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   "source": [
    "# MNIST Digit Classification with our own Framework\n",
    "\n",
    "Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).\n",
    "\n",
    "### Reading the Dataset\n",
    "\n",
    "This code download the dataset from the repository on the internet. You can also manually copy the dataset from `/data` directory of AI Curriculum repo."
   ]
  },
  {
   "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": [
    "Let's see what is the shape of data that we have:"
   ]
  },
  {
   "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": [
    "### Splitting the Data\n",
    "\n",
    "We will use Scikit Learn to split the data between training and test dataset:"
   ]
  },
  {
   "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": [
    "### Instructions\n",
    "\n",
    "1. Take the framework code from the lesson and paste it into this notebook, or (even better) into a separate Python module\n",
    "1. Define and train one-layered perceptron, observing training and validation accuracy during training\n",
    "1. Try to understand if overfitting took place, and adjust layer parameters to improve accuracy\n",
    "1. Repeat previous steps for 2- and 3-layered perceptrons. Try to experiment with different activation functions between layers.\n",
    "1. Try to answer the following questions:\n",
    "    - Does the inter-layer activation function affect network performance?\n",
    "    - Do we need 2- or 3-layered network for this task?\n",
    "    - Did you experience any problems training the network? Especially as the number of layers increased.\n",
    "    - How do weights of the network behave during training? You may plot max abs value of weights vs. epoch to understand the relation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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