diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index f3c7458..9446af9 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -21,6 +21,8 @@ jobs: folder: Chapter02 - name: chap3 folder: Chapter03 + - name: chap-nn + folder: ChapterNN - name: chap5 folder: Chapter05 - name: chap6 diff --git a/ChapterNN/01_ImageClassification_TensorFlow.ipynb b/ChapterNN/01_ImageClassification_TensorFlow.ipynb new file mode 100644 index 0000000..a81dd61 --- /dev/null +++ b/ChapterNN/01_ImageClassification_TensorFlow.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c4e9e09-b0c6-4671-86a0-ca6bc73056b6", + "metadata": {}, + "source": [ + "# Image Classification with TensorFlow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "012237ce-0396-4c88-8b13-994c7a830421", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.datasets import fashion_mnist\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "6080305f-eaac-49d2-a4cb-658a907186ac", + "metadata": {}, + "source": [ + "### Load and re-scale input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1135c9e8-1765-48cc-beb8-25fd6ff363d4", + "metadata": {}, + "outputs": [], + "source": [ + "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee2989f4-9b32-48f6-b669-8255aa9e9c79", + "metadata": {}, + "outputs": [], + "source": [ + "x_train = x_train.astype('float32') / 255.\n", + "x_test = x_test.astype('float32') / 255.\n", + "\n", + "print (x_train.shape)\n", + "print (x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0525097f-4b57-4e9c-b850-966540589a30", + "metadata": {}, + "outputs": [], + "source": [ + "classes = {\n", + " 0: \"T-shirt\",\n", + " 1: \"Trouser\",\n", + " 2: \"Pullover\",\n", + " 3: \"Dress\",\n", + " 4: \"Coat\",\n", + " 5: \"Sandal\",\n", + " 6: \"Shirt\",\n", + " 7: \"Sneaker\",\n", + " 8: \"Bag\",\n", + " 9: \"Ankle boot\", \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0e2bc95-ee33-4024-99d2-4ba7cb4fd0c6", + "metadata": {}, + "outputs": [], + "source": [ + "n = 6\n", + "plt.figure(figsize=(20, 4))\n", + "for i in range(n):\n", + " # display original\n", + " ax = plt.subplot(1, n, i + 1)\n", + " plt.imshow(x_test[i])\n", + " plt.title(classes[y_test[i]])\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c7ace701-0cd8-4173-982c-8682a860dd26", + "metadata": {}, + "source": [ + "### Build model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2d549c8-a410-4caa-95a4-b0bb20a05236", + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Flatten(input_shape=(28, 28)),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dropout(0.2),\n", + " tf.keras.layers.Dense(10)\n", + "])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f20ef704-3435-4f12-b41b-db42fcfb3b43", + "metadata": {}, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "9af979aa-9ccb-4b92-b0fd-ef39fcf6f317", + "metadata": {}, + "source": [ + "### Train the network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53f02c48-3d8f-4e41-a9d4-703016a0dc19", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", + "optimizer = tf.keras.optimizers.Adam()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6427e500-41d2-43f1-b235-622d1d59572e", + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer=optimizer,\n", + " loss=loss_fn,\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef1fea96-97cc-4c84-bb0c-78417a571575", + "metadata": {}, + "outputs": [], + "source": [ + "model.fit(\n", + " x_train, \n", + " y_train, \n", + " validation_data=(x_test, y_test), \n", + " epochs=20, \n", + " batch_size=128,\n", + " shuffle=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a8c64c99-cbab-4503-8a1d-84f80c6f2af7", + "metadata": {}, + "source": [ + "### More advanced model" + ] + }, + { + "cell_type": "markdown", + "id": "c4649c36-1157-438b-bff0-01e980aa7da3", + "metadata": {}, + "source": [ + "For a slightly more complex and deeper network, try to train the model below" + ] + }, + { + "cell_type": "raw", + "id": "e9af3bfc-7b19-4441-9dc4-46df1b3739cc", + "metadata": {}, + "source": [ + "input_img = tf.keras.layers.Input(shape=(28, 28, 1))\n", + "\n", + "x = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n", + "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n", + "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n", + "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n", + "x = tf.keras.layers.Flatten(input_shape=(26, 26))(x)\n", + "x = tf.keras.layers.Dense(128, activation='relu')(x)\n", + "x = tf.keras.layers.Dropout(0.2)(x)\n", + "x = tf.keras.layers.Dense(10)(x)\n", + "\n", + "model = tf.keras.Model(input_img, x)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chap-nn", + "language": "python", + "name": "chap-nn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ChapterNN/02_ImageClassification_Pytorch.ipynb b/ChapterNN/02_ImageClassification_Pytorch.ipynb new file mode 100644 index 0000000..0c3b17a --- /dev/null +++ b/ChapterNN/02_ImageClassification_Pytorch.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cfe91160-02ad-48cc-810b-1031f21397ff", + "metadata": {}, + "source": [ + "# Image Classification with PyTorch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60ea01e6-2184-4d88-a9e3-c05f70953f0a", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torchvision import datasets, transforms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83163df1-5732-459b-948a-b88d07d692cf", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "4067288c-39ac-4f51-a5b3-f3a043f0c3a9", + "metadata": {}, + "source": [ + "### Load and re-scale input data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55daa661-47bb-4499-b8a4-dce7f5cadc22", + "metadata": {}, + "outputs": [], + "source": [ + "transformer=transforms.Compose([\n", + " transforms.ToTensor(),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cf93aa8-eaa0-4143-84fc-2c617d402bd2", + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = datasets.FashionMNIST('./data', train=True, download=True, transform=transformer)\n", + "test_dataset = datasets.FashionMNIST('./data', train=False, transform=transformer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa2574d6-659d-4ef5-92a5-ffbe7036dc5b", + "metadata": {}, + "outputs": [], + "source": [ + "trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)\n", + "testloader = torch.utils.data.DataLoader(test_dataset, batch_size=test_dataset.data.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be30236f-5707-4517-9b6e-3eeb0601500f", + "metadata": {}, + "outputs": [], + "source": [ + "classes = {v: k for k, v in train_dataset.class_to_idx.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7b7f58e-291e-4084-933e-4e3357fe42fb", + "metadata": {}, + "outputs": [], + "source": [ + "n = 6\n", + "plt.figure(figsize=(20, 4))\n", + "for i in range(n):\n", + " # display original\n", + " ax = plt.subplot(1, n, i + 1)\n", + " plt.imshow(test_dataset[i][0][0])\n", + " plt.title(classes[test_dataset[i][1]])\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ef148e96-02d9-45d2-825d-01951a45f047", + "metadata": {}, + "source": [ + "### Build model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60570c8d-b61d-4558-8d3d-e831279e898f", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3cbed0a8-9d1c-49b1-9e41-59d15b2e134d", + "metadata": {}, + "outputs": [], + "source": [ + "class Model(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.flatten = nn.Flatten()\n", + " self.fc1 = nn.Linear(28*28, 128)\n", + " self.dropout = nn.Dropout(0.2)\n", + " self.fc2 = nn.Linear(128,10)\n", + "\n", + " def forward(self, x):\n", + " x = self.flatten(x)\n", + " x = F.relu(self.fc1(x))\n", + " x = self.dropout(x)\n", + " x = self.fc2(x)\n", + " return F.log_softmax(x, dim=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fa2d6f1-9db7-43d7-b00c-b44e5eba8a55", + "metadata": {}, + "outputs": [], + "source": [ + "model = Model()" + ] + }, + { + "cell_type": "markdown", + "id": "6bc8880f-47a3-4b0f-8fee-be0f4b8a0b85", + "metadata": {}, + "source": [ + "### Train the network " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0c04fcb-4769-4022-ab84-89ee2412febc", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.optim as optim\n", + "\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "679b9446-9820-416a-8bd2-96b8712c33b6", + "metadata": {}, + "outputs": [], + "source": [ + "from torchmetrics.classification