr690vg8FgMGwbSKEYOv502py3BtSKxg7HYDBsZ8iiIm6aNIo2VgXHj7yMXmdODGbke3+fPW27VAltBkKBwpDfjyBeKhh/5WP4eerS1xkhodKZNkPgZmgTwcdBEROB+6FItTfyCTyzUZDQHkktkShiMkiN3MKqJK4dWkiPEmlRqYPUv56y8H2J1aY11438grh2GPHp2ex85wIiFVWolatQgIg4QQrk1IAtS6bbN4G4UCJ0KDK0CB3bBShNq69LeL7Lfbg6goVgoWeDBN2sGJatBCnof/N0zv7iOt7585/Rjg1Ssn7YAP5w1994eMhBqFM8TrNPCMr3PGKVM0JRoQzae57H9N/34/3hf6ZS2/zuyDNg+SoIB5fJipT7owZfIaoSiMp4WJehAM5xwHEQgI7G0eUV9L3jJ0be0wldUcH8ZCmDC2ezT8Fc2lgVtLECCec/WwxCxKLcPOG//PbGSyh6c1xep8EcwvnZKba10vx232GseaGEj3f7BxO/L+DCX5xBixWTg3ZltjN+Lc6Z6dMwNbAtfV7oXAHxFuqfC/YndW7m7r+wUoLK7ezeka7TevaruuOnqCYUFTJ9LBsk9jYYDJsN74QEu188gn9e9ufGDsVgMNSDERgaDAaDwWAwbItIyc5OBYWyfmGGwWAwGAxbFAFW+HLeWlOBv2x5MF1aWMVF6FDkZ2PRP9KwFG2rLhrCqoEefS8Zy9/vO5ZOE9fVSItcZ0iWBUJkUllBkDJMa3DdwF0i5tPVLqRKJ3PWDdIhB+6FMsu9pPfLl1EyV+BUaloyqsY2+0SW0j9SSOfCtUxt0F4aDAaDYVtAaUnCs/BXGHGhwWDYAkjJzpEy9n33Wrq+r9IiF+15jR1Z7VQX3uRzY0uJdlIDc7IFeQ10b7M7tGfx0y3oeE0cb+78OpdNoariHPqn62j/5WIWH9MpmJYltMoWG1poXDJxpTwMA6dBRUQoHCFw8iRyTguRqqXaFRpcNAqJJRRSKyyhiKBAuMSEICZsHALBndICITS6rJwbHrgYoaDTSoXfqRRZmURUVaGTbuDyF4kgrJQoSgeCKiFynNWFbQdtHiEQvg+WxbzH+nF06/4kWsA/L3yICu1glUtYux5atUAkXRadswvtj1uQHmyFUmgLujtrwU3ir1sflB+6J8qCGMKxg8FbWkMySY93kpxQcR2TznsUUudw0kVIGYjypMykRPa8zDkenhvpfQMI56nKSmT7bix7rpRukam42uKt1QMZ9de9UJEg1qIlPhF3OdfcexntJizNyOfqEhlWnx4u6y9fQTzZimIRZSenArViFTrpIpygO10IEexv6ICYLTTM66JXfXtbwP1TV1VxzN030H7cMsqP2YvyC9Yhwow33heltH9kVLBdpTLnSgPa1Ns1Ofej8PyrLhRtok6tBsP2yKybduLIQ8c2dhgGg6EBGIGhwZBCWpSdsg/aguIFceQ3Exo7ohzUAXtS3iWK8KHZG+Oa9gsGg8FgMGx5XJfTpp/Ofb1eb+xIDAaDwWBIs/jI9hQMbEtsjU/k0x8yqbEEePicNPMoftf1PfaORihXcc6YfQKRVVU1uuy8mMAqCdw+Wr4wqn5xoQo6edKCwlRKKkjHkHKR0L6icvggenVajELXSIMMpMWFX1RFeXbpL/hXj8/o+pmbm8q5WmfDdZNPoVvLNUyZ1Ym+jKP8lMG0+H4ZJF3W7NeFktfGmrSaBoPBYDAYDIZcXJfTpp1Bz9ddrK/GpwVcGyVuaQxBTPbzeW3P6loDofiqumtYXVgWHZqtR9sFmU1kOc7l3ZTrUfqXUQS9J53S0310+AmLDk3oZFZLxBIKX8u0INARMC7RirtnH02RvzwVQJ6NqsA1TqvQ3T2VqlkTET4SFaRE1uAgGJ+Q3Djz1xT5y5ChEExVVdH2r2MRto3o3IFkl5Y4SQ8hLYTw0FIGArx0WuQwbkmuyI1QBAdpQV/Jq2MpAax+PWl1iYtUGtU5TsWg7hTNWI2IxSjbK87Hff4dFCgzbSlfC7QfHCurdw/W7dmWZh9MBscGpQNxpOdRfsRueAUCpyzTHhOh+FHHE4Fs0QriIUyDjJ86B1QgSEwJ9FKu82E9+q2K+HTPpxiXaAXAnPLWlD47BhlxgmWURmtF6V9G4cssF/q6BH/1MDahuWX2GUS9pZk2bT5S85TO3Vb6+FQXrUnSzoKb6VrVShNdrxFJF+UIOjZbjy0U93V/k0tiZ7BuxWAAWn06G7VqdcMK3dHFdTvyvhsMjcCuQ2fxaMexTErWv6zBYGhcjMDQYAiRRYW8+8CDlFpFDBhzOh2+ybNQIz5Urrqhkh8GPs9yv4Jz3j/cCAwNBoNhB0fF40SOX8X9nx7Z2KEYDAaDwZBmwi1P4mvFFYuHMvtjP9OxomG1nyBxVBl3fXwMb/b+jDkeJA5eAWpppoCwI6PdYyNp99hGBhF2pKFU0AGXcq5QKkx1pPjD/c9yUMzFQ6WFhUESMxX+LSjXCa784Rx6XbmcJWMqEX7d7cGOw3/CBfqyBGHb3HHPs9x478VEyjRv3vsA5793OKqyciN3ymAwGAwGg8GwPaLicSKHLwAWIFLpb6VEe25jh1Y7G+J+lhIqZYsMq8+rBW/hIji42jZF4DHYULIdyWvDR6TFhQAx4eIIhQPcP+8ICo6Yi8rafo57YcoRTvvIwkK0BS7ghmVJFBHh4wiFRFOhFbfMPjEoU1qBy6HMpOiVBQUQcUCDLI8HojspEGS5QKa2S1gVqRS/vp8ZZBXWmbCsTKpeIehkFRITVbyx39M80vtQlh3jQOuW2JGsOpUSEXHwHUGRVOlBXEsOa8+91/6Vh784AKQFQqXLPvaOL7mi5RSiwsYDtB2UgZDoykpwk+l0yXhertBU6WA/LAsIXfZcN10nwlPM8SKs9Qtpb6+j0E5SnhJa+n7gTm9lCQtT5W4oWY6Hd8wfRuSw+WhARKO5i4msAWxQQ+BZg/rmbyKyIMZzd/+ZC2+8luLXx+K9LfCE5C/fHcCnO7+JvE+wSlVx9vRLYOXKuq/fHcDdsDaRcl3CZYPBYDAYDAEbN3TDYDBsX2znDQaDwWDYHpFFRTzy06e81P2zxg7FYDAYDIb8hOmjtOshsl7Wq7CzTenN0w4RUuS6FIbbJpWKK9hY0PnkBqm4fC1R1XwTJQIbC5ugc+rwW66lx9kz8ZYt56KdDsf+8oca+1cb2vN4YLfBlD47ZrPso8FgMBgaDyk2QqRgMBgMG4iwrEBgJTey266x3/E3xClO640UfqVSKjdg3WrLqNC5UGlN9pwKJYlri4S28JFYYdtAhX/HhI8jBHZdvwHVtnXQ6BV8c9IDuBqSYfdrRPiBYBFNiVQce/+NxE5YCdJCSEGhncSyQpGi8gPBnq8QnkJXVHLwV/OY8bv+acf2VErklKsgSqOTSVQiEZhSuC46mUSnUhCHg65EJIKKOig0p59zJSe/eRUldhy1bj1YEmkF6aArtALXS5cfEwLRvBlWSQkd/vEjD++zf5iy2Q9iDs/bYiuOIywsEXrBx110IpleTlXF0Ylk8Ikn0vGl2mfa89CJRPCJJ4LtpweraVb4JbS319HFXk/raEU6nbJOOQdWFxjWR8pNM0swmpqeSi9cbxEbc81tzPlfD6qiguv2PIaSt34Izg3PQ/s+fngOflZVwDk7H4n+YWoDY0wJgndQtsAxMhgMDUNpUTNd+Y7ClnqOa+znQ8N2h3EwNBhCVEUlx197LVoK2i+M11ygkR+oS+8tYGjnS5G+pjjxfTBCLDU6Doxl+Y6OOfYGww6Hqopz3vXXcvCtI2tfyNwbDAaDwbA10OBqH0dY7HnX5bQavpBdWiwBgk6XmXfvza8P/5pSq4CSTwuY+m4vhs6/FMvVFKrRAKw+bwjJ49fS/oRaOj3qeyGWnSI55dQRTtd+MkzB5WP1703sqdXsEV2LIgKATdARlmKdquKIm6+h9KsFeIlEUEw+50GtWXrVUHqdNLOOwIqYvTDOKddeR1F8XB3LGQwGg6GpY7Vuhf1mhOR1pehxPzZ2OAaDYXtCyEAopYJn1rRoKvUIXD3Va1Yq2ZplbZl+AhmLoT9sjfun9thffl9zAZ3lgFf92T1fTLXFv6lxFhai3m1JkZPgVy0/SzuUu2j8UGToalAIXC0D90IUbjjIyBEeMeEiASt1AGpzLwTQGrtLZ6IvJxje7FssIfB1MKjKEV4oVvSwBESEQHqB2E4WFaLfacatXd5iVccifv/h8bQ8bRm6a0d0zMYuT4Ln8d6th9B7UXng0hc6AqZFcEKCBSISCeJyIoFToBueR75CeUlm/GUghXMdur80n33vGEH7n+bQZ1UrRk4bSOmnC6i8vwWOU4EjQkd3IcD3kaGDu44nUJWVlA3fG3HBCkpOWhaIEFOu8YAjfCSCtyuKuecPZ9Fq9czAQT6eCJbxfXQyzH3p+zXPB1VNfBqe70uvHMzup/1IhYrS2q7g0I+upftbipj8Kb3t9CqhQ2L62OQ5Xqn/hcysmzmWmevsD93f4Z7/Hg3Amru7E/vvlMzy2bFXd73TKnBdTA90q+M8z9fGrZ5CPHWdZF8vea5vf+3a3OnKZ9ItAzmoxUCshKKgbDse+JZKi533flit/pRv9IMGQxNGNlDgvV2yNfvyjKbEsAkYgaHBkEL5FL82urGjqBXx7QRKwr916oFZSALLeCMyNBgMhh2O8HfrxxEdGzsSg8FgMBjSJFpBy2hlzktBp0zwxtzdeWf+boze+58cOL8PJa98l7NeeRfBK3s8z9lXXEvHf03HX7mqdlFhvs7IlLhQyrS4MNXhlXLGUAcMYM6RMWb0fgVXx1AoZOjqMGj8KdzU5xNOKl6PhaCyraRVLBAgysJClp63O/kMF8u7K8rcGJ/1f4/9J53I+niQPksA3w58nusWH4xfZVPR1qJoI+rTYDAYDE0HEYnwRu8POKLVJTiNHYzBYNg+0Sqj08tJJVzNHTD1vboYKT1/8/cTaK2Zv6oVXRJBgLKwkKXn70HHt+bhd2jFqgEltHpuVO0FbC0HHa1ZsLoljuPRJlaeM8sHfA3JUFhYHUsoLDSO8LEESCEQQqPrEUPqwhiv9XqXNQoqtcbVVihcDMpKpVuOZh1HIQR/7/MqrWUBijh/2/klTr/4OkonJoisiSMSLrgehR/8EBxnxw4dAzPO7SLlaAhBe8i20MoCP0xdHAqrhuwyixW9ilm8rhsd352PqqjEa9aRik6CV3q9wnmRa/EmtGB/LsDzLHom1qGVonhhkoNGX0K3yrlopXEqFQtXNafEX5zxgs8SGQLMS5bS4vXxaNuuMS9HjJdO25ypE50lMhR2UOfl3RV/7vwRn1d2RgqF1SxJvFWMGARtv+x0tznpoHOvkWxBYf6DmDt/74jFm72DrDH7ll4abI96xIWkNlnHuV79Wq4mqswIIvNc/3Wdh3mud+fTcRv8vBI/dhB+VFD0RtPtK62V2lxUG+KuajAYmgwmXXntBL95G7iS0Y0YNjNGYGgwZGG3b4e/Zi06dKlo8mT/imhtbG4NBoNhB0VvphSTBoPBYDBsLDLsoHvjggdwkfx+/vHAcpCCbv83DqTA6tSBbz9zsNzw5Za0sNu1AcAr1PR3HF687s/c9L/zYeWquts46Y4iETp4hCmRRZAuOUipFbSXrDal4Pv8dLrDrGFP4OtUvEEZHj6tb7K4/Y5h7DPwL3S1i/nXlQ9y6byrKZw1F9miOWNueQxHBMJFV/tMdV36Ow79//0brJuaM+n1OM0vVxTNCRwYhW3z8yzFmBf2pOMKnzvu+QsPvDA4vxOiwWAwGJo8Wgu0UkxK+kjXWN8YDIatQHXXwrrI63rm1778xoSTSND1lMnp77J5M8bc8hgHLbuc5XtLHj75eR59bqdw4Xr6KjY0zlR5DXA9VFVVdD1lMlbrVnx74Z5w5X8z84C4lqiw7eIj8BG4OtNVKoXCEQoJOFjYUuFJAdSSgjfcz4T2qNQ6nXrZRxITSRyhiAmfQmkREzZaCGTEgbatSepUWmZFK+nz/IiHGfHbK4ktWJsZKKU04AcCQt9HOA5aZ9o6QZ144INWKh1PSggnC2J4WvJM738x+Zq2PP3G/oiIw5zhUd498UHmec2xK3x6PrMAtW49Kp5AN28GUmJ/+yNdRwmU6yGkIPrRD/T6wEdHo0ENhum8tevhaoslfiVzqtqEboThYK9sYUPWsctx+ctHalkFca2RYarqfw75K3/qNIzkmz7CtpGtWwWLrV4bppCWCKkyApXUNvKcZhslYqlebor09/A88bMdPYPztob7ZWpetZgaMz3okjMTtGhWSdEbjRbClsOIbAyGJo8y/VxbB3M/NGwCRmBoMITIkhKeHfMGJ19/PcWvflf/Co1JdWFhPjZklKL5ITEYDAaDwWAwGAybgeuOOgd/+hzQy0OHDQthSdaeuAf//L8H+M1uR1O4PnBDsNu14ZWxbxMVDhLBZ1WFPNR3V1A/1b6BPO4twpJpcaGwrEynj9bQsytvf/IyrvaxhECFosKUe6FEILF495OXGXT3FZz65vW8+38PcsOAoyjMSiMVpFYLOos+rSri0b67cNyPy0GDHvcjN/TcD/T8vCEXvzaa+97YA5QRFxoMBsO2iNLBb4e/bDm/7TUES/3QyBEZDIbtjpSYMFtA1wBxYY57WwPX2WBSQsFa+hD++/BTAHxcVbj5t52HhorC7DcjjO/9GKmeFF9rXA0uMp0aOXV/T2oLS2T6XBwUMSFwhIUUOiP6qi19LeDiE9eChLZwtYWFwkcQQ+OkTAbDtsjaE/fgv/c/gasj6enzvQLuHHo0LYuXBW6FlkQUFiCz+3mERLtuMJgKwnaPj3a9YHbKwU+pYACWbSMKYrSPraeNZaOQ+CtWYbVuhY5oYsJnkduSyKo4aI0oKUYKEQgbw3TGIlIQVqCPsKwgTXO4La11WkjnCJ8DX7+e3tePRTh2OqYaQrwQ4dhBjNlIUSNVsvQE65RFkUzQRiY49ukb6fJAMIBNex7Ln21Bh5Iy1KEK7WqEYwNWkG68DmqcRw25drSuU/grhMgVVAYbqrGdHAFkE6PHGZMaO4QNJzwmNe6HIbVNNxi2eUxWQ4PB0AgYgaHBkIUjRN60V9sM5kHCYDAYDAaDwWAwbG0EvFHRkr9cdjKR+VNB5dpDpDpZHAHay3T0+CtWcvw5v0m3wayEQqrxecqvpQNHikBcaFkZ94xUGi6t0a6H9H0kAkuIoFMRF0dY+FqDUEgsLCED6aCGVp/M5vTFV2KX/ZBpX4XuIH7YOTQ0uprPxgpOKZlGnxOWclv/E2h5zMwwqEyDcsQFV9Bx+nw8Iep1WjEYDAZD00Uh0Fpglbam/ydrmDxid8SoiY0d1gaz7sPeVHzZlo4PjGzsUAwGQ11sY+k8ra0Q7/qPelH2VTs6PzimwUKh5LWt6X/6b5hy+mM15gWuhVb4dyZ+C00ktLqzQpdDSShaq2u7WuNqha9FTurlmPCJCR8nLC/liA7gCIuEdnNiIhoBx0ZHgvTC0rYhJeRLLxgICPF9tOcF7aEwBh1PBO2i7OU9j2lX7cKwkj0QnsJhArqign7PrOPUyTdww7X/RsaT6Hg8ECRGIuhkkumP7oUo8ug3YiaoUGRpAU4gMNTZDn1SYKF45vi/clv/E2hx8tJ0ewzfD9prUPe5LWWu618KBXFtERMurSwLFQERcdBJF6SgzR8juHYbhLUaIXQg8vNVlgPixvWZ1Xleb4rDoFbBIDwRiBXznc+NKobbhvsYa6u39HQjxjJsL6TO5e3wfJZi+9unvOQMHqh7IIfB0NTYtloqBsMWRCeT7PPh1azcU1D2q30bOxyDwWAwGAwGg8Fg2CZoOxpue/MM7C++R1VVUXniYFZcOiRMYybSzhRFQjLnt7tj9e8DBGJD+4vvcT4PPvJ/ecSFKZeIaggp0sJCYVmIrO3g+2jfxz1gN6Zf3BqFxtc6dCEM3EtSKHJf4PkrVmD/JxAXrj5/CAv+MJQZV/fIWb5QOtzffjS/HHsxl/z3HNZOLg2DEqng0ErjfD0Rb/HSjBuNwWAwGLZJJJpjekxhzpV9eXPCXthrt01H2tWT2lDysxG8GwxNFq1qfupZRvt+7ctttrhqFzEoMtvpaa9mwR+GYndoX3d65I1g5cS2lCzYsH3S30+hZF7oXq51OkOuhc440yJRWqZdzqVQSKGwhEYKwfEzhjHz4155RWpCihxXQ0UgSE+lXU5tyxGBsYVE0uvL82g9NZ4uw0en2yMWGh1xULFI4CZYmQjaO6EjvLBkeMxDcWHKPTBbPCWrtTmkRPsKOW4qsa8mEx05Nagb34e5i2g5I0GfyDLwVVCOkBC6w5fMsimYFkuL/ladP4i1p+2VcUcMt699HyEE97w7nA/X7s453b6rGUdQYYEwL/UhGByW/qTEhak6FbnCzyKRDFJMp/SUqX2fPBMxfjoojbBkUC8NcAFNH79884TmjfJm9Pzs/BwRaN7zOjvtc/Z1ksd5v0a7MHTdMxgMhgazmX9fmwrTP+rDCTOPANgx7otaI5wIP982NHhH2kBxYV0CdGuXfvx821CEbfzlDFsWc4YZDCE6kaDvJWMp+m8bZu1USsm/GzuiOtB6u32IMBgMBoPBYDAYDNsWzf71Hc1fjyD790HNms+iE1yO22USU/9C2JEk0QKayRg/nfcEB0y+nJKpNR3/6nyhltVxI1IdbaGAEcsK5ocv2lKOFYsPiDLj1MfxUDmdn6n0yCl8rfisqgCnUoO0ghRKnkfiuLXcu8s7RIQfrqeZ5SaYmOhEiVVF5ztBLl4ALZpB/z4122jVvvs/zTAjkg0Gg2Eb5d52Ezjz7O+4oed++CqPw1Jj0wBXnj7PLEGvL6MJRm8wGPJdv7W9/8+77OYNpz6sZs1wu7fjnYpSYiIlwCrk3jNf4Km3joely+ovJDsddD30vHkUCIFuSNpoy0L26o5euCRnevUtpcSFKTGgryWOSLkXaiwES1/tRuenR6FFMKgpJ/ZQAGG1bEa8e8uwzMAZMa4dYsJFopGAE8bd77pF+MuWYw8fzAeVMYZGKxmVDNz5HKHBsVExG3u9B6vWQnFRuB0rOB88D5TKOPQJEQjtpAzaRpaVcYxPifxcNzMQS2WEq7JNa9Z3jdLPUek2lbAk2gsEhB0fG4f2fbQTdCN3PXsWc9e0hjd1kJ7Y9wPxoZBgCXreMoYPEoPZ65Bp4CfCGASoVGyZkzRwnc86j0MnP+Hk6bIW4AiFI5JIIggNIhbF6toRNWdBsJniImjbGr1gMVa7NuB6eIuW1CyrgWgt+Nui/ek3YiZlPyUbtpLSuc6G4b6nUypXF8ykviuJsHIdIU1K342nzrozbXHD9sB2qg/o/H8jmVc+FK5r7Ei2HiLicONZr/Pc5BMo+GnTy1uze0u+uuR+znno8JzsMQbD5sYIDA0Gg8FgMBi2ccSOYh1vMBgMhiaL3nsnPnzzRY4++GSE0ChdTeiQ9f6zxs9WQzoXU52J6ZRSoUNHljuG1hrcoIMzcHMh7V4okTkiQ0sI7DA9ckK7PLb/QbRaORbCTi0BdDhhKk/QF7t9OwaPfYtmMsYx/x1Bv8umB9utms70+wcy+ZRH06JFK3QnAXJSoC33Kzhnp8NRFRUNq1CDwWAwGDYzx38whvs+PI5e161q7FAMBkNDaEoO2CLj1IfyWXzOrrx43Z+5oed+UEN0PTXH2Tvvc/4W3DdZXMSLn/+d466vqVJIaomrJXHtBE6DOhOHi4WlVSgMBIlEZ+93SiiW2gUh0Foz9zd9+P7Ch1noQ5mKUKGiKCQx4WIJTUwIYsJOr4MQFL49hie+GMTOkz/klpsuobKdxT+ufxBtS4SvIJFElVdgRSOBiM+2EbYdeB26Xka8FraFgrZR8BGWRCsVtMVUUPdpQV/qWPg+065oz7iTH2RF6IiIJYP0x5VVqMrKQPAnA5d4HbosCqEDZyTbDqcH7S7h2Agp6Hb7d6y6HWRBQbWDknW8w5gyAjudKwhTOseZU+hAuBkTPq72QUH5/r357MknOHHgMPwVK1l1dD/evut+Ltj1aKIvVDB7dSntT2yYwHCTxXz1ncu1zE+dP0BQF0b9v1kwwkzDDoERy24XqMpK/tW/EwV6TDDBpEo2bCM0oRaKwdC4yMJCjvhxPU90f6uxQ9k8aM2C24dy+rTFnD5tMXb3ro0dkcFgMBgMBoPBsN2y4PdDsf7TMf199oP7suaDPunvM54dyNx/D8i/clMafZxyBayG1aYNw39agdp/j5zpBV+34/Rpi7nspTeDCVqjlQjSjtWyX7ff/Tdm/GWfzASt6nyBJiyrxifdSaXCFGGuB66bTq9V/lEPnj/3sbzlpQSACs0fV+zMCcecg7d8ZeDQ4XrpNGALXtuN4T+t4Ogvf+JnX7JGVaH9QDipq6qIfdGaN054hKiwiQoHR1jYWNy7aheGHXs2Rx5zJkcedxZHHv9rzj7uYnQika4Tq00bTpq6PF2fdo9u6bbb6dMW43zVIR3v7Af3zZmX+hz6YxkyFqu13gwGg8Gw+fA3Z7rRRuLtk/an3wPzGjsMg8HQUDblvrO5BXyptLOhmLDjP6dx03Hn5hEXhu2DqYtw/tM+mJ9KsZydanlT76l1rO+vL+fXwy6ixUf57YB8BFIE61tCYaX+RmOFQrog3bFCZKfbzUaKdIpi4UNCe/ha4GobF4uktvDDNoeFyHFPF5aFsB1w7MDUTwi0ABmMjkJomHV+e7p84wQORNkpkC0LEYsiIpFA1Gfbgetftjteym0xHJCVTqWcEkgKGYgVLU1U2MS15Ffv/4/5F/cD24JoNChH+TmuelLonAHO2lcsuWIQXUfFgjaZkCy4fQjRr9rVrC+lMp/0tIyLocgSG6brO33OBF8dNEdf8ht6Pjef4m9nc9zw8xn08QLWnL4P0teUWgWcOXYKD3d/C6Wz0h9npWTO3pauLmzMQgiNFKnU1SKIQWW1WbNiz93PcHrK3VHn2d/wo1NpqVPzqsWXF63wD9wz3R5c++shtS9rMBgMhiaNLCzkiMnrOHXqUk6ftpi9f/Bz3ZKzST2P1Pb8Ez5LGAxbA+NgaDCksCzObj6ZUqu4sSNpOPWo2JMtFTtHF3Hma1fSp2x63mXEnrsw89cl9LnpB7TbQLt3g8FgMDQZpFCc23s0T913BD1v+s6McDIYDIZGos1EjwWJ7nRiMXPvHoIWmsRnbYAgFXDptw5rdrGZ9ed9az57N+K9e+3ZQ6joKOh0z8i885deMxSnXNPu/bmc13werxbaOSMVZ37ci8+HJbiyw2f0+ceV9Fk1HWie7rRDBoLFFlPK6PvK5Uw+9VEOL5SUtCtvWIAp15B0R0uQHjm7cyqVniudKkwrhradyz5RgUJjCYEf1rFE4ggr7WY4uGg2f7/gANADQUDJbIuOT//AzLv24JZd3+a0knm4WrFaKQZ9cRXWaofZt+1Oz9+PZeYnvTi51VVhECLtzFi4RNB+/Mg8KZOzOhZti3Oazeehq3yad9uXVh/P4K53T0rPj64WdCJw3VBtkrSx13PV2+fy3WkPUmoVAbDcn8uX1uENq0eDwWAwbBJWU3ISy0cdzxJWmzZM+31P+t+7EG/J0q0YlMFg2K7Ius/4q1bDqtU5s1P3GjlGMkX3zHmerVGOEJlO8gakeM9sP7djPcd9LkuYpiZOZenVQ2h19KIgXjR+uFggJFS4WOnvCA9fS6RQWGFaYyDtYCikyCva1CoQwPloXKwcV0SlZVCWkEhEOnKtNCmjcyec1vb7Ck74x3X0UqvA9fFKFCe1Hsejer/MxqREANq2ESqsw2xhHgTqRClAC9AyV9AXxi9k6AQfrlqpbY4v/pk7m2Xc9LTSrLxkCF6hoMPj40ArZv+7L1Zco/WSwGURaD0lyTcf7E43/T1oRaKl4rDSqXyQbBO22YIyta8Cd8XaBtYJST4LvwW3D2XnobOD4ySgcNYa1Lr1CEsif5zNyx8dSLd5cawql/7//k1YF4JWU0g7MGbqJsuJsgFIofGSSQb/4zp6TltffWa4rQ1sx+uax6PmInWX6RVYnNtsOQCPlBg1icFgMGyzSBnqUorwtWJV0Vz2feTa4FEn9VMQ3uY7/QcK3xydOyi8+rOT+UkwbCWMwNBgSOH7PLxqX1rZFZQtLqFD/Ws0eaIrJLfMPomeN46q1WG9qlMRpx08kvFWIdrdquEZDIathIhGUXvvhDVhJqqysrHDMWwBrm01h0NPfYgbbt4PtMmpYTAYDI1BwTtj6PRO8PfAA6cx9eX+tH0yI9pr9fwoCo8dxH53fteknr1X7wKF/dakv1t9e4Ft4f80A73vAA4443tmrm9DxeIuAKzpHaFN+e7p5Tt9XcF3u/bg2NZt6fF2JbqiEmtplK+b96Y90zKpsyZOo++sQipPcSkkUn9g2WnVsjujpERYgTuiTqX90jr4W6uwk0+jtEShc9Iip5xDJIKn1/ZmaOFMDiuoYuaJT6WXuXThAcydsDPHHjSOuYk23J9sDYCvJb2f8VixZ5Qup84hCXT+shxrZRlq/qJg21kOH2lhpFZYrVri7dQVLcCZMh9/zRq05/Pgql25qP+3PD3rCJq/vJqeN47KWxXWkii//XE4vW8exx0HHUT3WJDacrVXlHZbNBgagt2zO2775o0dhiEPVru2eL2CN1HW5DmosrKc+Xa3LvitmqHHT2mM8HZ4pizqwMedonS0440dykYhigsZcfBnfP70PrCwMQMRMHg3rOk/469ZU/tie+6CtXId3s+NGazB0ESoLoaqS4iXFuw1jiA6fa85bR/8KfnNDtKkRYabMNBKK7SqJhrLKq/PiTN4peeneFSXJea6FUqhkGSEhxYaS4RtB0HtbkKp7erQ8VDnplxO8Z+qNlSoKMOLllCxV1ci6zogy+Lw8zIsIVjXQ9J8Qjndf/8j9OmBcH2iKy3+uWJwze1ZFkLKIHJP5LQFtNYI34dQOJlqhwVfqsUlBNFVFi+u78FeBfMoU34wWEoIROgUv3pPH6tZMt3GavvkqMB5MRI6K2qF89n3dP0siEsrjdDQ3KpADToIa+KswMGdUOyHRZ7qqUmW2PDyUz9geMkUVis7EGNaEhFxEM2bkejems6fJ4n8vAq1fCV9bg3TQfvV2mXpClLpWLKnpc+hLFYvasG69UX0Siylxy2jwIkEKaSrky1grL4P2ddu1t9p4WO12Bqaqtkp9/jz6p4AxNZu++7Km53q98GUc+d24ERtMBi2M0JdSnO7kp1jiziyAOYM/wu+VkxIenxZ0T+96AuLj6AQjLmIoUlgBIYGQ4iqrGTsHhbQjL6M2fIbrN6A3gJp0brcNRLuqnuZ2Ptj+P59CWybL0kNBkP9yO5d+OC15znmhHNg7OTGDsewmVHpkdFmiJLBYNhK1DZS0pBm1X5raEtNR8DY+2P4/kMHVNN59u55U66oben9Fh2brUcPK+alV58M3PI6AX8FcBh/25PpZX2tchydTnnteYYdeho9bw4cdYVtg20HacCqOVYIyLSJpAVkUk4JJxQgahV0ollWUFaqA0dIkAqhwkG9QiCssIvQD5whfWSOuBDAERYyHNL70fF78dDvDmHmoc+iMl1vPN35fyx/8RPO3+lwVKVHqoMuiHkibUdC4ong+/DnvuCBD4fR6/q5mY2khIXp75LVR/Xlu/uexteKX1xzOcWvfoe/YgVfDygAUUgPvsttD6auLRlsu+ctY4LOL2D6PjBdN8vaq6ZzLhmaPlNvb8XMQ5+txYnttq0ejyHD4lN7M+GW4P568HkXEvlkXM78add04urDPuL9XVrWXsimCjUMtdLzjAncfMUFvHjdnxs7lI3CmzufT3ZtBuQR/GzF5zpZXMxLrz3FcddfR8kr39W63AkvfxX+vhqBoWHTEbZdU7STJ63vBiHzCM42tcx81HZd5utHqC5m2pLXdC33DW/ufD7ZrTno6ZnltmT/h9Y1B9lmbUPpwM3c1T7JbPdFBD4CR3jpNMYQCA0d4SOFpsaTUqodErZpdM5+kV4+rh1cbeMIL1gNuO3Zs2kz0eVXf/sLXz37V3ytGDbjWDg+iH3ilY+z55Az6XAiQcpl16PnCz+z6tliEIma+x2KALWd3b3rI7KbPpYFyssdjJQlvBKWRY9HpvLaf4/k6BceYbEfRXigbQtREENIQb+rJgb7miX4074fCBuVzivi0wLOLFnOaa89y/CDf4WeMz8zz/cRWlCbk6CwrLQzoMBCK5/VXhEJDQ6KQmmBr9AVlZTv35uPn3gcSwj2vfsq2j61KFOOFGmxXrZbZLDJhqXo7ntpVv9g6LapfYJzzsoIOIO6rnY+QA3RYfV7UPA9U38NFRcCyG8mhNeZphm1/5buUOS7F2R/N4PxDYZtAqF3rD6ujC6lhJevOJ9jwvcBlpCc8vVl9Dnvh/Synck/IDmN1ghFkL3FvBMwbGGaeG6H7ZgtICYzbAdUv/Gnvtf2MRgMBoMBGDLxJG496OQt80LbYDAYDJuNipMGc/G0mcjCwk0raAPbk1bvHtwyexK3zJ7E/D8OzZk384nBiC87bXQoB158MT0+uIi/rWvPcfsPR82cm9tWsYMOGK10TsfLF3s/y/Apy0FaOF+2ZeYjg2oWHnZ+5bgZWlbYwaMC5wzfRycSqHiCFefsxa2zx3PL7Enc3va/SCQ2FlHhEBU2EpEjrNrpuvnsd9PlKBTDjj+HE/Y/kYF3j8DKTjlSB+8dvgd9/zQ1d6LWwe+xVlmfQIwJQadbveSkPAnWSzunCGneJxg2iSaf5tXAQ08/wcxHM65FXUcXIXz4+IhdAThgUpwl1wb3cuFEOGnqclafP6RRYt1RWPleX169/v7GDmPL0IjvGK12bblx9mT8g/bKmZ7397Uas/6xJ0vf7l/nMgYD1J9udLtlSz4vpu4ZqWfW2raV796yqf0b9a0Tlissi8RpFns8fkWQvjjlMhiKCy10WlxoZQ1KstA4KCwgKuyGpRusJSQpFFZqfa1J6EB06OHzt16vcvOE/xIVgeu6zm4kJJKQdDPpjaunZxYi7WRYK6nz3rICIVyqjOxybBs/KokJgasthMoIQrXSzHx2Z66fMpbzp0xHxqLpVNHa9WoI9LTSdB5ZwBcnPMBLZe0ZfuCpqPm5IvG0a1+qjVQdrTKivXB+uR8lqSUxoSgUkdDBMELJ6PmcfPhZnHDEr+nwz+D3Qsai6W0Ervabx7FORCI1HCBrTfWcopaU2g3aXra7YXZdGQc+g8Fg2GH49pePcOvs8enP/D/uW+86Ld6exIW/OMNksTNscYyDYVPCjDTesah+rDfTsbfbt2P6n4O0OqUfxmj+DzOKyWDY4ZFBZ/oONPhnh+PC7t/yp98dS9+LFppnCYPBsOUx95mNptmPq/jDM2fRKbmJjukbegxWrmbEM5cC0HlMruNd1w8Vq8Z1pRWB60Phiy1YFWlJC+pPw2kJScHSSmRFMZUqijd3fs78ujp0S60ihhbO5i3a07/ZUqaUdAxmhA4R1Ttk0im/pED7Kt3hJmJRpj+yM9LWDOw+nV/EABS+jgEwzU1w2tPX8fhFT3Pn3GOZN6kjM04LRgX7K1fR+n+F7P3YVVRdXYWULenXcS4NxVu4qPaZWcdIaCjXCYY8dR3dxy+jwcMBsjqT8qXuEtEoM/66C9JWFI4rpMOfa7pmGgzZdH3Vokflxcw9/pnGDsVQDZF1uxz+0RVYCcmMJwfR9/Ix/PDcAPROmmnXdqH3tYt4/dlfUt7HZ+7dQ+j5+7EcUDCLp2ICPWQAsy6x6Hvhj2g32Xg7sx3Srfka+jpFTErGQ5ehxo6oYcy9Zwiqa1WN6XppjN7XNv77Ql1WztV/uYSusxew7qTBLDpE0/fyMXX/voZ0eCuCH41uhSgNBkNeqjsTpvq26kzhvJnakQ0sx1u6jMj6nhu9mWyX82xHuvSgKVVzf1U+TxdBDRFmKyvKL2woVwKFClwTI064fDWHyFS7SCmQMlPflhW4aaYGMqXc4LN/pKQMXY1UJj1xOPBLuEnsuE+F0rjaDhwQtUbLwB2+efNKfqjqzosvHEEnN3BWTrXvarTztGKvZvO5cOYZrHm9E23njc2aVcvxsqxgsFgoBsxORW11aM/0e0u5uPhNLKGJCVAolvyfpPAf/Wn23kRYXwZCsGDE7niF0PPRaah4IjdFbsp5cEOp3lebrlMrIy6UMiMCzVk1FDlKQMlAbFhHDNXTJefUV3ZqX5Pm12AwGLZb2n+zjl0fvbzW+Z1H15+9RFVWouYt2JxhGQx5MQLDxsJ0yhnysQlpSdQBe5Js7lDR3mLWQU8B0HvJpTT/x+YM0GAwGAxNkXObLWafQx/nBrGfSftgMBgaH5NCuVb86bPoOH1WbSYXW267a9fR6d784rPoB2PJ7p4ven108EdJSYPKXrZvM9CKZ2bsR0d+qrlAnvPgz6t7Mr2yHSvixVQN68EXC9cRmxtGkZ0yy8pNP6fDlB+oMO2bUmDbfHXwo7SSNo6w8HVmna/iDrfPPJlO94zkisG/Qn7dgt4jy+A0WPGL9rRVGm/OPDo/sISHZv2X/pFCpiSruHLBMPA374jfhFZ0e3gifkVF7gytG+Ywk+1ckeo8jEQYc/BjlFpFDCg8HbbNzJ2GjaWhbkFZ9+ToB2Pps3p3Ltl7CI92+i9R4WzZGA0bRdF8m2RzTfueKwFoMTPJ2p0cOuy8nPiwQXR4dgLxm/bA650rHHNLHAb1nsM6S6Ldxoh8+2X8tO4806Yj+xbM2WhHosbg+MO/4/7243OmvVtRyG1TjgfAPXwgBbNW4s2Z1wjRBZ1gHe8biQfEj+xCp57LGrxu0Rujt1xgBsP2gGmLNYhs90II0ier0LZQETiR5xv8RB6HPB+Br2XokChRoaJPi+CjyC8Ok0ikCFLviupCRq3TaYPT7Z/6nCNrEfTlpHZWCtW3KysGxKjUFkltIXwQno+OOAhLsm52S55c+kv6PTwmV/SW131QY6FYuKoFnWckg9TEdaX9TdVnVvrl9D4DOhbh4F4zKZFVvF+2G+PWdaPAchnScR5jmu+J9jxkYSHa96nqoFAlHqRSRldvX9Unyst3fIMKyxV5ahXUrVWz3mt1NJS1Pa9niQZTZWS7L+YULnP/NiJDg8Fg2O7Q46fQaXz9yxkMTYFt543IjkBDOxYM2xcbccxFNFrj0/+hH/ng6Uf56g8PUa7ilKs4wjfnk8FgIP1iSZh3i9slSWVRrhOUqUhjh2IwGAwB1Z0rhEg/s25T7R0hEI65t9bHtzc/jFDQ6VezkbFYzWOc3UGiFGVK88YfD2PeoCoSp0reefxhWjxaTNc7MgJI7QedTEIIhG0H7hDVUSrogPIVfrgJX+scp5GL/ncuxcOC1Fwdh/9E+4dHpp2dx/7pKeae3rFGsbfOP4G1B5Wj4vWPDt4sVK+vHKeMOjqXAJSiUmvKVRzfN693DLVQ7Z4sRk1k/lCXiUnS7w7K1VY63w01EE4EEY2i7CCVermK890Vf8Zt76bvX6c98RFCQdEfivngqUcRHdvR7faR9DpzPFgW61QUBES/mrx17187CCIape8l43jyyRNQWjTZjv3UuZT9SSg75zovV3Gue+scOpwQpJP8zeOvMu+0mr+FjUHrv46i6Mg5jR2GYXsklWI0+7M5ysz3t7R2zPZD6lljW2rrZWHVNfxLZ6XbTQnMslz8hBMhNb7JD9Mvu9rG1/mfzX2t8LXGD5/NJBKJQEoVpOLVGjwP7bpBmmTXC84r28oMvkrVt1I5QjVhBZ+UqE2nloHc9pQU4PvMOLeYUTc8TJmK4GIhPYL0zKE7Yp+bx7PTlZORhYUIx0ZYVlaaY521XQsRjeIjGb/fsxz+yH/T9VOD7DTNqffVlgVCon0/SL+sNPrnxfy8v8sbqwfyzJtHsOqA9Sw+RDP3AGjzz4lBTCXFiEiEPjf8QL9LJ4HngfIz8Sk/Nx13Xem567g/5KSY1ioj0qyDHJfLUGyZ+tRHvctuQwMdDAaDYYPQUKHNIEiDoaljHAwNhsakrs6kWpBFRfzxx69oIxM508+58lpO/SbLZUQIeldOrGVcnMFg2NHwm2gniGHT0ceVcXrkmPCl1ZrGDsdgMBhq4B6yF3/72yMAnPKnG2j911GNHFHDKD9lMA/e8wR/3O0XqOquc02FHGeGxhlJMOzsy9DHwoPTvyImfK447Bz8mVkCgZTbnhSoykp+s9dxlKz7Hg14y5Zz5l7HE1kzMejWC/cn1cmUQliZv7UfOPgF4kIftMIRQeecJQSSQEAIMOnQJ/j7xD68u3PrBu/PK73fZ/x0yR93PQBVuXldDPOSrzO2tmNZ7XlOVVZy6Z7HAdA1Ps+0/QwNRnsef9jr8BxXlY9XNGJAOyjZ73dK5JdMcyU3DD4ePI/YFRGGTVySc/8S437i1D2OwV81Lz1NJxLcsfehuH9bxxM3fsFSv5A/7br/1rl/7QCkjtE1N44AAVLo2t2YGpFUnC1kkBrbQuMjuGjENZw6MteRuHfF+PTvxd8G702Xyu+3urOywbDVyefuW2PgxkZkg6j2bLbwpsFc/uv3NujZc5umRhrZpn03kYBEE0ER31jvlZTbXShGK/26hGc634erQWkZigsFNFBrWaldXHw+2uN5/je6A38bug+6sioYcKU0IuIgPA8NCCtMbQw1nN5zUDo9YCsHKYNUyam2lBK4+BRKRYV2ER7oqipoVhy0x1JugmG7KyO0zE3dLPv04LGPn8cRsNur19PvzukIqyJdX7lplf1AOFft+hNSBAlZlI+IOOhdenHP688hheYra49AcFdYiC4rCwaa+T6EAyqEY6OTSfzVm+mdaNo5PozbrybkTLXd8qRHroEUwfta4zxoMBgM9dLu2e+565+HoD3Tx2UwNGWMwLCpk2PDLZp8I82wgTTkeFY/7lLS005SahVzwYL9mfjsbgC0Gz0Hb9XqLRSowWDY1rHM6Mbtlpm/35V2uy1jbUUBnU9ea54VDAZDk0Pbkh5OMQC+s+24WrQYs5gr7hxBq6oxjR1K3WzhzorsQQrZzxMTEgkuvOtq2o6fBsP60TfbrSX7ucOSQQeYZYHn4a9anZPSy1+1OohfWunOplxBoR9kzQodOILOsKDTzD9wd/zfrqKFtENxoWSRX8nwu25A+FB+eDn/2fcpXvrwXITQuG+2pe2b0xn4+8t47raHOOO0L/nogJ0RQBc72GZUOPR0KracA0td7fpqzp/B/zKnQyvo1Mw4h/imDWjYSPw15qV9o5P1fgdgsR/HX7EqELko6B5ZAbTmhT8cR6+5FejUPTQl3N57F9bemaDVGSto+3QBp3UN7n2t4k38d2sbQicS/ObOK1k1LMEpu41GaYGwLPTGCJG2JFLS3U7SWhbk/FZbCVXn74S5Dxh2CPI9d4XXSa0pSeuj+ju28Fmt60dr+cf8Y2jGdxsc5raAjMVY/lpXYo7H6pHtcxzIAezOnVj7bBSA+FvtKH3mu8Z/RyXACtV+bpj+2EcghUIpJxQCBsdPaYlCggiaH77WCK1DkZzMcu/LtI86xNbRStqsVR5AIC7cACwEFoJi4dDdWRmEbNtpAWHaPY8sV7z0QKtQuKb8mu1BKUOHQ11zekq86EOl8gEbV9sMPXU8n/bblf4PrYOCGCK1DaXRQoJ2M4OjRFZ9CEFXu4BKnUR6An/t2sCNMeu6qtWNL4xbtmzJmheb0fx3BeiJM5BxDx/BehXl2CNH82bHPel/zayM2HFAH6ruLKf4rHLUuvW5wsfNjRTZWZwbREqEqFMprjf34ITGvq4MBoNhC1B55O74l60EWtLs5hhqwk+NHZLBYMiDERg2NcyDoaEO7G5dWHZoZ06Y0gbH8lkyshPdng0a8l4jx2YwGAyGxmHA4Fm82fszFnjlnHTuDbT5cDb+suWNHZbBYNiekBZrzxyEtqDZ/ATWf35o0Gplp+2LVyCoaiM4eMrxADT7eRt6atUayw3SGTVps4GtEJxCI6vZcPgIpAcrj9sJ0Jwz71D+3v1zFh7bjk5fRJCr1rPi0K6UjlwOVpA6S2/MoDnfz01DFYoLvYP3YN4wh2k7v8nR007hDz3eYd8orFU2bZ77Hu0mqeg4lA77F/O/3V9BIhiYPIOVyX5IF5QW3FY6jdtKp4UFx2psOnHUPthxv8HnfD68X+7N8kFZE/I56FQnzzS7fTuWH9kDgDbfLkckXVYe0IkWL4/ZOMcdw7aPeX+07eO6DPvxbAodl/3bzOak5t/nXUx6GqF0jsuc2n8Plg4tpKW1BADn03G069aFlb/ovBUC33HQnker50bR5dw23N32ByYkm+hACdfl2EnnUhRJIoWmwHZ5v+9HjR3VBiEH7MSqvVoCUPrONCN+NGwVNtmRNCU0DJ/H1YSfaDah7lXs9u1YeXhPWr7yAzqRwA7v3Rv0TJdvUMpWImL7xGwPbQUp5Ff/ai9KP56NbteKxQe0opW9EICqRh7nXNuxTaUxTqG0xBKqxjIbWr7KWk8hc8oQOviksITgm3iM++cfyT/6vEqhsPiksjm/m3I8ne3V4DiIUCCoXTe1QVBBpWpJ5hyoPhCp+n7nMTARQqC1j1AQD2cltcXVbb+g2d5xptAH4Thg20FbLJGssf854lytUSgcLLRMiR5TwsysdNK1iAy10ohohK8G/Jsj2l5OwS69WXRgCxZ5LSiSCWypEFKj44kgHt8n0TrGF7u+wKBTr6DDp0tzHfQBq28vVg5tG24gFXTwX/o3pr603nXM02E9bhL1mAHkPYeb9IsJg8Fg2DTWd7WZMOBNAH7Z9kJMsmSDoWliBIZNnW3IYt6wkWxAQ2Tt4E785bZHuG2Xg1CVlXRj/hYMzGAwbE+YFMnbL2sShaz0K+hqFzP2rqf45aILcT41AkODwbD5kEWFfHD3A5RaRQwYczod/tOw9f5w1984vNDl6iUDmbp3Sli47Ty/rh3ciUfvfIzb3/llkIqpKZHqKEp1imyJtqJSzPEi7Op4RIWNQjPPLaezHSUqHPaORhhz91MA9HzjEpb9rie8BO9ffR9HR26kdHIx/7vrUY4/4Xys9eXo7DReOZ1coWtFxEHEovhr14EOXDuEEGjPCzqdLAsRcZAlxejKKtZcu57Ju7/MpCTYw1Zx2zvD+Xu/l5nntgWtkCUlqGiwHUcE2x438J8wMBVE8KrS1T5L/Cq62sWs9Kulwr5mBQtWN6NHA8/5fKy5upzZA19heX39xdXb/ulj7COLili3XzfG3hXU94AHLieyTvPhHx7gnDcPb7opvA2NjozFEJEI/vr1jR2KIQ8qHqfZUbNBCN4ZcSAnXZdfYHjI77/hXx/9gh7jMtNmXyw5bpcxTN3bS5vqrN6vE+/dbe4LmxurZUsqvQhrVBU00W4uFY/T8piZ6e+6fTtmjy5HeNvOu+QFx7Tio8vuA+DiiZcgq6rS9y+rRXNUVRydSDRylIbthlSa2821vpAE+V3rp2pAF1668wGu/ugE/BUrWDu4E0/e8Qi3vXFQk09vn/7dAro7S5E9uvCvO+7n4p+vYungKFed/TZv7NwOtKYNCxo3WK2QxcX40cwkC40Vqs2UDkSA1qYkidea7FzIrrYDcaGW6fJ9DcoBrzA4Xywh8bTPIwsPwzpP4v5Ps04lueGHk+lx1jTo3AFVUgBCIFwfsb4CHY+n2wXBdvP0J0kRChCzzkMZpPPNSZcsROBiGJaT/ba4TDusdQvBV+DYgQOibSGiEaQlUWvXob2gXZ8Sv8mIgyoOBmo5wgJRh1shZGIR1epda+a4Ln5UMufk5nz86/v4uqon7e11vP3hEPrcOgolLaxmxaiqOEIH23vvpvs43r2BNnMXIIuLgrYksPiIdky45cl08eUqTpny6GAXc+j883FGVaHCFMv1kl1/WftWXWSYPSiuLvFhMIBR1llPeclJT22y3RkMhu0P6WoWeOUA21Q7xmDY0TD5Eg2GrUUqDVhWOrBaSXUWpj4hxa+PTYsLDQaDYUMwKZK3X6JHL+GQP9/Q2GEYDAbDdkfxa6P5/U77o5qKuDC7DbEVOhNURQW/32l/zppzDArNpKTPiJ2P4PfL96mx7I/DH+P1vz8GwCVHX0jnhwKRTKVys9Iaq5ouEak0n5bFvBv34uzRk2qUrX0fGXFAChL79eeKb77imO/m8q9dn+fsuUfzu72OQFVVET16CZf0PZSndhuA9jx6fOky6dxHc8qyhMz5ANywdDCX7HIUU5JVHHrfDRxz6/Xp5WPHLKbnWVs4JUsD0iVPe6w/nz78WPr72Ose4b9/fGTLxmXYLph51x7s/82yxg7DsImMHlRMj9+atMeNgSwq4rEJ77Hu2S4c8ucbcITKFWo0UbylyxjR71DsTXDg3dp0vnc0F/c5hIv7HIIePyXn/nXBmPH8fO3ejRyhYbtiM74j21CRkPPZ91zV7xD8FSuAjXzfn3qe3sruhdksu3ggt378GiN2PgL7Pz/Q+d7RvLlHl9pXqN430tB+ko1Fa8S7JfzvmgdxUwOasmZXdym0Qqmdj8DVkoYO01ZZYr9AUChxtUVcO/g62OJXF9/P+48+nF7OxuLfvd7l79/8m7ZWIcfdch09z5mBsCTakuiIHXwcC7RGV8XRSTdn3/K2IaQIUitbFsKSmVTLUuZ+AIREO5oOVoQulqKFVUkEhdIC4YaDA5Muyw7rzCvfvsarEz5g/UkDQVrBwC8pEFIw78a9eOWtZ4hrD4kI3BVFZhvCyiyf+l4dIQX+ylVcu9Mv2fPWH/junAexBDjCp4/t4hVqRDSKkCLn9yChXS454HRKnx3D+pMH8tjED5CFhXmP04AvLuf8Yy8E4O2XnmTWHXs24OgGdZ0a8AakHSKFEPlFhKnlwnpOLScsGRwLITP10ID7UM79xZjRGAyG7Zw2z4xJtwe2pXaMwbCjYRwMDYatRQM6jupF+UZcaDAYNohVFwyh5FeLOWDEJZRMn0rT7woxbCjaTdL5rYUcMO8SAEq+n2mOs8Fg2KyoikpOHHENWgo6LKr5LGp36UzLV8qRQjHx37vS/pGRANx95bncGZVE17pYbIMvhrTeoZ1yZCxGwacl/KHre4BDT9ujzecWF7f6BigGAodkhSYqbKIieL0gEknm3bQ3J534P6LCRq6rRJdXgO8jbDvsnFGZjpWww6/7m6t5YvKpFBf9GHTIKAVSYrdozsKnW1FZGcWrEPxu2vEMaT+X/QpmEfcd/LWrgmPlJikbvi+xixdjH76IIivBIZNPQ/ylDQA33/93bphwMq1fLsrZz+haF6vsBy4fcRWdJv8MSZeT111DQdX3myX1cJu7oxzQ4RKE0kGZ2WR3SoV/L3pjZ6yvm6evI4B+j1UxcPbV/PSbwIWj/xsj6PQf8pdpyIu1c19K/7YUpSXT/9qfVs+P2uAyarvXNWX6PreGzz85AIdx9S9saDRmPLUP1//ivVrnL/xXb/bq8DPjFnWly8k/1rpcq//MY/h119Hpk5msun0n7C/M/aEurJ370urZ5aw5tQhv0eJgYtY7OvfwgbS4bT5nX38drf43l1axKFfMvJKY2jbEng12ZmoqKB+dyPzuZt+/Hrv6NLrNXFqjnesePpBmt/5M/PC1O/Qzm2EjyPeMJ0R+wU/eZWW1rwKdz1Uu33t/rVGJBMjwOVgrVFVVAwOvVm4jiow6frCQ386/lFhFeE/U4TWc5/k28z2r3rLd2LYQEekTEzZxPCwBvgYZuhi6gK8llqj9eV+RlX44H0IgQyc+v9qupMSFCnAQOFhUapdDbr6MHpdMp31sPd//KRDKtR6zAN/zEJFIUOa6SnBstC3RSTeYDmnxmk4m0+6EgdNgStQWpi32vFBcGIrZIJNaV+mMZ6OGtyo68PQtJ/Pru96jV2QZCytaYCeSgVOh0rQduYrDbrwa6WpajlyAp/zMuS4kCCgWUap0kr3vHUHfr1bn94RMpXAGhO3kxIvSYVya1ckiVqtguQMK5nPQgzdCN8XcF/vR/fQpFMlEtmkkOp5g9r2DuOjIzymRgWB1xtODIJrggBHBe1ItoO+8ckQiEE7ud/+19Pl8VcPenYb3hZRAEhk4QKYGG6REhtqvJkkN27I538N9DlJUp2rJyhy/sH4yYkY//30lFZcRGRoMhu2Nau0Bg8HQNDECQ4PBYDAYtjNkLMbiy/ai82vzia5XzJvTlr5vjjais+0Yb94CCucF6WfqO84rLhtCm+/LYczkLR+YwWDYPlA+Be/U3pmuC2P8o/v7APTuunN6evTDsVs8tO2axu40cBye7vE2rWUBCo2LZtyirqxqH6UXgbgwL1qT6Jng9jYTUAhEPIlKJJB9e7LoyFI6PjYO7SnE7jux8PDmCAVd31iMmjWP4jkSEXEyHVBCQjTCOb1HsyTZnHGrupL0LbrEVlMoPQ5oPYuXrz0MgM7vLcMp85m7sA199M+89cW+lMwVtH0rEIFdccg5tBkjKXwrv7As9v4YUom8C95ZumF1leo8zXe8vptEfh+N/FQuLqZ0bcbpccWl++IVCuIdMr/wzadZFL7V9MVtTQX/oL2Yc3SUD7u9CsDux3dgrTeEFi9tmMiwtntdU8afMh1nSmNHYaiP6HKLceu7c0BhkOJ27dlDcEs0V353OkXXFfDkHk/yn7Kd+WZx/zrL8ZYspdlHFTz54MccV3odJVsj+G0YEU8ybmEXennLAbC7deHnkzoD0Omz1djlLpN+7kzvt8fjuUkAYvMaOeXndoTcvT9LD2hJ2ydGpX8/rRbNWXjBLnR+cWbO/Sv64di87Vy7wuPHhR3prdZsvcANOwQp17C00GfDVs76Uo+ILvXMuw0Khbx5Czb+nrg19rkeZ0S/WnK36t9rFJfPqVJIyON16COR6CBdMgIfjQrliqWfzmHGyW0obxal8K3RAHihK2C6WNdDSwlO4ESotczUl+8Hf7su2rICrZ2T1c0bCtjIFsRlx2sFZQhL0mqy4Hf2yez0nxnc8/kwDtwndG5XKhgYlkigCiOUd5Z0fvQHvGpC7jW/HkRsr9V44R63H7keNXl63e589bj2eVqS1JKYULSzorT/rpxZ3Qo5p/9ovtaFQVrrQWuY37FZep3YSsHzU/flr+7+9PZ+Irrcwim3M+0+IQPRY58eAHT4dj3+lOl1xpEbckZ8LFKum0pnBJ3Vz2Wl0RKqnzFpx0MpEaHgUOtMOXl/6KpnAMgtMHc5g8FgMBgMhq2AERgaDAaDwbCdIVu2YMz1j3DUlMsofm00fV9r7IgMTYl7rnuWq5+/iC7bhvGGwWDYFvB9vosHb8OtysZL07Xdk925sJU6EJJa44U9HT97Dl3PnMkb3+3DoHYT0imGLWCBV85q32G3iIPbvjl2LEjh5WuN36YFlhAsObA1n19zP+e+fzYikeTnXzbn2UseQwrFVYuuoOXYoONMF0YRS1YhigvxWxSjHclqL3AdbB2roLkTp4uzmpjQXNBiAvv/ZjoWmhvmXk7hW2Po81FQN71uyBWP9RkxeqvU2QaRp8Ooz4jROQ46p13+Oee1mEBbK6iD7xNJ7LjpQEphtWuLiEXBV3gLF+Vd5ufDorx2ysNAFICJg/7F1V0GMvWl2suVRUXI0lYA+IuXod2kudcZtgh2ty70+ttCxsR3Q130OQAdLpjD8pld6P205s4XH2PPqOL8t35B35vGYXXrgvfz4toLVIrRidZYSXOfqA9vzjy6nUogLheCqt5teOY3j7FPVDCwagRtnxxJr5Hkd2IybBxKMTLeBiupWTGoBVeNeJ3XPhgCSqHXl0PrFjzzm8f44xe/hjB9bF2IbyfQ61tzjAybn3qFhamBMNnLbmzaZSFB74BDgrfCYCopFLIe4aCPQCJRuprgsJ7QhBRopZFCI5FYWY+GSsvAITGUmCnAr3ankiLTDhAyJQjMCNCQIhAZFsQQVfHAUVDr0O1PBcI0zwvEgqkBWiksHaYyliBEZstSBNMBfJ/SVybR5nUL2ram3w0T+e89e9Bl56XYNuhYBJIu6/oUc+dFL/LMO0cif16cya4lJAdfPYo/tf2ehPbrrWft+8F+Zl07YWVlYlOaxeXNWeyX0N1ex+iEg0j4oKHQSmB37cuPVeU8NeBlSvZI4muJ6tiGLo9NAKWQ7dpA+7b0uHt84OKbOscEWM2KSXZolhtUA9Nzy+5dYOVqVFU9zsDV9y2nEJlxMcz3PV9ZBoPBYDAYDE0QIzA0GAwGg2E7RCKpLYuCwWAwGAybE3/WXG7vNRCA7nrD044a8lBbZ0ddDgZbgIgIO9xQ+Ai066HyPGAc/Mb19Ho9zkevPsc7/34GSwhA4giLd95/gbj2cHXg2vHM53+nSEj2H3MRf+g7GNm8Gft/Npo/tRuDrzWOsBh2wrnMPKOIaac+QaVOosLOxxvdw1gwJM7fI/148PXDaFdchntw4DZYqJuggHAz8OXuzXjm7yOYfcjzJLTL74cMo9VSc52liLwqeKP3O0xOutzUa2jeNIbdbx3Fje9fzKdv/L3B5S49b3fG3fI4AIeddzHOp+OCe13vQUGZ+rvNswOGHRpZUsLz3/yb1rIAgMmBSR5VBy0n9rteXPH8P7m910BO+mkZAHqv/rz71t8ZduBJtZapKip4tE//7faeuMXQGufz7/lDvyE8NPOrxo5mu0VVVPBE334U6tEUAq99OJDXRr9GVNj0/uRi+p4/Lnim1D81dqgGQ8AGinyqu9zVqhvcWDFiU6CuAU/pQTL5UwunRHk5y6faN5tbbKg1nrZwtZ9uS1RHIdMK5SBdciBI9BEZUaAGtEKrzL6kHO2EBdm2c76W+OH6aImvJa6WJLWPRJHMikNpkZs2WIjQ+U+gpUBbFtgSHYsgtEYkBNp1c9IKaz+QLVq+AlumBYVEnIx4TesgVTISLCtInRxx0JVVyKJCdFHwDCLblNL3hfW4LVpR1V8Sm7MKHY/T7N+jeeajvfnbpBc4+frrKX71u/Rx9ZTE1T4+OkdeGKQP9lOVlVvpKVdFpQM3RhHGbVnopEvhMT8z4rZLeOCs53i0T38Q0+CcfbisxUwuGzmT4QedxpezikkcOZAvnv0LH7z/EgPvGkFsreKb+59EoTnomt8EcaYr22feFbsw/tJH8LXFhmCVlPDMly9xys3X0/yN8WitEdmug+mdruVekd12T6VMruZkWMMBsS624rsAg8FgMBgMhnxswy0Zg8FgMBgMBsPmInHMPuz6vUTGYo0disFg2BbR2qTl2VSqpziqrT4buZ6VFjnpkX2teP74pzn52U+xRCAqBFAoJAIbCwcLC4GvNS2kTaF0eGbPl7A/b4NaX856L4arfaLCxtVB2q+dHlvKkNtHEBM2jpDs/fY1jH9mAAPGKURJCaW3WnjXtw464rbHc0+r4KN8dJaoc4M6oHYApFBYQgYuMHUgsuqtx9sXM+3CfnUurwVYQqadOjMz1DabytDQ9FDl5Zx5+m+4Zsng3HMtlS5W5J5r8sfZHPmrC1B1OBjKwkIGfA+Jo/bZYnFvDyy8ZSgVH/cMvqTSHQqBdpNcdcZllO1fyQGT4vT/3jbto81IjfNTKRxhsfc9I+j/f6uDaQ28v854chAHTIqbNqxhy7KBaV610jmf6sy9ZwiL39q5+kqbGuXWJXWN1iV0SrVl0s9NwUf7fu7+psrazM9VsrCQAT8IHuj+Rs50BShE2llQhkJIP3QvdLWN0oGbYVxbJLTixmv+zcy/75nZj2ooLYLBWDp7mkxvo/r2tdYkPZu7u7/FgHEKWViYccMM60H4CuH7gUjQ88Hz0UqBr8LUvMHy6oAB7Ds+Dq1bpEV6pFIty2riTtsOxIWp42ZJtFKIiipYV45f2pwVA5uz6KAYK3eLwNr1TLuvP0MnJNjly/Wcdd5VNP9yZuiMaKUH9QwZdw4nn345w86+jMiDK1l48xBEJIKwrMz/UmTEtzrYB1lcxO5jPX7x/TqmPTQgENvFosjiInq8upKHLjwDYTvIWBRta6LCwcbKOe+qP6un2qJaUOP81AJsLNRGeN5umCSxgWzMOV/XNWeEhwaDwWAwGLYSRmBoMGxjCCfC/DuGMPfuIaw7a9/GDsdgMDRBVHkFfd+5jIJ5axs7FMM2RGxZFW+N3Aftb2Mvtw0Gg2EbQu66E/PvGIJwIo0dSr3oeIJ9376WN8pLg7Rf1TpjfK3SQsP9oooLmi1Mf5dI/lNVTO/3LmW5H6TRcoTECTs+4tqjn1PF77u9y5w79uYXzWfklD39wkJmnd+BVQN9/LDzpWOfFaz+RYLftvkWYVus2qMFc04sYf5tgxq1PuPHDmLxdUM2X8doQzuHTCcSADPf7UOPdy9m+JtX19lBby9ZQ493L6bHuxfT7T2NHj+lznJLJ8fTy8fmr83M2AbE1FbrVsy9O3hnkDjaiMyaNFojv5nAwsoWADWEsj3t1cy9ewj3fDkMLWD2bbsz/6gYsrCQ0i+jvDtqb+b9aUggTkhhWdzU5huSzbZId/h2Q7yd4rQu3+edJ0ZOpFlxFbeVTuOmNl/VEGkYNoFq52fq3UXHT5fjz5yzQUW1+NHmbyN/Ydqwhi1GdTfCeqkmpks/l6REzEDzGeB/36LmMtsadQ2Eagr7FN5reti1i4+rp0RWofNg6uNqSVxrjipazHUDP2P+H/dFRCKhm6GuU3CYwtU2rpahsDHojJ1xY0+O6TGFbrbmhjbfgJSBE2GY7hitIekiEi4i4SNcL3Au9Lxg28kk2vUAsFdV8eLI/RCul3Oeac9Dx+PoeCJYFzLzUy6HAIkEuqoKtCLZpoCKzoJE9wSVHRS6fRuK59i88N1+vDFqEM5/J+OvSgnBQ2EmkkEdFrB4/wLsryYw/ZseNJunAjdG2w7EhSnhY3VRrhTcUPot7y3aleZTA/GjiDiBM+P8RUSnL2bOHXsj27cNthVuc9qVpfgH7Unh/HXpZ/XWP8VpPrOCHh9eSKVK1nd2bDrV2mHCtMsMBoPBYDDsQJgUyY3FlrB9N+wQiFiUb857gFKriAFjTqf5Pxo7IoPB0NRQZWX0vXwMPmC1aQNtWuL/NKPe9Qw7Nnrcj/QZx0aM5TUYDIYNZAduC1X0bsalJ33EJ/d2QrtZnR/V66Su1GP52AJ1qt0kfa4Yzb+/HsQpvT+qITDMRqFRZMSFEsHoil70+2sFq4+0aGUFacIKRZBu+f2KzsSEyy8LljLjnKcoV/EwFXOQInnGsKeo1ElcrXCRWAi+2O0V5G6ShJbE+3ei8MwlfNL/nyz14dr7D8utz63IwkMlVx/2Ae8/0HLTC0t1vNXXMWs6sdJ0fGBkg5bz5v9M30t/bnC51lc/0Per4O/ashs2SYSA0lbMOPcpAHpHL6XXh40ck2Gj6R8pZMa5T3HkcWcx86xifjj7Id6p6MQ/n9mPli+Motm8vTjr6fd5+ZNjcH6cB76Pv1M3XinbCaeiCQg8mjCRNZIvVu4ELKvx+yl33Ynyyggvl7Vmmdszk2bSsNnJfnexobR9ciRtw793zKdKw5YmnwNh3SvUlTIYEND6xbG0Uk1/sEKDqCu1cSM+q1rNmuHt0iNnmhQCdK7bihQKstLl+ohMimMhSSKJa0FMK85rNpvTznuAMz+8DFnlItdX4s1fWGPbKmsLgQuiQxKJq8FBExGCWac/TbmKp+97qn93ZNxNCwDF+kp00kVAIM5LJKEqnk7Jq5IuaIWwLPT0OfS93EW3boWIRoMCQ4GirqoK0vlGIoFwD3JEiGiNaNkCVVSAXLmGeCubRGuflq3LWCOKqepWQtfXFqKWLg/K6d8TISVyXQX+wiXI3fvT3J7Eve1HMerCUdz74Wn0vGtiIH50sp0SLYTWaPy8D9X65TZ0eGcyRCMgJLprW2RZBdqxuemEt3hu/AmgNC+VtUeimDr8cfZcchVd7h5PvxFW+jrVQP9ri6g8ws3Uw5Y+D5VGkyddckPY3O337eGeYjAYDAaDYZvACAwNBoPBYNiOmXtZHx44+zke7b1TY4diMBgMBsMOT8HbY/jo7RYgKnNnbGyHQKrTsik4hYSoUGr429LJ8O5kIHQXFEGMFhZP/P4UKttKjrv5MRLaxUcT+HpkOvliwsYhlSY0SLUMEBUO77z4BJXaxwUiYjvppM0mJ1Vqvp64rdBhZjAYmhzPr+vH+7u0BBYAgRD237t25dFZT3DGfdcTW6t4594HOWenw4lVjGnUWJs6Xf84kqo/1pwubJs/v/8cZ9x3PS/+qks4Nb5VYzMYDE2IzfyMnXa+217Ifiat73k8b1rpanWxGZ7p1xy7M6MeeBpfF+Skw3UI9G0SjYUmgk8l0fR8X0sUEhcLFCgROLhbJPFFkhJp8+4bf8PGYs+xZ9JheP7jKIUOHBCVzVq/kNV+MRYaKZMUiYwTH0BzGeHdt54L1kMyw01y48G/CpwFtULYFjqZRFXFEREHEYkgY1FUPBGeS27+QWoQOLtqFbgiKjtw45UiZ5mp17fnxoPf573D98AtEAgPqhIRYkVJ3KJiEALteciSEl7+4DlaWoUMGHM6XS5rxT8/eA4LQVxr9ox6vPvhPxh2/DkwYVqQChvCdM4icPhLCQF9H3To1ggoSwQCSMuCRIIBL03j1fED6Xv+OF7dpRPnTHmX+z48jn/t1BGA9rPWkc4+LSRbYyiQZdpdBoPBYDAYDGlMjgeDYRtDlZdz9pHnc9Thv6LLFWWNHY7BYGiCWG3acPb0n1EH7EmPJ6fzxDHHNnZIhibEw8ccT/enp9WYXnXCII77aRUyVnsKGYPBYNhUZjy/NxdMn8Pwn1aY+01DqK0zI7sjbwt3UkqhsIRkl4jNxdNmclvbb2ouQyAAjAoHGwsbC0dYSASOCP62s8SDd9/9DJVDKzjqzIvwtcYJl/9PVYzjjjiTN8pLiQqHqLCxhAhSNIvg4wiLuPYpFBbNZYRoI/f36NoOkW1zwKQ4a88ekjN90Zu7MOPZgVkLylAkqkH5wSfPMb19xS4MP/ws1KrVIMM0Y6azq1bsnt25YMZcLpgxlwW/H9rY4Ww9tEbNnsdRh/+Kow7/FX3v27CUo4atiywsZPhPK3i6x9t5539RZXHU4b+CyTNrzJv18L5UfNwT7Xlcd/S5dPjnFFq8N4Wzh1/C8HFziQ8btIWj3/7wD96Li36aQW8nWv/Cho0i9U6zxUc/1fsbtuAPQyn6bxsA+n9vs+zKHehebjBsi4hc8VrQTskaCJQvhXT2MhsqLqzlHtLivSkcccKvWaOqkAgsIbBCRVqQqjhIgZwilRJZIUlqi7iK4GKFaZIt4toirqFS+8S1R0J7vLXnXxk6IQFC5qREllmCSYUgoRwqVJQKHaFSW/hapx3bHYLvce1RppKhe3vQJtCeF6RE9vx03amqOKq8ApSi/1jBBdNmUfVx99x2gSUh4iBiUUQsCpYViBGrqtCJBPgqWM730UmX/vcv4Z0zDgRLIhRYcUG8MoKUmnhLgS6MIWwbnUxyxlFBf5Qe2ZLzvx6Jg8Uvb72GvV67huayAIng6n+/xvzfDkK7XpDK2feDbYXpnYPjJkFaqKo4Zx95Pm0+CZ9VlUb7isln9SOyxKHX2Bgy4vD2yQfQ788Lgv2UVvBO86+zcA/Zi4umTOPiaTNZ8+tBVB4/kBNGz+KsEy6m+YdTWH3eEC6ePpsLZsxFHbAnAJaQ6Tg3pH3gax1YJGa7mqb+TrkXptp04TRhWeSlLkFuuL6QohZBrsFgMBgMBkPjY55SGhPTGWDYGLTGnzId9eM0vJ9rWvEbDAaDsC1OLV6OX2Dhr1yFP31WY4dkaELMuKiUyn17ZSaEzyNFs9bz+CvDghd/BoPBUBfVO482YJ1WpWWcWryOE0tmBC4FTRixz27MeHIQwolsdBnWzn2Z8fQgZElJVsENSIeU7oirtpwQWLv0Y8bTAzNlbkyHXAMQ0SgznhzE2R1GAeAIi5OK19PSKqyxbEr8B9D3tcu5YnHQYaPQ9P7npfxp5U5hh47ExuKgAkWsIIkzdjp7/e1qPqosRSLp5axh2pUl7BZdnC7bJhAqArxdUUy/5y+jUmuGjjuPAc9fyWHP3YhONiw9ctmv9mXeXUNqzqjtnK7rPM9aR4r8Is/hzcbjFuVO61u6nGalFVmdS8G6dvt2zHhmH+xuXaiBhl8UT2PqFc2RJSUIKXa8jqcNvO9o2+LU4nWcWrwOt/lGinA35l7XBNCeh/pxGurHafjLljd2OIZaEPvsxrTHd+bsZnPZ/9vL6PfcZZzy8jXMeGovrP59QEBcO6gfpzHn9r1AwbPPHgPA7Af2RfhQ8W57EIJpl7YkuUcv9E7dmX5RAQ++cTxFs9c27g5uA9jduzLjmX2w2gQitsjPa/jtK2ey63MjaDe2bJOfA1afP4Sff2dEcdmISISpI5qj+nat99ml3RiX2W/1AeDz1wbRZmLV1gjRYGgY25ML4aYg8oir0v+L3P/rWn8zosrKsKbNrzE9GLSUH18H4kKlgyVcbeNqO0t8CEmtcXXg1d7ZjnJZq7HM+MteDGsxIWsbCquaK2NKqOhqiQ+Be3t4/1OosExwtcoRPgJBymTHASEpO3Ufpj++O9Me35Vr23zF43N/Sdm7HYI2QVq8l5WW2JIkDtqNWX/eJ0uw6IHroZVCCIFauhymzQmcCiVoCULqYNyTLdC2BCnRSTf9bNnxqzJueudMAIqXuHR/z6Xf85ex83O/4ap/nU+7sW44YCoYCLfynH0C0aHvo5UO2jHhewD/pxnoeDyIW/ngusw5tTVuieKzL/Zk2hO7Iirj+MuWIywLIQX+9Fn4K1YR+3kdv33tTA4oWIJbJCieuY4H3zie6RcXovt2xS0WnFS8nlOL1+EX5L53OLIwEbQP8glc8wheLSHIOTQ6TIucnRo59T3l2Jh9PLKOZxqlQCl09m+hua8YDAaDwWDYBjApkhuL6imNGtLJZDAYDAZDA9Cuy+3L98Qudxs7FEMTZOd95rFoZg+q+3KoH6fR9UcwTyOGzY3doT3JXu2R30xo7FAMm4uNabeE66xZ0JLbu+7CGrcwnRapqRJvE2PYoPHMdBy02zABW3W8loWcPvg7vj1oX4rHzkd7HsndumF9Nb7hIsNquC0LOHXQWCZHW8MWMjS3SltTNbAnJw4ex/jKbozPyujshx1vSgukSKUwznSG7PTgQj6v2pPbjy4jrhw6fa14ocMQ3F0tJJrbSn9ML6sqKuh2+0hGHtub9vY4Plo/hLMHj+TVdQPxtaTUKePqlvPwteKDyhi3TjqeHreP5fZDjqLo1eZ0+XIO8Z07Z9Jw1cO6HpJWey2rOaO2Y1HXMQrnFSyVvLd0AN4v2+N8+2PgDkKQAu/eJUcQW5NbxsT5nUGAf+CeWF/9EJajoCDG2YNH8skBB9DqOxt/zoL0OvaiKP8p25lTB4/hx2i79HEXloVWm/k6EgL1iz3QYYeZcNUG3b+tFs1J7tEL+5tJGzVowe7ZnUS3VsHfI6ek63ND7zsi6XLb8t2whKJg2UYKMc07GsMWJN4mxpl7fcfdK/em44tRoh99h9W6Fbt8upqJrXYnthKeWngwsIQ2ey1j/Zftaf/QSCB8nv9nD0r/MhKkxYGDfmL6yF2IrLcYtvcEZu5v4cdNWt/6UMWFnD14JOMKewLgz5pL99/NBYI2UeLofYLnAMtCN6Bpbffsjte2GXw3CYB1fcDus35Lhb9NIiyL4wf9wMR39iRC4Pbr7T8gLdyQCR8xciIA0Q/H0v7DYHqne0Y2TsCGHZMNFfls6PJCgm7a7aAGk+W4plOObmF9WL26k+jaqo51wVkdR4+fsgnbz9OnJgRaa/60/EAuL/2aHnYMiUZWS6WbEg+mUiMrLfGRSBRWer7E1ZIkEkdkhIAArWUBs47+C5aQlIengIUORYZBTKlBSH5YhiJwQowKnXZVDP7PfMcPhGfp/XMckIL13SWX7/cZ17acySNrBrBsTHt6jCkLjoGfWV5bEmFJsG3KOzr8Yt8fWfCL3YlNX4JOJIKthG0n0a0zXusi5KLVGYGhAKUyz86yVUsSvdthfR22X8dMpt/P7VGnh4OkvppAj/+KWp/71/cOfwu1Du71qXPGsvAO3BN7/ir0+rJA+Ogr7N3WoaY1o/cLyxn82lS+KxoQCBOrjU1Us+fR/XdzuemQIylYrVA/TqPnvSW89NPH/GLBDQgdOMAD2GUuBSsc/ry6J9e2yu/ubfXrjWpWgB47OTNRa7Tvc+/yA4isV7mCQlJVLsJFdc73vFTvE863SCjCzNoAVkkJiYF9iHw3FVVVU2xvlZaS3KVL5hhtJ9TaLjQYDAaDwdCoCF3HA8dh8pTt52nEYDAYDNssn6nXGjSk1fxuZWGE64Z8VD8v6krNYTBsJpaPGMrz1z3ETb2GBqPStzeqp4ESgs/8VxtsxWB+u7YS9f0uVn/Rvxnvi8KJcN/M/3LuvdcQXat5894HOH+Xo1Blm6AO3By/87WVIQTrzhjMu/c8yDk7HY6qqNj4Tdg298z8lvPuv4a2T43CKinh5Skf09IqZMCY0+lwwlQQgj1+0Lw6YW/6nvd9bgGDduPDt/4OwIAnRtD57pE5ca8/fd+gPnc6HFVZWX3zW49Bu/H+Wy9w/IEn48+aW+/iy64YysvXPch1/Q7K7SgRguOmrOTPHx9DnxvHAdTorBN2OE5USIRjb/b9liUl/P2nj2ktCwCYnHS5ue8BaC9U16TOmVqumeSR+/Des49x6t7HbZR73qyH92XGKU+i0A2uT8PmwbS5DIZNY9ZD+3LF4R/z0S4tGjuUbQa7Q3teG/suUWGj0HxQ2Zyn+vRu7LAM2wgN/t2yf1Xzd2tztEsb4r6XT4CXj1S64MZCZFzltNK56WyzFwuFTzX2JXv58O9ZDw1m2ilP1LnZ42cMwz94cToGhMwVV2U2DFqltyukCAYYpeLTKmfbCAnKJ/p1e97o/QEA5SrBaqXClMcWcW2zVhWmB09Vqig+EguFFIoimaBExong4wifQuERE4qYAEcIYsLCERZR4VCpkqxUSeZ5xfzstma1V0yZH6PEitPRWROWVUUrGYj/W1uaqJBYCKLCAQI3w6lJxa1Dj8dfvQZh28iSYnSzYvSS5eh4AjWwP2++9hdOPeQspt3ajGN3nsyM/SQyGkWUFKMLY+jCKHJ9JYSpkUVxMa989wbHXnwFBV//FAxQCtMWz7p7T8497CtGHd6Npcf1ZH1P8Nq6WDGPZv8toP3XK1mzZ2v++X8PcHn/w9PiNrtjB/7+3Wucet6VOF9OCM6d1DEIj0f2sapxLmmFLC7i6UkfcNpN19P89R/S66fbHEIG5cowpXMovMsW4GnPy2kbypJAYFhqFbH7mNNpP3xazmlk7dSbD794DYDe/7qUXtd9lz73Zr24B8N2nszUvWsKJa1mzdJ1htIIS9YQFGqtg7+lzMRU3zuGUEhao8udWOsAAEBaSURBVI8+S6yrlUYP3pVP3/g7Rx90Ev6seel5KdafPph37t2Atnu1ay19rMLjqH2/5rvj+u5P0kI41fyMUnW2kcx6eF9mn/o0AEcfeCL+zPzi0G0J094yGAyGjaQhz91b8lm6sbffSNT2u2UcDA0Gg8Fg2B6QVvoFqdWuLTeO/Jw/XXAu1lc/NG5chqbNdvjQa2h6tH9uAre+NRzU4voX3hYx15EhH1kv4bWb5Jb9TqTdmolo3+eC78/hmvHvcucN51P41ugNLtM7ZG/u+OtfuWvIkRud/lTsvQt3vP4Cf/zlyXhzc1OIzXl5dz7e7wGonqJrI9Cex637Dyf56DpWH92HNmcszZlvtWvLzaM+ZWAkyavsXTPOH6Zy3OBhAHRdM5Fkat8HH4G/YgXN3xzPhf89FVXZuPcX8cNUThh8HP7iBfUvHLKTE+Wuaf/jpvMvxfrPD+mXVR8esgv9qqbi+37QyZPdoSMtREFBujNKODZsZl2lKivjvCGnZr63bsbvpr9MTHic8sEV9BkRnLP9xtqc0/pbIHB4SZ1L0S8mctq+J+Ev3/hj8sL6jrx52N4bVJ8Gg8HQ2PS7/Sc+u7cfkMcp15AXb+kyTtl3eGaC7wNLa13eYNhktnYK0pRoqi5xYRMhJ0aR3/25QfsRrtvvj9M54aETMmKpFFJkpsXDgTbSCletpf1R23HLFrVV2z6Ad7LPHhdewaQRjyPDFMkSjSMUrtaB06BQaZGhqy18BLHQeTCuHJCE3oMEDoYQOhxqpNb44TwJWKi0QDHl8O4jSGqLuHZwSeJqSQudDNLuZlcLMtxCuGueh66sQpe2RFiS2XfszaOnPMc38ebc+OGbXPb3S5l9U2tQq2tWiyUR0QgohVq9htP2P5WCVdPSjnz4fpAi2dG0tCvAstCpalOgPIkfFWgnmO4IgkFoH/Ti0f7/QmlJLGUpqFVQI0JmOfxZoLxccWEoFMzGIXBNDOblP8SZY+qnz7+0IM6J8OsfZ/PQfafS+m+jal296OtSbu3yAY4YBcRqzD908npebv44f1p+YB1BBGJCnf/SyE9tA7M2ASFFKMTMOlc2svjs67nWa6/hhTX5DBUGg8Fg2I4x/TNbFSMwNBgMBoNhe0Ar5v1pCE6ZoNuLcxgUjaMiEqv+NQ07Osbt0rCFUZWVNR22tufzbnvdr22djUxHvLm25y3KiK3UnPnc+MhFdJy8pM5+lNrKjM1YxuWPjKDjuo0fRCAXLOeiR66i06ofa8zr8EqEE8fciPSgbdUGCCBrwVu0mNYvdsaPCFTlHA6+73q0BS1neeh1c7ny4cvREnr8VDMNtfY8vIWL0t/T+14W7LtOJHLqtrGoHmddzPvTEJKlHnvdPwKAzjPn40H6+HpLQ2GKkOnONKSFsCyEJRERJ+iU3YId5Nn7Ileu4vJHRoCA7lnHaNQTA/m6+T7p76lzSbvJBtdFPnq+leAvE4bTcmHtnYQGg8HQFPHXr4f1JiXyBqH1Jv1mGAwbTNphbSsJYWoR6tVwNNzW0yZn7WdKqOSvWw/rat4Ta+y7tBokbspeRyudW7fV3PGC52iBv2IFXT9ozQAxgm8ue6DebfhaggBX28SVBpkM0gVLKBIeSR2kUE6iAYUlBCpMh2wRpEiOSRdH+UihcLVFpYqS0g46wsdC4ZMMkzEH//xqbUdh24HA0PMQSReiUbxixa6RVYyOd6RPdBX7HzORr4oH0POmZUAMlEL4CpIeRCNoy0+nQ9ar16bLJeX+CPR42+P5ScfSTixAqHB4mRagBG4xqMIILSet5dgHbqR9YgzWC6WcccJFTD/wORI6IyjUvh8ew/AtdMplMPu4VBOa6niCIx+8kY6TV4Nt5xevpsSL+dAK7cMDj5xG+9GryW4Z9XjvIrq+nzmnFj/Ti/PbXE2ipWbahU/VKOrfjx7OP4qOoGC5ojnf1dxUdUc/SDt+ptNZV8eyMvOkrLms1sH02tZPbUcK7LlL2f2+y+m0Ymp637MWoNW4lRx+3w20T4yps6y60EojnHqEng0oo0Z8m0DPtxLsPu9yADotq/newmAwGAwGQ+NgBIYGg8FgMGwPaE2ytY+2LbTrctacY4isjmNkLgaDwWDYYWmiaeC159HusZEb/e7e+3kh7R9ZyKa8tvdXrKD9wyvyxlDwzhgKNqHsfBS8E3R2aKDdYyPT0xXQ7tGR+VfKw+bY90YhS1SdbO3jrLVo/3Cw33mTRoWdcanOPyEFwpLgOEFZVtj5sxVcIlQ8TvtHah6jVs/nCgA3VyTy6/G0/HozFWYwGAwGg8FQjUDgtsU3krM9yCOQ286pns42nBj8Z5E3rfJmDgBQqEnT6L68Hf5luXUuhUKi8EMBm08QW8rN0EJhaRsfhaMtXC0D50NSz/UaB4UjApFgqkxHeESER0x4uNoirhwkCktqylSMmHBRQWT4aHzthdsTpB3kZaYtIBPJQIBnaywCl8UVfgG/bf8JnY9Ywxcj96f4f7OCmJQCz0cXOQhAawdRUIC2LESq3ZAlbHP+N5nSbwWUtkYEu5TGL9T4MRtr2nzaPzkL7XkULkug1kSDeIUInPPSxy8jLsx7PKtN055Hh6e/RxTEAgdF3Ex8QUGZMquXl/qufNo+ObJG27DDlxax975Lvw9o/vJomgNW315wYc3QSp8ZVfcg2KzpNdIg10V4HIUQQRrkfELDVJnkSZUc1q23fCXtH1mOn51CPCv9upo9j3aPz665/oaSui9tjOBZa6wWzVh3WD+avT+p5iDjjUB+PZ72YbtwG5ZfGwwGg2FrsD2bWTRBjMDQYDAYDIbthL6XBZ33PlDxC4AVjRmOoSliHrINTYXt+VzcjOlvDJtIeJ6JaDT90l7F440ZkWFHJTwXZSxG38vH1noPlIWFQQdbMol2PWTEASnRvo/2FTImg3WFCNxHkqarxWAwGAwGg6E+sl3ztpq4L1/63rqW3U5Ip6+VIuPyVvcKDSo3n1CzzjKr1b2FwBJBWmRVx/sIH5kWGTo6cCL0tSSubfxQ5uSHDoYVKJxQiGUJgYXGwadQJkhqG9e3cLWNq31c7eOHboauBlcrXDS+1lhCEBUSEOhQfCbsoOtWxxMIxwGC9MxJbbHIa4mP4NTm4zjrkTFcud9paNcNHQg1eAotggFKOuoEdac0eB7a8wOHRN8PHdKDY6RFmGpXahAa5YAflRCNIuIJhG1TdOdCpvf6GAVUKhehyXUuDEWMmWOUEjVmxKTCstJpsoUIXQ4dgSSWJ/OFRBbE0EkXXVVV/3FXinVKI5QGaSFjgRhSxRM1BY5W0DYDUIlMqm4ZCepaJV1QfrrcNCmRYMp9MCUiTP2dEiBmbyt1vtXjVpgXIRFSZfR+6fPaz3WJFLrhokCt01rW4LuqKfTNFmo08P2d6t2Z9x/8M2f+cCZi/qJggFy+5fK8kxHRaOC26eUdfmcwGAwGQ9Mk9Y52B6OBLRyDwWAwGAwGw3aDEDvkg6/BsMVIXVPmumqSdPra4bkZn3Py+HmNHYphB0aWlHDvtK+oOGlQ3vlWn578ddqnPDflI2bfuRd68K48Pv1zHpn6GQuv3jtYSOmg4y7pBh1tpgPGYDAYDAaDoV4azTFQq4ZtW6SESVbwqf73xn620D41WBCZvVxqvQ1Zv0Zx9dSl1lmiqJSoLVMXMSGICY1Eo7TEEgory7bPR+JqC6VF6G4oUEhcLOLawUcQJh5GEQgF49rH1Zk0x6kyLRRx5VCpIsS1Q1w7uNqmzC8gri3iWlGmNGuVZK2CypSYLRTpzbt+d04eNxu1ajVozc53LOCCM68griPMSrRjWqIjM91SZrst0YkkJBLgegjPR7hhamXPD93PLbAk2DYi4oAUCMdmxl27MfSb5UF1WaAdjYz4RApc7HKBH5PQohmiuIh7p33F8z3fRaGY4SY5c89hRL6ckE5XrT0PrXTwva5jrVWwjynBaCLB4jN34sofRgeOl7aNiEaRhYWIiMPgr1cw+/e7Z45t9id1jofHWFVWcsXux1Dy1vesuGQQz037lOemfYp38B41TpWJJz/MczM+568zPsfq1R2A5GF78tyMz3luxuesvDB/my29PcgIClN/p1Aq8/H94KNU7vmZWi4f1VKG55z3WoUpm1WmroGGpBnPQfmZj9ZpMWVu6vYNK1N//xNnDjgGf/Y8pj+5e7ou/xp+/jLjc/449X/BoLpqdPraYfrje23YPhgMBoPBsDWo7fdwezbQqAcjMDQYDAaDoT6MYMSwvbEDP/waDFuM7Bfdhsal2u92q0gFHexi2jtrGycegwHQVVVc8turaf7dwvwLCEFnu5hSqwBla7Ql6G4X0kYKfnXml0x/fHdUVRwdT6ASCeJD+xF/r0Pg9mAwGAwGg8Fg2LbJdtyrngp1Yz6bwiaIADNFBGKzbDFUXZ+GkK6ThsSXqoOUk7gQSIIOUUtoHOFjoZEoIsLHEalUxZlyU/NTKC1RWqAQJLUMUx2nHA1JixUd4RGTLo700usltY2rLZLaCgWGENcWLhJXS3zARwRiQaDbh2U89uSJ6E/ak+zRlvln96Tl/y0gJoL5gRjSxhIK9UqEdYfthI7HA5Gh6+WK16q/p7AssCx6vRbnozsPDBZJpTwGPNdGORq3SLLkkLbYbzj0cyycdDppgSorD1wQU8dEWoFrZcq5Mvs8TL8rUWlBoiwsZO1bnUi+W8q63VzuuPW89DFddeZeLPxHN3QiQYfIWm46/i34onP6s+6sfWs97P669WjPQ9mCDnYxHexilF3zvX6xjNHBLqazXZx+f6Bl7evUmX64nn6D4BrIFgk24HyXoobQML257OsgVeRmEFHnigs34j6ifPy1a0FrRNRP12Vnu5gOViFd7WJ2j4D1YXP4ojPz7xiSXnX2nf3p8/fEJu+DwWAwGLZzGqPvpSHb3MH6g0yKZIPBYDAYDIYdkQ1MdWEwGOrAXEdNmjdGDuLrHr1ZtaaY3oxv7HAMOyja82j2z++oy3PQ1wqFolXf1fx8WCkJ7eGi+U3LH2i+fxUvnH80yhJ0+HoV8dY2b+70D863DmdHuwPZPbqx7JCOtH5uTCZ1mcFgMBgMBkMestMjbzWqp2NtDAdFIRueMhUantJ5I8SLm7z/aaGaqjktZzlRo21u7dKPn49sjYOFQgM6dBjUaVGhjyImXCLCI1mty1QhsUI3wyRWIDjU4AhFUkuSWmGRTgYclC0UFgqlJVJkYva1BAGutqjUFmUqgiN8FBqFT4l0WXr2bnT4dCli+nzae1344OZ3ObToYip6eDzX/QM+ryplYTJwWnR14Kz4jz6vss/RVyJ0H5p9PQfh2IH7Zd66TDlcSqwJMykZ50Gb0kB5KUBrgfaD1d1CgYrAqqrCtEPjW+Vt+cPEY+nOjJxjUDMddiAs9Q/ak3grh6I3RoeHLRTO2TbP9X+JFlJx8OrLKHntp/AQa8q7CP5v1/9v777j5Krr/Y+/vt/vnJnZ3Wx6DyGkQ6RIQv9xlSZNpV1BKdaLigiKgO0qdr2CIghKEFAseBH1YqWIAjY6gRBCQkIKJCG9b3Z3Zs75fr+/P86ZurOb3WTTyOf5eOwj2TNnzpw5ZfaU93w+f2R6NIH/eeJ0hg3fiPPlsF2q8oSqk3Bfv1cjjnj+XABap6bJjD0K26BKw96739NcPuC18hO6uKbTIVzofccAZ+3/i9uic+A8Hhu3hIYOLZQ76Gx/8Q7vNKlRI1h52miG3vUCvlDolUDwttDNzay+4EAABr/QCk/OKr3/Pi9kec/4E/jV2EcAMMm2klEBf570AAAHt5xfmlbmvmd25qwLIYTY0+yOrYh3t/nZiSRgKIQQQgixt6tzEVYIIfZoFSf5Ey+Lb2YM2FXzIkQNlcmgm/tg162PByTfhp0f5hid0jx0yE9pOdix3pWrolzUdy4f+dIClkV53h1+mnSLJ7eX/ulef/QI7v/yd3n/3SfjWlt39ewIIYQQYje2Q8J9u/CG4k4JTFaG90qthretKmKx2lrlPNcb1qWK8FT8XFP1/NIyUXGAr2Jklp84iFlX3RIH8rCkUTg8GWUpoDF4ChiyKiRQFus1Fo1Dk/MBQfL1oKwKybkAo138Gh608uS9K7WJq2yhrJUj9IY+OkQrl1Q+NBjlyPkAHKxzTTTrHGksoQ8Zk0rz2Odv5NTXL6PPhk24bAqHR4cendMsjRxDzGZCvy9tLk1WZSh4w6ZgLU+ddBPvG3MePJSHbAZSpuO1Pq3ik4tiGLAYdFMKZygvO6dQHsImxYBXQhp+vIb1LxUYqNN8ffbpjDnvxbjdcmUlPaVL66ncJtmz8D2GaVMW0vJ/8frAmFIYsc2nCJ0jikwcirQ2bpNsodXFVdonf3RWXAEw+VKRGTQQ37q0XFey4nUrwwfZPz1N9k/x4Lc+t4DvDH+e+9qy3DRhfwBuvPE0Lj/v1qrNTHVZpDDZZm1FULC0XJPlWKflsU8qN+I0XifT6aw1cmevrePQZ7wuHa0HjeDxL9/EOX96J3bN2h5Nq1uqgrydXy9WI4cx48vTAZhw9yWMf7L82IjrH2fVS4fDT+KA4WrbykYXB3DHB31YEW0h157u0Wzp5maU0diNm3r2foQQQogdbS+7vyotkoUQQojdzV78zQexC8j2JoQQQuxUqy6exleeeRCVCkrD7ILFfGricfxs80S0UmSTv8/Wk7RM87S4Ap849j0MvvMZ/N58NccTV1HZiy7eCSGEEGIPVCeUpzppe9rreqOi2XZMo9gut/L3yn87G1bxYM2sdDzu22pQ0VlqS31rICBuk9ykoqRVcrlNcrH6oCYOCOZcQMHHgbhi6DD0Bosi9Dppd6wJUfHw5CA9rpBok2qJ8XJscVlaXSZpkxywMuxPm8tQIJ6eTm7Xeq1QmQy2IcUWl0dbx+QvzeET7/04Q0w7oTdsCJtYGzUT+hTrXIZ1VtEWpvHe4zNBdZW8YvBOKUgZVGUgLll23oBXoLQnlYnQeYUOk2XW1EjoIcTivepQHbHYCttHET6KqrabSR97ji3HbwRVsT24uOKewTNER6TTETiPSkKL3kB/01oKlFW65MknWXLl1I7reid1aekQLqxkTLzciz9Vw1XXz+0u71EuPhfyzsXLfUcGjntpeZ583af5xITjuXzqGWywbZzzuasZe+HsHk3j5ZsmMfqhQq/MjxBCCCG2nVQwFEIIIbZmZ9+8lJulYmeS7U0I8QahGxsZ8nCK/kF71XBXJ4n1z7unMeJ7jwNQ+OsY3tR/JX955QDGXTBzZ8yq2Jspxcg/L+WzCz5GOppRGpbadx/G/HYNZ/aZS0CaMLkRaYr34YhviE75w+s8/u0jGfjIYj70kStIt8/YJW9jV1lww1EEo1q54MN733sXQgghdrTi8fSyL08ieOjZXT07vcP1oE1w0jq2qiLb9lwzqdN2eHvDQN7a3r+OUwpndbGsio8Vl1EnuqqwWC9I2GngsqYdstI1lRS9q6pgWDXNhgaG/xWaTIHz+tyL9Y7QW1zFcgvwoBw5bwh9Kq4qCAQqfp+hj2+d5nxAzqfJuYC+JhcHD5PbqlkVJcFAj01a+OZ8gFGOrC7Qx+QwuLgyYhIeDJSlSecxeEJvKHiDdgE5XQwxOpTzYC06dLR4B9aX1nuj8lXtgi2aFpfllWg4bxm2gL//ZiJ9r0xBZDt+obgYevMev6WVpZcfQsOxaxn60S3xl5eUx1tF5AzpPGjrWXV4wOlfW0Nzsoy19qVqg/Fq8KT2HcXY36xi0fvHY+e+0mE9Vm5WPozwgNq4iU997DIA9l3VFgfw0mm8dYz935Vc/9iFpHMvdmj/+/1Lzme/Ra/HdSW971Yb8BcuP4Tj+h2GyTlSxOcPk6ev5dBXLuX5L9zCm3+9gM1RA2MaHiH0lrde9XFG/vs1ojrbuvc+DmUWA4NaVy/nygpGWpcLajpXHlfrePn1sJJhMdjZ8OwiTv/wx8muex68p3DKVIZds5BNJ7bhcrn6z618L519rtX7rCy+t9rWz4B/bRnHXfzh+P9vg4nPxFUnF589mGjZ61WTufzSe3n+fWNotSFnXnYFA55chO3JZzOw/w2tLG6ejGJmj54nhBDiDaLyb9euuKcp91FLJGAohBBCCCGEEPXsZeXt93jG8L19HmCwadrqqBNGHVr6/+fH3s/JjSFXaMvcHTl/Ys+wE/b7aOly0kuXV7Xx8pk0N498HEcDobeYpH2bJm4CF/+r+PPCAxm+2WLXriP917V73WdU01JNtLaZ9F8e39WzIoQQQrzxJMfTZwyYQrD1sd9YKgM4lf/WCzBVHMN1a9K92dZ4Nz/225b36Z3v1cqOKpXi2lF/ZoDOAhDhcDi0UljvMUphiKthB8oRqCgOBuJxuKTVsS6FAkNvgDRZHxIok1Q41ITexEFBFbda1sqRVWFpPrIqLE0D6n/xrRhkDL3GJV8wUhZ8FKGsI+fjdsVoDUbRrA26opevwWG9ptVlGBZs5uL9/s2vo/9ARRZvKgJtxJUR4/94fCGkabln7SsDGcqWcq87r0iylygHhb6Obw1/ipzXnDnnfMy/+iULuaI1cS7PA/OmsH9uQ6n9cbwddNJe2zt85Mn87fm4Ap/S6HQQt3D2DrvwNYKFr8W/10g9PCNpWl1vxdeMn+wr6rGZZGpGtfMWMHRAIwDfGjarNDzvHf0fXUS0anV11cVazsctj6HLFsklunpdlGcxmcd6nyc1FUSLnyN2/UYyDz5bem6wqcBT88Yx2c7qMIl1Fx9N0ypL9r4Z8XtROmm33HW4zxwwkSVnDGGf7z2Lj8K658gulyNz/zPxLycfxQ9GPQXA6Q3nAJBd1sKERz/IjLfewgNrD+T5V0fjQsPk+2diw55XInSzXqb3PiWEEEIIsa0kYCiEEEIIIYQQIIHCPZ1z/K1tH4akNjPStHBAurFbT3usdRJGvcxLG0egWbqDZ1Ls9nbmZ0ByI8n0709+n/645KZm8eZiMVxolKLFeV4oDGPclRtw6zdAJoOyFpfrWeWHPV2x8qgQQgghxA7XWYvgzsKFlaGr4u/b/NJ79nlpp6HB2mXU1fjdXH4dnmstD7aO4YTGVxmo03G4EA3EFSVD7+I8nYLAO7IqohWHUXG40BCH/crtjh3OK0Kfwvo4NGjwFBRoHGnvQEE2qU4IcSXDQEUYNNZrnFel6dnivAAWBT7F61F/1tmQI7ObaR9o6NO3GRXF43iVBNCs59l8H4yKhwfKktUh6aTNs0XRYhuIhjSTWr05DhmmA3zKlEOxSQU+s88IBj2+ksH/jofHFQyLy7287SkPYRJGC+8YzvDfPhUH/5RO1o8lWrWaiR9Yh1UajInnNYoA02mVSZSKWwcXWzRbB7Z6u+jxPtCD8zgzaCBbhscB1MdyjpwP6K/bOTDdzQhbRbtnlUqVwn5bnYfKSoZVs17zvIoKnUDHfaEYflYK9dRsJj+j4vbUNYZe+Bovvziaifd1502VtRwwkC9+6G5+9oMpccBwK1JbFA+3x+HScHg/Aj8WtbGFCe9fwF2zJvHaTyYy4c4n4rfWs1kRQgghxG5m289whBBCCNG7lOrRN6CFEELsYJUXeeUzerfnWlu5c/IYrht/EOdff3W3n/fkIQHXjT8IfaKEC8VOoHT5BltyY27xJw7g4bt+jCNu3xZ6h01uvQRKkVGab608hekTJ+C3tKIasqhspkMFDCGEEEIIsX26VUmvGK5SGmVM6af2sb1Rt5dfxf9Ly6+Hy0xpVfqpZLe08sv99+GSRefWfZ5BkVWKAEgrR6DicGFWhVU/phQC1IQ+RYvNJi2TA1p9mtAbcj6gQDGI6Es/gbKklUUnwUUAh8J5hfPl6ohNOk+zbueKx87n+/9xIm3O8vdvfp+5Xx6ICh3DjMY2pqAhS/DSa1w/7ViWtQ9gcGoLjclzG3We0Bs2RY202Czf/eWtrDtqGIRJ4KwYLKy4vnHuA09w/z9/x5n3PR0HDCsXoUoqGFrAqdIyAzpW1QtS6IYGVDqNSgeoVKrLcxSlVXmfSaXKFQ+jEJ/L1w8V7oAvgM27ZhL/vOU2AL55yru4bvxBXHXpx7f6PKVU/J4zGXQmg0qn4weK1QuL1406u3ZUs2xqKxfGbZfjYZXbd2UV1A4twXUS1qzDHr+ciZ94qm679q403vsUd04eg2vPlfdP3fk+ut8Xn+C68Qdx3YSD+fSdd3H/P3/H+N+vxkcRf5wyiIFJuFAIIYQQe7698yxHCCGE2N1IaEUIIXZvNRfkhRBimziLt7bqdzxYX7yBGX/OOJLqhcnNPJ3cmKT4XOfxhZ63luquxb86mPnTj9hh0xdCCCGE2B31tGqad77001vT3JPUhp22+71WBpi8K//0cJ5UkCalys8LlEGjy0E5wBLfIA1wNOscgYpIK0ugIhp1nqwOyeqQQFm0iqvc5VyQ/Jsm9Cmc1zivCb2m1ZcbxhkcgbIY5dE4tPJofFV7Y4CNtokQA9pDOojbNyuFdwpdiAOCynpoz+HzeTCG/kEbgYri10eXQo9bbIZNtoF/tE7mos/dz5yvD0eFUd3rGJXVGQG8oePdYl+97OJhybZeXC/W4sMo/imE8U9yvlI1HpRCcN75uNqe1iiTfPnK+/J4lc/p4brvrv2vX8aRn/tY1TDV1aardfmneA1fl8OApceKisvcuXL40Pv4XK74U8v5uJJjzT5U+/lSWq41z93RVG2IsW5b5/J81G7rQgghhHhjkBbJQgghhBBCCAESIHwDGfJ8G+N/fQkAt73zdk5s2LvayIrdx/oPHU3UoBj6w6S1b82NmOWfPoaxx7+KwxN6i/O+FDI0ShEoTaAMpvIGTRThbdKOq04rrO2hgjQLvzENt8IzaLZ8AWZnaj/rCJb/R/Wd3cblmhHXS1toIYQQYofzntS+I3n5k6PioJFP/g5/r84xXFINrLKq2Pa99o4JUe0M2/3+i5XValtL11smnQ2vQxnN4nvH87bT3sMjB92Dw2GUAq8JVPy6cTdcj0vaHAeqfM5oUQQqwnpFWpWPt0OfwniPUSEWVfoJAJeE9rRyUBngUxrj40qGaRWVvzgEtLoMzbqdYyYs4vGrJxOUKgUqcI42Z3n1gw5/xhSyazX7fX82GR0RKEvoUxS8ocVl2WKztNs0KW3J+RRva5rDHQOOgUIIqZpgmHdYNNY7mnSecPSgeHY9eKfAKpSFVM5j8gqNRqt4lor7QTHkVjjlMFYck2LMV54G72g95whWHKtK4cQx94cEDz8XrxOl8EqjDpnIgouawcOEu1tg1iugy+2UaytcguvWtZrieYxPVY87afoq7ILFVcOipcvoP39g1bCGReuZcs/lTNryYvV0a4N0VZ0udPytsOI5WTFk6Lo3z3VVbOOd7l++OnCosykWfn0aE+7eiJv1cnlfqZyHHlYxrBQdezBLTskw9gtPdwwWFitkJi6/+2KiZk/j65qRyHmMEEII8UYjAUMhhBBid+D9Lq9iqA59ExiF3tyOnb9wl86LEEII0WNKoaZOQb+yFP49kwn/jgc/+JaDObHh+V07b2KvFZ25gRF9N2OnG9TUA9ALX8du3Fi6cXTq+U/w7WEziHDkvKV4O8l6j1GKjArQKFLKxseKWuGTSiQqnYZcrlfnVwUpLj79bzzwuePI3PdMr05bdO314zQLz7u1atgnlh/OvOt30QwJIYQQexk3oJnPnvZHQm9wXnPnwqPgxjqtR5PjuF6tTLinfNltR7R+rg0NbiVE2Fmr2FrDb3qCjRuO4q7Pj+aivkvjKnxJ+E8rT+CBJGSYU460t6DiVsc5H2DwpJWl4OOgYLHVsfUKqxQ2aXVslS4HDZXF4CFpu2yJw3ZGObRyccvk5IhfK0/oDaFP8bHhj/DVM/5MzoP2EdnmPK0TBtDm4YXjphMow90to/jfR06nj3mCQEVoFdDmMrS5DJuiBtptQMprQpfiddsPox2F/YYQrNxUbtubbGfOK2YULPevP4jWUdl4OTjAKnCgI0hvcaTaUjgcd20eTdBau548GyYFHH3yiyz/UhzOXD/F8NlTf4/zCq0832s5i3HLJ+LmL4rDd9axcUozC86/lWXRFs575mr6zlYonSpXZ0+2seL69a57IUOVzfDEBd9lsGmqGn7CXy4mqAkY1mPnL2TClQtxtS2OtY6rLzpX/nKXMSjqb39KxS2LvbVdtosG8Nuy39fsH2ZAP8IDx/DEhd/ljJlX0fclE79+GIHvhS9aKs2mcRnOPOVJXrzGJOuj+FjxP+X1s981vdgOuatwZ/HxPeWzUwghhHiDkIChEEIIsTvZRSfFKpXiht/dzgHpRt4x/zTscbtkNoQQomty8VB0Qffpwy9+fxtnXH0Vzfc8WXcc6x1mR9wUE6ITSnlS2kHfPvzyd7fx9k9fSfM9T1WNU6peCBS8x5F0KPMejcIoXW4x5ZJ27c7tkBbJrq2NRw5qIoOEC3c1uwdXMxJCCCH2ONrgX1rAvQeNjINB3jOEeaDrBAyh64p7xd/foOcd5eCXrxpWN3DZg6qDXbxg9x6rCBx6G79m/188yb0PT+Wip5eiUWgMWnmcL1Y0dKCgWVty3oIHmwQDmzTkfAAOXNIiuOBTWJ+meMBukmBiSLydpJN2yMYrsiqMWxErh/YuDh5C6V/nFc26HYAW1wDAGtuX4WYT9xx+O+ZwT84bNroIR8QJjYs4+57p/KN9ECuj/uRdwKaokS02w/pCE5vCLG1RmlXtfVnYNoR3j32Ofre18ce3TkE1N+GD5HawiZfBBb/9BJO+9TLutDgEqSKFtwrdFGJyKVKtFpNPsTzK839HTiLbUj4/UFrhMQz7wVOsuEXFldWdZ/Q3n+L/vjGsdN2m8LMcE36+mPlHFVeMK7UiziqVBBt9/f0l2XbibUsn4+7kzgRKo9IBKB2HC53HWxt/0UvpeJur3A8qAnHKmDhA2Ek1w+6EC4v7lSoGFpN5KgUHlWb9qZP413U/RNMQV6JUOm7BXLvfbcvnkXd4CwN/+jSz7oS4sXjNtOScRQghhNirSMBQCLHd7PFTufr2XwLwpW98iAE/7cVvKQmxN9lFoRl7/FQuu+3XfOrcj6I3tbHquKF88uVnuHvqJFxb2y6ZJyGEqEvChaIbbv72Taz5ZjNzcqP4y8EDgPIFbwkXvnG88vOp3HLML1kdNe/WxywjProJa0zpxtPN/3MT7zrlY0z64AwgbrPmkm20GC4sCupUt/ZRFN9ccr7qJqp446kKlgohhBBit2D6NHHhMy8xxGzucjyH5opf/hdjvvpU5yPtocGcekHCXq3m2HHi8b81le06jGMrfy3Pj121mrPfdiFYx7yPDmbhe27F4bDel9olF/A0qohQOXJJVcEQ0MStk+OWwgpD3F449CnS3mK9JkcAQFaFaOXJKotRngJb0DjafIYcAVo5jPJVLZLX2z6EPodRjv60MdxsYmSqnaxSFLxnkzPMC/uxzvahxTYQesOS/CDyLkW7S7Ox0MCWKENLIUPBGgpRilyYYk1DE2vyfdi3cQMHPbSG5y4fRTB3CapPE95a2lwatEdlMzijUB50QeFTCpfRZDc40qtbGf3rVXz8zx/A5ZeijKlatp1VkFTGJIG8kP0/s4yF2eGgVpXWoU+e1qji5bb2A9O46IoHePCQweX1lgTbSuu2G9eCXEsL7zvhvaWqd9HgPtz3m5+UHk+N24/LH3qA77/rP3Ez56BmvMzpx/0nduGrXU+4GC4MQ5TRqCAFYYhLvuylG7LJDDhKc6l1PK6Lg4TFcKBSqlTVsLb1si8+uyaUW1zOVSHe5LkLfnYwdx97M4ujAp8888MMeHVuHJ4dPIiP/utf3Hzxu9H/eL68HHvjesjOvKaytfUu1wiFEGKPpJuauOi5lxlktvCxf11UukYq9gwSMBRCbLfMq+v41C/+i39c/B1GfnARr4w4hn3+5/FdPVtCiG7KvLqO//7p+xjz0kxsWxvDUoZrB53H6MLTu3rWhBBCiB6blknz36sm86e7j2Wk3zFffHn160fT/CoM+rF8sWZX6d+/lVMb86y267lbTd62ifRSVdTU2DHM/fLg0u/pJWnGfCneNqIVK+P23amAY2+5Gp+CEfPr30w2lIqhYIAguXljvcP5eF5LlSsgrkyxg5m+fZn7/Ykc8J0Wtkzqz7LjNRM/WadCqFKs/fBRtO5D6b2L7ee8hEiFEEKIncI79Jh9eflLA8ApvIfsaxn2/VpNQFArTm9cygDTuNVJhs1bCRAWgzp7UtBwe0JKPXlevQppla9dOazmOb7mGNk7j391Gb5QIL15aGm4UapUfM4AaRUfc6dxhCqi4E08Dg6TvLbDQcXpQ1zZMH69yuCgBppURIuKx08rS6Ourj6uladZ5Wg27WRVSM4HtPo0za5AqGyp7bL1mhbbwKqwH3mfIu9ShN5gvSLyms35LJHTWKexLj52tE7TUsiwhAFM67+EhR/RDPzHZIb+YT54j/NJ0C+V3CL2cYFAHSrU6jRNr7ei123EbdgYh+uKy7YiuOmd77xNdbJe7KrV8aoJ0h1Gyaj4taMGxXGN83iQwR3GiafVxflaxfmcymSY+9mBqFT8eyoToSvaGPsNm7jyZ//F2JVxG2QfhdhXFtV/vWL4r3L7MiYODqZS+Hy+dC7mrauuUuhcHCJ0Fb8n//d4sA5lavYFrZMq9fVDhnX/rzT9+7dySBrmhQa1aBmuPRc/1xhOatjIjYFmu+KA3gNdfD5V7X/FZdaN82vpTiKEEHs3rTmlcQmDTRPNA3bPL4yLzknAUIg9hH7zFHLDGlHWEzz8XLcOwM2k8YRDm9H/nhn/PmUS7aP7ojykH5mJjyLMsKHkDtmX4K8zuj3N9rEDAMj8/UV8Pk+0+DX2/foyLnvbGYzvs4YXJozervcqhNi5osWvMfqbr5UuF9g589lnznzkNF/sDDqbJffWAwFoWLS+/sU9IQQAurmZ3DFdB6l6cqy4O9umzwZr+fzyk+mbauf3/zyCCdfFX3j504KDqkI6Fk3j8p5dZleHH4QqRLgX5sa/T97CFtfMoB5N5Y3BH30IYd8Ak3eYvz/XrefoN0/BBwY/Yw7hSYfitSK7shU3c842z8fGxQO4atRUNoaNYCMAUvuMIjd5OKmHt/7NV3v8VGzGEGwqoJ54ofpBpQhPnEr2hSXYNWs6vp9k+8z+ey5q1HBWnDiURSdP56trprA5yvKn7IFV45uhQ8gdsi/7fPuJTvdNjY4rFiaPB0phUHxt7UFsihp4cMEBjGVWVeXC2punO0Q64J0HzWJe3wNoHWYY+6bXq+e7+LmkFOsPiwj65nf8PHWhuH12pngOawYMoP2I8eXh63L4Z2fvjFnsVONyzVUrppZ+t+jyehdCCCF2huR4OrMx2tVzstOZCWNZedIwFp00HesdEZbr1x3IX598S9V4UYNGqwc7PN96V79aek31vU6HJVL7jKLtTSMAaHjqFezGTdv2hmqmmZs0nNQj8TFyauwY2iYNQUee9JMvo0YNr7qHUKvyvkT67y9stVphZQtlpWuCUT0JGWqFbsjSdswkGh+fj9vS2nkYs2K65ddPKsUZE4fCrMUr2OTauWblW/jM0EcZqNNxS2MU2ns0Pmlx7DHK4bzG4tDKYb1GE7dUtr7r9+GAQMVtkQMVYZWmSecJVHnfMjiyOiSrQgA2uywtroF1OkeTKpRbKaOTqomGnAvQyhNgcUqTSkKRAEp5jPZ47zDa4b2iNUyzsG0wXzj8fv4ndSr9Xt2PzDOv8MvFh5FdoyFl8Bp0SKlNcnatIrVyI27TZlwhjCsSetfleq8NHpZXRkVQz7lSi+Q2V+Cra44gszFeJ19ZcgawrnaiuLe8mWDFZvyK1eSOmVw6lq9HpVKcdNBc+qbaS8M+s/Iwgs1xsNNu2MDorz9OVKdKfMc35DvOe5FWYAzKubjNcfGcrHIc5/C6Zhup2naTwGYxHOnqb9d1Q5xKx62ia9+GLs+vL4RcveItbBqXZtiUSdi5ryStlrfyvrtSOo/tXiDaDBhA7vDxpYqVmXU5/HNze+16wA61lesBxXH29OtuQgixyyTnHJ8e/lDVYH/0IaQ2tWPnzN9FMya6o/cChsU/ppV/VIv/1xVHLW4HXQDvzkFhPXIAsH26ezAuttvyr3iePnw6q2yej+5/crdagM27ZAiXnPxX/nZgMwAvf7YPr5x0G3kf8a5p78CuWs2GE8fxh2uv5/37n4xrbd36NC8dwsLzbgXg9LeeU77Z6yybjl3HJmASz27z+3xDqt1PaveJ3jgZqXwN2eeEEHsQNWYfHr3zDgAm3H0J46+SgKEQnXEH7MeDP5ne5Tg9OVbcnW3LZ4Nra2PJkfH/J1CusDbmvBepjg05RtCzatuN169kwfrBjDirPM291SE/nMV3hj/PfW1Zbpqwf7ees/KrjhF9N+LPaOTnd9zIEJNh2lMfYNQ5W3liF1UQJn7ySWYrBX5Ladjys8bwo6u+zzXjj6q+9lBnOp+5/Rec3BhyxYrDmDut+jVN//786s6bOf0zV9L3V2s7vH5x+zzlrPey4Pwm5px3E3kPT/3nZOyCxR0CYRtOGMc91363031To0kphVGK0FssHuc9Gs0z5+6PXfAqY3kJtMFbi9Kp+AaS0pR7h+0Ydu065h0GMIvBTwI/qn688nNp6g8/yT7femaHzs/WFLfPzhTPYduPnMDffvwjHB6H48x5Z8EJO28+6xl53ePMvq5yiJNwoRBCiJ2qeDydfgNd21WpVMdgVKn6XXn4vEuHMv+8H2IrRr1q0Gw+++O5OHxVJbSIFHkfotGl4Q6P8xaNIu8jjFKoyhxOvWBcnfl4/ewxzPz8LQAc/8GLST9U88WZzq77Vl4bLobtkuPh5WeNYfqVN/PlCUegtGLhB0bx0sU/4HXbxoff9TEWXNDEJScl9xBU8RizPL8rv+p48rDpLI/yXHrgafjWnXeu6fcZwV9uv4Uzz/pQ/GWQ2iBhveBmzePKaHzSzlY5+FvbMOYf5fnDrAO4uN8i8IY8DqMgTTmwZ/CEFdOzFV9aMypulVzJeY1REaHX5JLtQitHlhCjPVkKBFjSOHI+rkIY+IgW14D1CkccYFwaDqK/aaW/jpezRZFVBfql2mgrpGk0hVLAscGEDG1soaWQJW9TGOXJpCAwlrS2NKRCImd4fssYPvXmh9lwQxP/ntaXYR9Yi0pvxvdvxmvIrvPYBkXU4Mls8PhNLfF5R2W4sCIYG4fVaoKHxQBpZXtfU7437AphHDB0sCByzDq2iXTbs6SB9l/Vrvh4mufe+heuu/8Mxv+miUfvvKP6flRxvGTbjz+/6u0fs8rbdeW2UucejTImrtiYvE+VSuFz+bhKYWlDSM6/tEY5h8vl423NmHLbY+/LgUNj4uVgbWk63rpyIJAkaFi5HKnTdlzpOCDoHejyrX2NJqsKeOvwYRKmXLuWhUfACTOe5LdHT2XSB+MSlT4Ke3bvqHhvv6qCYp0AqdLlFs7J+Wn7kRP4/R03kVUpUhjOXnA60Tn9+NWPb2KAaeTgp88vXV/pEd11SrIy4Fw5rNOAbJ1tQffpw6/vvIkzrr6K5nsqAoa125Gq/3whxG6o9j78VqquqlSq/PdA9LriOcfn/nlW1fBDfjiL374wlUn/ZeoeJ3dLvcyFZCh6Ve8FDIsro3Kl+OoDIiHEthv1vtc5u+HtALi21d16zqRrXuTRb44B1lcNd5XfMvJg5cN015LlL4QQQojueHYOZ099e92HiheyvffdPlYUYmdRKq4EEra08JHD41Thvu1L6e1o3PA7nuMr95wOrpMqA92w5V1HcMd3bmCAaeSea7/Lie+8jHEXzKw7rnKeSV98kbO/He+XdvWrdcfrd+/zXPLIOXX3zfkn9+VNn76M+e+bDh50sfWaim8WYXT5xpx3qFSKxT+fyIDmNvq+vf7r7VQVn0v7tszsZj2L3UOE5eyTL4K1G9D5Lb2+PQohhBBiD6KoqkBokvCY9a4UIjRKs9a28v63XIBva2fJByYw+5O3YL0jSEqDPdQWcOP/OxWlFBNbX9q1x0ZJGGb4T2bytXtOBdbHwbBkpkaYBm789Y945/9dyaPHjwO9HhVU3DJMAjkjPrSac9LvxDuHb1/f8XVqFKuuddo+Fzq2gK19rPjcBa9yzlFnw8p5xYlXhda6w1tXeq2xN8zmR387hxsWTGdCkEKjsUlUMCBudhxXMYxbG1sUoU8R+hRplYzpXVxV0CsK3qBxaFypEqFFkfOGNpcptTgOVESjsnFrYxQFTCmgaL3CKI/BklUhgYpoUgWaVEigHDkfxJUOXVj/DQJGJ22cKZ97FZyhPRcQZTQbCw202jT7N63kghcX8abMcs794yeY/OONcUhKgVegLPRZEeHz+fgaQ5B8wQkbV4T0rmK9mVLorVRZvWadVlbgU1rhLXgNw42N2wJvr+4UQNlKIK1S8X2oIM2md03lG9+4neuPPA6/qSUebsptuOPlE2CymXgbszZ+vvPlts0qDh6W50XF4zmP8p0EZJN9oxiKKwbjitVA8ycdyg3Tf8AXjj2byBrevfBU8hekca0rO7511Qv3nboRoFv8rSM49aRnmXe4BW1QWpH9+4ucP+1MADb8tJkHD7qL5TN8t9rLb9/sdtG6u56efGmvJuC8tWquYi8l1S33eOFJ0/je7bfw30e8o/MqpmKHee6km/nZi1N44MAB2z4R2Q93qN5vkVxnhS39wtE0HLmWfBiwz/kLOi1hvUvIBib2EHbzZti8uUfPca2t0EVVwqVfPIbD3p7Uc+nmCd343+Q4dP6lAAxfmVRukf1o6yoruwohhChbsZpDvxH/XRk3s+PfLHXom1j91fjbYn1v7Uvm/l1bGUmIXcpZ7KruhQcXf/togs2Kfb7Vs0p9O4vKZFh29wRGXh+gHpvZcYTks+GGq26t//yd/Nmw5oZxDMztSdGpHeexa4/k0EFHE7R5BvBEt57T99a+bE73o4EV3d6Gga2fY9Q87nI5yOW6NZ0vfuNiPtuoaFzr6FNR8dIFitEpzSHXXooOYdTiOjfyku1zxOKXsVs55wLw+Ty+ZQuv3/smRnwvqGo/Z9etZ9xvNnPokktR3uOVQiXz65VixJp5FTeywEcRQ+5uxGaaUMEyfH4Xx+J68Lm0qxz9wn+S+9MwoHwOWypAs3bDbj//QgghhNh2peBT1cCOx4aV17yraPjDZ65j31QfIG4O6taux23Zwuj7BnJoS/Kc5NgiaPUMWP1kx+l008hH1nGojac5cvarRNtzTdnH1cTaTjiQFRfmGXvhenCWMX/exGFrLsObeL7HPt+O27ChFNwrVWEzKv4C2+akYvhW2uR2b57qt0murjbmIGlv7PG4NWvj4cbE4xUDW5XzolXpOaXXqfjXWwtK49raSL28hIuuu4prr7yd4xtyGKXIYjDx2sXhSHtHG5TaFMchwHIVw/grQRrnNShIq/J25rxKWivH4cLKxwA2ugZyPqBZt5ce00k75WIb5Uadp1FHGDxZFSbBQ5vMiy5VWawMkGnlccrjvcIB1sUhyA25BtLG0m4Dci5gUnoVF/30CsY8VXGe40B50KEisy4fV24qFEqBuVIFw9rWwcm6LIYHO5WMt+SaIzj8tNnktnael7zGT755BuMWtXd8rLOAar37VJ21JK+3TxXDZt4x4Lm1XH39RxlRmF0OvBoT30crFNjw7sNYe3qeCR+ag0rV3Gp3HnS8/5TO7SqqFKoglQQRXUWAsKLFcZ39rBica3x5FR+67gqGbZ6N0c1E3uDWb+j4XqC0nWwLlcnw+q/Gk05F5J4YXP/aUvLebMazX3Yd83zfJLCq8dbhVq8B72nLDyKrUgwxhdJ59sB659m9pHb5SRBQCFH3+mInf4t8SnNQOmDTL5oJbtpP7kftZANMI/tnVvAA2xEwhI7HBJJj6TW9HzCsI3vEOm478C6uXnDuzni53iOhKfEGlz1iHT8f809W9KDKr3psJkMfi/8vt1iFEEJsL7t5M0OnJwGVOsdd+WGNPHfY7QCMPeMjjMtPI/XwjA7jCSES2tBy7uG4fduJFjX06qTDk6ZhcrYqHLWtlFIM69uCSw+mXj0Bu3kzQ295nI/8x3tpXtTxBlTlZ8Oh+13K0O2eo641/u6pHfwKe47me56kuYfP2R0vxg34aefhyDZvGXnHC/EXtuoobp/djvYphUql+Pvht3H0GVezH2+u2o/8jJcYOqM8bunvoTb4bKZ8E9V7fOgJWi067IWKH3uJNS8NYfwt8Q2xynNYjWbVmeNJbxlH46pQji+EEEKIvVjlNe8iM2wo608ax0Vz38vXJvyB4xqKFeLigJN7aR5DX6o3seQ4bRs6e9mX5jF0ThwKirbn3lTFa9uMYmC/5LhWKfxzcxj6HKh0Oh7mfHXVwFrF4U5DT2o+93Q5lMKAoFIVx7paJy1nNUq7cvV+TXXIsNPJljuteaexGzYxdPoTfPzYC/nC1Pu5sHkFgQKLx6DQyXIvVjE0ymFqKsEZ5ZMMmSOtLIGKCJRNqh/6uKIhIShKwcCcDzB4NrssoU/R1+TIuaAULoynEU8njSPAYxRkVURa2Xg+khbOte2ZKzkflyN0yU8hMmRTERqPQ2FR7PeHjajXVuBHDyt/8caB9mBa8hXrvLzswhOnYvIuPo+p3TaNKQXmutJ4+Fru3PfvvG4ptxDuQr+74rCuOWAi75h/Gqo9X9OitvMW2dsirkoXbzNu0RKGL12OpyJ0W/G6Nq1oaMzj83m2vPPNNC1rRz0/Lwm7+lLIsEoSHtRpg3dRdevppCLk1t6HXbacIbcuxSnNljkDmd2nP5PV7O1+77VUOs2jh93OYNPEwfb8rY6vVfV8m2FDWPfW0fT/7fOlYTnvGXn7TFzbjm+z3usVBiu2gdqqnEJUkVzJG4JRmscOvnenXHMW3bS9uS3JffWa3r8i3cmK+fJrZ5J+22s7t3qh0uWf2uFFsiGJvZCu2fUtdOuETmwHpaR6oRBCbKfFZ9xGwzXLd/VsCLFbM32auO+732PoH7Ls98XuVZfrrv5fWsKqq3vnfM7lcqROWoJ59Lkuxxt7/gsM/eHuWYVRiJLisX5nx/vexxUrgFcumr71/SiZlgpS8b/GxD+pFEorNl7WQvPVS/FhD74pJsqUSm72GmZ8ZTpPfPdWOb4QQggh9laVx3FKxe1Uk3/bpo3hb9feSJ/3tXLlSxXFM7bSfrPUHrhOpb5O71n1lqrwVfyemn73LP3esTgelAriSoVKx61ai1XqoBSK8rX3zJwHa0ttW0vHpsn7rBqW/HT5/iorynUVQtSq47KunG5leKirIFGpmqEHZ0uV98ZdMJOvP3BO3adY4iqEEFcrDFR83J1WNg774QhU3NK4Uedp1gX66wJN2pFVlqyyDNQ5hph2hps8Q3R8/B+oiNCn4rbHKkQrV6p2CHF1QoNDqzhcqImrKBbbMG+t5a2vCBb6imFNQYGmVAGNJ+cDvKpYtiquXqh83CKZQpiE43SpkjrE1wNWX1WuFu9rAoXxuUv1elc6aRGcrEvnNBGWoMt3UWd9zH2F8LgVRMter2nTXEd37sF0sh9WtXpO5t1bW94nSi2UUwy6awYjz54DwEVf+zMLzmvceuC1+HpKJdUhbcfHoGr/qpo/5+PnJdvy2M89waQrevAlqR32uZMEaZUqrZ8tbx7FP667Gd2/H1p5Upi6Xy7dkSqDgJ3qYTC1R+2XhSjqQat2sRPIvXohtovqcLIghBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIvZ701BFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUQHEjAUQgghhBBCCCGEEEIIIYQQQgghhBBCCCFEBxIwFEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRAcSMBRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUQHEjAUQgghhBBCCCGEEEIIIYQQQgghhBBCCCFEBxIwFEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRAf/H5/yMaBgHebSAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bwframes = [cv2.cvtColor(x,cv2.COLOR_BGR2GRAY) for x in frames]\n", + "diffs = [(p2-p1) for p1,p2 in zip(bwframes[:-1],bwframes[1:])]\n", + "diff_amps = np.array([np.linalg.norm(x) for x in diffs])\n", + "plt.plot(diff_amps)\n", + "display_images(diffs[::150],titles=diff_amps[::150])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suponha que queremos criar um relatório que mostre o que aconteceu na frente da câmera exibindo a imagem adequada cada vez que algo acontece. Para isso, provavelmente queremos descobrir o quadro inicial e final de um \"evento\" e exibir o quadro do meio. Para remover alguns ruídos, também suavizaremos a curva acima com a função de média móvel:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def moving_average(x, w):\n", + " return np.convolve(x, np.ones(w), 'valid') / w\n", + "\n", + "threshold = 13000\n", + "\n", + "plt.plot(moving_average(diff_amps,10))\n", + "plt.axhline(y=threshold, color='r', linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora podemos encontrar quadros que têm a quantidade de alterações acima do limite usando `np.where` e extrair uma sequência de quadros consecutivos que seja maior que 30 quadros:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322]\n" + ] + } + ], + "source": [ + "active_frames = np.where(diff_amps>threshold)[0]\n", + "\n", + "def subsequence(seq,min_length=30):\n", + " ss = []\n", + " for i,x in enumerate(seq[:-1]):\n", + " ss.append(x)\n", + " if x+1 != seq[i+1]:\n", + " if len(ss)>min_length:\n", + " return ss\n", + " ss.clear()\n", + "\n", + "sub = subsequence(active_frames)\n", + "print(sub)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(frames[(sub[0]+sub[-1])//2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode notar que o esquema de cores nesta imagem não parece correto! Isso ocorre porque o OpenCV, por razões históricas, carrega imagens no espaço de cores BGR, enquanto o matplotlib usa a ordem de cores RGB mais tradicional. Na maioria das vezes, faz sentido converter as imagens para RGB imediatamente após carregá-las.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(cv2.cvtColor(frames[(sub[0]+sub[-1])//2],cv2.COLOR_BGR2RGB))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extraia Movimento usando Fluxo Óptico\n", + "\n", + "Embora comparar dois quadros consecutivos nos permita ver a quantidade de mudanças, isso não fornece informações sobre o que está realmente se movendo e onde. Para obter essas informações, existe uma técnica chamada **[fluxo óptico](https://docs.opencv.org/3.4/d4/dee/tutorial_optical_flow.html)**:\n", + "\n", + "* **Fluxo Óptico Denso** calcula o campo vetorial que mostra para cada pixel para onde ele está se movendo.\n", + "* **Fluxo Óptico Esparso** baseia-se em identificar algumas características distintivas na imagem (por exemplo, bordas) e construir sua trajetória de quadro a quadro.\n", + "\n", + "Leia mais sobre fluxo óptico [neste excelente tutorial](https://learnopencv.com/optical-flow-in-opencv/).\n", + "\n", + "Vamos calcular o fluxo óptico denso entre nossos quadros:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(180, 320, 2)" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "flows = [cv2.calcOpticalFlowFarneback(f1, f2, None, 0.5, 3, 15, 3, 5, 1.2, 0) \n", + " for f1,f2 in zip(bwframes[:-1],bwframes[1:])]\n", + "flows[0].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como você pode ver, para cada quadro, o fluxo tem a dimensão do quadro e 2 canais, correspondendo aos componentes x e y do vetor de fluxo óptico.