Minor notebook corrections / translations
This commit is contained in:
Dmitri Soshnikov
parent
b338181e13
commit
c8315af964
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@ -289,7 +289,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We will define our system as a class that subсlasses `KnowledgeEngine`. Each rule is defined by a separate function with `@Rule` annotation, which specifies when the rule should fire. Inside the rule, we can add new facts using `declare` function, and adding those facts will result in some more rules being called by forward inference engine. "
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"We will define our system as a class that subclasses `KnowledgeEngine`. Each rule is defined by a separate function with `@Rule` annotation, which specifies when the rule should fire. Inside the rule, we can add new facts using `declare` function, and adding those facts will result in some more rules being called by forward inference engine. "
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]
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},
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{
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@ -19,7 +19,7 @@
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"\n",
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"However, we might want to use raw (unlabeled) data for training CNN feature extractors, which is called **self-supervised learning**. Instead of labels, we will use training images as both network input and output. The main idea of **autoencoder** is that we will have an **encoder network** that converts input image into some **latent space** (normally it is just a vector of some smaller size), the then **decoder network**, whose goal would be to reconstruct the original image.\n",
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"\n",
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"Since we are training autoencoder to capture as much of the information from the original image as possible for accurate reconstruction, the network tries to find the best **embedding** of input images to capture the meaning.л.\n",
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"Since we are training autoencoder to capture as much of the information from the original image as possible for accurate reconstruction, the network tries to find the best **embedding** of input images to capture the meaning.\n",
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"\n",
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"\n",
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"\n",
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@ -41,7 +41,10 @@
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "48ua_UfDIqum"
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"id": "48ua_UfDIqum",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -62,7 +65,10 @@
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"height": 109
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},
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"id": "-VvA6_e_Ytv9",
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"outputId": "7f43ddd9-2044-4c88-a57d-7dffbb0741fe"
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"outputId": "7f43ddd9-2044-4c88-a57d-7dffbb0741fe",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -93,7 +99,10 @@
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "XVXKfTiKZGzs"
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"id": "XVXKfTiKZGzs",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -132,7 +141,10 @@
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "xRVImJGBZ2kt"
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"id": "xRVImJGBZ2kt",
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"vscode": {
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"languageId": "python"
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"outputs": [],
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"source": [
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@ -150,7 +162,10 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "H6YD611WaAAG",
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"outputId": "3cf9f53c-8898-4bde-89b7-3670804c2373"
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"outputId": "3cf9f53c-8898-4bde-89b7-3670804c2373",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -255,7 +270,10 @@
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"height": 256
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},
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"id": "CTEBvy2FaRBF",
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"outputId": "b18d0d16-c88f-47bb-fe2e-2ba5871f0563"
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"outputId": "b18d0d16-c88f-47bb-fe2e-2ba5871f0563",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -307,7 +325,10 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "44u_8PR7kaoY",
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"outputId": "71916b80-0821-4cd2-c481-bbbd5dac2fd4"
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"outputId": "71916b80-0821-4cd2-c481-bbbd5dac2fd4",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -333,7 +354,10 @@
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"height": 164
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},
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"id": "DJS4ZdO5oOFn",
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"outputId": "89df7031-c58b-46ae-9fc7-4ddbf5e46ef9"
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"outputId": "89df7031-c58b-46ae-9fc7-4ddbf5e46ef9",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -363,7 +387,10 @@
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"height": 109
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},
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"id": "WXIys9NGz8bF",
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"outputId": "c0b2ede8-a041-4cac-ccf6-3e40817df8c5"
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"outputId": "c0b2ede8-a041-4cac-ccf6-3e40817df8c5",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -414,7 +441,7 @@
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"\n",
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"Autoencoders can be effectively used to remove noise from images. In order to train denoiser, we will start with noise-free images, and add artificial noise to them. Then, we will feed autoencoder with noisy images as input, and noise-free images as output.\n",
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"\n",
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"Let's see how this works for MNISТ: "