import MulticlassAccuracy\n", + "\n", + "accuracy = MulticlassAccuracy(num_classes=len(train_dataset.classes))\n", + "\n", + "for epoch in range(20): # loop over the dataset multiple times\n", + "\n", + " running_loss = 0.0\n", + " for i, data in enumerate(trainloader, 0):\n", + " # get the inputs; data is a list of [inputs, labels]\n", + " inputs, labels = data\n", + "\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + "\n", + " # forward + backward + optimize\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # print statistics\n", + " running_loss += loss.item()\n", + " if i % 200 == 199: # print every 2000 mini-batches\n", + " print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}') \n", + " running_loss = 0.0\n", + "\n", + " # Evaluate accuracy\n", + " for inputs, labels in testloader:\n", + " preds = model(inputs)\n", + " print(f\"Accuracy on validation set: {float(accuracy(preds, labels))}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e53fcbf-19c3-46f2-b4bb-90dc307c24e4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chap-nn", + "language": "python", + "name": "chap-nn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ChapterNN/03_Autoencoders.ipynb b/ChapterNN/03_Autoencoders.ipynb new file mode 100644 index 0000000..af8b3e0 --- /dev/null +++ b/ChapterNN/03_Autoencoders.ipynb @@ -0,0 +1,627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AutoEncoder " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following we will show you how to create, train and use a simple autoencoder. We will then show you how to make an auto-encoder more robust against noise. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.datasets import fashion_mnist" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x_train = x_train.astype('float32') / 255.\n", + "x_test = x_test.astype('float32') / 255.\n", + "\n", + "print (x_train.shape)\n", + "print (x_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classes = {\n", + " 0:\"T-shirt/top\",\n", + " 1: \"Trouser\",\n", + " 2: \"Pullover\",\n", + " 3: \"Dress\",\n", + " 4: \"Coat\",\n", + " 5: \"Sandal\",\n", + " 6: \"Shirt\",\n", + " 7: \"Sneaker\",\n", + " 8: \"Bag\",\n", + " 9: \"Ankle boot\", \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n = 6\n", + "plt.figure(figsize=(20, 4))\n", + "for i in range(n):\n", + " # display original\n", + " ax = plt.subplot(1, n, i + 1)\n", + " plt.imshow(x_test[i])\n", + " plt.title(classes[y_test[i]])\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()\n", + "# plt.savefig(\"TrainingSet.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Autoencoder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import Flatten, Conv2D, Dropout, MaxPooling2D, UpSampling2D, Input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_img = Input(shape=(28, 28, 1))\n", + "\n", + "x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n", + "x = MaxPooling2D((2, 2), padding='same')(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = MaxPooling2D((2, 2), padding='same')(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "encoded = MaxPooling2D((2, 2), padding='same')(x)\n", + "\n", + "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n", + "\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n", + "x = UpSampling2D((2, 2))(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = UpSampling2D((2, 2))(x)\n", + "x = Conv2D(16, (3, 3), activation='relu')(x)\n", + "x = UpSampling2D((2, 2))(x)\n", + "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n", + "\n", + "autoencoder = Model(input_img, decoded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Model(input_img, encoded).summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.callbacks import TensorBoard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.fit(x_train, x_train,\n", + " epochs=50,\n", + " batch_size=128,\n", + " shuffle=True,\n", + " validation_data=(x_test, x_test),\n", + " callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.save(\"./data/Batch50.p\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import load_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder_first = load_model(\"./data/Batch50.p\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decoded_imgs = autoencoder_first.predict(x_test)\n", + "\n", + "n = 6\n", + "plt.figure(figsize=(20, 7))\n", + "for i in range(1, n + 1):\n", + " # Display original\n", + " ax = plt.subplot(2, n, i)\n", + " plt.imshow(x_test[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " # Display reconstruction\n", + " ax = plt.subplot(2, n, i + n)\n", + " plt.imshow(decoded_imgs[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.optimizers import Adam" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.compile(optimizer=Adam(learning_rate=0.0005), loss='binary_crossentropy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.fit(x_train, x_train,\n", + " epochs=50,\n", + " batch_size=128,\n", + " shuffle=True,\n", + " validation_data=(x_test, x_test),\n", + " callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.save(\"./data/Batch100.p\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decoded_imgs = autoencoder.predict(x_test)\n", + "\n", + "n = 10\n", + "plt.figure(figsize=(20, 4))\n", + "for i in range(1, n + 1):\n", + " # Display original\n", + " ax = plt.subplot(2, n, i)\n", + " plt.imshow(x_test[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " # Display reconstruction\n", + " ax = plt.subplot(2, n, i + n)\n", + " plt.imshow(decoded_imgs[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the trained layers in order to get the core representation in the middle layer of the autoencoder, and we represent them with the TSNE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = Model(input_img, Flatten()(encoded)).predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tsne = TSNE(n_components=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "emb2d = tsne.fit_transform(embeddings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x,y = np.squeeze(emb2d[:, 0]), np.squeeze(emb2d[:, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.cm import tab10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summary = pd.DataFrame({\"x\": x, \"y\": y, \"target\": y_test, \"size\": 10})\n", + "\n", + "plt.figure(figsize=(10,8))\n", + "\n", + "for key, sel in summary.groupby(\"target\"):\n", + " plt.scatter(sel[\"x\"], sel[\"y\"], s=10, color=tab10.colors[key], label=classes[key])\n", + " \n", + "plt.legend()\n", + "plt.axis(\"off\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Denoising" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Introducing noise in order to train more robust auto-encoders" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.layers import GaussianNoise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_img = Input(shape=(28, 28, 1))\n", + "\n", + "noisy_input = GaussianNoise(0.1)(input_img)\n", + "\n", + "x = Conv2D(16, (3, 3), activation='relu', padding='same')(noisy_input)\n", + "x = MaxPooling2D((2, 2), padding='same')(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = MaxPooling2D((2, 2), padding='same')(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "encoded = MaxPooling2D((2, 2), padding='same')(x)\n", + "\n", + "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n", + "\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n", + "x = UpSampling2D((2, 2))(x)\n", + "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n", + "x = UpSampling2D((2, 2))(x)\n", + "x = Conv2D(16, (3, 3), activation='relu')(x)\n", + "x = UpSampling2D((2, 2))(x)\n", + "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n", + "\n", + "noisy_autoencoder = Model(input_img, decoded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noisy_autoencoder.compile(optimizer='adam', loss='binary_crossentropy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noisy_autoencoder.fit(x_train, x_train,\n", + " epochs=50,\n", + " batch_size=128,\n", + " shuffle=True,\n", + " validation_data=(x_test, x_test),\n", + " callbacks=[TensorBoard(log_dir='/tmp/noisy_autoencoder')])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "autoencoder.save(\"./data/DenoisingAutoencoder.p\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noise_factor = 0.1\n", + "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n", + "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n", + "\n", + "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n", + "x_test_noisy = np.clip(x_test_noisy, 0., 1.