\n", + "\n", + "Exibir o fluxo óptico em 2D é um pouco desafiador, mas podemos usar uma ideia inteligente. Se convertermos o fluxo óptico para coordenadas polares, obteremos dois componentes para cada pixel: *direção* e *intensidade*. Podemos representar a intensidade pela intensidade do pixel e a direção por diferentes cores. Vamos criar uma imagem no [espaço de cores HSV (Matiz-Saturação-Valor)](https://en.wikipedia.org/wiki/HSV_color_space), onde o matiz será definido pela direção, o valor pela intensidade e a saturação será 255.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "195 322\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def flow_to_hsv(flow):\n", + " hsvImg = np.zeros((flow.shape[0],flow.shape[1],3),dtype=np.uint8)\n", + " mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])\n", + " hsvImg[..., 0] = 0.5 * ang * 180 / np.pi\n", + " hsvImg[..., 1] = 255\n", + " hsvImg[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n", + " return cv2.cvtColor(hsvImg, cv2.COLOR_HSV2BGR)\n", + "\n", + "start = sub[0]\n", + "stop = sub[-1]\n", + "print(start,stop)\n", + "\n", + "frms = [flow_to_hsv(x) for x in flows[start:stop]]\n", + "display_images(frms[::25])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Então, nesses quadros, a cor esverdeada corresponde ao movimento para a esquerda, enquanto a azul - ao movimento para a direita.\n", + "\n", + "O fluxo óptico pode ser uma ótima ferramenta para tirar conclusões sobre a direção geral do movimento. Por exemplo, se você perceber que todos os pixels em um quadro estão se movendo mais ou menos em uma única direção - você pode concluir que há movimento da câmera e tentar compensar isso.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f" + }, + "kernelspec": { + "display_name": "Python 3.8.12 ('py38')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "orig_nbformat": 4, + "coopTranslator": { + "original_hash": "339b2129f86b24024393ff92b8bd376f", + "translation_date": "2025-09-07T15:03:29+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/README.md b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/README.md new file mode 100644 index 00000000..02519882 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/README.md @@ -0,0 +1,112 @@ +# Introdução à Visão Computacional + +[Visão Computacional](https://wikipedia.org/wiki/Computer_vision) é uma disciplina cujo objetivo é permitir que computadores compreendam imagens digitais em um nível avançado. Essa é uma definição bastante ampla, pois *compreender* pode significar muitas coisas diferentes, incluindo encontrar um objeto em uma imagem (**detecção de objetos**), entender o que está acontecendo (**detecção de eventos**), descrever uma imagem em texto ou reconstruir uma cena em 3D. Existem também tarefas específicas relacionadas a imagens humanas: estimativa de idade e emoções, detecção e identificação de rostos, e estimativa de pose em 3D, para citar algumas. + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/11) + +Uma das tarefas mais simples da visão computacional é a **classificação de imagens**. + +A visão computacional é frequentemente considerada um ramo da IA. Atualmente, a maioria das tarefas de visão computacional é resolvida usando redes neurais. Vamos aprender mais sobre o tipo especial de redes neurais usadas para visão computacional, as [redes neurais convolucionais](../07-ConvNets/README.md), ao longo desta seção. + +No entanto, antes de passar a imagem para uma rede neural, em muitos casos faz sentido usar algumas técnicas algorítmicas para melhorar a imagem. + +Existem várias bibliotecas Python disponíveis para processamento de imagens: + +* **[imageio](https://imageio.readthedocs.io/en/stable/)** pode ser usada para ler/escrever diferentes formatos de imagem. Também suporta ffmpeg, uma ferramenta útil para converter quadros de vídeo em imagens. +* **[Pillow](https://pillow.readthedocs.io/en/stable/index.html)** (também conhecida como PIL) é um pouco mais poderosa e também suporta algumas manipulações de imagem, como morphing, ajustes de paleta e mais. +* **[OpenCV](https://opencv.org/)** é uma poderosa biblioteca de processamento de imagens escrita em C++, que se tornou o padrão *de facto* para processamento de imagens. Possui uma interface conveniente em Python. +* **[dlib](http://dlib.net/)** é uma biblioteca em C++ que implementa muitos algoritmos de aprendizado de máquina, incluindo alguns algoritmos de visão computacional. Também possui uma interface em Python e pode ser usada para tarefas desafiadoras, como detecção de rostos e pontos faciais. + +## OpenCV + +[OpenCV](https://opencv.org/) é considerado o padrão *de facto* para processamento de imagens. Ele contém muitos algoritmos úteis, implementados em C++. Você também pode chamar o OpenCV a partir do Python. + +Um bom lugar para aprender OpenCV é [este curso Learn OpenCV](https://learnopencv.com/getting-started-with-opencv/). Em nosso currículo, nosso objetivo não é aprender OpenCV, mas mostrar alguns exemplos de quando ele pode ser usado e como. + +### Carregando Imagens + +Imagens em Python podem ser convenientemente representadas por arrays NumPy. Por exemplo, imagens em escala de cinza com tamanho de 320x200 pixels seriam armazenadas em um array 200x320, e imagens coloridas da mesma dimensão teriam forma 200x320x3 (para 3 canais de cor). Para carregar uma imagem, você pode usar o seguinte código: + +```python +import cv2 +import matplotlib.pyplot as plt + +im = cv2.imread('image.jpeg') +plt.imshow(im) +``` + +Tradicionalmente, o OpenCV usa codificação BGR (Azul-Verde-Vermelho) para imagens coloridas, enquanto o restante das ferramentas Python usa o mais tradicional RGB (Vermelho-Verde-Azul). Para que a imagem fique correta, você precisa convertê-la para o espaço de cores RGB, seja trocando as dimensões no array NumPy ou chamando uma função do OpenCV: + +```python +im = cv2.cvtColor(im,cv2.COLOR_BGR2RGB) +``` + +A mesma função `cvtColor` pode ser usada para realizar outras transformações de espaço de cores, como converter uma imagem para escala de cinza ou para o espaço de cores HSV (Matiz-Saturação-Valor). + +Você também pode usar o OpenCV para carregar quadros de vídeo, um por um - um exemplo é dado no exercício [OpenCV Notebook](OpenCV.ipynb). + +### Processamento de Imagens + +Antes de alimentar uma imagem para uma rede neural, você pode querer aplicar várias etapas de pré-processamento. O OpenCV pode fazer muitas coisas, incluindo: + +* **Redimensionar** a imagem usando `im = cv2.resize(im, (320,200),interpolation=cv2.INTER_LANCZOS)` +* **Desfocar** a imagem usando `im = cv2.medianBlur(im,3)` ou `im = cv2.GaussianBlur(im, (3,3), 0)` +* Alterar o **brilho e contraste** da imagem pode ser feito por manipulações de arrays NumPy, conforme descrito [nesta nota do Stackoverflow](https://stackoverflow.com/questions/39308030/how-do-i-increase-the-contrast-of-an-image-in-python-opencv). +* Usar [limiarização](https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html) chamando as funções `cv2.threshold`/`cv2.adaptiveThreshold`, que muitas vezes é preferível a ajustar brilho ou contraste. +* Aplicar diferentes [transformações](https://docs.opencv.org/4.5.5/da/d6e/tutorial_py_geometric_transformations.html) à imagem: + - **[Transformações Afins](https://docs.opencv.org/4.5.5/d4/d61/tutorial_warp_affine.html)** podem ser úteis se você precisar combinar rotação, redimensionamento e distorção na imagem e souber a localização de origem e destino de três pontos na imagem. Transformações afins mantêm linhas paralelas paralelas. + - **[Transformações de Perspectiva](https://medium.com/analytics-vidhya/opencv-perspective-transformation-9edffefb2143)** podem ser úteis quando você conhece as posições de origem e destino de 4 pontos na imagem. Por exemplo, se você tirar uma foto de um documento retangular com uma câmera de smartphone de algum ângulo e quiser criar uma imagem retangular do próprio documento. +* Entender o movimento dentro da imagem usando **[fluxo óptico](https://docs.opencv.org/4.5.5/d4/dee/tutorial_optical_flow.html)**. + +## Exemplos de Uso da Visão Computacional + +Em nosso [OpenCV Notebook](OpenCV.ipynb), damos alguns exemplos de quando a visão computacional pode ser usada para realizar tarefas específicas: + +* **Pré-processamento de uma fotografia de um livro em Braille**. Focamos em como podemos usar limiarização, detecção de características, transformação de perspectiva e manipulações NumPy para separar símbolos individuais em Braille para posterior classificação por uma rede neural. + +![Imagem Braille](../../../../../translated_images/pt-BR/braille.341962ff76b1bd70.webp) | ![Imagem Braille Pré-processada](../../../../../translated_images/pt-BR/braille-result.46530fea020b03c7.webp) | ![Símbolos Braille](../../../../../translated_images/pt-BR/braille-symbols.0159185ab69d5339.webp) +----|-----|----- + +> Imagem de [OpenCV.ipynb](OpenCV.ipynb) + +* **Detectando movimento em vídeo usando diferença de quadros**. Se a câmera estiver fixa, os quadros do feed da câmera devem ser bastante semelhantes entre si. Como os quadros são representados como arrays, apenas subtraindo esses arrays de dois quadros subsequentes obteremos a diferença de pixels, que deve ser baixa para quadros estáticos e se tornar maior quando houver movimento substancial na imagem. + +![Imagem de quadros de vídeo e diferenças de quadros](../../../../../translated_images/pt-BR/frame-difference.706f805491a0883c.webp) + +> Imagem de [OpenCV.ipynb](OpenCV.ipynb) + +* **Detectando movimento usando Fluxo Óptico**. [Fluxo óptico](https://docs.opencv.org/3.4/d4/dee/tutorial_optical_flow.html) nos permite entender como os pixels individuais nos quadros de vídeo se movem. Existem dois tipos de fluxo óptico: + + - **Fluxo Óptico Denso** calcula o campo vetorial que mostra para cada pixel onde ele está se movendo. + - **Fluxo Óptico Esparso** é baseado em pegar algumas características distintivas na imagem (por exemplo, bordas) e construir sua trajetória de quadro a quadro. + +![Imagem de Fluxo Óptico](../../../../../translated_images/pt-BR/optical.1f4a94464579a83a.webp) + +> Imagem de [OpenCV.ipynb](OpenCV.ipynb) + +## ✍️ Notebooks de Exemplo: OpenCV [experimente o OpenCV em Ação](OpenCV.ipynb) + +Vamos fazer alguns experimentos com OpenCV explorando o [OpenCV Notebook](OpenCV.ipynb) + +## Conclusão + +Às vezes, tarefas relativamente complexas, como detecção de movimento ou detecção de pontas dos dedos, podem ser resolvidas puramente por visão computacional. Assim, é muito útil conhecer as técnicas básicas de visão computacional e o que bibliotecas como OpenCV podem fazer. + +## 🚀 Desafio + +Assista a [este vídeo](https://docs.microsoft.com/shows/ai-show/ai-show--2021-opencv-ai-competition--grand-prize-winners--cortic-tigers--episode-32?WT.mc_id=academic-77998-cacaste) do AI Show para aprender sobre o projeto Cortic Tigers e como eles construíram uma solução baseada em blocos para democratizar tarefas de visão computacional via um robô. Pesquise outros projetos como este que ajudam novos aprendizes a ingressar na área. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/12) + +## Revisão e Autoestudo + +Leia mais sobre fluxo óptico [neste ótimo tutorial](https://learnopencv.com/optical-flow-in-opencv/). + +## [Tarefa](lab/README.md) + +Neste laboratório, você gravará um vídeo com gestos simples, e seu objetivo será extrair movimentos para cima/baixo/esquerda/direita usando fluxo óptico. + +Quadro de Movimento da Palma + +--- + diff --git a/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb new file mode 100644 index 00000000..c0826337 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detecção de Movimento da Palma usando Fluxo Óptico\n", + "\n", + "Este laboratório faz parte do [Currículo de IA para Iniciantes](http://aka.ms/ai-beginners).\n", + "\n", + "Considere [este vídeo](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), no qual a palma de uma pessoa se move para a esquerda/direita/cima/baixo em um fundo estável.\n", + "\n", + "**Seu objetivo** será usar Fluxo Óptico para determinar quais partes do vídeo contêm movimentos para cima/baixo/esquerda/direita.\n", + "\n", + "Comece obtendo os quadros do vídeo conforme descrito na aula:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Agora, calcule os quadros de fluxo óptico denso conforme descrito na aula e converta o fluxo óptico denso para coordenadas polares:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Construa um histograma de direções para cada quadro do fluxo óptico. Um histograma mostra quantos vetores se enquadram em determinado intervalo, e ele deve separar diferentes direções de movimento no quadro.\n", + "\n", + "> Você também pode zerar todos os vetores cuja magnitude esteja abaixo de um determinado limite. Isso eliminará pequenos movimentos extras no vídeo, como olhos e cabeça.\n", + "\n", + "Plote os histogramas para alguns dos quadros.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ao olhar para os histogramas, deve ser bastante simples determinar a direção do movimento. Você precisa selecionar os intervalos que correspondem às direções para cima/baixo/esquerda/direita e que estão acima de um certo limite.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Code here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parabéns! Se você realizou todas as etapas acima, você concluiu o laboratório!\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4, + "coopTranslator": { + "original_hash": "153d9e417e079bf62f8f693002d0deaf", + "translation_date": "2025-08-28T13:14:48+00:00", + "source_file": "lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/README.md b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/README.md new file mode 100644 index 00000000..6789bb9c --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/06-IntroCV/lab/README.md @@ -0,0 +1,22 @@ +# Detectando Movimentos usando Optical Flow + +Tarefa de laboratório do [Currículo de IA para Iniciantes](https://aka.ms/ai-beginners). + +## Tarefa + +Considere [este vídeo](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/palm-movement.mp4), no qual a palma da mão de uma pessoa se move para a esquerda/direita/cima/baixo em um fundo estável. + +**Seu objetivo** será usar Optical Flow para determinar quais partes do vídeo contêm movimentos para cima, para baixo, para a esquerda ou para a direita. + +**Objetivo adicional** seria rastrear o movimento da palma/dos dedos usando o tom de pele, conforme descrito [neste post do blog](https://dev.to/amarlearning/finger-detection-and-tracking-using-opencv-and-python-586m) ou [aqui](http://www.benmeline.com/finger-tracking-with-opencv-and-python/). + +## Notebook Inicial + +Comece o laboratório abrindo [MovementDetection.ipynb](../../../../../../lessons/4-ComputerVision/06-IntroCV/lab/MovementDetection.ipynb) + +## Conclusão + +Às vezes, tarefas relativamente complexas, como detecção de movimento ou detecção de ponta dos dedos, podem ser resolvidas puramente por visão computacional. Por isso, é muito útil saber o que bibliotecas como OpenCV podem fazer. + +**Aviso Legal**: +Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte oficial. Para informações críticas, recomenda-se a tradução profissional feita por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução. \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md new file mode 100644 index 00000000..b3fec284 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md @@ -0,0 +1,64 @@ +# Arquiteturas Conhecidas de CNN + +### VGG-16 + +VGG-16 é uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014. Ela possui a seguinte estrutura de camadas: + +![Camadas do ImageNet](../../../../../translated_images/pt-BR/vgg-16-arch1.d901a5583b3a51ba.webp) + +Como você pode ver, a VGG segue uma arquitetura tradicional em forma de pirâmide, que é uma sequência de camadas de convolução e pooling. + +![Pirâmide do ImageNet](../../../../../translated_images/pt-BR/vgg-16-arch.64ff2137f50dd49f.webp) + +> Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) + +### ResNet + +ResNet é uma família de modelos proposta pela Microsoft Research em 2015. A ideia principal da ResNet é usar **blocos residuais**: + + + +> Imagem deste [artigo](https://arxiv.org/pdf/1512.03385.pdf) + +A razão para usar a passagem de identidade é fazer com que nossa camada preveja **a diferença** entre o resultado de uma camada anterior e a saída do bloco residual - daí o nome *residual*. Esses blocos são muito mais fáceis de treinar, e é possível construir redes com centenas desses blocos (as variantes mais comuns são ResNet-52, ResNet-101 e ResNet-152). + +Você também pode pensar nessa rede como sendo capaz de ajustar sua complexidade ao conjunto de dados. Inicialmente, quando você começa a treinar a rede, os valores dos pesos são pequenos, e a maior parte do sinal passa pelas camadas de identidade. À medida que o treinamento avança e os pesos se tornam maiores, a importância dos parâmetros da rede cresce, e a rede se ajusta para acomodar o poder expressivo necessário para classificar corretamente as imagens de treinamento. + +### Google Inception + +A arquitetura Google Inception leva essa ideia um passo adiante e constrói cada camada da rede como uma combinação de vários caminhos diferentes: + + + +> Imagem de [Researchgate](https://www.researchgate.net/figure/Inception-module-with-dimension-reductions-left-and-schema-for-Inception-ResNet-v1_fig2_355547454) + +Aqui, precisamos enfatizar o papel das convoluções 1x1, porque, à primeira vista, elas não fazem sentido. Por que precisaríamos passar pela imagem com um filtro 1x1? No entanto, é importante lembrar que os filtros de convolução também trabalham com vários canais de profundidade (originalmente - cores RGB, em camadas subsequentes - canais para diferentes filtros), e a convolução 1x1 é usada para misturar esses canais de entrada usando diferentes pesos treináveis. Ela também pode ser vista como uma redução de dimensão (pooling) sobre a dimensão dos canais. + +Aqui está [um bom post de blog](https://medium.com/analytics-vidhya/talented-mr-1x1-comprehensive-look-at-1x1-convolution-in-deep-learning-f6b355825578) sobre o assunto, e [o artigo original](https://arxiv.org/pdf/1312.4400.pdf). + +### MobileNet + +MobileNet é uma família de modelos com tamanho reduzido, adequada para dispositivos móveis. Use-os se você tiver poucos recursos e puder sacrificar um pouco de precisão. A ideia principal por trás deles é a chamada **convolução separável por profundidade**, que permite representar filtros de convolução por uma composição de convoluções espaciais e convolução 1x1 sobre canais de profundidade. Isso reduz significativamente o número de parâmetros, tornando a rede menor em tamanho e também mais fácil de treinar com menos dados. + +Aqui está [um bom post de blog sobre MobileNet](https://medium.com/analytics-vidhya/image-classification-with-mobilenet-cc6fbb2cd470). + +## Conclusão + +Nesta unidade, você aprendeu o conceito principal por trás das redes neurais de visão computacional - redes convolucionais. Arquiteturas reais que alimentam classificação de imagens, detecção de objetos e até redes de geração de imagens são todas baseadas em CNNs, apenas com mais camadas e alguns truques adicionais de treinamento. + +## 🚀 Desafio + +Nos notebooks que acompanham, há notas no final sobre como obter maior precisão. Faça alguns experimentos para ver se você consegue alcançar maior precisão. + +## [Quiz pós-aula](https://ff-quizzes.netlify.app/en/ai/quiz/14) + +## Revisão e Autoestudo + +Embora as CNNs sejam mais frequentemente usadas para tarefas de Visão Computacional, elas são geralmente boas para extrair padrões de tamanho fixo. Por exemplo, se estivermos lidando com sons, também podemos querer usar CNNs para procurar padrões específicos no sinal de áudio - nesse caso, os filtros seriam unidimensionais (e essa CNN seria chamada de 1D-CNN). Além disso, às vezes 3D-CNN é usada para extrair características em espaço multidimensional, como certos eventos ocorrendo em vídeos - a CNN pode capturar certos padrões de mudança de características ao longo do tempo. Faça uma revisão e autoestudo sobre outras tarefas que podem ser realizadas com CNNs. + +## [Tarefa](lab/README.md) + +Neste laboratório, você será encarregado de classificar diferentes raças de gatos e cães. Essas imagens são mais complexas do que o conjunto de dados MNIST, possuem dimensões maiores e há mais de 10 classes. + +--- + diff --git a/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb new file mode 100644 index 00000000..80d4f6f4 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Redes neurais convolucionais\n", + "\n", + "Na unidade anterior, aprendemos como definir uma rede neural com múltiplas camadas usando a definição de classes, mas essas redes eram genéricas e não especializadas para tarefas de visão computacional. Nesta unidade, aprenderemos sobre as **Redes Neurais Convolucionais (CNNs)**, que são projetadas especificamente para visão computacional.