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"Let's see how this works for MNIST:"
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]
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},
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{
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@ -426,7 +453,10 @@
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"height": 109
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},
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"id": "uVprJu59lte0",
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"outputId": "3e9e924b-ec01-4ff3-c49a-3dd70db84db1"
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"outputId": "3e9e924b-ec01-4ff3-c49a-3dd70db84db1",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -461,7 +491,10 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "mlwxbuaHomTr",
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"outputId": "24c6c3be-e755-4216-815a-46561448fe7f"
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"outputId": "24c6c3be-e755-4216-815a-46561448fe7f",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -551,7 +584,10 @@
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"height": 201
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},
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"id": "7kctWkcwv3kd",
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"outputId": "00bfe5d9-882b-46d3-b8b2-8310756bc9f9"
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"outputId": "00bfe5d9-882b-46d3-b8b2-8310756bc9f9",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -618,7 +654,10 @@
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"height": 108
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},
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"id": "JGV724C9VQ7m",
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"outputId": "d63d8ad4-55bc-431b-bc95-79f994713ad3"
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"outputId": "d63d8ad4-55bc-431b-bc95-79f994713ad3",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -645,7 +684,10 @@
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"id": "VXIFG2ulYuGM"
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"id": "VXIFG2ulYuGM",
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"vscode": {
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"languageId": "python"
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}
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"outputs": [],
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"source": [
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@ -686,7 +728,10 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "tie6d1OCY2ql",
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"outputId": "0a97cea6-3fd3-41e3-c743-e332535fa779"
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"outputId": "0a97cea6-3fd3-41e3-c743-e332535fa779",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -775,7 +820,10 @@
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"height": 200
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},
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"id": "C-wLl0BiZGR4",
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"outputId": "f0104552-e056-4cbf-a238-09820839d70f"
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"outputId": "f0104552-e056-4cbf-a238-09820839d70f",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -847,7 +895,10 @@
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"id": "a8bqQpG6wuJ9"
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"id": "a8bqQpG6wuJ9",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -867,7 +918,10 @@
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"id": "y6cJ93kwQkM3"
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"id": "y6cJ93kwQkM3",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -885,7 +939,10 @@
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"id": "Bf3UqBMXQwpQ"
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"id": "Bf3UqBMXQwpQ",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -917,7 +974,10 @@
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"id": "czXAjHUJR-BE"
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"id": "czXAjHUJR-BE",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -939,7 +999,10 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "zky-60lERlG-",
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"outputId": "baed11e4-b165-42d5-d86b-1037741554c8"
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"outputId": "baed11e4-b165-42d5-d86b-1037741554c8",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -1047,7 +1110,10 @@
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"height": 256
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},
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"id": "YF8hHAZVR5x_",
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"outputId": "146edef1-676d-4592-8b9f-250ff4b98fa6"
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"outputId": "146edef1-676d-4592-8b9f-250ff4b98fa6",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -1100,7 +1166,10 @@
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"height": 433
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},
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"id": "jZ0PzYmInDi5",
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"outputId": "b114fa04-ba23-47e3-b63e-1e3eac248463"
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"outputId": "b114fa04-ba23-47e3-b63e-1e3eac248463",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -1142,7 +1211,10 @@
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"height": 303
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},
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"id": "AZQfTelyOigw",
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"outputId": "3c0fc864-e3e5-4b57-d84f-572358f2e61b"
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"outputId": "3c0fc864-e3e5-4b57-d84f-572358f2e61b",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -23,7 +23,10 @@
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"id": "vh9JiNcw80sd"
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"id": "vh9JiNcw80sd",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"id": "iOWm55Bd5DNl"
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"id": "iOWm55Bd5DNl",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -81,9 +87,9 @@
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},
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"source": [
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"A few tricks used in generator:\n",