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decoded_imgs = autoencoder.predict(x_test_noisy)\n", + "\n", + "decoded_imgs_denoised = noisy_autoencoder.predict(x_test_noisy)\n", + "\n", + "n = 6\n", + "plt.figure(figsize=(20, 10))\n", + "for i in range(1, n + 1):\n", + " # Display original\n", + " ax = plt.subplot(3, n, i)\n", + " plt.imshow(x_test_noisy[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " if i==0:\n", + " plt.ylabel(\"Original\")\n", + " else:\n", + " ax.get_yaxis().set_visible(False)\n", + " \n", + " # Display reconstruction\n", + " ax = plt.subplot(3, n, i + n)\n", + " plt.imshow(decoded_imgs[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " if i==0:\n", + " plt.ylabel(\"Vanilla Autoencoder\")\n", + " else:\n", + " ax.get_yaxis().set_visible(False)\n", + " \n", + " ax = plt.subplot(3, n, i + 2*n)\n", + " plt.imshow(decoded_imgs_denoised[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " if i==0:\n", + " plt.ylabel(\"Denoising Autoencoder\")\n", + " else:\n", + " ax.get_yaxis().set_visible(False)\n", + " \n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "decoded_imgs = noisy_autoencoder.predict(x_test_noisy)\n", + "\n", + "n = 10\n", + "plt.figure(figsize=(20, 4))\n", + "for i in range(1, n + 1):\n", + " # Display original\n", + " ax = plt.subplot(2, n, i)\n", + " plt.imshow(x_test_noisy[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "\n", + " # Display reconstruction\n", + " ax = plt.subplot(2, n, i + n)\n", + " plt.imshow(decoded_imgs[i].reshape(28, 28))\n", + " plt.gray()\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chap-nn", + "language": "python", + "name": "chap-nn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/ChapterNN/04_GraphAutoEncoder_PyGeometric.ipynb b/ChapterNN/04_GraphAutoEncoder_PyGeometric.ipynb new file mode 100644 index 0000000..5de12fb --- /dev/null +++ b/ChapterNN/04_GraphAutoEncoder_PyGeometric.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f5d647f0-6d7b-4e4b-b5cd-c674dba76a22", + "metadata": {}, + "source": [ + "# Graph Auto Encoder with PyG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9403ccd6-3353-4edd-9ba2-beaab617afa3", + "metadata": {}, + "outputs": [], + "source": [ + "import argparse\n", + "import os\n", + "import time\n", + "\n", + "import torch\n", + "\n", + "import torch_geometric.transforms as T\n", + "from torch_geometric.datasets import Planetoid\n", + "\n", + "from torch_geometric.nn import GAE, GCNConv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c80d137d-313f-4b8b-8473-c787a59c97ba", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device('cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2694e0b8-7ad2-4ab0-bd66-ec9e9615c9cc", + "metadata": {}, + "outputs": [], + "source": [ + "DATASET_NAME=\"Cora\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "409d5939-8d16-4db9-a17f-c5cab6a2d4aa", + "metadata": {}, + "outputs": [], + "source": [ + "transform = T.Compose([\n", + " T.NormalizeFeatures(),\n", + " T.RandomLinkSplit(num_val=0., num_test=0.1, is_undirected=True,\n", + " split_labels=True, add_negative_train_samples=False),\n", + "])\n", + "# path = os.path.join(\"/home/deusebio/Personal/graph_machine_learning/data\", 'data')\n", + "path = os.path.join(os.getcwd(), 'data')\n", + "dataset = Planetoid(path, DATASET_NAME, transform=transform)\n", + "train_data, val_data, test_data = dataset[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe46bed-054c-4f52-a4da-ed3728a3f41c", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Train edges (positive): {train_data.pos_edge_label_index.shape[1]}\")\n", + "print(f\"Test edges (positive): {test_data.pos_edge_label_index.shape[1]}\")\n", + "print(f\"Test edges (negative): {test_data.neg_edge_label_index.shape[1]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dedf968d-6e87-451a-aaa2-972ce21f4aca", + "metadata": {}, + "outputs": [], + "source": [ + "class GCNEncoder(torch.nn.Module):\n", + " def __init__(self, num_node_features, num_embedding):\n", + " super().