\n", + "\n", + "A visão computacional é diferente da classificação genérica, porque, quando tentamos encontrar um determinado objeto em uma imagem, estamos analisando a imagem em busca de **padrões** específicos e suas combinações. Por exemplo, ao procurar por um gato, podemos primeiro buscar por linhas horizontais, que podem formar os bigodes, e então uma certa combinação de bigodes pode nos indicar que se trata de uma imagem de um gato. A posição relativa e a presença de certos padrões são importantes, e não sua posição exata na imagem.\n", + "\n", + "Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Mas, antes disso, vamos carregar todas as dependências e funções que definimos nas unidades anteriores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torchvision\n", + "import matplotlib.pyplot as plt\n", + "from torchinfo import summary\n", + "import numpy as np\n", + "\n", + "from pytorchcv import load_mnist, train, plot_results, plot_convolution, display_dataset\n", + "load_mnist(batch_size=128)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtros convolucionais\n", + "\n", + "Filtros convolucionais são pequenas janelas que percorrem cada pixel da imagem e calculam a média ponderada dos pixels vizinhos.\n", + "\n", + "\n", + "\n", + "Eles são definidos por matrizes de coeficientes de peso. Vamos ver exemplos de aplicação de dois filtros convolucionais diferentes sobre nossos dígitos manuscritos do MNIST:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_convolution(torch.tensor([[-1.,0.,1.],[-1.,0.,1.],[-1.,0.,1.]]),'Vertical edge filter')\n", + "plot_convolution(torch.tensor([[-1.,-1.,-1.],[0.,0.,0.],[1.,1.,1.]]),'Horizontal edge filter')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O primeiro filtro é chamado de **filtro de borda vertical** e é definido pela seguinte matriz:\n", + "$$\n", + "\\left(\n", + " \\begin{matrix}\n", + " -1 & 0 & 1 \\cr\n", + " -1 & 0 & 1 \\cr\n", + " -1 & 0 & 1 \\cr\n", + " \\end{matrix}\n", + "\\right)\n", + "$$\n", + "Quando este filtro passa por um campo de pixels relativamente uniforme, todos os valores somam 0. No entanto, quando encontra uma borda vertical na imagem, é gerado um valor de pico elevado. É por isso que, nas imagens acima, você pode ver bordas verticais representadas por valores altos e baixos, enquanto as bordas horizontais são suavizadas.\n", + "\n", + "O oposto acontece quando aplicamos o filtro de borda horizontal - as linhas horizontais são amplificadas, e as verticais são suavizadas.\n", + "\n", + "Na visão computacional clássica, múltiplos filtros eram aplicados à imagem para gerar características, que então eram usadas por um algoritmo de aprendizado de máquina para construir um classificador. No entanto, no aprendizado profundo, construímos redes que **aprendem** os melhores filtros de convolução para resolver o problema de classificação.\n", + "\n", + "Para isso, introduzimos as **camadas convolucionais**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Camadas Convolucionais\n", + "\n", + "As camadas convolucionais são definidas usando a construção `nn.Conv2d`. Precisamos especificar o seguinte:\n", + "* `in_channels` - número de canais de entrada. No nosso caso, estamos lidando com uma imagem em escala de cinza, portanto, o número de canais de entrada é 1.\n", + "* `out_channels` - número de filtros a serem usados. Utilizaremos 9 filtros diferentes, o que dará à rede muitas oportunidades para explorar quais filtros funcionam melhor para o nosso cenário.\n", + "* `kernel_size` é o tamanho da janela deslizante. Geralmente, são usados filtros de 3x3 ou 5x5.\n", + "\n", + "A CNN mais simples conterá uma camada convolucional. Dado o tamanho de entrada 28x28, após aplicar nove filtros de 5x5, terminaremos com um tensor de 9x24x24 (o tamanho espacial é menor porque há apenas 24 posições onde um intervalo deslizante de comprimento 5 pode se ajustar em 28 pixels).\n", + "\n", + "Após a convolução, achatamos o tensor 9x24x24 em um vetor de tamanho 5184 e, em seguida, adicionamos uma camada linear para produzir 10 classes. Também usamos a função de ativação `relu` entre as camadas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "==========================================================================================\n", + "Layer (type:depth-idx) Output Shape Param #\n", + "==========================================================================================\n", + "├─Conv2d: 1-1 [1, 9, 24, 24] 234\n", + "├─Flatten: 1-2 [1, 5184] --\n", + "├─Linear: 1-3 [1, 10] 51,850\n", + "==========================================================================================\n", + "Total params: 52,084\n", + "Trainable params: 52,084\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.18\n", + "==========================================================================================\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.04\n", + "Params size (MB): 0.21\n", + "Estimated Total Size (MB): 0.25\n", + "==========================================================================================" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class OneConv(nn.Module):\n", + " def __init__(self):\n", + " super(OneConv, self).__init__()\n", + " self.conv = nn.Conv2d(in_channels=1,out_channels=9,kernel_size=(5,5))\n", + " self.flatten = nn.Flatten()\n", + " self.fc = nn.Linear(5184,10)\n", + "\n", + " def forward(self, x):\n", + " x = nn.functional.relu(self.conv(x))\n", + " x = self.flatten(x)\n", + " x = nn.functional.log_softmax(self.fc(x),dim=1)\n", + " return x\n", + "\n", + "net = OneConv()\n", + "\n", + "summary(net,input_size=(1,1,28,28))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode ver que esta rede contém cerca de 50k parâmetros treináveis, em comparação com cerca de 80k em redes totalmente conectadas com múltiplas camadas. Isso nos permite alcançar bons resultados mesmo em conjuntos de dados menores, porque redes convolucionais generalizam muito melhor.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0, Train acc=0.947, Val acc=0.969, Train loss=0.001, Val loss=0.001\n", + "Epoch 1, Train acc=0.979, Val acc=0.975, Train loss=0.001, Val loss=0.001\n", + "Epoch 2, Train acc=0.985, Val acc=0.977, Train loss=0.000, Val loss=0.001\n", + "Epoch 3, Train acc=0.988, Val acc=0.975, Train loss=0.000, Val loss=0.001\n", + "Epoch 4, Train acc=0.988, Val acc=0.976, Train loss=0.000, Val loss=0.001\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hist = train(net,train_loader,test_loader,epochs=5)\n", + "plot_results(hist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como você pode ver, conseguimos alcançar maior precisão, e de forma muito mais rápida, em comparação com as redes totalmente conectadas da unidade anterior.\n", + "\n", + "Também podemos visualizar os pesos de nossas camadas convolucionais treinadas, para tentar entender melhor o que está acontecendo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(1,9)\n", + "with torch.no_grad():\n", + " p = next(net.conv.parameters())\n", + " for i,x in enumerate(p):\n", + " ax[i].imshow(x.detach().cpu()[0,...])\n", + " ax[i].axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode ver que alguns desses filtros parecem reconhecer alguns traços oblíquos, enquanto outros parecem bem aleatórios.\n", + "\n", + "## CNNs Multicamadas e Camadas de Pooling\n", + "\n", + "As primeiras camadas de convolução procuram padrões primitivos, como linhas horizontais ou verticais, mas podemos aplicar camadas de convolução adicionais sobre elas para buscar padrões de nível mais alto, como formas primitivas. Em seguida, mais camadas de convolução podem combinar essas formas em partes da imagem, até chegar ao objeto final que estamos tentando classificar.\n", + "\n", + "Ao fazer isso, podemos também aplicar um truque: reduzir o tamanho espacial da imagem. Uma vez que detectamos que há um traço horizontal dentro de uma janela deslizante 3x3, não é tão importante em qual pixel exato isso ocorreu. Assim, podemos \"reduzir\" o tamanho da imagem, o que é feito usando uma das **camadas de pooling**:\n", + "\n", + " * **Pooling Médio** utiliza uma janela deslizante (por exemplo, 2x2 pixels) e calcula a média dos valores dentro da janela.\n", + " * **Pooling Máximo** substitui a janela pelo valor máximo. A ideia por trás do pooling máximo é detectar a presença de um determinado padrão dentro da janela deslizante.\n", + "\n", + "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque à medida que os padrões se tornam mais avançados, há mais combinações interessantes possíveis que precisamos buscar.\n", + "\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/pt-BR/cnn-pyramid.85915455759ef0ce.webp)\n", + "\n", + "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "==========================================================================================\n", + "Layer (type:depth-idx) Output Shape Param #\n", + "==========================================================================================\n", + "├─Conv2d: 1-1 [1, 10, 24, 24] 260\n", + "├─MaxPool2d: 1-2 [1, 10, 12, 12] --\n", + "├─Conv2d: 1-3 [1, 20, 8, 8] 5,020\n", + "├─MaxPool2d: 1-4 [1, 20, 4, 4] --\n", + "├─Linear: 1-5 [1, 10] 3,210\n", + "==========================================================================================\n", + "Total params: 8,490\n", + "Trainable params: 8,490\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.47\n", + "==========================================================================================\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 0.06\n", + "Params size (MB): 0.03\n", + "Estimated Total Size (MB): 0.09\n", + "==========================================================================================" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class MultiLayerCNN(nn.Module):\n", + " def __init__(self):\n", + " super(MultiLayerCNN, self).__init__()\n", + " self.conv1 = nn.Conv2d(1, 10, 5)\n", + " self.pool = nn.MaxPool2d(2, 2)\n", + " self.conv2 = nn.Conv2d(10, 20, 5)\n", + " self.fc = nn.Linear(320,10)\n", + "\n", + " def forward(self, x):\n", + " x = self.pool(nn.functional.relu(self.conv1(x)))\n", + " x = self.pool(nn.functional.relu(self.conv2(x)))\n", + " x = x.view(-1, 320)\n", + " x = nn.functional.log_softmax(self.fc(x),dim=1)\n", + " return x\n", + "\n", + "net = MultiLayerCNN()\n", + "summary(net,input_size=(1,1,28,28))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Observe algumas coisas sobre esta definição:\n", + "* Em vez de usar a camada `Flatten`, estamos achatando o tensor dentro da função `forward` usando a função `view`. Como a camada de achatamento não possui pesos treináveis, não é essencial criar uma instância de camada separada dentro da nossa classe.\n", + "* Usamos apenas uma instância da camada de pooling em nosso modelo, também porque ela não contém parâmetros treináveis, e essa única instância pode ser reutilizada de forma eficiente.\n", + "* O número de parâmetros treináveis (~8,5K) é dramaticamente menor do que nos casos anteriores. Isso ocorre porque as camadas convolucionais geralmente possuem poucos parâmetros, e a dimensionalidade da imagem antes de aplicar a camada densa final é significativamente reduzida. Um número pequeno de parâmetros tem um impacto positivo em nossos modelos, pois ajuda a evitar overfitting, mesmo em conjuntos de dados menores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0, Train acc=0.952, Val acc=0.977, Train loss=0.001, Val loss=0.001\n", + "Epoch 1, Train acc=0.982, Val acc=0.983, Train loss=0.000, Val loss=0.000\n", + "Epoch 2, Train acc=0.986, Val acc=0.983, Train loss=0.000, Val loss=0.000\n", + "Epoch 3, Train acc=0.986, Val acc=0.978, Train loss=0.000, Val loss=0.001\n", + "Epoch 4, Train acc=0.987, Val acc=0.981, Train loss=0.000, Val loss=0.000\n" + ] + } + ], + "source": [ + "hist = train(net,train_loader,test_loader,epochs=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O que você provavelmente deve observar é que conseguimos alcançar uma precisão maior do que com apenas uma camada, e muito mais rápido - com apenas 1 ou 2 épocas. Isso significa que arquiteturas de rede sofisticadas precisam de muito menos dados para entender o que está acontecendo e para extrair padrões genéricos das nossas imagens.\n", + "\n", + "## Trabalhando com imagens reais do conjunto de dados CIFAR-10\n", + "\n", + "Embora nosso problema de reconhecimento de dígitos escritos à mão possa parecer um problema simples, agora estamos prontos para fazer algo mais sério. Vamos explorar um conjunto de dados mais avançado com imagens de diferentes objetos, chamado [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html). Ele contém 60 mil imagens de 32x32, divididas em 10 classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "77b339f50a6b48e98e5db22e0ddd7eb6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./data/cifar-10-python.tar.gz to ./data\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "transform = torchvision.transforms.Compose(\n", + " [torchvision.transforms.ToTensor(),\n", + " torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n", + "\n", + "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n", + "trainloader = torch.utils.data.DataLoader(trainset, batch_size=14, shuffle=True)\n", + "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n", + "testloader = torch.utils.data.DataLoader(testset, batch_size=14, shuffle=False)\n", + "classes = ('plane', 'car', 'bird', 'cat',\n", + " 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "display_dataset(trainset,classes=classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Uma arquitetura bem conhecida para o CIFAR-10 é chamada [LeNet](https://en.wikipedia.org/wiki/LeNet), e foi proposta por *Yann LeCun*. Ela segue os mesmos princípios que mencionamos acima, com a principal diferença sendo 3 canais de cores de entrada em vez de 1.\n", + "\n", + "Também fazemos mais uma simplificação neste modelo - não utilizamos `log_softmax` como função de ativação de saída, e apenas retornamos a saída da última camada totalmente conectada. Nesse caso, podemos simplesmente usar a função de perda `CrossEntropyLoss` para otimizar o modelo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "==========================================================================================\n", + "Layer (type:depth-idx) Output Shape Param #\n", + "==========================================================================================\n", + "├─Conv2d: 1-1 [1, 6, 28, 28] 456\n", + "├─MaxPool2d: 1-2 [1, 6, 14, 14] --\n", + "├─Conv2d: 1-3 [1, 16, 10, 10] 2,416\n", + "├─MaxPool2d: 1-4 [1, 16, 5, 5] --\n", + "├─Conv2d: 1-5 [1, 120, 1, 1] 48,120\n", + "├─Flatten: 1-6 [1, 120] --\n", + "├─Linear: 1-7 [1, 64] 7,744\n", + "├─Linear: 1-8 [1, 10] 650\n", + "==========================================================================================\n", + "Total params: 59,386\n", + "Trainable params: 59,386\n", + "Non-trainable params: 0\n", + "Total mult-adds (M): 0.65\n", + "==========================================================================================\n", + "Input size (MB): 0.01\n", + "Forward/backward pass size (MB): 0.05\n", + "Params size (MB): 0.24\n", + "Estimated Total Size (MB): 0.30\n", + "==========================================================================================" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class LeNet(nn.Module):\n", + " def __init__(self):\n", + " super(LeNet, self).__init__()\n", + " self.conv1 = nn.Conv2d(3, 6, 5)\n", + " self.pool = nn.MaxPool2d(2)\n", + " self.conv2 = nn.Conv2d(6, 16, 5)\n", + " self.conv3 = nn.Conv2d(16,120,5)\n", + " self.flat = nn.Flatten()\n", + " self.fc1 = nn.Linear(120,64)\n", + " self.fc2 = nn.Linear(64,10)\n", + "\n", + " def forward(self, x):\n", + " x = self.pool(nn.functional.relu(self.conv1(x)))\n", + " x = self.pool(nn.functional.relu(self.conv2(x)))\n", + " x = nn.functional.relu(self.conv3(x))\n", + " x = self.flat(x)\n", + " x = nn.functional.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "net = LeNet()\n", + "\n", + "summary(net,input_size=(1,3,32,32))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treinar esta rede adequadamente levará uma quantidade significativa de tempo e deve, preferencialmente, ser feito em computação com suporte a GPU.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 0, Train acc=0.261, Val acc=0.388, Train loss=0.143, Val loss=0.121\n", + "Epoch 1, Train acc=0.437, Val acc=0.491, Train loss=0.110, Val loss=0.101\n", + "Epoch 2, Train acc=0.508, Val acc=0.522, Train loss=0.097, Val loss=0.094\n" + ] + } + ], + "source": [ + "opt = torch.optim.SGD(net.parameters(),lr=0.001,momentum=0.9)\n", + "hist = train(net, trainloader, testloader, epochs=3, optimizer=opt, loss_fn=nn.CrossEntropyLoss())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A precisão que conseguimos alcançar com 3 épocas de treinamento não parece muito boa. No entanto, lembre-se de que adivinhar cegamente nos daria apenas 10% de precisão, e que nosso problema é significativamente mais difícil do que a classificação de dígitos do MNIST. Conseguir mais de 50% de precisão em um tempo de treinamento tão curto parece ser uma boa conquista.\n", + "\n", + "## Conclusões\n", + "\n", + "Nesta unidade, aprendemos o conceito principal por trás das redes neurais de visão computacional - redes convolucionais. Arquiteturas reais que impulsionam a classificação de imagens, detecção de objetos e até mesmo redes de geração de imagens são todas baseadas em CNNs, apenas com mais camadas e alguns truques adicionais de treinamento.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py37_pytorch", + "language": "python", + "name": "conda-env-py37_pytorch-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "coopTranslator": { + "original_hash": "28ab2e2e61409b818d9189e56987b49e", + "translation_date": "2025-08-28T12:53:28+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb new file mode 100644 index 00000000..354b5716 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb @@ -0,0 +1,683 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Redes neurais convolucionais\n", + "\n", + "Já vimos anteriormente que redes neurais são bastante eficazes ao lidar com imagens, e até mesmo um perceptron de uma camada consegue reconhecer dígitos escritos à mão do conjunto de dados MNIST com uma precisão razoável. No entanto, o conjunto de dados MNIST é muito especial, e todos os dígitos estão centralizados dentro da imagem, o que torna a tarefa mais simples.\n", + "\n", + "Na vida real, queremos ser capazes de reconhecer objetos em uma imagem independentemente de sua localização exata. Visão computacional é diferente de classificação genérica, porque, ao tentar encontrar um determinado objeto na imagem, estamos escaneando a imagem em busca de **padrões** específicos e suas combinações. Por exemplo, ao procurar por um gato, podemos primeiro buscar por linhas horizontais, que podem formar os bigodes, e então uma certa combinação de bigodes pode nos indicar que se trata de uma imagem de um gato. A posição relativa e a presença de certos padrões são importantes, e não sua posição exata na imagem.\n", + "\n", + "Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Mas antes, vamos carregar todas as dependências e funções que definimos nas unidades anteriores. Também importaremos a biblioteca auxiliar `tfcv`, que contém algumas funções úteis que não queremos definir dentro deste notebook para manter o código curto e organizado.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from tfcv import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Neste exemplo, vamos focar no conjunto de dados MNIST que vimos anteriormente e na classificação de imagens. Vamos começar carregando o conjunto de dados usando as funções integradas do Keras.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "(x_train,y_train),(x_test,y_test) = keras.datasets.mnist.load_data()\n", + "x_train = x_train.astype(np.float32) / 255.0\n", + "x_test = x_test.astype(np.float32) / 255.