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"* Instead of ReLU, we use **Leaky ReLU**, i.e. a ReLU which is not exactly 0 for negative $x$, but rather another linear function with very small slope.\n",
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"* Instead of ReLU, we use **Leaky ReLU**, i.e. a ReLU which is not exactly 0 for negative $x$, but rather another linear function with very small slope. This is important, because it helps gradient descent to propagate values even if we are on the negative side of ReLU (where values are 0)\n",
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"* We use Batch Normalization in order to stabilize training\n",
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"* The activation function on last layer is `tanh`, so the output is in the range [-1,1].\n",
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"* The activation function on last layer is `tanh`, so the output is in the range [-1,1]\n",
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"\n",
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"## Discriminator\n",
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"\n",
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"id": "krxyG2oh87pU"
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"id": "krxyG2oh87pU",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "TsJH1VWQ9yeB"
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"id": "TsJH1VWQ9yeB",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"id": "ZzX0kMrW-dQA"
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"id": "ZzX0kMrW-dQA",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"\n",
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"On each step of the training, we have two phases:\n",
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"\n",
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"* Training discriminator. We generate some random vectors `noise` (training happens in minibatches, so we use 100 vectors at a time), produce для этого передаём ему на вход последовательность из 50% реальных и 50% искусственных изображений (и соответствующие labels)\n",
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" * Обучаем генератор (неявно через обучение генеративно-состязательной модели с замороженными весами дискриминатора) на искусственных данных"
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"* Training discriminator:\n",
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" - We generate some random vectors `noise`. Training happens in minibatches, so we use `batch//2` vectors to produce `batch//2` generated images\n",
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" - Sample `batch//2` random images from the dataset\n",
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" - Train discriminator on 50% real and 50% generated images, providing corresponding labels (0 or 1)\n",
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" * Train the generator by using combined adversarial model, passing random vectors as input, and expecting 1's as output (which corresponds to real images)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"id": "aISx_wo4W1qJ"
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"id": "aISx_wo4W1qJ",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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@ -196,7 +217,10 @@
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"height": 673
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},
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"id": "mNdcpEa3_Pqs",
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"outputId": "75ebec09-e608-4747-8298-0cb2281d95af"
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"outputId": "75ebec09-e608-4747-8298-0cb2281d95af",
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [
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{
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@ -357,7 +381,9 @@
|
|||
"source": [
|
||||
"## DCGAN\n",
|
||||
"\n",
|
||||
"**Deep Convolutional GAN** is pretty obvious idea of using convolutional layers for generator and discriminator. The main difference here is using `Conv2DTranspose` layer in the generator."
|
||||
"In the previous example, we have used dense networks for both generator and discriminator, but we know than CNNs provide better performance when dealing with images. **Deep Convolutional GAN** is similar to the architecture above, but it uses convolutional layers for generator and discriminator. \n",
|
||||
"\n",
|
||||
"The main difficulty here is to build an architecture for denerator, because it has to do an inverse task comaring to traditional CNN - it has to generate image from feature vector. In a way, this is similar to decoder part of autoencoders.That's why we will be using `Conv2DTranspose` layers in the generator."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -368,7 +394,10 @@
|
|||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "fkD2WufskBdm",
|
||||
"outputId": "b4a22c66-a493-4abd-dc79-55c9fd7be137"
|
||||
"outputId": "b4a22c66-a493-4abd-dc79-55c9fd7be137",
|
||||
"vscode": {
|
||||
"languageId": "python"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -393,7 +422,10 @@
|
|||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "tAk-hsF0TzEv",
|
||||
"outputId": "68bba515-be2d-44f5-f079-b461456ed082"
|
||||
"outputId": "68bba515-be2d-44f5-f079-b461456ed082",
|
||||
"vscode": {
|
||||
"languageId": "python"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -460,7 +492,10 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"id": "t9FZHScTUsCd"
|
||||
"id": "t9FZHScTUsCd",
|
||||
"vscode": {
|
||||
"languageId": "python"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -492,7 +527,10 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"id": "2Jz166pQWRba"
|
||||
"id": "2Jz166pQWRba",
|
||||
"vscode": {
|
||||
"languageId": "python"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -512,7 +550,10 @@
|
|||
"height": 1000
|
||||
},
|
||||
"id": "kohaucz-WTue",
|
||||
"outputId": "edf64874-5ec9-4c10-cb36-95e2bb5111cf"
|
||||
"outputId": "edf64874-5ec9-4c10-cb36-95e2bb5111cf",
|
||||
"vscode": {
|
||||
"languageId": "python"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -23,7 +23,7 @@ CNN discriminator consists of the following layers: several convolutions+pooling
|
|||
|
||||
### Generator
|
||||
|
||||
Generator is slightly more tricky. You can consider it to be a reversed discriminator - starting from latent vector (in place of a feature vector), it has fully-connected layer to convert it into required size/shape, followed by deconvolutions+upscaling.