__init__()\n", + " self.conv1 = GCNConv(num_node_features, 2 * num_embedding)\n", + " self.conv2 = GCNConv(2 * num_embedding, num_embedding)\n", + "\n", + " def forward(self, x, edge_index):\n", + " x = self.conv1(x, edge_index).relu()\n", + " return self.conv2(x, edge_index)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40de5839-fdf8-4935-b2d8-f46adb5ab4eb", + "metadata": {}, + "outputs": [], + "source": [ + "n_features = dataset.num_features\n", + "n_embeddings = 20" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9240419-00b2-4f01-885a-169814989d21", + "metadata": {}, + "outputs": [], + "source": [ + "model = GAE(GCNEncoder(n_features, n_embeddings))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "baa87287-e068-463f-9cb5-be032a2273ac", + "metadata": {}, + "outputs": [], + "source": [ + "model = model.to(device)\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da9b26c1-c070-4e98-a0a1-26783b0ed7d7", + "metadata": {}, + "outputs": [], + "source": [ + "for epoch in range(20): # loop over the dataset multiple times\n", + "\n", + " model.train()\n", + "\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + "\n", + " z = model.encode(train_data.x, train_data.edge_index)\n", + " loss = model.recon_loss(z, train_data.pos_edge_label_index)\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " # Test/Evaluate\n", + " model.eval()\n", + " z = model.encode(test_data.x, test_data.edge_index)\n", + " auc, ap = model.test(z, test_data.pos_edge_label_index, test_data.neg_edge_label_index)\n", + " \n", + " print(f\"Performance on validation set => AUC: {auc} AP: {ap}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43533588-03f7-45c9-8d97-1e618148a9c5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "chap-nn", + "language": "python", + "name": "chap-nn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ChapterNN/05_GraphAutoEncoder_StellarGraph.ipynb b/ChapterNN/05_GraphAutoEncoder_StellarGraph.ipynb new file mode 100644 index 0000000..887b9a8 --- /dev/null +++ b/ChapterNN/05_GraphAutoEncoder_StellarGraph.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "393d1f8c-162d-43c6-9d3a-3e795bf6467a", + "metadata": {}, + "source": [ + "# Graph AutoEncoder with StellarGraph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65a6d0fb-bb0f-4af0-8ba9-6a0c902cec49", + "metadata": {}, + "outputs": [], + "source": [ + "from stellargraph.data import EdgeSplitter\n", + "from stellargraph.mapper import FullBatchLinkGenerator\n", + "from stellargraph.layer import GCN, LinkEmbedding\n", + "\n", + "\n", + "from tensorflow import keras\n", + "from stellargraph import datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d27bd90-c522-48e2-9929-7417b3ce904b", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = datasets.Cora()\n", + "G, _ = dataset.load()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e59277b-12b7-4e8f-9cdc-eaf4c96d1771", + "metadata": {}, + "outputs": [], + "source": [ + "print(G.info())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab4de94-e416-49b2-8e02-5217d5f410e5", + "metadata": {}, + "outputs": [], + "source": [ + "edge_splitter_test = EdgeSplitter(G)\n", + "\n", + "G_test, edge_ids_test, edge_labels_test = edge_splitter_test.train_test_split(\n", + " p=0.1, method=\"global\", keep_connected=True\n", + ")\n", + "\n", + "edge_splitter_train = EdgeSplitter(G_test)\n", + "\n", + "G_train, edge_ids_train, edge_labels_train = edge_splitter_test.train_test_split(\n", + " p=0.1, method=\"global\", keep_connected=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "261e8cdd-455d-4607-a95c-c26ec6aaf109", + "metadata": {}, + "outputs": [], + "source": [ + "train_gen = FullBatchLinkGenerator(G, method=\"gcn\")\n", + "train_flow = train_gen.flow(edge_ids_train, edge_labels_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5820186d-1728-494b-9e2c-6a906d7ff3f5", + "metadata": {}, + "outputs": [], + "source": [ + "test_gen = FullBatchLinkGenerator(G, method=\"gcn\")\n", + "test_flow = train_gen.flow(edge_ids_test, edge_labels_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7897bc20-a2eb-4887-8bde-3ca5756cd62d", + "metadata": {}, + "outputs": [], + "source": [ + "gcn = GCN(\n", + " layer_sizes=[16, 16], activations=[\"relu\", \"relu\"], generator=train_gen, dropout=0.3\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27562a4-9f93-4c23-87b1-ad3a0f46c19a", + "metadata": {}, + "outputs": [], + "source": [ + "x_inp, x_out = gcn.in_out_tensors()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be2d54a1-9561-410a-9c8f-54b805450dc3", + "metadata": {}, + "outputs": [], + "source": [ + "prediction = LinkEmbedding(activation=\"relu\", method=\"ip\")(x_out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38cd0093-ef71-4110-8f59-43e510b86edc", + "metadata": {}, + "outputs": [], + "source": [ + "prediction = keras.layers.Reshape((-1,))(prediction)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33ccc882-df0c-40ad-a401-725b3a64aac3", + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.Model(inputs=x_inp, outputs=prediction)\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(lr=0.01),\n", + " loss=keras.losses.binary_crossentropy,\n", + " metrics=[\"binary_accuracy\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dbc5d61-4e11-4ec8-bb06-8b206beb698d", + "metadata": {}, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5760e12-f39a-4ce8-8c9d-9ba8ae61d4ab", + "metadata": {}, + "outputs": [], + "source": [ + "history = model.fit(train_flow, validation_data=test_flow, epochs=50)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d6f0caf-529a-4609-ade3-61715426e4b3", + "metadata": {}, + "outputs": [], + "source": [ + "from stellargraph.utils import plot_history\n", + "\n", + "plot_history(history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e2130e3-6710-4d60-afa2-c0c7254921a5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + + "metadata": { + "kernelspec": { + "display_name": "chap-nn", + "language": "python", + "name": "chap-nn" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 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+matplotlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9" +mpmath==1.3.0 ; python_version >= "3.8" and python_version < "3.9" +multidict==6.1.0 ; python_version >= "3.8" and python_version < "3.9" +nest-asyncio==1.6.0 ; python_version >= "3.8" and python_version < "3.9" +networkx==3.1 ; python_version >= "3.8" and python_version < "3.9" +numpy==1.24.4 ; python_version >= "3.8" and python_version < "3.9" +nvidia-cublas-cu12==12.1.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-cupti-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-nvrtc-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cuda-runtime-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cudnn-cu12==8.9.2.26 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cufft-cu12==11.0.2.54 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-curand-cu12==10.3.2.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cusolver-cu12==11.4.5.107 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-cusparse-cu12==12.1.0.106 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nccl-cu12==2.18.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nvjitlink-cu12==12.6.77 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +nvidia-nvtx-cu12==12.1.105 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +oauthlib==3.2.2 ; python_version >= "3.8" and python_version < "3.9" +opt-einsum==3.4.0 ; python_version >= "3.8" and python_version < "3.9" +packaging==24.1 ; python_version >= "3.8" and python_version < "3.9" +pandas==2.0.3 ; python_version >= "3.8" and python_version < "3.9" +parso==0.8.4 ; python_version >= "3.8" and python_version < "3.9" +pexpect==4.9.