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtros convolucionais\n", + "\n", + "Filtros convolucionais são pequenas janelas que percorrem cada pixel da imagem e calculam a média ponderada dos pixels vizinhos.\n", + "\n", + "Eles são definidos por matrizes de coeficientes de peso. Vamos ver exemplos de aplicação de dois filtros convolucionais diferentes sobre nossos dígitos manuscritos do MNIST:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2ryW3UWDKSgVOHCGZCjnzpwQ//PBXaTpDxpw+gUiZcHq4ImNC9glEhhQWF0NdGnxR+bbnNxguZQ7/6Fu/H/VyBWcgCGK47Nb42eAIIgOvAzK1eB1LGFmmLvZQL17CJgkm2R3t5FYgfR9cF3F4jmS6xvK7A079oRe4t77MDze/wbyyhNK75j1bxvKV4V0815/ntx97GH9Vcfr3z/Dzxz7BX5n5JB/60y/wpc5Jfu/TjxAuNZj/TABPPLdLd3gVEfh0jrr0DkM6ZqCRgg0IHQcbx7twQYKs6pCOZwTjEadaywB82T90569lr+G5xC1IxzQz7hahUMi3Um0UIVALc6RzLbpHAzYeEKR1Q/1Ih7Gwz/1jVzjkX/XZfmrlFBc/dQivA17XIlNLb0ESTVnCRcHUExFuO0K+eB5TxhfsCW6rhml9RRYqZmc3+JPjX2ZKZkyqGwnDG19GajUGgy58bi4KV+SeEG0tfetiVwKql0Allp1WXJla/LZGxQZnkCKjDLnaJtvvQQZSIWpVRBCQTFXpz3kMZg0fnXyak/4V5pWlJv1XvE0jiI1LJ/Px1hXhEiwNGgxMyrxS/EhthVmnzcfn7ifKfHTo7Q2lSSrSuiAd04hmQrUekVUCkLuwEBeBacYVqFpGrRJTUSmZKbxz2r41NYEiut1UXLKaRYQZVRkjkdvPb88Y2gbOx5PIFMQBGyvheggl0eM1BrM+/TlJdnRIqzHgI4ef44i/zvvDM5woljqJZNLt8vePzJK2FemWQKaCwSGDmh0wsFWiyy4I8Ny9E5v5Vue2fRM2y1DrXXwludINuZi1cJ0NJm8QzHMjtkzEF6MZ1rMaWzpkYDy+IzzL+4I2xloiaziXztF8XjD7hQ0wBvSOREJrEVECxuT+SqPzqjb7HNVssPqH76F3WJCeGvDQoTN8pHmZ91depiUNvvBu+L4pKfgDtWcZd3p8qXY/2pNcaTf4+a23cX9wmQ8FXeoyYnqyw3LSIqs6RUzt7iLCgO49Ke964GWu9Bu0hzdhqr9NqHodUQ3ZOubyUw9+nlDFfHHjbi50xqgtGuSFpQMxx14vzsI86eFJ1h8Kufs953nH2EUe9BcBxcsZnEsn+JeLH+L0549TWRXMfK2Ls9bFbHV2+9JvCTIIMG+/h2jCZ/GDDuMPrXJvY5MPjr3EuNPjpHeFQGiejef4dL9JSw0YVz2qMuYn3/9FYuPQySqkVnI42GTObfPb8w/yrfoJKpcCjr3ceMtHXu8Vbt/WRWtsr4+q+KRxjfWsxqy6+QdkYC3PDhdYjFusxDUGmUcoE97hb6CtJbKwkdWoLWn0M6dv223sNURYoX0P1O7b4M/f/WX+TOul4pWr4s1gXmEKC6XLKQmRvYzxDdaRxAOXx7aOIoXhA8EWgYDpao92o4Lxdk8wXYNS1Gd6/MDkE3xcPEh7OLt71+L72FpIPA4/1vwGXevw21ceZGOryuG2Rq9v7N617SK2UWWwENBfgB+Ze4x3BeeZV7n2uKqrvBTP8vTZBU7+Zg+10YP1NiaOMdEumNRvA8LzGMxX6M0rmm9b4x/e+ytMqSGHlIsSAolkYDM+2Rvjsa2jTPo9Fvw2R701/sL4Y4Qyf3Z3PrMtNWBlUOeynMSGe+RZLLmNGqbW2GGEbHcJnp3i73rfx/GpdT40+RI97XNuMEHVSfjp6U/zgHf1MjZNxMtpwOf6j/Avv/Bh/FWFjPOw/X82c4x/vvAhGvUB75m9wIudKdzePg7eeR3IIEBOTpAensQeGfL7Dr3EKX/xmoesZ2IeT+qs6xrf6B/n8rDF94w/zY/Wl0itZsNknEsX8DYU4bIhPRPw5ehuNk6G/ETjWdTeMMJeg5UCAajdDq2UiuiRo6w96JE90KMqBauZYq1XRW95qPgtVvRBCJyZaWy9ytq7J1h9X8bUoQ1OeldoSo0UihTNb7Yf4Xdfvo/gRR9ndQ3b7WGHQ6w27OvSUkLk6VxTE2TTTZbeJ/FPbPH9h55jVg1oG49PJi3OJ1P8+tLDrPaq9F9uEqxKjA/at2TzMeff8QVOBUt8h39lO6od4ErWZGmtibehEPHBiejf79w+DdNaTL+PzTJmvpHQWa1y9niVcyfHifse3iWPrGp510fP8oB3cftti5nD5/r38msXH+auX0lwn7mAHQyxSYI6NE9yaJzO8Qk+9l0NSCSnOkMOjifk1RHVkPTIJN2jFb7j6Av81MQXmVUauLr7bBvDpzoP8FJ/iq+fPo53xWX1vTV++NR/JrKai1nIS9EswaqgfjHCGXrElxxOu3OkJy0SkNg9l04ohUWxu4urUIrVhz0WPnqeD0yeIRSKgfHpdCq4mwq5GwFIu4hQCr0wSTQbsvpuzc9+16+w4GxyvxttuwVim/G5S3fjPVqn+XKGWVrGRAfAZF34smWjTnx8ku5hnw9+8Gn++tzHaUloSp+Lkc9vbz7M4yuHyT42SXNJc+iZNeylJYTngePQf+9x/n3rnbx95jKHZz7FzA531VLSQlzxCVYFHKAUuP3O7fcma423PqTmCKx06NkalaEgXLakdcmnN+7liLvOKXedQ06FC9kYn145xfJSi1ZniB0Mc9ONyU287rpPNVBUX/ARGcjOkIOsYwrfR4Yh9vAsK++sMpizfKR2hbrIcAvtctNEXMxcvhHdw6+99DDRZoC36uD0BOfWxvmlueMsp00+v3o3l9ZbTCwZnHZExYAzcNjoOCTWEgjLXbVV+plHd2yBcGIcO4x2pYSgcBxkrYqph1T9IS01wFe7q8UZBxpeRE1FSCSRdbFbHv6mQA6zt8TGbRul8lrFhxzcsQELziZTaogrFAbDxcywakLaqzXmLxuC1fjAlF5U42MwMUa80GT5XQHDacP9tUXqwrKuJecy+O2th/m95+9DrnjMLWoqyxGiN8DEMbJew0yOMZxQHG61uStcJRQZBkFkM2JrODeYoHJFEq4YbFoKzL3CbReYNssQT79E5XmHaqOObdQQaYbtdGFqnMcW7uHFU5P81F1f5s80z/Hpzv1c+PwRJi5b1Moi2XC4HU2nN9qIrS7+BZcjT4RgDWZrD+QK3kbU5ATZ4UlW3lnjwz/5Nd5ff5F3+ZeZUd52bttTSYNfXPlOvn7pKDO/EFC52KV/vEE0puhT439Z+wH8FcXcl2KOt2Pk+TOYTgdHKRwhqN73MAMrmFeKn5r4Es/XZ/gf7/5xGqcO4y61MWfP3/H7lrUq9tgC/WM1TjQvctJdp+kO7/h17MQ6MOn3aal8A7GS1am9rBg/naI2OhwMcXBzCMdh416Pzttjvvv4Gd7mxbi4uEKxZSI+1nuQp3oLNJ/0aH786TyV64AITHN8nrVH6rRPWf7q9/0Gbw8ucMKNCIXD7w6O8OnNe/ni4/dx8l8PURtbsLKGjWKyIvhQL0yy/nCDjbdZ/vbCZ3iHv0FYRP8vZpZF3eCxS4c5+sUezkrnLV+beC9xR+KVbRzn/7IMGcWYLMP0Bziui9OfotsP2MrCHW8ofprrqs0YjTU6r/By0POSpEIohW1UiSYDogl4b+0M7/Qv05J5uH5qLak1XE5nOLM1SbReIbjSRyyu4jcCjOOjPQlCUVmxBBfasNXFtLewabI9zCoBbQWuUEyphK6zSdI0RNM+qlfJCyXc6RQAxyFr+iR1ScOJCQW7Z5aVCuE6GAfqTkQgUpQQpNZBReD09VvLbCYEwvNIGzA+2eVIZYNAONtpJF1jOT2Y4fnNafy2RXcORjSsajTA9+nPhvQXBMwPeX94hrsdyZqxrGrLY93jfGt5geCKwrm0ju100b3+NaUmdc3Ly3+OJ8yqDs0iZ1pby8WsyWODE8TtALW5ie32D1QZz/3OHU3wsUmC0fpqey1rUTEkkcvA5JPmvbUzPPqe46ydG2f6S3XYxZqhu4YQODNT2HqV9XdNsvpuQ+vIOvf7S4xLyYYxRFZwOWuwqhv88uJ7WPv6DK0lgVrZQm91cZ+7gHc2oOm52IqPGMaYlTVskmKzGy/uEklNuMw7Qw6/7Qrng2lmv9ii8bQEe4cf2rEma2+r0D9kORVeoS4d/BuUS7ztSIVqNhC1KsmY5p3Vsxxz1/LIR+Pjdwzu2gB7QCI+vy3FeDA1TnrfgJ+779c47GwBLgObsKY134rn+cRjD9J4wWHmxYOxsZVBQO+776V9l0P/kSF/9u2f4oS3ymFlWDMZf2fpe3l6Y5atR2eY/kZKsLyFWVvHFFrlTjbvCTj8B8/z8NhlZlQKo9KBaH723Ee59NUFpp8Hllcx/eGBMWUfBO5sRqy113z51ubFBmwsGWoXg2HW2eKRqUt8tlfBBF6uZWn91hKaQmJrIboVMpgWNA9vcf/kMuMywxUOXaNYNyEvJ9NcSsa5uNkiXBJUV3QegZgmbzjFwRWKUAjuH7vC1kJANDZOczd6ZXou8VheNWbc6W0XrLBW3NFapEIKRFjB1kNsYFhwNmnJBINHahUqtog4ectoAcJ1ENUQXQ+YGe/wXZUI8DEYYmtoG4/FdIzKokPzXIaz3tv/MQZSIQKf7rxD966M9x0/x0+3nsMVitQ6LGrN0xuzXLk4ztyLhvCrZ7BR/MoAp6KkZdwUfGTmGU76VwiLvqGp1cTWcHF1jLHTUL8Y58Jyl+oll9yYXS0hYXt9Zr4eUb/g8Ru1h3mweolApHz/2BP0M58z991Lk1OoC0tvmRw34TjIMGT9vTO0T4Fz3xY/cfdXmHc3McCzqeIvPPdjLC+2cNZc/LYg2LA0zie4nRQbv/kHTCGY99vMNTpcrIy/+Zt6A5jAIRkzuK2Iusx9lxeG4/RWqrQ63DEBJWtV1j98hO4Ryam7z3PUGTCwgicSeK4/hzswufZ+wLUAGQSIZgMzP8W5jzQZzmn+m/nHrjnm+bTKP7j4EV5cmWLieU3t2XVY39+VtdTEOIN338Vg2qH/wT4/dM9TvL/+Eima82nG5wYn+Ub3GJ0vzDD/oqZxuo3tD65RDITj5P+OHiKbqDI4ZLjfv8yC08FFsaFjfm79AzyxuYD/zSrjT2wi2z2yV7EEleweuyowzWCA+7XTtMKQzXtO8snj9/Oe5ln+dPM07fGn+V8O348T1Wi06/BWEJhC5O2rwgqb98HR91zi+2ef4s80XyBFs6Hh5WSazW9MMfe0pX6+j3NxLe8C47mIJEXfghq5UggmnS6Hq5uc9469+ft6AxjPQTczJhsDGjJCW8taVMXddPB6Nvdv3wFEtcr6g4LafRv8odknmVMh57IBz8dzXOiNoYYGGyf7u5j/TSACH8ab9I/VmPjwEh+Ze5YfaDzBzoIZLyfTPHX6MMFll8Zz6+gXzuzeBd8iRL3G2sMug8Oa/+5tj/KXx58vtMHc3/jx1bfx/PI0C1+N8b72AjaKr9UKhciFZaVCdLhJb8FDzQw46W7SkhJXKNpG8ttnHyA6W+fQUynmied2OYmq5NXY3SKFhYlWxDGNc5ZHv3mKy/c0+SP1Z5hwegweiIjHfaLWPNV7p3GGGqeXIDtDzNmLB85cIRwXNTWJmWySTqe8f/Jl7vWXUELwzdjnH176Xl5Ym6Z5BmoXh6j1HnYwBCURsZPvag/wwr0xDPE3BF5XY2+xiV44ea9N2WrCeBNTCxjOVBhOKryTHd49d56T3hUAnkpm+fmL38m5c9Oc2hpgo+hAjzuAqNUYHm7Sm1O8o7nK/ZXL1EUGuFzIhpxJx/jE+gPUXnIJlyyit8+bs48CvZpV+kczZo5ucNxfAeB8lvFUMsfHNh7i6a+doLIi8JY3sMnVjZNwPWStihhrsvXIDHFTsnUS0smM7zx6nqoUbBjDo3GVbw4eIH6xQeslCFYGb630pH3Grlf1tXGMThImv7BI88UW579vnnN31TjstPl77/k1VrMGv/r2d3BprYVd9alcCahdrDG+uoHePFgCU1YC0iOTDOYCTp24xF8a/zqukIDiY52HeflXTzJ2WdP4+kX00hW0sa+slnJAfb0Gw8ZWlenzhsryrRdQwvcRgU921xybp0IGswL7zg5zrQ5/4+in+Q7/Ci3pAB6f3rqP5c8uMLloUYvrZHuhDdptxkw2Wb/Po3fM8L3jT/FdlVUCkQerPJXM8isr7+LLz9/FqU93UJfX0Bv72xQrXAdZrxHNVHnfQy/ykzNf5F5vE4PP16Mj/MqVd/HsM0e49/9YwV6+ktcQ3hHcI2tVmJ+me7LJ1o93ee/8eb5//Ane4V8hFIK69Hg+dfnnl76LF65MsfC5jOrjFzCdbikw9zC7LjCBXNPsDXDWHYK1Kr/ZfoRjwRqH3XWOeqvcN3YF38m45LXo+xWscBg7MotTq+YpElHR39Ls812+79NfCOjNK+6rtqlJny0TsayzPJF5zVBZjrGDwS2PnFPCIhGY/fC4vkYpIuF6CK8wE0qJEAJRDcFxctO1o66ew1HoRoBxJGmgMK6kt+DQOyxIxjQz1SFj/oC6HBIUNUEBulmAvwV+5+AnlctqFRGGDGerDBYsYiZiWnW3fW+RhS/33sk3Lh3GW3KRWxvY/mDfa9yicI8YRzDuDZh1uoTFvDsTz/D85Vn8ZYUYRFitcaYnwfewYYAJPJKWT3/Wo3dIct/UMm+vX+Cku8qMutpJqK1DLrRbpBsBbicpGrcfLCXgoLE3BCZgNjcR3S4zX6vxO+H7GC5o/tp3/xYfDF/ip6c/A9MwOO7SMQH/Ye09POo/RLAyxszXu6ilDcxWZ280PH4zTI+z+L2au48v8ofGvwnA00mdj3Ue4ssvH+fUk5vYC4vo/q01d1kBCosSEqxBIZC7Xbv1BnheRlIT6EAhxY3be6mJMfTCZBGRKMh8RfueCklTkFYhCy1IsBKyhuaR+89ypLqJK3T+T2p8kbGeVjndnUEKS2RdBtaiyHCFYrHfpHEuo7I8xB7k7iRCwMmjdE/UWX635E9/5NOcCpY45XZIUXxueJinh4f41Uffw7Hf0HjrHezi8iu0rX2JlAjHwXh5ANxRx+IW/tpfO/sQC//Oxd8c5O6Qw/OsvW+GwbSgf0xTXegyXV/jQxMXmPPafHf1eaZkRl1eu9w+Fy2QfnOMiUsWb7FNVjaK3vPsGYFpswybZThrXZpnK4DiTDTNvf4SR50O40rhonFFxJXW83x64T6M4xBNBoSDWh7a3+sVJ9tnk67oJ6irHmPTXd4zcY4Fpw0oVnSds/0JTMdFbPXQd2hTkFqV93ncpaEU1uaSvEAiCbyUtC5Img7BxNgrI4KlwEyPEc2GWCFAQFaR9OcFScug6xpZzQpZaplq9vmRmcc46S2ji8LzbR3SNiGGOTpxwDBz6Ruf1IIWNk+f0A5uL0P2ooMb7FPMyWS8Qm9Okc7EfHftWeadIXXpkFrDlazJheEY3pqi8tIytttHHwRhOUIIrBC4MsNFbVfWShIHr5siUo0eb6BrHr0FQTRjmDi2yYfnX+SuYIUPhi9Rl5px6eCKq5qltvk82siq+BsQrmsYRvtv3XoLsmcE5jbLa7Qes9QuNPjd5H38xuR7WfiORb5v7mneVTnLdwYp31k5x198/+/xXH+O32s+RGVxgqlvNah+U2KjaN+VknJmpknunmPz3gofmH+M72k8xbxKcn9J7wRff/441fPOLUkZuRHC5g2mtTUYLJHVPNNf4LErh/F2ayiNhUwQpw6JzRerP3P3F/l4821c2Gpx6XuOcqNQQm8i4vjUIo40SGFxhOadYZuGE6GEwRWalaTOuf4EmZH8q0sfINWKpc0GSd9DrbtUVgXOAMIVQ39M8LE//RD3zi4T2gxtLVvDgLnLbVgtEtMPGlKhJsYRtZCz3+3xgT/wJG+vX9wu/5bnHxouRuO82J7Cb4Pd2DwYmuUIYyDLUInhcjzGJZ0yJQWhdPmZB3+PX/6f3k2sFdoKPNXju1pXmPW3uNtf5pi7BkDb+HSNZUNkSKGZV5q69OjZlHUteKo9z+TTEf7ZNcxme3fvt+Sm2HMCU3c60OmglqrMD4+RTAScbc3w1aBPUw15X3CeI06Fn26d4ULtaV66b4qzzUl6Kz7hS9VcTxCdfbVbs/UqvcMBg1nB26qXud/tE4j8q7k4HMNfdKmsWriNuX7aXvVfGmBx0KS7VWFiaPPKTHcYYUFoQWYkKQqJ5Adqp3l/eIaLWYunjx8mta/sRv7u8Ay/rzJAIpGF1hjbDIOhbTIGVvB8MsXnxL1cHI7x+IXD6K5L5ZJLawPqlzJqz28gBhFmfYPm8cOc+aFJohlFSn6eJFOwubXvNmY3i5ACUQvRrRr27j7/2+HPFa9c25dxI63S7oWEA4vp9Q9URRprLVYbhIZuGrCqK9TFgBD4U43L/KnGr20fa26wc1vMYk6nE2gECosrMsblFnUgspYNU2FtUGXq/AbZuQt37sZK3hR7TmCOsEmKWm0TDCpMfXWKZy/fwzeOHuc/nVjiba1F/urU56lLwR+ae5LnGnN8In2AeHyW1kua6u+0sfup3ZKUWJUX9w5EgiskXZMR2YzTa9O0XrBUl9Jbd09FnVpx/10MjtTZukdTlYaeifl63OTF5BhPPnWMsSclrReHu9K3UK20mflSlcF0i78ufpD/OHuJab/LpNtlKwtZjhs3fF9qFYvZKstpk29sHaGTBKz2q8SpS79dQXQdVCRwewKZQGs9L89Y2Uhx+hp3bQAbW1AL0Y/cQ/dIwP3Np5lVMZEVvJxBHHn7akP2epFhSOfts/TmFcenLtywIXnXGh49fxznWzUa59Nd2VTdVrTGDgYESz0+98W38cXDJ/hzb/s8P1p/mlAqQuExsAmXslyTfHRwksvxGE9sLLC40SSNHWzPQdQyvu++Z7ivusis6jGl4PF4lt9Yf4TVSy2mksXdvtOS18HeFZhpQnbpMkjF5Mo6U2GF4QPzXLn/CC/cO8+f+N6v8DZP8OdaL5I2n+cXa5f4vbvv57kvnuCuT/nofSUwBdoVGMcSyDSPQDR5t/r2UoP7vrGO6PTJblG9UuE6CM9j46EWq+82HL03D3XfMIbf2XqYpzbnmf6KYPzjL+TtvXZBOGTnL9K4uMjY1ARL+m6+eKQFh4Ycm1l/zfed643zFY5zYWMM8WQdtwfVRUO9qzlyZhPzcrGb37EJ2F7sbaErWItqnWD14ZD+Ics7GueZUxWeSTKeT2bRAyc3GR9QRFhh7UFFemrIH518GW0tiGs3TV2jEKerHPpUF2e5TXZQTLEFo5gKdX6R478ZMJgJ+c+tR/jwPaeZJCVU0DWaZ5IFzsbT/NIL72awWmXsCcWRp4aoKEF2OwzuHuML/+0JWIC3B+cBzdd6J/jc6ZOE593b5mYpuT3sWYG5jdG5ZmUN3uqQ+iVFNKWIrANoJBJXwDFvlbc1F3li/CjMTKI8N0852WdmIlWYRTWCxCpEKvLQ9VvReFcIpO8jjh8ma4V0jgtaR9vc01xhy1jOZU2+unKU5eUWh9sGO4ywu+mjM3kwRO2yRmjJsBfy8krw7d8HuB1B/YLFHRgqqylOP0V0ejdf7GJHLdtRO68z6RSPdu9GddSuaN23m1EPUjveJBk3zE5sMePmZmddbJoGNuXlzOPZ+ChuR6DaA+zg4EYK2yTFWR8SAmefnuGn7Y8Ruil1N6KbBixtNYgiF/VyhVpbUFvMcNf7udXId8gqkul6j7vDZVoyBhx62scOHFTMgZxHB5m9LzABMxzCcIh49iUaZ3wy/36uZC1SbwVf5N6q9/jrPOit8aW7T9B5eJbKlQbuk+m+bS0UWUXXVFBDiVld326i/YYpWjLJiXGWPjxJ7yi87f0v8neP/AaxVZzLmnx86yG2Hp1h8oKlemYNvQfC3HW3S+2Tz1L3XChqct7cG3W+yTBFwX9j8n6EN0nWCBjelbAwv8GCs4nB8PHNB/nUc/fSOC+x6f7aiN0MslbF3HWIweEqEyfX+ZNHvsq7KmevOWZZS/7F8nfxzMYszbM6r7h1UCOFydcecfplHNfh1AsNqARY6RCJOq61HNW9PEAoTnKtNE4wUYxamGV4aprevOLH5x7nxxovbbdAW4treGsKr21BlwJzP7F3BWYR1i6UzOtYuh7CUXmfxIrAFdcuWK6QuFbvyfzB14vB0NZ1rmRNZEK+OL9RYVmU+JKVANFqoicbDOYgm485Xl2nKg3nkzE+1bmfL68cJ1i1hGsZojfcdWEJgLW7k18rBcrXVN1ke66tRjXEpovbs3tjbG4xolJhcKhKb05xrN7msLdOXaaAS4pmYDSXszGe2Zhl+UqL41vZgStP+QqszXvHpglm8Dryn63FOgLtQUsNaMrKdhR6L/NxewJ3YPZ9gYe3GntWYErfRxxZwNQDNu+tM5yWxGOWeFIzfnide71VfHHVPHc6dXg2PsrZS1Pc+1TeJeF1TfA9RGQ1v775Tr68eIxwSbwps42amsDOTjA4VOPKexziSc1H3/VNvrPxIi9FM/ydpe/lk8/ey8JvOQQbKc3zSzAYYg5oBOgbJbWa567MMPEtQeN8fCA1zPjeeXr/9RaPTF/mT0x9mVPuFtWiQMT5TPC5/gN8bOUB9H+c4q6zMf7zixy8UbhFSIn2JbZYYUfC0mB4YXmKw18Z4q4N8lSckn3D3hOYQuQRnJUKerxK0vToHpEMFzTe9ICHZ1a4v7FEa0fQnsGwoWtcSsah58DaRl7Lcp9qAaO0jq3NKhODN5jWUfTeE9WQeCKkN+dg7ulzYrLNHxv/Ou/wuvxcNMsTa/NUzvjUP/EUptstF8DXIBm4hCsZbjvCHkDfU9J0+NHjj/PR+lPMK00ovO3X2ibg9GCW8xtjLDw/wHn2HLo/3MWr3eMURQ5GtTd2lpxMBh7exXVst3+gzdkHkT0jMNXYGKJWJT00weapkLgp6NyboRop98yf457GCnPeFsf9FWadLcKiobDBkFrNSlbn7GASOZS5mWO/CUtR/AN8Ifnw5GlCJ+HJ5+9DvM4GzsJxsO+4j+F8hfX7HOIHB0y2VvnJQ09RVxGf793Lb2YV/suX38nU1yTzFyLsLYrALdm/GCWYcbeYVZpAXJvj+tXBXfz2kw8SXPRw1vLyd7bs1/iq2I1N6s84ZMEk67oGrO32JZXcAvaMwBS1KnqmRfuekNUPpNQn+/z393ye+4PLnHR6TKrKde/I6zpqa9FYtnSV5aieR57tt5D/HcXElTAEwuGD4Quc8Fb4+ti98Cp1U1/1dJ5H+2SV9inB2LuW+ef3/jJNmTLv+CzrmL98/o/y7PIsM18SNP7dl4Fdq4BXsoewEqacDmMywGC2I2MBnu/NUX/Wo7poYLOzv/KcdwHd3oL2FrXpOltZuNuXU3KL2B2BKRUy8BG+D/PT6LrP2skqvUOC4SHNA3df5kh1k5P+FabkgOA6gdEzMRe1ZFVX+Z32w1wcjvHYy0dxL/iMPWf3dcV/bfN7bckE7WxhD0XEv/9h3K0E99J6fm9JClojmg1srYJuVhjOBBhXkFQlOoCNhzWN+S6PTF6mb13aWYUnkpCX42m+ee4w8nJAsFFqCDeDwiKRKF+TNFy8jsfr28LsbdTdxxnePcnGfYoJ2S8qJMlrci+30oBgzRJsZLe14tRBQdbryMlx2rMedVX6KQ8KuyIwpecix8ewzRrL3znOYFYQvHOD//qur3HCX+H9wTIuAl84KOG+osrIqrH8bu8Bnuou8JXPPED9PNz91ADn6WewSYK5FTmLdxJrcxVvh5o3oxzGZcr3nXqa3/yht+MvVZl71MXtpDidCNKMwYlxenMOvaMC9+2bTNX6/P6pM8y5bd5ROccxJ2Exc7iQjXE6nuMTy/dzebNJ4ysVGuczKi9vUHpQbg4lBPVqxGA6xOu6BEodGMGx9cg0Sz+QcvLQEoecIUrUikCzq8/dyqBO88wQd7mDHZa+y2+HnByn+/AMWycU405vty+n5BZxZwTmqBSb5yI8DzHWZHj3JHHLoXsc0qmERyaXuT+4zLyzRVN61wjJLRMxsJbFrMKFbJznhgt87PL9rLVr1C8JqlcynLXevs25vBGjggwnKqtMzm+xphq0T3o4fRevFyA1dA8phtOWZD7h4allZoMOj4TnGVc9qiIjspbLusmTwyM82VngpXMzqE2HsVWDvxEjhqVZ7fUghMUosPLV+3HuJ4TvIzyPuCmZnOxyrL6OV7gHRhGdl7KYKzpktVPj6DBFRAmmzB38tljXIQ0l2ucVKXAl+5fbLzCFQDUbiLCCmWwSTYW0T3rw0Q2OtTb4ialnuMtbZl51mVV5PuVOYZlazRejGZ4dLvAfXn4H8RNjVFZh6psDxroRcmMVOxzmTWsPEEoIFIofrj/N+x94kcVTY3zpkZN0soD1uEpiHL67dZGHKhdpqT6zqkcgNHUp0NbyueFhTkdzPLp2ghcvT+Oe97nnt/uo9Q1od/OuLmWgz02z7c/bEZy1r5EKtTCHnqjTuRv+78e+xgl/mVAoUquJbcrAav7Z2of4+Ln7EN+qo5bPY7Y6ZWTnTWBDn8GUJBk3VGX5nB0Ubo/ALFIaZCVAeB5MtDDVgHimSn/WpT8Pf/jQC7y9eoHfVznPjKoAeb84gyG2KRpL12i6RvLE4AhPdebpLNWZetlSXc5wnnoZ3e3uSp3TW46xSJ135+gbj8he7b83pXymFJxwlplVW/Stx2rWILIu7wgucMrNoxm1VRgEkdX0LZyO5vhm+zDn1sZRV3zCJYF68RJ6fWOXb3b/MeqVqaQldeAGTVL2HUIKbMUnq7pkoeGEv8yC2trerKbWEFnL6e4Mw8UarXWbV05K07Kc201ghSgaldvCB361GxAHoLjKW5VbLjCF6yGrFZibZvEPThFNWuJpjaqnjDU7HG+t8+HqOn+s9XUmZEzrui7k57OER4fHeWawwG+cfoi07VN/ySFcNhy/khJc3ED0h3uibNutQmx1aZ6pYVSF31l/CCUs7woucNS5mgcXCMUJNyKxQ445W2gE4xJAMTApG8ZwLmvyy6vv40xnkpXPzzN2WjPXN7idIU47wnRKX8obRQnB8dY6jx0dw9tSVJXa95HFJnDIqgpb1Zx012lJUMIjtZrzmeKyHue5bx3lxK8neKt9TLeXF2w4IM/d7US1ezRfrmJch7YOUSK+Wty/ZN9yGwSmgwgCkskaW29POLywzgemz/BAeJl7vSUe8kbbcwlcnyoCq7rC472jPL52GP/xKuNLhrFvbcDZi9gkRR/AUlx2MMRZ3iIc8zizOcmT4WHuclc46lx9vHzh4gt3+/edj15kDevG50wyw1cuH2WwUuWuL0Wozzy+4/iSN8ts0EGNx6T18JpUoP2KVRLtSaSfbDdHlkhSUtZNlYvJBNWLEvXZx8vgsNeJHQypLEcMpqtE9upze30AY8n+4s0JTKlwZqaw1QrDuyboz7qkdUE0AfGE5sP3P8v9tUXuDy4zqzpMqYTrhWRsU1Jr+N3BAl/p3cUXFk/Qe2ICf1Mw+XSCtxkj19voJD2wvhMTx8h2h/CCz8bnJ/lPUxN85v6TfMf0RT7cfI4fCFe5Ps5EW8snhuN8uXc3T7Xnef7CLGy5NE8r6m2Dd6ldLnK3iFFaSUnJzWJ7fZwrbSpzFZ7qH+K54ByzCkLp4niabLKOUhLR3sIesNZoB5k3JTCl56IPTRFPBFz+kEPr4TVOtdb5voknmXW2eIffpi5zs6JEcb2wNBgiq+kay7+/8m6eePYozWcd7v71C9heH9PtYrUmO+AmIBvH6DhGdHsc6QwwjZDF757i4yfGWXmkxvcd/y3gWseZwfAb64/w2WdOEZ7xuOvzA5z2Fpy/jOkP0KWf6ZZzEAr7l9wZTL+P6fcJp5s8szXHE7UFvOAioQQ/SImnqgQWhJLYMh163/DmNEzXJZoMGMw4pNMpD08ucryyxjFvjZaMCIRCItk0EX1jWdZ5WogpkvM7psJXt06wNGzw3HOHqL/kUL+cdzq3w2GuUR5wYXkNWmP7QyRQv9QEq/iGc4I/rn8QRxhkkUhurCSzkmdOH6J61qV2yeBuDHLf7pttA1YCgEw0ZjNkMWzQMQGwz3J7bwIZpbjdDNNzeTYNmFUDDjn+bl/WgUJGKS9emeK/+G9narbDISdivtHhyskW1ZpibGkC0emie/3yud0HvCmBKXyPzXtcuicMH37baf7m3O8SCLEtKN0iRP3ZpM6ZZJrfXn2QJ84fwhqRVyXuOcx9DurnBty3uQrtDjZO0L3eW0tQFtgsQ69vIDYFjY02Dc/lUBBgKhVu5Lm9f7iYd2xPE0x/CNbsu4bZexW5NaBxuk6/3+TcvVOYynmM3f9+yxFWa9RqmyDOCC9M82/X388D1cv8ifpp3NdZirHk1ZEbXepfGuexc/dy7KPrfCD4Bj8y/xj/5vsV587MUFmbxVuuI89f3p02diWvizcd9CNTUJHgUr/FV6N5lLjWFBgZl6/3jnNxOMYzS3OoywGYPLLa6Qnq57rIly9j+4P9V6HndmA01nCgijDsR0ScEGwadEXy6Y17aak+T24skLW9A9MP0yYJYhDhteHry0e40qhTlTGu0Dw5OMzlYSu/15I3jE1TKusG40nODSZY1TEtNeDBsUWWJ+sMpmsIE+ItB/AWVRT2E29KYJqtLnOfWMLWKiSfmObnmj92w+NUZBDacrSXoXrt7UkhUg1LK5j+sOx8ULKnMMurTHwBxgOf5SdO8E+qJ/HaGac6A9RGj2y/F32wFtPpIfoD5j7hkjw7Rttr8K+qJwBQiUFkltkXl8qWb28Cu9Wh9c01ahdrfPnBu/gn4Qe4r7LIT0x8iQW/zb/46AdRSz4n+rPIfh+bJKWVaA/zpgSmTRP0S2e3T3QzJytDUUr2AyaKMOcvAuCdhlFGrIUDI0BsmuQBJy+dRb10FsWoB9BVDsq97hYmSVHLq7hJirM6zxObC8x5be52Ne3wZT53+CQvOtOkDY/Ac/O4jVJg7ln2THuvkpKSkgOHNZhhhKTN/BdmWL5wlH/84AJPvP0w426fj04/w0K4xZPzDxFMTSDXNtFl67Q9SykwS0pKSm4X1m6njYWfe46q7+FvneSz/j0cO7TGn7/n85zyF/naxMPosSpO72DVxD5olAKzpKSk5A4w6tNbvxgTPV7hyvkFPrr206SRw5GXMtRGr2ydtscpBWZJSUnJHcDGMTaOUY8+w9xjLkiJ8FwwFjMYoNOsLGy/xykFZklJSckdJA+2Ong1sd8KCFvm/ZSUlJSUlHxbypIeJSUlJSUlN0EpMEtKSkpKSm6CUmCWlJSUlJTcBKXALCkpKSkpuQlKgVlSUlJSUnITlAKzpKSkpKTkJigFZklJSUlJyU1QCsySkpKSkpKboBSYJSUlJSUlN0EpMEtKSkpKSm6CUmCWlJSUlJTcBKXALCkpKSkpuQlKgVlSUlJSUnITlAKzpKSkpKTkJigFZklJSUlJyU1QCsySkpKSkpKboBSYJSUlJSUlN0EpMEtKSkpKSm6CUmCWlJSUlJTcBKXALCkpKSkpuQlKgVlSUlJSUnITlAKzpKSkpKTkJigFZklJSUlJyU1QCsySkpKSkpKbwHmtF4/9039g79SF7EXO/fRfFa/3Pcf/8T98S4/Z2b/4V17XmJXj9frGC+D4/+8tPmZ/4XXOsZ97i4/XX3r9c6zkxpQaZklJSUlJyU1QCsySkpKSkpKboBSYJSUlJSUlN0EpMEtKSkpKSm6CUmCWlJSUlJTcBK8ZJVuyDxFgBaAsVloQ+d+EFohUIDT5T8P2a1aAVfbqe8uYupKSkpJXUArMg4QA6+aCUlQ0yjVIaVCOIYkd7IaHSCXelkDFYB0wCowLulIIS9fmP0tKSkpKrmF/CMwbaT2jzCpDvtKLQpuShbZkQRiRH2d2/N9yjWa1/b4DgJUW6xhQFulpXFcjpUFKS5YqNMXtGpAZWANSgDZglcA6Fr0/ZsTuMZo3sphjWmz/FLb4DuS1x79VuOZ5Go1B8cwJnVs38t/zQRlZNYC31DiV7F/2zvJ4I29qYTa0vkG4ZvthtFpAIsEInIFCZGA8MJ7F+ppwbIi1gqjnY1OJ2lKooUDFuWZlHNAVi1WQhWCd/Z3XbCW5CdYzhBMDAi+l7icETko38elFPkJcvceR0PQ6FndgyQJBWoWsIhhOFwtZySuwjs3nimdwqwk6U9h1HxUJ3K5ARZCFgqyaC8233Dgqi61ohGPwKimOY4gjF50obMfBX1UIAyrJD08aAu3bYlx399JLSm6G3Z2m1+0qbbFrBxBWXH3dMchCYArASIHR+VZfGJCpwCoAi3AsjTAi04okdtFGIHQhLCNw+xbjCqwUWBeEsez7ZU3YfHF2LPVKTN2PqbsRgcpIjaKHf+3xxa5fxRa3p5GZJFfNLWKkhb/iM27/bdwUu3ltEpC59l4LY6LEJcJHWFBxPresI9Bhfp37fl69TmwxNsoxVCsxgZuxBUSAwUHFINN83gFoX2BcwAgOwFNY8hbg9gnMbbOnveZ3IDdnjXbfI1OpsoWZK//dGoFI5I1XHZu/T2iBLASh8QEJwjGEbkokbC5UE4nbE3hb+YLm9SxpBbQn0KNz7VO2NUvf4NZjKkHKseYGdScmsxJtBVLkS5G1YntsR9+FE1n8jRjjKpy+Imk6DKfltnZkdzOGWlzV/K3Mf8pYItPCBEoh9++gz9W6BhlmhNWY2XqXjWFIrGvIWOB2Lf5WvhlLa4CTa/JvBX/wSPMWYcbEeI/QTWn4EZ7MSDKHKHKRqcBvW1QMXt8AoAOVC025jx/CkrcUt1XD3I7SHFEIRVH42ABMlptWhZP72iy5sLSZxKYi1zSvP68VuaDV+Y5VpuRCVlqUtFScFGMFVgtEJnAGufnR6xu8jkZoRdJUWFX4Nver1FT5QiUrGVOtHnUv5kS4Rk3FrKR1umkA5ONlC218dKtWgIoManOAchSq5yB0gEx9hAYrBcLuXgCQ3SEwGc2jOPe9UpiUreTO+lxdgx+kNCoRC+EWxgrWzEi7BL+jSWv5Rs6+hYSAdSz4Gr9ydcNWUSkAV/qNfAlIBV7X4g4MXjt/bTAZkNYKLbOkZB9w65cbUQhKCbKWbgtBbG5GtboI0HkTWC2QkURGuTB0hpa0UbwGGCvQRoIWUAQbyMzmKRWZzQXHPsYWpkHrG2Ql13gmKgMaboQUFoNgK62wHldZ74f0OwF26OAOBTIVZAFoF6IJhTNsIGON6seoSON1AAQ6yP3CVoHZDR+vzH2FQlqEU3xhPYWMc8EvdHFt3h3yFQqQrqEaJExUBhytrNPPPEQmkEmuNblbKWrcya9vn8+x14W0OL6mVok5Gm7QdIZoKzEIfCfLD9Hg9TRuL8PdGADgREH+TOrdvPhbgCieSWFBXbWIjMwgO4MPsaLY5BeWEpu/13h5TAXF3BFGbI+LVVcDzd4KFou9zC0XmFbmvjTha6YnOoRuSqoVxgo2+xWG3eDN+5wyidsRqKHA37S4Q0vcksXEFKRGkRoJmcwnZwIqsajEIFODzPapg2lkVXUNSPAaMWP1AdPVHvc1rhDIFIklMi7LwzrLvRrtjRpq2csX9Y7ASogmLSYwCKPQnk+4qqmu93CEIFzxcQaSeEyQVQS6YjG74Om2jsUNExzH4Lv5ottZ9XF7+QIr01xYZrU7d01+kDJX73CytsJ3hGeJjMs3MnB7lmA9wVts4096yNTBSgF2P06y14/wDLVqxEJ9iw/WT9NSA65kTbq6QsU5AoCMBf5qhGoPsJevAODd00IlCuMXY7WfhYFT5D37BuUXkk5YrBGYNLeikcp8gzDM1y/INxJGQTwO1jOFoMw3Yc4gj+PIQltsXMVVV1bJrnB7lkIBQlkaXkzTH5Joh8xKBonLUNprIja3KWSYNbkWao3IBaABtMAaQBSBQJnAGQjUMPfDqaQIVhFgtKCXeAxirwhlFyDzyaY9iQgVWZCbgbZ3bvuBHTmWeAahLJ6XETgZnsxQGFKjGBiPxDh04oBh7GFTWaSR5A8hku2x0gHELYETScKKh3UkTpRve7OKyPM0vV0yWQvw/QzPyQhGAtOCikBqi8jI58MdujQrwHE0DTeiolI0gtg4ReS1BW3zQDIhrqZXHHC2fcuuoR7ENLwhDRnhoenqCmtZnXZUIRs4+DEIbfLAMtcBIbefv31bLGOkWUqbb2Idi/I1jpvlbhAjMNel14z871z/WBUZACPrhEwFTkQRTyAwTpGypO7sLZZcy20xySItjqu5r3WFI/4GGoGxki9wN51eZfuwnfMl90uS+y6T3Jwqo1xoZo7F7MjXcjqK+gWD2zc4fZ1PsETlAUZDxfJyC5tKnJ5EJqC9PIQ9qQuEkWQVQdIE49p9k1JiXYto5BpXvTak4mZU3YSqGxOojIHx6Gc+53rj9BKPlfUGpucih4XATMDfygN5ovFcEMZTGfGcJau6+Fs1VGTw1+I8ptYGxInESkFa34Ub9gzHxzcKM7MhMQ4ryQzVZb0dyZvUJIM7YM6zKncxjIdDHm5cJBAZq1mDi8MxglVL9UqGzAw28PMIbCffjO1LIXCTbPuYlaXZ6POOyYscC9aYUn1SK/lm7ygvdqa4fG6S8LxDeMUiMgOuA/MzWFeR1GSxcd0fz+D1WJmnvOEaqmNDQj/BVRpXGvqJR3fgYzIFab6e5ZW2BBbQfmGK3jbn5ueUWW458zpQXTLF8yrRPsRFkZGS3eO2GduktIw5AyadDqZIsqy58SsE5Tb2qnYpUgmZQGZi2wdgzdXiBMLmfkunr1FxsWsdLZxGYONigha+AKvyqNjRpNT+SFiybxY1Ky2up/G8jEYQU3UTPJnhyNxZNtQu/cyjPawwTFxMrLZL4I20aJXkAlNmEkxuPvLrMWnbIa0qsOBuZAitcYY+WZA/1LsR7SmkpeFGtNxhcX+m+N6vOgeVL+7MhRVRw4GTMq76aCRdXaGX+jgROJHOx9ORGFUspPtkXr0RtjVoCTiWipsx6faoywiJJUGyllRZG4SovsTrgjscRTxLbOBgPJULyx3CYl8xKl7h5jnioZ/QDPL7B4iVuhqZXgQfbvsyBdtFGwy5BWx7vtg8sE3FFrdvsA6ksQAh3lp+8T3KbROYQlhCFdNSAwKR4glNVSV5qgf5BBECVBHQkQ0cRKRwepJgI9c2R/Z97QtsaFFBhu+nDHtq23wh4wxhLDItqq7kn54LTgXGt6QiTyjf9gE6FuPn5o29Hs04Sh0RFc2hiTZNb8h8pUPViVmJ63lgT1SlPcxNsMPVEBlLRBHqb0S+OXCGCr+da+PDCZmbxA7F/L5jL/FFdYKtjSaVFUH4wgDaHfyqD/gkzd1J1ZWu4Vi4zqTbpacDtrIKMgV/I8YqifEkOpA3jKK+lVgJ1DJcP+NEfZ27vBVeSGZ5vHuEs+vjTK5r3PUB1lW5EHDyikkHVWju9KG7rYhaGHOsscGc20YJw+PxYRbTFk8uzTNcDalfltQWNc7AYCouVknicY+sIknqAuNbzH4yM4qrz6StaBoTfUI/4Xhjg5Y3ZHHQZCMK6Q4Dks0AkQrcrkRmuXZoRR7go6v22nM6Jg8sSyVuD4JNS3ixi/UctFcl0SCyfRzRf0C4rQLTFZpApIXQ1PgqyzVFcg3CkgtMISyZzs2Hblfgr9vttAHjFmW1pMVxNNUgYeDl2zRhLSI1CK2R2ubpKTt8BVba3KcUsB1UMCpdZp19EmRQFCVwPM1c2GHS7zHnbRHKhG4WkBlJN/bZ6lTRAwdvXSFTQdIyVyvTkI+lM9CIzOAOHHRF4Pop766f5eL4GC+ON1CRQEQJur2F6k7hVBxkWqxmo+f0Do2ZEJZJt8uss8UqltSqvABFN8Z4DuAi05HqexsXEWlx/IxqGDPldZlSfV4AFvtNhj0ft5chexG6WcX6hV+uqANxINnWLA21MGa61mPG79BSA/rG41IyzvlogmE7wNtQ+JsWfyNFWItxFdqXxE1JFgi0X5iu98tY7cgjt06eGjdZ69P0hhwON2mqIRtJSGokSewghxIZC5z+1ZQu4xYb9TA33Yhiwy7s1bgNFVncnkFudLGBhzOsoF25QyEo2S1uvcC0gBYkscsLg1li4zLpdKmrIWtxFaxAKINfSXEcTasS4SrNy30PmYIzhHAtt6+mVUlmbmCKUJakphDGwQqB1Abt5yqnRWybbfMdvr02WFHs+LeHGYWp21DjVlPGm33uqq7SVLmJcmA8LvTHOLc+znArwF12cdI8zWb0fhFobCYgk4UZKM9rHUWZCmGZcHocq21wen6GoQ6w1QrCcRBJijNIUXGeOsGdKmRQbGocVzPrbDHldFjNGkTGRSYg+kOkDTCVO6T5CqiGMTP1LgveJlPK0NMBZ1cmUMs+TruD7faxk3XShkcWsC8sF28YCaKS4XiaQ80tTtTWmPa6pFaxljX4xtYRLvea+IsulSuC6kqKuzFA13zicZ+sIojGJVmF7QjsfTFWhWXKKgsVjRemVCsxc2Fu7Rlql6F2eXFjko3lBmrLobKSa5YjX6UZbRCaKQuzm0hh0VaQZA4bm1Vs7OAMIGhbvG6KHQ4RqigkIsWeX7PeCtzyVUcYgc0gixWnt6ZZi6vMBh0aTsR6VMUagXShEUZUvYS7G6vUVMz5lXFkKnB7lnBxmAu7uQrYIl1k52e4hqQusEJhlEBqiw4oBGGhUd3qG7vTqPzhdKsp8xNbHKq1eahykUAmvBjPMtAVFjsNouUq/rqkfg6wFuMV/lppcSspaezkgVQCjJtXThK6SLWRlnHV457wCucWxnk+m8PUfITrIpIU1YlworAoMC6w3u0fVVvs3j03Y97dZEIOeQrBULt5h5VuDyEEtCq3/VpG1zNZ63N3fY1j7irTqsqWrmAuV6guCdTaFrq9hXUXiJsKXRFYZQ6kORZy31ulmlCrxNzXuMI7q2dJrUNiFUtJk2dXZulvVpg4D/VLCcGVPnJlE+tNETclaU0wnLKF8NjlalKvE+vmEephI2K+1aHpDbmrugrApeEYW2nAxkqDyjkPtwvhSuFuquRR+UlDYFxLvTXgI/PPAtDVAWtxjc917kakEqcHwXqK046wgyHCdYvNLqXA3APclm26KKrstIcBxgqMFfRdn27sFwXVLRU3zf1x/hbjTh/Py9AyD96RvQikREU+xskd5tYIjBHoIvgnq0LheUfq3ckVvC3s8LPiGoJKwnjQp+FGpFaR6gqX4zGW4zrdXgXVkzh9gVuUG0sRebTwdbLNSosORB50ofIH0FGahoiZcrocrm5yrjaODj3csBBGaZbnr0YyT6x274BfTgGOxXczPDQSS2xculmQV/nR+s7mN0qouxFTXpeqjNEWtrIKbkfidixkecqLcSXaE1fn4QFc3EbJ87VKzGTYZ85rM626XEjHuZBMcnE4Rr9dQbUd/K7F7WWgLVQCdMXNC9NXRj7eHUn4e3msdmiWIsx92dUgoeokeFKzmYZkVnGmM8nmoILadPC28qBEmY1SjcCoIqc5NIyFQ+bcNpF1GWgPAJOovBBLZFHDLI8oDitQCcgCedV8XbKr3B6TrAVSyeZGjS03ZDWo4bspnW4ImUQAC9UtFiptvqv+LLOqz39qPMKiW88XxcvLoBR+6CGsj4oUJJLMcYhcFyEtwymLaoLTE0gtSGv7Xqfcjp6zyqLqKX6QcKTV5h2ti/gyZV3X6OmAx9aPsNKpYZcCwmVBsG6pXokxSiImXVIrXuHvsA7EDYXM8hxL40HTj5h3MqS4jNvKWI1qrEyfwN0Yg/4Q0RvgdjL8tiILc58Tt9l8Zh2DU0sZC4aEMkUJy0pSZ2nQQMUWm6RFPt9tvYz8WpQFx3Cyvsp7qmeYVQMyfC70x2ics1SvpNgoBinIQpV33/AObrAP0iJ8w73jy9xTXeFdlZc55Q45k0zz6PoJzq5NUDnj4W9C7UIPZ3kLU62gJxtEUx7DyaKC1ChCfR9gVe4WkZ5maqLLZJhvXse9AX3t8VJ3iq04YOmlKdy2pHkB6pfTXFAW0fnGzXOe0/GM1kyXt09c4l2Vc1zJ6qykDWLjQM/B25RUNjKclU4eETk1TtoKicYESVOg74CFp+S1uW0GEWEENpXoWJHEDsPY246QHaEwVEVCS0LoJmiv8GlYm2sSWV6ZRxjyHE0tyLI8XNu6FuPmfgHtsa9MO6/FKLfNcTNCP6XmxtRUBMBaWudy3GK9HzLs5W2lZFqU/cssUuda5igOxlquFrcnH6es6BBhFHhK4yKoioxp1WXMG+bFsIN8NbOZRqUGFRdRyHfieVW5/9J3MlwMCsvQeAxSLy9WYMyOAK7beB0CUCAcSygT6nKIRtA1Cf3Uxx0Y1CADrRFCYNyrxTAOGttl2RyLdA3j3oAxp08gcu16S4d5Ifq+h9sDr2cRsYZMgxLo0MnnnVcIy/3wrI7MoAqkr3PN0ku2C1cAxNphYxiy0a3idPLG7O7AFuU3LUaJvEG7l282ZSWjFQ4ZcwZURYYShq2sQicNkEOJMwQZW0SmQQhM4KErzlXLxX4YtwPO7dvnWfKelQLSVJJKm2uXWqAzSTupUHWqKCw14fJAc4nzR8cYrDeRk+OQZghjkInBGYDqK0wmSJIiXl/m/rq0aBa9X3asr4V1LaqR4noZxyfXWQi3mPK6KCzn4jG+sHiCbq8CZ0PCTh7go+I8oti4eapFGoo8hQaLThU2Uqhe3ocwHs+FZ1aFLLBM+T1q0scXmrrs8kBtkW9MPoS/GVLd7GO7XZzNIZU1D6Elw6nbPwbS10w1ehwK29SlIbZwrjfO0nqTqb7FFiZQ60qsErdNio8KRYS1mDmvzbiMWNUVXk4rXNgY49Bqsl0TVQQ+WSDJwjxl4CAxqgttA43fiJlo9HkwvMQxb5V1XWVVS764cRfLF8bxVxSNixpvK0MmGTiKZDKkP+fmgT6h3TfR6da12EAjA83CVJuGHzFX6dByB2wkVS4MxrjYbtE7PYbTE4ydtfidLK9x7AjSqiJq5Sbo4awlqxruml/jA5NnuK9yGYDL6Rifv3wX7Y0azbOC2pLG34xzYdmoMDgUktQkaSMvj7ev0m8OKLdVYG6bBUdBOyNzrREMszyqDMAVimmvw0RtwJV6A1vxQeXbKaENMiu6VAixXQThqvnytt3BnUWMihNkhEHMZNBjPmjjiyzXbNKAdruK7XhUNwRe52rvSmEpymcV2mNRDcRkMi8OXnT40H6RRx1YjGepqBRXKCQCVygmnU4RvZiPsUlS1DDBGRhUVd6RdU4qS82LqTsRnhDE1jJIPbLIQaYWq/MIaivFbdVUrMoLRVS8lKqM8YShaypcSCdIIgc1yBBRkkcvKoVxuJqIf5Ao5qVw8x6XTT9iwunRkkMuZuO0dcjKoI6zpXA7Am8rw+kmYAzWUehAklYFWSXXLveLlmSlRXgGz8+YrPSY8Ae03AE1FbNGLW/M3g/wNwTelqWynuF2UrKqgw5kbs0J8/vOqgZb08yFWxz3V5hQPTSCLV1lqxMi2i5B2+C3U2SU5eucp0iq+diNLG+32x1S8u25M3rZ9QEomWBzkAeWfCM6SmQvM676fGD6DL96pMnm28fxOgZ/PQZyLcrpC6wjMKOk8CLXLa/yv78nki0qpuAbDo23mQp63FNd4ZC3weO9ozy5scDiehP/dAVnmLcqk0lefFz7ggSwwi16MYqi7JaASIHOzTmiqFYDuTkWCbLI11FCgjUEMiUL85Jz1nXAGkSU4G0lZFXJ1cKYtw/laCaDHuNOH5e8Z+lar4racHBGdfCUJKsoMv8WCM3RXm6UMlA0D1CBZqw+oBUM6Rufi1mDz3Tv44nNBeRigNxagSRF1OvYwCsiIfeJufF1sB0ZWo85Nb7KXLBFKGJSq/jm4Cgv9qa5vDhO66LA3zS4WzEiychaIdaTDCadopA/+0Kz3E45CwwT4z1alSEPNy8z7vQ5F01wbjjB41cO0b3QwN9QNM4ZnMggtEUHinjMIW4K4jHBcNZgAoPbjAkqCTUnYWB8no0O8bh1+MzqKZyzAcGGIFhPcDoxxlPouVbh85XooPgOyk4le4JdEZhoQX/oA/BCNAtAXQ35UP0035o7xLnjxwg2BG5fISOdd2mPyAWkyvORjAdGFTuv/a5lFsUJlK85VtvgeGWNE/4KU06HTyb3c/7SJM6qWzycNq8mI692rLdK5NWQFGTBqNgDyChfvW+kiV//8Ckh8YRGB5Y0pBCYFpskqF6MM/QQd0AaOM5VH5kUAm1hOPDwOhInSnL/pVLFLv7W5KaNUllwDMIzOF5GEKRMhn1a3pCB8bicjfFUe54XL08TrgpEf4hNUhhvYupBPv7O/tGgbgYrAMeggtz3dn99iUmnSyBTEhRn+lM8tzaDu+xSXSpMsZ08TzibrZHUFXFLkDT2kSWoiFJXvmahvsV8uMXD4QXqcsiZaIrlYZ3OSo3GGYXfttQuRghtSGsuxpPEDcFwUpCMG9z5PPq/VYmoOCkVmRBZl4vROOcH47y0PEn9EgQbGn89QnYj0uk6yZjHcEyRtHKt3DilsNwr7I7nzwrS2KEPnB+MY6zgZGWZCdVjIdziucMZWVXhdTzcQZFjCYgMnIR8Use5oECC1lfLkY3qze4nrGuR1ZSwGnG0ss4hb53EKi6mE5zZmsBbdPE3BMF6hkwN8biLdgXGy3fuI9OslXkQlFHXLdw3OR4SU5jgdrzBWIR+ZZrK7UIIi190X1EIDHnIvUpAZKOqRaIoQcc1qQnXLCo758HoZ9GPcLtajSBPwdmRv4sRGK3QWmOsILOSzaxKahUX2y3kUkCwYbFxDEJiGhXSurdd9uwgLGxWUBSqsKgwo1kfMFvtMOe2cUXGi/EsXRPwzOos7cUG9TWB28sDxGzoYx1J3FQkdYmuFIUc1D56LkUedHests6ct8WU6qCwvNSd4oWlabxlh3DF4MSWrKKwymE46eRFGSYEadOiq4aKq3GVRhZzazWpoZG80Jnm/MYY2UqFyrrBaxd++cAlrbtELVU01t5f9a7fCuyKwBSZwPYckkjxnD/DcrVObTrmAf8Sb69fYP3+kPNbY7SzCdyu3J4wMgZnkAe5qLTQtIRAVgrNyrcgxP4z0XqGVqvPQqPDu8MzLDgdvjC4mzPRNFcujTP3tMVvZ1TOt0FK0kYLUxVkIaQN+0phATc1BvIGATO5NiquClyTRzveqcLPUuRRqYHMIxFTKyGWOH2Q6cgkm7doyzVMu+O6r63PuS0IRxupTCKyYmxcc23Fp0KYWg3aSlKl0EaSaMWl4RhSWPqX6kw8D/ULSZ5UXq0STwTELUVW2WdC4bUQV9tVjTf722kk9/qLtE3If9l4hIv9Mbovtmidk1SXNP7KEJQga/pkFYfBlCJtQFrP/eX7ZVxGQU7VSsz76y8y62xx1BnQNZIXrkzhPxVSv2BovtDFBC6DOZ80lHSP5pq0rmfIWorvZVT9BEcalDQYBEuDJpf7Lc5dmUBdCGhcEdTObCEHMabmo6s+0YRiMFc0eQ8Onol/v7NLGmaRdmIgThw6ymc9rbKqGxgrmfAH9EOfzdYYVuWBK9ud2YfFGpiCMBYV5eZJJQEprq0estcf0kLQCdfQCGJa3mC7CfRaVuficAzZzYumO70UtAGZmyJ1QFFWjO3ADODNqzj2usBTIfJx3aWxlIUGaBUYV+EEATZw864gO6uf3Kh+6+hGdmqbOwLPrtEsi2pGGPL7NRIhLFJYDHnxDRlJvK7BGWR5eosU6ECQBWL/mBy/DXkKCeDljZCbQZ5z6MuUvvFZz2osDxus9mu4XYnXsTiDvJ6zdVyyikMWyqvpXvuxQo0AVxlackBDxNseH50pghhUSp7mJvPvPqsUUayVvNWXkBYpr1YcGxVviTKHTCtMzyVs58FCMkoQaYbxqtvpN7pIv9l34/YWYPeSMYoo2ngrIO57PO4exlhJ1Ym5p3qFKa9LfK9DJ/LpdEP0UKFXXFQsEAOLOzDbi5/28sTetFYkCNfIQ+Hl3p50o+LotXrEd0xcYMHfpCoSYqt4dP0Ez1+YpfmSJHx+MT8+DNBVj+GEJJosdqCj4urb9/nGtWtDEVWbFmZYAKWwnpNrnXcIg0AXN6TIK6ykdYdoyqN+ZIF4MiSr5puG0X2PigxsMxJ+o//b3LIhE5FH2KbFa0XRYaHJC/6r/HvRvsRTmkBlRbUhB68tqL/UQ7V76CxDOg6DCUU8kVdx2e/awLYp1jWMT3aZDAc8NHaZt1cv0NUBjw1OcD4a5+nLc5hNn8lzluaZITJKEalG1wN6cw5ZVRC38lqx+87aU2w+AydjwekwLjVKCGKrMJEqLFwWU3FImi6DGUlag3Q8RdUK06oRaC1JMoWSEmMF1grW2zWyoUP1rMPkkyleO4bVDXAc4olphpOKaDIvwrKvmtu/hdjd7EULZAKMpB97XInqzAZwyNukpmJmqx0Cp0JmJEPho4M8ibew1iGsRaV5eoWOyZv3ynzxw+79BczKvEiB52RMex3GVQ9V9BPcGIbQcfG6Nq+f6rrYRphH0fmg/R0P1et9sIpE9BHaGgyWxOb5mqPxQwiElBhHXusvvI1YIDUKgyS1BhBIx6A9SxaIPMAmuNp38hX3tfNEows2xcbJUHS02emTzY+R2UjAFmZ9I5BYXHm1Q7XMQPYjiOLiD7Ko3btPNanrEXZ7Ttb9hImgX/S5zCOF19Mqq1EN3XVxuhKvZ1DdKN9ciTyKPavk5kTj5cEq+3VM8m5LBiVeeQNWiuI5FNvPIq5FKo3RKu/ra3IhaaxFG4m1Ap3m7gW3D95GhOpGkKRYlZ8rq+TBjPslV/WtyK4LTGEE1kKnU+F5PcOVsMFQu0hhqaoEr5IhhWUQeiy7dbphBaejMK6DGlr8ri3CugVeJ0+rEDoPiEmaJg/J3qs4FlnJmKr2eTi4QENGdKzPlazJ8sUxWs8raosxNooRYUg8XSVpOUVOG28oL8sWSeh4hqaTRzRumiEvZx7f6B/Pg4s2NWiDDENsq048FRLX1R2JAI0il5f6eYWEl/1LtE2F8Waf1TmHbs9DZnWS2qjEGq9cWDKRN+zVuTYJbG8sZJJvtqwS2ybsUcCUTEGmRR6rgcwIxvwB427eMFpmft55otvHJgkoBa6DDsSB8TVZxyLrKX4l5aHxy5wKr9BSeXGGpaTF4xuHudJuEJ5zCTYs1ctDxNI6TI+TzNQYTrrEY0UXkpFJcb8t/MWa1It9nornmHXazKsBUliCZsxw1sN4AqO8PIWr+O7FQJEmARRN69OKJHY1jqNRstA8E4UaSNyuxVnrQppBWMHWQuKmJG7l57tRTELJ3mD36+MYEAjM0GGgJcYIlvwGoZMwHfSoinyHn3gOQljW3YxeEBJ3fRxP4PUsMrV5SSkLwhQBIRVI63csuPONoSzK0TS8iGNuG4XlTDrGatbA2XSoLWncjSivbiMFacMhqcmru9DXy8hn5xkcX+dFEayhaywX0wmWogZuP+/0jrUIz8MEHmmtyAe75QPwSoxWbMYhq26dVV0nsi7NIKLbDEhaLsPx3G9oHG5strJFd/tMINPcFLstMEetlq4XbqP3pCBE3nsVCxWVUnNiIuOSSCc32UZxXg5PKaxTFCzYLy2qvh2S7eLix4J17vUXiayLtpKe9lnrVYnaAePrlnDNoNZ7mE4HOTVGWsvnZlYtmrMXvvX9SpIpFtMxAKbUMK9IVonZaFQRmUTFuTZtilQiGQtsMup7KdDKonXuB8fNNVZ0PiedyGK7fSCvEmWDvO3ZqJpPKSz3LrsvMEdogU0k0cDjomrhORmDmkegUmQRfLFQ3eJEfZ1L9RYvMgNdB2cgEUbib2mcboowLlY6pLXcr2llYS7bgwuaUBbP09SdmFBYUgsbusZK2sDbEgSrCbIfYZXC+h5xPe9S/0aEpVU2b8/lGKrNiHolwpUZF7IBX48X+K31h3l86TD1tsXtpljPRY41SccDomZeceROjKFJJev9ECUMj1eObQfceG5Gt2aIx1URAFTk9m2ngwBZrlnm1Y0EMq/ocDV61YDUAoNFjVZzU8QE5dZftG/JWprq2JCHqhdpqQHf6B9jIwmRCdgkQbgOolrFhv624N7PwmEUGYqvmWz0mQ67HPLWmVJ9nk9meCma5XR3ht5GiLPpEGwa/M0Ukel8U1X1iVqKrFoE3e1HzXKEFWAsUezydH+eZa+BLm7GdzJMKyMxDkLn0fsyA1tolcB2A3EtLWP1AYGT0fAjMiNZNWM4fYETaWwUIXwPW6ug635eheuA1iI+SOwZgSkyAZnAZoJeVEV4hkwrKl5KKxhSc2Puqq7yndUXuNic4DOVeznXGWfjyixOJAnWM5z1HmroI5OAeMxlMFNoAGJvmsyEYwj9hLobUZeKrtGs6xqLUQu/bfEub8KwEJiBS9IUJHVef01JQV5IPMxwvIxDrTbTlbxG7YvpGF/t3sVXzh1HL1WYXk1RWxHWV2RhnWg8r9QyKqt3u7GxpNvL/dbf8g5tp76EfkK3mRLpUcR0sUCNKiUagQVEmgtLkeUm1ryd3LUFDqQRkI0+sHj/KFg2NPjjQ46Nb/D+8Awuhq/3jrMZhVe7pbgOohaShW7u291vgS3XI3OLheNrTjTXOBS0ucdd4bAyfEVXebY3x7nNMZxVl2BDUFnNm0KTaYTvkdQ94jFBWi3GYg8+azeLsHnQTho5PLc5y2U/b6sXyJSqm1Af79MVIbH1kBmooUDqq5Gz2i/S25TlUL1N041ouQN62ud5fRi3B25PY/oDlONgagFpw0UHlN1I9gF7RmBej7XkeXCZItYOjsxDHqsypqUGTPo9OpWA1dCSFc2jRZyAEDiuQgcKYdSetsmO4gkUV6M7ezqgm/m5dhTnlW2E72F8Nw8ucbm5BWnkBxnlJ3oGP8jzwwKVoYTlQjzORlblW5uHyNYD/K2iMpCr0KGH9iVpmJuAjQe3q9D5NRiBThSRdFkZ1FHSMEhdkizfJeTCaSQId1xPYdrPfUiFxjh6eRRJu12s4FoT6s72odY3NKoR436falHH93LUYqlbJ4xsrk44Tt62quLse9Mj5MJShBlhGDPjd5n2OnSsz0UdcSGe4HK/Sb8X4Pfzgv8y1og4wwYewnXIqurVfcr7jZExIpW0hwGpkVz2WlRUSqx3LJeiCIobpWGNfsrcRC99zaGwTdMZ4osMRxpEJnD7FhXp7XmUVV2yUJWa5T5hzwpMrCBNFdZCX3loI9FW0pIxOG0eDC/hy4ynxo+gBg72ZYHd6sDAQw0ifMYQoyrke5iRuVkhSC0sJU2WB3W8nsVsthHVKqJRJ617eZeRmwzV307k9w1OJcMPUuaaHSpO3jIM4CvLx1hr17BXAlov5g8zMk8+H065JDVJNCFImjvq995mZCyxiSQdKi5mCilzP68QgBFXIwivK6QgtACda5UivWoi2944iNGYFDmd10ciFnI4GI94ZOoS91WXGJeSVWN5ZmWWwdkG4xsam2WIICCerhKNO/tfuwRkNWV2aouj9U0+WD9NVca8nEzT1iFfXjvO+cUJ1LJPuGTxtwxOe4AYROiZFmnDpz+tiFt22xy5rxkJv6Giu1Kj5xni1MF3M6LExRh5janlevOz9iw6tDSbAz7SfIq6HNI1Fc4lkzgDQW0xw2kP0YDwPYbTHnEjr+pTsvfZfYF5TWmzwpzjWIQyCGERArTJ8/Ji49C3DunOXl7yanqF1QYhMkglZHemyfAb5lV24pnJK8xIbbFphgCsktsCy177vN7wvFawXbBZehrXy/CcDFdqJJZB5mKsT7tXwWz6eF2JM7SoZFSnVuVd3gut4Y5WsBnt1DOBiVUu5KxACIs1Oy7iRtezU6MU9mqJPznSKq+O36tpho6jaTgRdRnhCom2hjhycQYCmRRSWsntWrb7XkCQd4hp+nmBgpYa4KHp6gorSYP2MMAOHdRQ4AwNztAWBTQEOnDIQlXUMd6HOZevhSlcREgGkUeqFboIShxt1izk69UosEySm+gdi+9mjKteYaWQBDJFZAI1NHl0LOTzyKWYRwdo7A4wuyswxY7F2Ncoz6AcTeCnKGlwVD4zMy3pRT4vdqf5pPM2UqtYT6ssDpuIVF5rKZSiqE6zK3d0SzA2j7azWZqXpoOrVYxeY1HaThlxLH49puKnuI4mcPIHdJB6bGrF6mYdPXTwFl0aK3nUnhPlwRqDKQer2O4wsVspE0ILGKg8EGeUAqJFkUf56u+zjkUXglKYkYOyeG0UJfwawt+RhjFnQF3lKTexVaRtn+aywO3lCcDW94jGFEnjYFT4qdeGfGDiDEf9NU44PQZW8NxgjufaM2wsNqlcdqgsW+oXYmScYQMXG/oM5nyGE5Kkyf5NI3kVhAGRSGxmidcrxIKr/u5UbHcYykKubvKArGaw1YypsM9hFeMKQWQTJHlfX3+5h+j08jxxNw9OzKplsM9+YW9omDLv5O4VC3w9iLeDPYwVpFmudXVTn0vxGKlRdDOfraRSlDQrTjVyCkr5ikCP/c5NBdzIIoHa1YRBQrMSIYXFEYbMSgapS5S46I6H6kn8TUFl3Wz7/KxDkeN5tZalGc2QOz2WRWWe/KNv8sNH5tcdZcleL0JYApniCY1EosnNxM7AImKdn9NRRacYcWf8ureJ0Zzy3YxD3jqzTpuqyAtGbCQhG/0Q2Ve4PfB6FqcTgbZYV2F9RRrKvEi4z77eoN6QkWnWiO15+IpDRtXERgiwnkG6htBJqEsHiUQVM1GmIAYRNh1VXsnnkLlDRUFK3jx3VmDu8CXhGFAWN8hwXE3gpVS8FCUsrso7Rejr+i+u9ap0oiPEmaLfDTBDh3BZ4m2BzCyi2cCGAdlYSDzh5yZFb29GyL4ajsw7HAwrAjUxjqhUsFIitEUNi7Zegb2aJrJT45R5izCpNErmpsxYKwZWMIg9uu0QGymq5xy8rdwf5XUMWZi3JdK+KLSFHZWEDpKZ7Q0iNEgNwtp86ROi8IPubav/a2FFYY1wLUcbm7wzuIgrDKsGLmZNznfG6GyGhGuSyoqhspIi17bAc4mOT5LWVR4Z27Bob7fvZm+isVd7GCBRCdhB0RYO8rrQPgdzw3FAueMCc1R6SwYa5WiatYiKm1Jx0jzPyQqyopRUXBQtHjEY+HRiBZHCW1eoWBCsWdyivqMNA3SzQjQVELVUvujvM2e6wuBIQxYIRKOOdRSo3OysYrBOUQ1EkRfIDgp/SOHnc1yNLDokAHlZwcRlMPCR6y5OX1C7ZAg2NDIxyMyifZe0LskCSJq2DEC4DlGksmDyMbVC3Lg0335CWESgcYOMo+EG97geA5vwXOJxOR1jsxsiOi5eGyprGf7aELO+gWg2SOsqb0FVh7RWlnG7ntwjdPUZ0gi0lXl7umGUV4qC3Ifp5YFCpYa5P7i9AnOkUY78Rm7eoFc5hkoY40iD72S4SueVMChMsEaRakU38km1YtDzsbFCxBIVFdUy+oKi4hRGCdKqg5yskdZd4obMc8L22YLmCvKAEzdipSbQE/U8vwbQnryaYE++8ci7IhiskdtNOLQWGKPoxx5x6tAfeqR9D9FXVNYkTgQqKc5ZUaSOIG7Iq3UsD0CaxK3GyiKAqDD5C2uvpq7sM7ZN1hK8SkqrNqSphhgMXaM5k05zJp4hGbqoocAdWNxOikgyCHxEJSCt5oUsyo4aN0Da7S43AAZDal1Sq/az9b6k4PYJzJE2KQAnj9h0qwnNWoTnZIwFQ6SwZIXZdTTBtJHEmcMgcdnaCrGxwll3cPsCFYMajvLs7Laz3bgQjSnSWkAa5hV+sgCss79WNE8IJt0u85UOz05Y+odDVGRQkUFX5NXC6MVGRCiDUhYjDDrLQ4WNzlNxskTl0aUbHpW1vOBzeMWgUouK8rGOG5KkIUgagngs/47Kne4r2Ta/ymIHZu12u7l9N1yiCBxzLJONPqdaKxzy1kmtZll7PN47ypneFHRc3J4gaGe4y1ugNVSrmGaVaEzm3Ug89uEA3GYkKEfjSr1dTSqyLpF196/9vmSbWyswd/ooReFfK3a0QlqUsjhK40qDIwxSGDLktlaZGckwdekOfdLEwfadPOAiysucyTT3Ve6MShtpkXmlDIHe7pawxx3pNlcejRWkVpFag7aWQKRUVIIOIKlJHCcPXtLeq/ektEZgighSm0hG/R6FEahBvtGQydUxM17eeDsLrtZlRe4vX++dQmFf2VTb2rxziX7Nt+5dJKAsDT9ixu/QUBGpNfRtwFLUZG1YRQ0kKgIZ2zwNQsnc5RF6eaqRQ+l3uxFFKtzIJKuxaESpYR4Qbp3AHGmUvsGrJShlcB2NFJY4ddBaEngpvsqDWgCMldvm141BheHAJ+u7uOsOIoVqLze75s2iyfOfdkw6K0YtvUBX8h2v8ex2KsQbKlB+B7FaMExcumlA1xq0hZYasOBvomdiut0Apy/wuleLjV/zfiswRqAziR04iFhSWc2DC0RRaFymoFKbl+1yBdrLS5gZ52r/UOOWmuWrIUUeXJWFAuM5uc6QZrhDm1e22V9GjG3XiAo0DzSX+J7G09RlxIYxvBjP8tTKHL2NkMaiIFw1+OtRXnB+vEl8qEk85uTzZj/2urwDCGlx3YyKSrf/1jc+A+0Xm+RyzPYzb1xgimt/jsyvwjF4Xl4KKvDySaOLBqpixxbLFG+MM4dEK6LIJRs4yL7C6eaC0hkUZq9r+heOPo+8EbCT11bVXtFWyMmF5n4wFVmTC7zYOPQL07QrMlyRB2Oktbz8jNCiaIhdaM2Fw9LqvFGtSRUiEag4L12mYoom0PnnbGvjqhiroo+f8fZfFPGuoEaF3vNfhbF5VLZ+7ZzQPckojUvm7d1mVA+AxEoGxmc49BBDlfsuBwaZ6DwX2FGkNUVaKXqjfrsCGm9VrstmM3bkyyz830Jcs5SJHdaykr3PGxOYsjC7yjzSTjom90GKvPtGrcijVMVMGE0gW0TAZqbICUwdNtfr2EjhbiqqW3m/QjUshOSI7QVfbFfTyIJcAOQpFoWPaVT4eb88yJkkjjyWBg0eHZ4gkCmpVRgrOTTR5qKFYc8j2XKK/o6ABRULZKIQm3nDZzctzK4peJ3cXLhd0cYBXZgTR1pqWi8i80adXMon9lVR5ClQeZNomU8tY5BJPs77zi8lQHkGz8+YdjvMKtgwhlVdYS2rofsublcSrmjCS31kZ5AHk1U9+rOKtCq205r2zXN2B7GZJE4cOpnPwGok0JARY04f7QtEWMnTk+L4Wl/4K3WCkj3IGxKYduSfVBYvSPG8vISbkgavqCyzM4hnZxECXeRXxqlDkjqw5eL1BP66wN+024WM81ZL1+1iRwLAgyy0+f8Du+dNr6+KFphU0okCzsZT1FVEKBM0gplKFz0uWfOq9FUAsUJ1VZ4PmILSAjUk9zPpqxqlE+URnNoT25rAyOeUL/p5PVpTdka4aYQabS6KyagNQluktvtOzbIi1y5dpWnIITXp0zZD+tajl/l5JPpQ4G1lqPVu3gAAML5D0sirP22n1OyvW78zaIHOFIlxSKzFE4JApNRllMcKuC5CFSYdM4q23lFGqGRP84Y1TBVmKEcz2+rS8CPizCHWznZItbGCROcVenr9gGzoEP9f7Z1ZbxzHFYW/W9XbLFxkiZIQGYkBP9jJ//8ByWse/RAEAQzYiWGtpIYczkwvteShupukIicjRssMeb8nghxwaXbPrVt17jm24DKriF6IjQUnlAtDtknbiNcbnShpXCTaXsDTd5EhT8bZoRzk/h/pSnwBpM8AXa5L/nbxlHnecFKkLbK1S9PgIkkwNdjixSjJyOZ6KsdwjibpbJKYOvCQSxJDVX3h7MNu9/mafWp8MJz7CatQEgjkEsgKh58mn10LSfTTBYzbPz8z6WebfRTqmNNFTwBy8f0uUXqdXXfE5WU/VGgJuRmtEncxW3Zn8IKrM07rGT+7Kcem4cC0PM0XNF9B+4dH5C9yZLWBGMlWSVAW8jTfq0r13eZ2BdNGJtOGWdnypwcveFJc8GtzxGkzow0Zq64gRqHuMjpvcZc5dtm/uUQwTsjqlFmYL9Nc4LDdONJ3kyEniVTKJLIIw5DvXXhonSDB0KwKfjx7xKToWMymFMaxcSlpZSyYg8l8iBCvRkwG1TD0i4z+P+qrVCzdNOJm+hBuiw+Gczdh6Ss8EUukqjpW00goBkm2x3bJ9EH28MKms3NDHXKamIaZCzy5eKKkXR67avFnbzHzOWY2xZcGN4sp61F5P7H3mQ2Gs/WEH9vHPM3O+b54yzN7TnPiWH5TcRgi9vkriJFima6nmySv6IDcdO9Sdor/SyUrEjFE7LUJ7sZlrNqC1lk2TYF3JhmkX/NjHLddIze2YMc0iSx1Sm7CKOIZzijvkqu/9KMldIZNneO8Ieu3y1KcWXpj++1vMKxKr3YGhySO5AnLlU/l/r2vfxGcM5y1U97kB/zi4DTMCcH0loQGe3xEPJjhKvtfR312lpgKpnOGN+6An51lEeYswpQLN0kvMenM0h4fI7MpcVLiK6P30DYEQQSaLueX9iEA3+SLFON30LF+PKE8L5kfHxGLPIUeWKGtk4iRMuL3b+Pi3vDRxkpCNLhguWhL3izmhK4fdXBCtjKYRsYuSd4ZD4G+M8rSjGA3S0WzO4hX24hbJE3sHTFty1IbOlfR2Ui9KbA2hT0XmcP79xRMSSuM1GkPi4peATt5RwR1l67XZ6BrMn66eEjtc47shqWv6DoLJlIfGWbffo07LGkeZLQHe5hWEkl5o5Lxw8UzcvF00VKHnH+uH6Rt/SJSP5kw8b8jVDmhMDRH5k4tVj8V6YhE2KwK/vr2G17MDvm+fM5MHN99/ZK/h6dEU1GePYYYqU478kuDL3K6RmiPZcxq3bvF2D3g1gUzDuKdkLH2BRufU/uM1tlULPuuUlzy4ZRAugne/T6DmKLvjHzRd0Z5KpajivMO3zwSBBzJqae1RCtpGzb2M5bepHw+GB+kMQi57yIHBWzIGWdQ9azyw4k+mWecNxNedodc+jLlcEpalPhpkTIgi97wYR/vyyBEb7hoK161B6MyezgGSMIwg58X+NKmRI3dz2LfDYbjEW9YNBOm2ZyFn4Jdc1xsmB40dIcl7XGOaQK29liSWC9kfULPMDu2j/fWHed2BTNA11k2kvPT5Ve8zua8XM9ZbirqOidu0hjEUCzfKwCT4cEEqQannnjjzT5m9+SmiX3RDKQ2USztOqMVenGPXEVwSW82ECN+kr4+rkblqqvU1entiLXlbDFjVRcE+jGozhJNpDkWlr8vcZWMsVZ713UNwcjB8q+3x1y2JZlJYe2L9SQtDPLI+sTgi5KQJaP55kh0/OgDiLXl19MjLuqSP0//yOPigmeTBbNnLX9pv+Wlm1OdwqMfNtjaMXltKM8Nochw837GfN/urXvA7QpmFII3OGe5bFMa+aopqOsc39i0ShoUnPFmZzTSfzysXH3VO/TcFUHPh9L/yWMGZPcbFU/6BcVn+rXuHQFCY2mA09WUGIXorzrMbp7cknzF3naY4oVIpKkLFkIf1u6p277D7M/AxUuafR6U6nv4t34pxAvdJmdlIs+bIzyGuW04yjacHD/h+ckE01kwIC6QrRyhs5jW7p971D3i1h2mW2c4k/GqyRAT01ZiZ1Kgc0+0gIl4SclIYzGMY1LSWChCfrcEPcp+Ik6IGEJneNvN0ydri3SCm0Y2j6WfbY3/GSC8BwxCM/GCv8xYNXb0P439EQqSYt7cVEZ/aL8n7lk7gwcaQys5/1ic8LI84MlkyVG+obAeedDS1BX1w4K8tBAiGEnCH10O7yy3KpjiBTZJ7RBXdvz33niehu1BkoDnPaeXt/nRivJJSdmX/Z28uXkI7MuIvwNjFdKfh8vGwuba56+9xh0M8nXlNohP4QchwuvFnGVVjl8rrePwYMNildEcZkQD2SakzlJ1BzvN5w2QVpR9Rjss5QORILg6Z+0trzNPFyzrLk++2nlMSmtjCJkgvjf01zGwnUULpqIoysdmmDPvhHiREQycdobzckJeOKqiw5ae+iTSzYXiXLBdxFVowdxhdANAURTlEzIo4GNItoRxFELG0azluvmIsruI5rMpiqIoyv9GO0xFURRF2QItmIqiKIqyBVowFUVRFGULtGAqiqIoyhZowVQURVGULdCCqSiKoihboAVTURRFUbZAC6aiKIqibIEWTEVRFEXZAi2YiqIoirIFWjAVRVEUZQv+DZZLbf/M1kIAAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_convolution(x_train[:5],[[-1.,0.,1.],[-1.,0.,1.],[-1.,0.,1.]],'Vertical edge filter')\n", + "plot_convolution(x_train[:5],[[-1.,-1.,-1.],[0.,0.,0.],[1.,1.,1.]],'Horizontal edge filter')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O primeiro filtro é chamado de **filtro de borda vertical** e é definido pela seguinte matriz: \n", + "$$\n", + "\\left(\n", + " \\begin{matrix}\n", + " -1 & 0 & 1 \\cr\n", + " -1 & 0 & 1 \\cr\n", + " -1 & 0 & 1 \\cr\n", + " \\end{matrix}\n", + "\\right)\n", + "$$ \n", + "Quando esse filtro passa por uma área de pixels relativamente uniforme, todos os valores somam 0. No entanto, quando encontra uma borda vertical na imagem, é gerado um valor de pico alto. É por isso que, nas imagens acima, você pode ver bordas verticais representadas por valores altos e baixos, enquanto as bordas horizontais são suavizadas.\n", + "\n", + "O oposto acontece quando aplicamos o filtro de borda horizontal - as linhas horizontais são amplificadas, e as verticais são suavizadas.\n", + "\n", + "Na visão computacional clássica, múltiplos filtros eram aplicados à imagem para gerar características, que então eram usadas por um algoritmo de aprendizado de máquina para construir um classificador. Esses filtros, na verdade, são semelhantes às estruturas neurais disponíveis no sistema de visão de alguns animais.\n", + "\n", + "\n", + "\n", + "No entanto, no aprendizado profundo, construímos redes que **aprendem** os melhores filtros convolucionais para resolver o problema de classificação. Para isso, introduzimos as **camadas convolucionais**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Camadas Convolucionais\n", + "\n", + "Para tornar os pesos de uma camada convolucional treináveis, precisamos reduzir o processo de aplicação da janela do filtro convolucional à imagem para operações matriciais, que podem então ser submetidas ao treinamento por retropropagação. Para isso, usamos uma transformação matricial inteligente, chamada **im2col**.\n", + "\n", + "Suponha que temos uma pequena imagem $\\mathbf{x}$, com os seguintes pixels:\n", + "\n", + "$$\n", + "\\mathbf{x} = \\left(\n", + " \\begin{array}{ccccc}\n", + " a & b & c & d & e \\\\\n", + " f & g & h & i & j \\\\\n", + " k & l & m & n & o \\\\\n", + " p & q & r & s & t \\\\\n", + " u & v & w & x & y \\\\\n", + " \\end{array}\n", + " \\right)\n", + "$$\n", + "\n", + "E queremos aplicar dois filtros convolucionais, com os seguintes pesos:\n", + "$$\n", + "W^{(i)} = \\left(\\begin{array}{ccc}\n", + " w^{(i)}_{00} & w^{(i)}_{01} & w^{(i)}_{02} \\\\\n", + " w^{(i)}_{10} & w^{(i)}_{11} & w^{(i)}_{12} \\\\\n", + " w^{(i)}_{20} & w^{(i)}_{21} & w^{(i)}_{22} \\\\\n", + " \\end{array}\\right) \n", + "$$\n", + "\n", + "Ao aplicar a convolução, o primeiro pixel do resultado seria obtido pela multiplicação elemento a elemento de \n", + "$\\left(\\begin{array}{ccc}\n", + " a & b & c \\\\\n", + " f & g & h \\\\\n", + " k & l & m \\\\\n", + "\\end{array}\\right)$ e $W^{(i)}$, o segundo elemento - pela multiplicação de $\\left(\\begin{array}{ccc}\n", + " b & c & d \\\\\n", + " g & h & i \\\\\n", + " l & m & n \\\\\n", + "\\end{array}\\right)$ por $W^{(i)}$, e assim por diante.\n", + "\n", + "Para formalizar esse processo, vamos extrair todos os fragmentos $3\\times3$ da imagem original $x$ na seguinte matriz:\n", + "\n", + "$$\n", + "\\mathrm{im2col}(x) = \\left[\n", + " \\begin{array}{cccccc}\n", + " a & b & \\ldots & g & \\ldots & m \\\\\n", + " b & c & \\ldots & h & \\ldots & n \\\\\n", + " c & d & \\ldots & i & \\ldots & o \\\\\n", + " f & g & \\ldots & l & \\ldots & r \\\\\n", + " g & h & \\ldots & m & \\ldots & s \\\\\n", + " h & i & \\ldots & n & \\ldots & t \\\\\n", + " k & l & \\ldots & q & \\ldots & w \\\\\n", + " l & m & \\ldots & r & \\ldots & x \\\\\n", + " m & n & \\ldots & s & \\ldots & y \\\\\n", + " \\end{array}\n", + " \\right]\n", + "$$\n", + "\n", + "Cada coluna dessa matriz corresponde a cada sub-região $3\\times3$ da imagem original. Agora, para obter o resultado da convolução, basta multiplicar essa matriz pela matriz de pesos:\n", + "$$\n", + "\\mathbf{W} = \\left[\n", + " \\begin{array}{cccccccc}\n", + " w^{(0)}_{00} & w^{(0)}_{01} & w^{(0)}_{02} & w^{(0)}_{10} & w^{(0)}_{11} & \\ldots & w^{(0)}_{21} & w^{(0)}_{22} \\\\\n", + " w^{(1)}_{00} & w^{(1)}_{01} & w^{(1)}_{02} & w^{(1)}_{10} & w^{(1)}_{11} & \\ldots & w^{(1)}_{21} & w^{(1)}_{22} \\\\\n", + " \\end{array}\n", + " \\right]\n", + "$$\n", + "(cada linha dessa matriz contém os pesos do $i$-ésimo filtro, achatados em uma única linha)\n", + "\n", + "Assim, a aplicação de um filtro convolucional à imagem original pode ser substituída por uma multiplicação matricial, que já sabemos como lidar usando retropropagação:\n", + "$$\n", + "C(x) = W\\times\\mathbf{im2col}(x)\n", + "$$\n", + "\n", + "As camadas convolucionais são definidas usando a classe `Conv2d`. Precisamos especificar o seguinte:\n", + "* `filters` - número de filtros a serem usados. Usaremos 9 filtros diferentes, o que dará à rede muitas oportunidades para explorar quais filtros funcionam melhor para nosso cenário.