|
||||
Generator is slightly more tricky. You can consider it to be a reversed discriminator - starting from latent vector (in place of a feature vector), it has fully-connected layer to convert it into required size/shape, followed by deconvolutions+upscaling. This is similar to *decoder* part of [autoencoder](../09-Autoencoders/README.md).
|
||||
|
||||
> Because convolution layer is implemented as a linear filter traversing the image, deconvolution is essentially similar to convolution, and can be implemented using the same layer logic.
|
||||
|
||||
|
|
@ -33,7 +33,7 @@ Generator is slightly more tricky. You can consider it to be a reversed discrimi
|
|||
|
||||
### Training the GAN
|
||||
|
||||
GANs are called **adversarial** because there is a constant competition between generator and discriminator. During this cometition, both generator and discriminator improve, thus the network learns to produce better and better pictures.
|
||||
GANs are called **adversarial** because there is a constant competition between generator and discriminator. During this competition, both generator and discriminator improve, thus the network learns to produce better and better pictures.
|
||||
|
||||
The training happens in two stages:
|
||||
|
||||
|
|
@ -43,8 +43,8 @@ The training happens in two stages:
|
|||
During this process, both generator and discriminator losses are not going down significantly. In the ideal situation, they should oscillate, corresponding to both networks improving their performance.
|
||||
|
||||
## Go to Notebook
|
||||
* [GAN Notebook in TensorFlow/Keras](GANs.ipynb)
|
||||
|
||||
* [GAN Notebook in TensorFlow/Keras](GANTF.ipynb)
|
||||
* [GAN Notebook in PyTorch](GANPyTorch.ipynb)
|
||||
### Problems with GAN training
|
||||
|
||||
GANs are known to be especially difficult to train. Here are a few problems:
|
||||
|
|
|
|||
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|
@ -63,7 +63,7 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking the
|
|||
<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
|
||||
<tr><td>8</td><td>Pre-trained Networks and Transfer Learning<br/>Training Tricks</td><td><a href="4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">TensorFlow</a><br/><a href="4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td><a href="4-ComputerVision/08-TransferLearning/lab/README.md">Lab</a></td></tr>
|
||||
<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td><a href="4-ComputerVision/09-Autoencoders/AutoEncodersPytorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">TensorFlow</a></td><td></td></tr>
|
||||
<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="4-ComputerVision/10-GANs/README.md">Text</a></td><td><a href="4-ComputerVision/10-GANs/GANPytorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/10-GANs/GANs.ipynb">TensorFlow</a></td><td></td></tr>
|
||||
<tr><td>10</td><td>Generative Adversarial Networks</td><td><a href="4-ComputerVision/10-GANs/README.md">Text</a></td><td><a href="4-ComputerVision/10-GANs/GANPyTorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/10-GANs/GANTF.ipynb">TensorFlow</a></td><td></td></tr>
|
||||
<tr><td>11</td><td>Object Detection</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
|
||||
<tr><td>12</td><td>Semantic Segmentation. U-Net</td><td><a href="4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
|
||||
<tr><td>V</td><td colspan="2"><b><a href="5-NLP/README.md">Natural Language Processing</a></b></td>
|
||||
|
|
|
|||
Loading…
Reference in New Issue