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32" +pickleshare==0.7.5 ; python_version >= "3.8" and python_version < "3.9" +pillow==10.4.0 ; python_version >= "3.8" and python_version < "3.9" +platformdirs==4.3.6 ; python_version >= "3.8" and python_version < "3.9" +prompt-toolkit==3.0.48 ; python_version >= "3.8" and python_version < "3.9" +protobuf==3.20.3 ; python_version >= "3.8" and python_version < "3.9" +psutil==6.0.0 ; python_version >= "3.8" and python_version < "3.9" +ptyprocess==0.7.0 ; python_version >= "3.8" and python_version < "3.9" and sys_platform != "win32" +pure-eval==0.2.3 ; python_version >= "3.8" and python_version < "3.9" +pyasn1-modules==0.4.1 ; python_version >= "3.8" and python_version < "3.9" +pyasn1==0.6.1 ; python_version >= "3.8" and python_version < "3.9" +pycparser==2.22 ; python_version >= "3.8" and python_version < "3.9" and implementation_name == "pypy" +pygments==2.18.0 ; python_version >= "3.8" and python_version < "3.9" +pyparsing==3.1.4 ; python_version >= "3.8" and python_version < "3.9" +python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "3.9" +pytz==2024.2 ; python_version >= "3.8" and python_version < "3.9" +pywin32==306 ; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.8" and python_version < "3.9" +pyzmq==26.2.0 ; python_version >= "3.8" and python_version < "3.9" +requests-oauthlib==2.0.0 ; python_version >= "3.8" and python_version < "3.9" +requests==2.32.3 ; python_version >= "3.8" and python_version < "3.9" +rsa==4.9 ; python_version >= "3.8" and python_version < "3.9" +scikit-learn==1.3.2 ; python_version >= "3.8" and python_version < "3.9" +scipy==1.10.1 ; python_version >= "3.8" and python_version < "3.9" +setuptools==75.1.0 ; python_version >= "3.8" and python_version < "3.9" +six==1.16.0 ; python_version >= "3.8" and python_version < "3.9" +smart-open==7.0.4 ; python_version >= "3.8" and python_version < "3.9" +stack-data==0.6.3 ; python_version >= "3.8" and python_version < "3.9" +stellargraph==1.2.1 ; python_version >= "3.8" and python_version < "3.9" +sympy==1.13.3 ; python_version >= "3.8" and python_version < "3.9" +tensorboard-data-server==0.7.2 ; python_version >= "3.8" and python_version < "3.9" +tensorboard==2.14.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow-estimator==2.7.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow-io-gcs-filesystem==0.21.0 ; python_version >= "3.8" and python_version < "3.9" +tensorflow==2.7.2 ; python_version >= "3.8" and python_version < "3.9" +termcolor==2.4.0 ; python_version >= "3.8" and python_version < "3.9" +threadpoolctl==3.5.0 ; python_version >= "3.8" and python_version < "3.9" +torch-geometric==2.6.1 ; python_version >= "3.8" and python_version < "3.9" +torch==2.1.2 ; python_version >= "3.8" and python_version < "3.9" +torchmetrics==1.4.2 ; python_version >= "3.8" and python_version < "3.9" +torchvision==0.16.2 ; python_version >= "3.8" and python_version < "3.9" +tornado==6.4.1 ; python_version >= "3.8" and python_version < "3.9" +tqdm==4.66.5 ; python_version >= "3.8" and python_version < "3.9" +traitlets==5.14.3 ; python_version >= "3.8" and python_version < "3.9" +triton==2.1.0 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.8" and python_version < "3.9" +typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "3.9" +tzdata==2024.2 ; python_version >= "3.8" and python_version < "3.9" +urllib3==2.2.3 ; python_version >= "3.8" and python_version < "3.9" +wcwidth==0.2.13 ; python_version >= "3.8" and python_version < "3.9" +werkzeug==3.0.4 ; python_version >= "3.8" and python_version < "3.9" +wheel==0.44.0 ; python_version >= "3.8" and python_version < "3.9" +wrapt==1.16.0 ; python_version >= "3.8" and python_version < "3.9" +yarl==1.13.1 ; python_version >= "3.8" and python_version < "3.9" +zipp==3.20.2 ; python_version >= "3.8" and python_version < "3.9" diff --git a/docker/Dockerfile b/docker/Dockerfile index c39533c..d4e495a 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,7 +1,7 @@ FROM jupyter/scipy-notebook as base ARG user=euler -ARG branch=chap6 +ARG branch=main USER root @@ -49,6 +49,12 @@ RUN conda create -n chap5 python=3.8 RUN conda run -n chap5 pip install -r Chapter05/requirements.txt RUN conda run -n chap5 python -m ipykernel install --name chap5 --user +FROM base as chap-nn +RUN ls -d -1 */ | grep -v -e ChapterNN | xargs rm -rf +RUN conda create -n chap-nn python=3.8 +RUN conda run -n chap-nn pip install -r ChapterNN/requirements.txt +RUN conda run -n chap-nn python -m ipykernel install --name chap-nn --user + FROM base as chap6 RUN ls -d -1 */ | grep -v -e Chapter06 | xargs rm -rf RUN conda create -n chap6 python=3.8