\n", + "* `kernel_size` - tamanho da janela deslizante. Normalmente, filtros de 3x3 ou 5x5 são usados.\n", + "\n", + "A CNN mais simples conterá uma camada convolucional. Dado o tamanho de entrada 28x28, após aplicar nove filtros 5x5, terminaremos com um tensor de 24x24x9. A dimensão espacial é menor, porque há apenas 24 posições onde um intervalo deslizante de comprimento 5 pode se ajustar em 28 pixels.\n", + "\n", + "Após a convolução, achatamos o tensor 24x24x9 em um vetor de tamanho 5184 e, em seguida, adicionamos uma camada linear para produzir 10 classes. Também usamos a função de ativação `relu` entre as camadas.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d (Conv2D) (None, 24, 24, 9) 234 \n", + " \n", + " flatten (Flatten) (None, 5184) 0 \n", + " \n", + " dense (Dense) (None, 10) 51850 \n", + " \n", + "=================================================================\n", + "Total params: 52,084\n", + "Trainable params: 52,084\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential([\n", + " keras.layers.Conv2D(filters=9, kernel_size=(5,5), input_shape=(28,28,1),activation='relu'),\n", + " keras.layers.Flatten(),\n", + " keras.layers.Dense(10)\n", + "])\n", + "\n", + "model.compile(loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['acc'])\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode ver que essa rede contém cerca de 50 mil parâmetros treináveis, em comparação com cerca de 80 mil em redes totalmente conectadas com múltiplas camadas. Isso nos permite alcançar bons resultados mesmo em conjuntos de dados menores, porque redes convolucionais generalizam muito melhor.\n", + "\n", + "> **Nota**: Na maioria dos casos práticos, queremos aplicar camadas convolucionais a imagens coloridas. Assim, a camada `Conv2D` espera que a entrada tenha o formato $W\\times H\\times C$, onde $W$ e $H$ são a largura e a altura da imagem, e $C$ é o número de canais de cor. Para imagens em escala de cinza, precisamos do mesmo formato com $C=1$.\n", + "\n", + "Precisamos remodelar nossos dados antes de iniciar o treinamento:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "1875/1875 [==============================] - 15s 7ms/step - loss: 0.2099 - acc: 0.9410 - val_loss: 0.0879 - val_acc: 0.9735\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0858 - acc: 0.9753 - val_loss: 0.0682 - val_acc: 0.9791\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0665 - acc: 0.9808 - val_loss: 0.0553 - val_acc: 0.9829\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0582 - acc: 0.9835 - val_loss: 0.0513 - val_acc: 0.9835\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0527 - acc: 0.9847 - val_loss: 0.0503 - val_acc: 0.9833\n" + ] + } + ], + "source": [ + "x_train_c = np.expand_dims(x_train,3)\n", + "x_test_c = np.expand_dims(x_test,3)\n", + "hist = model.fit(x_train_c,y_train,validation_data=(x_test_c,y_test),epochs=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(hist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Como você pode ver, conseguimos alcançar maior precisão e de forma muito mais rápida (em termos de número de épocas), comparado às redes totalmente conectadas da unidade anterior. No entanto, o treinamento em si exige mais recursos e pode ser mais lento em computadores sem GPU.\n", + "\n", + "## Visualizando Camadas Convolucionais\n", + "\n", + "Também podemos visualizar os pesos das nossas camadas convolucionais treinadas para tentar entender melhor o que está acontecendo:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots(1,9)\n", + "l = model.layers[0].weights[0]\n", + "for i in range(9):\n", + " ax[i].imshow(l[...,0,i])\n", + " ax[i].axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Você pode ver que alguns desses filtros parecem reconhecer alguns traços oblíquos, enquanto outros parecem bem aleatórios.\n", + "\n", + "> **Tarefa**: Treine a mesma rede com filtros 3x3 e visualize-os. Você vê padrões mais familiares?\n", + "\n", + "## CNNs com múltiplas camadas e camadas de pooling\n", + "\n", + "As primeiras camadas de convolução procuram padrões primitivos, como linhas horizontais ou verticais, mas podemos aplicar camadas de convolução adicionais sobre elas para buscar padrões de nível mais alto, como formas primitivas. Depois, mais camadas de convolução podem combinar essas formas em partes da imagem, até chegar ao objeto final que estamos tentando classificar.\n", + "\n", + "Ao fazer isso, também podemos aplicar um truque: reduzir o tamanho espacial da imagem. Uma vez que detectamos que há um traço horizontal dentro de uma janela deslizante 3x3, não é tão importante em qual pixel exato ele ocorreu. Assim, podemos \"reduzir\" o tamanho da imagem, o que é feito usando uma das **camadas de pooling**:\n", + "\n", + " * **Pooling Médio** utiliza uma janela deslizante (por exemplo, 2x2 pixels) e calcula a média dos valores dentro da janela.\n", + " * **Pooling Máximo** substitui a janela pelo valor máximo. A ideia por trás do pooling máximo é detectar a presença de um determinado padrão dentro da janela deslizante.\n", + "\n", + "Assim, em uma CNN típica, haveria várias camadas de convolução, com camadas de pooling entre elas para diminuir as dimensões da imagem. Também aumentaríamos o número de filtros, porque, à medida que os padrões se tornam mais avançados, há mais combinações interessantes que precisamos procurar.\n", + "\n", + "![Uma imagem mostrando várias camadas de convolução com camadas de pooling.](../../../../../translated_images/pt-BR/cnn-pyramid.85915455759ef0ce.webp)\n", + "\n", + "Devido à diminuição das dimensões espaciais e ao aumento das dimensões de características/filtros, essa arquitetura também é chamada de **arquitetura piramidal**.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_1 (Conv2D) (None, 24, 24, 10) 260 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 12, 12, 10) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 8, 8, 20) 5020 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 20) 0 \n", + "_________________________________________________________________\n", + "flatten_1 (Flatten) (None, 320) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 10) 3210 \n", + "=================================================================\n", + "Total params: 8,490\n", + "Trainable params: 8,490\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential([\n", + " keras.layers.Conv2D(filters=10, kernel_size=(5,5), input_shape=(28,28,1),activation='relu'),\n", + " keras.layers.MaxPooling2D(),\n", + " keras.layers.Conv2D(filters=20, kernel_size=(5,5), activation='relu'),\n", + " keras.layers.MaxPooling2D(), \n", + " keras.layers.Flatten(),\n", + " keras.layers.Dense(10)\n", + "])\n", + "\n", + "model.compile(loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['acc'])\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note que o número de parâmetros treináveis (~8,5K) é dramaticamente menor do que nos casos anteriores. Isso acontece porque as camadas convolucionais, em geral, possuem poucos parâmetros, e a dimensionalidade da imagem antes de aplicar a camada densa final é significativamente reduzida. Um pequeno número de parâmetros tem um impacto positivo em nossos modelos, pois ajuda a prevenir overfitting, mesmo em conjuntos de dados menores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0723 - acc: 0.9780 - val_loss: 0.0423 - val_acc: 0.9861\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0523 - acc: 0.9842 - val_loss: 0.0425 - val_acc: 0.9866\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0448 - acc: 0.9868 - val_loss: 0.0403 - val_acc: 0.9865\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0383 - acc: 0.9886 - val_loss: 0.0323 - val_acc: 0.9888\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0338 - acc: 0.9895 - val_loss: 0.0331 - val_acc: 0.9896\n" + ] + } + ], + "source": [ + "hist = model.fit(x_train_c,y_train,validation_data=(x_test_c,y_test),epochs=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(hist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O que você provavelmente vai perceber é que conseguimos alcançar uma precisão maior do que com apenas uma camada, e de forma muito mais rápida em termos de número de épocas - com apenas 1 ou 2 épocas. Isso significa que arquiteturas de rede sofisticadas precisam de muito menos dados para entender o que está acontecendo e para extrair padrões genéricos de nossas imagens. No entanto, o treinamento também leva mais tempo e requer uma GPU.\n", + "\n", + "## Explorando imagens reais do conjunto de dados CIFAR-10\n", + "\n", + "Embora nosso problema de reconhecimento de dígitos manuscritos possa parecer algo simples, agora estamos prontos para fazer algo mais sério. Vamos explorar um conjunto de dados mais avançado com imagens de diferentes objetos, chamado [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html). Ele contém 60 mil imagens de 32x32, divididas em 10 classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(x_train,y_train),(x_test,y_test) = keras.datasets.cifar10.load_data()\n", + "x_train = x_train.astype(np.float32) / 255.0\n", + "x_test = x_test.astype(np.float32) / 255.0\n", + "classes = ('plane', 'car', 'bird', 'cat',\n", + " 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "display_dataset(x_train,y_train,classes=classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv2d_8 (Conv2D) (None, 28, 28, 6) 456 \n", + "_________________________________________________________________\n", + "max_pooling2d_6 (MaxPooling2 (None, 14, 14, 6) 0 \n", + "_________________________________________________________________\n", + "conv2d_9 (Conv2D) (None, 10, 10, 16) 2416 \n", + "_________________________________________________________________\n", + "max_pooling2d_7 (MaxPooling2 (None, 5, 5, 16) 0 \n", + "_________________________________________________________________\n", + "flatten_3 (Flatten) (None, 400) 0 \n", + "_________________________________________________________________\n", + "dense_9 (Dense) (None, 120) 48120 \n", + "_________________________________________________________________\n", + "dense_10 (Dense) (None, 84) 10164 \n", + "_________________________________________________________________\n", + "dense_11 (Dense) (None, 10) 850 \n", + "=================================================================\n", + "Total params: 62,006\n", + "Trainable params: 62,006\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = keras.models.Sequential([\n", + " keras.layers.Conv2D(filters = 6, kernel_size = 5, strides = 1, activation = 'relu', input_shape = (32,32,3)),\n", + " keras.layers.MaxPooling2D(pool_size = 2, strides = 2),\n", + " keras.layers.Conv2D(filters = 16, kernel_size = 5, strides = 1, activation = 'relu'),\n", + " keras.layers.MaxPooling2D(pool_size = 2, strides = 2),\n", + " keras.layers.Flatten(),\n", + " keras.layers.Dense(120, activation = 'relu'),\n", + " keras.layers.Dense(84, activation = 'relu'),\n", + " keras.layers.Dense(10, activation = 'softmax')])\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Treinar esta rede adequadamente levará uma quantidade significativa de tempo e deve, preferencialmente, ser feito em computação com suporte a GPU.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "1563/1563 [==============================] - 6s 4ms/step - loss: 1.6205 - acc: 0.4033 - val_loss: 1.4287 - val_acc: 0.4743\n", + "Epoch 2/10\n", + "1563/1563 [==============================] - 6s 4ms/step - loss: 1.3435 - acc: 0.5154 - val_loss: 1.2804 - val_acc: 0.5412\n", + "Epoch 3/10\n", + "1563/1563 [==============================] - 5s 3ms/step - loss: 1.2225 - acc: 0.5645 - val_loss: 1.2164 - val_acc: 0.5600\n", + "Epoch 4/10\n", + "1563/1563 [==============================] - 5s 4ms/step - loss: 1.1360 - acc: 0.5957 - val_loss: 1.1918 - val_acc: 0.5768\n", + "Epoch 5/10\n", + "1563/1563 [==============================] - 5s 3ms/step - loss: 1.0776 - acc: 0.6178 - val_loss: 1.1451 - val_acc: 0.5906\n", + "Epoch 6/10\n", + "1563/1563 [==============================] - 5s 4ms/step - loss: 1.0228 - acc: 0.6370 - val_loss: 1.1178 - val_acc: 0.6098\n", + "Epoch 7/10\n", + "1563/1563 [==============================] - 5s 4ms/step - loss: 0.9769 - acc: 0.6544 - val_loss: 1.0793 - val_acc: 0.6202\n", + "Epoch 8/10\n", + "1563/1563 [==============================] - 5s 3ms/step - loss: 0.9340 - acc: 0.6711 - val_loss: 1.0783 - val_acc: 0.6271\n", + "Epoch 9/10\n", + "1563/1563 [==============================] - 5s 4ms/step - loss: 0.8983 - acc: 0.6824 - val_loss: 1.0952 - val_acc: 0.6203\n", + "Epoch 10/10\n", + "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8648 - acc: 0.6939 - val_loss: 1.1103 - val_acc: 0.6217\n" + ] + } + ], + "source": [ + "model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['acc'])\n", + "hist = model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_results(hist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A precisão que conseguimos alcançar com poucas épocas de treinamento não parece tão boa. No entanto, lembre-se de que um palpite aleatório nos daria apenas 10% de precisão, e que nosso problema é significativamente mais difícil do que a classificação de dígitos do MNIST. Conseguir mais de 50% de precisão em um tempo de treinamento tão curto parece ser uma boa conquista.\n", + "\n", + "## Conclusões\n", + "\n", + "Nesta unidade, aprendemos o conceito principal por trás das redes neurais de visão computacional - redes convolucionais. Arquiteturas reais que impulsionam a classificação de imagens, detecção de objetos e até mesmo redes de geração de imagens são todas baseadas em CNNs, apenas com mais camadas e alguns truques adicionais de treinamento.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n---\n\n**Aviso Legal**: \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5" + }, + "kernelspec": { + "display_name": "Python 3.8.12 64-bit (conda)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + }, + "coopTranslator": { + "original_hash": "96896b1704c43955ccd2cf60c0c284d9", + "translation_date": "2025-08-28T12:55:58+00:00", + "source_file": "lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb", + "language_code": "br" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/README.md b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/README.md new file mode 100644 index 00000000..5f28f580 --- /dev/null +++ b/translations/pt-BR/lessons/4-ComputerVision/07-ConvNets/README.md @@ -0,0 +1,60 @@ +# Redes Neurais Convolucionais + +Já vimos anteriormente que redes neurais são bastante eficazes ao lidar com imagens, e até mesmo um perceptron de uma camada consegue reconhecer dígitos manuscritos do conjunto de dados MNIST com uma precisão razoável. No entanto, o conjunto de dados MNIST é muito especial, pois todos os dígitos estão centralizados na imagem, o que torna a tarefa mais simples. + +## [Quiz pré-aula](https://ff-quizzes.netlify.app/en/ai/quiz/13) + +Na vida real, queremos ser capazes de reconhecer objetos em uma imagem independentemente de sua localização exata. Visão computacional é diferente de classificação genérica, porque, ao tentar encontrar um determinado objeto na imagem, estamos escaneando a imagem em busca de **padrões** específicos e suas combinações. Por exemplo, ao procurar um gato, podemos primeiro buscar linhas horizontais, que podem formar os bigodes, e então uma certa combinação de bigodes pode nos indicar que se trata de uma imagem de um gato. A posição relativa e a presença de certos padrões são importantes, e não sua posição exata na imagem. + +Para extrair padrões, utilizaremos o conceito de **filtros convolucionais**. Como você sabe, uma imagem é representada por uma matriz 2D ou um tensor 3D com profundidade de cor. Aplicar um filtro significa que pegamos uma matriz relativamente pequena chamada **kernel do filtro**, e para cada pixel na imagem original calculamos a média ponderada com os pontos vizinhos. Podemos imaginar isso como uma pequena janela deslizando sobre toda a imagem e calculando a média de todos os pixels de acordo com os pesos na matriz do kernel do filtro. + +![Filtro de Borda Vertical](../../../../../translated_images/pt-BR/filter-vert.b7148390ca0bc356.webp) | ![Filtro de Borda Horizontal](../../../../../translated_images/pt-BR/filter-horiz.59b80ed4feb946ef.webp) +----|---- + +> Imagem por Dmitry Soshnikov + +Por exemplo, se aplicarmos filtros de borda vertical e horizontal de 3x3 aos dígitos do MNIST, podemos destacar (por exemplo, valores altos) onde há bordas verticais e horizontais na imagem original. Assim, esses dois filtros podem ser usados para "procurar" bordas. Da mesma forma, podemos projetar diferentes filtros para buscar outros padrões de baixo nível: + + + +> Imagem do [Leung-Malik Filter Bank](https://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html) + +No entanto, enquanto podemos projetar filtros manualmente para extrair alguns padrões, também podemos projetar a rede de forma que ela aprenda os padrões automaticamente. Essa é uma das principais ideias por trás das CNNs. + +## Principais ideias por trás das CNNs + +O funcionamento das CNNs é baseado nas seguintes ideias importantes: + +* Filtros convolucionais podem extrair padrões +* Podemos projetar a rede de forma que os filtros sejam treinados automaticamente +* Podemos usar a mesma abordagem para encontrar padrões em características de alto nível, não apenas na imagem original. Assim, a extração de características pelas CNNs funciona em uma hierarquia de características, começando com combinações de pixels de baixo nível até combinações de alto nível de partes da imagem. + +![Extração Hierárquica de Características](../../../../../translated_images/pt-BR/FeatureExtractionCNN.d9b456cbdae7cb64.webp) + +> Imagem de [um artigo de Hislop-Lynch](https://www.semanticscholar.org/paper/Computer-vision-based-pedestrian-trajectory-Hislop-Lynch/26e6f74853fc9bbb7487b06dc2cf095d36c9021d), baseado em [sua pesquisa](https://dl.acm.org/doi/abs/10.1145/1553374.1553453) + +## ✍️ Exercícios: Redes Neurais Convolucionais + +Vamos continuar explorando como as redes neurais convolucionais funcionam e como podemos obter filtros treináveis, trabalhando nos notebooks correspondentes: + +* [Redes Neurais Convolucionais - PyTorch](ConvNetsPyTorch.ipynb) +* [Redes Neurais Convolucionais - TensorFlow](ConvNetsTF.ipynb) + +## Arquitetura de Pirâmide + +A maioria das CNNs usadas para processamento de imagens segue a chamada arquitetura de pirâmide. A primeira camada convolucional aplicada às imagens originais geralmente possui um número relativamente baixo de filtros (8-16), que correspondem a diferentes combinações de pixels, como linhas horizontais/verticais ou traços. No próximo nível, reduzimos a dimensão espacial da rede e aumentamos o número de filtros, o que corresponde a mais combinações possíveis de características simples. Com cada camada, à medida que avançamos para o classificador final, as dimensões espaciais da imagem diminuem e o número de filtros aumenta. + +Como exemplo, vejamos a arquitetura do VGG-16, uma rede que alcançou 92,7% de precisão na classificação top-5 do ImageNet em 2014: + +![Camadas do ImageNet](../../../../../translated_images/pt-BR/vgg-16-arch1.d901a5583b3a51ba.webp) + +![Pirâmide do ImageNet](../../../../../translated_images/pt-BR/vgg-16-arch.64ff2137f50dd49f.webp) + +> Imagem de [Researchgate](https://www.researchgate.net/figure/Vgg16-model-structure-To-get-the-VGG-NIN-model-we-replace-the-2-nd-4-th-6-th-7-th_fig2_335194493) + +## Arquiteturas de CNN Mais Conhecidas + +[Continue seus estudos sobre as arquiteturas de CNN mais conhecidas](CNN_Architectures.md) + +--- +