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lavanyashuklalavanyashukla
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notebooks for oreilly class
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‎examples/keras-cifar/cifar-cnn.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import wandb\n",
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"\n",
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"# Set Hyper-parameters\n",
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"wandb.init()\n",
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"config = wandb.config\n",
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"config.batch_size = 128\n",
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"config.epochs = 10\n",
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"config.learn_rate = 0.001\n",
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"config.dropout = 0.3\n",
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"config.dense_layer_nodes = 128\n",
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"\n",
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"# Load data\n",
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"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
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" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"num_classes = len(class_names)\n",
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"\n",
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"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
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"X_train = X_train.astype('float32') / 255.\n",
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"X_test = X_test.astype('float32') / 255.\n",
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"\n",
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"# Convert class vectors to binary class matrices.\n",
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"y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = tf.keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"# Define model\n",
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"model = tf.keras.models.Sequential()\n",
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"model.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same',\n",
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" input_shape=X_train.shape[1:], activation='relu'))\n",
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"model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
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"model.add(tf.keras.layers.Dropout(config.dropout))\n",
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"\n",
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"model.add(tf.keras.layers.Flatten())\n",
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"model.add(tf.keras.layers.Dense(config.dense_layer_nodes, activation='relu'))\n",
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"model.add(tf.keras.layers.Dropout(config.dropout))\n",
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"model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer=tf.keras.optimizers.Adam(config.learn_rate),\n",
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" metrics=['accuracy'])\n",
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"# log the number of total parameters\n",
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"config.total_params = model.count_params()\n",
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"print(\"Total params: \", config.total_params)\n",
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"\n",
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"model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),\n",
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" callbacks=[wandb.keras.WandbCallback(data_type=\"image\", labels=class_names, save_model=False)])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/keras-cifar/cifar-gen.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.callbacks import TensorBoard\n",
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"from tensorflow.keras.datasets import cifar10\n",
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"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
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"\n",
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"import numpy as np\n",
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"import os\n",
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"import wandb\n",
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"from wandb.keras import WandbCallback\n",
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"import tensorflow as tf\n",
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"\n",
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"run = wandb.init()\n",
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"config = run.config\n",
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"config.dropout = 0.25\n",
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"config.dense_layer_nodes = 100\n",
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"config.learn_rate = 0.08\n",
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"config.batch_size = 128\n",
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"config.epochs = 10\n",
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"\n",
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"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
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" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"num_classes = len(class_names)\n",
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"\n",
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"(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n",
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"\n",
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"# Convert class vectors to binary class matrices.\n",
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"y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = tf.keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"model = tf.keras.models.Sequential()\n",
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"model.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same',\n",
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" input_shape=X_train.shape[1:], activation='relu'))\n",
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"model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
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"model.add(tf.keras.layers.Dropout(config.dropout))\n",
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"\n",
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"model.add(tf.keras.layers.Flatten())\n",
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"model.add(tf.keras.layers.Dense(config.dense_layer_nodes, activation='relu'))\n",
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"model.add(tf.keras.layers.Dropout(config.dropout))\n",
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"model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))\n",
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"\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer=\"adam\",\n",
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" metrics=['accuracy'])\n",
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"# log the number of total parameters\n",
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"config.total_params = model.count_params()\n",
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"print(\"Total params: \", config.total_params)\n",
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"X_train = X_train.astype('float32') / 255.\n",
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"X_test = X_test.astype('float32') / 255.\n",
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"\n",
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"datagen = ImageDataGenerator(width_shift_range=0.1)\n",
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"datagen.fit(X_train)\n",
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"\n",
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"\n",
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"# Fit the model on the batches generated by datagen.flow().\n",
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"model.fit_generator(datagen.flow(X_train, y_train,\n",
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" batch_size=config.batch_size),\n",
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" steps_per_epoch=X_train.shape[0] // config.batch_size,\n",
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" epochs=config.epochs,\n",
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" validation_data=(X_test, y_test),\n",
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" callbacks=[WandbCallback(data_type=\"image\", labels=class_names)])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/keras-cifar/cifar-transfer.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from tensorflow.keras.optimizers import Adam\n",
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"# Importing the ResNet50 model\n",
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"from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\n",
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"import numpy as np\n",
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"import os\n",
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"import wandb\n",
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"from wandb.keras import WandbCallback\n",
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"\n",
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"# Set hyper parameters\n",
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"wandb.init()\n",
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"config = wandb.config\n",
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"config.batch_size = 128\n",
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"config.epochs = 10\n",
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"config.learn_rate = 0.001\n",
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"config.dropout = 0.3\n",
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"config.dense_layer_nodes = 128\n",
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"\n",
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"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
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" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"num_classes = len(class_names)\n",
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"\n",
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"# Load and normalize data\n",
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"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
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"X_train = X_train.astype('float32') / 255.\n",
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"X_test = X_test.astype('float32') / 255.\n",
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"\n",
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"# Convert class vectors to binary class matrices.\n",
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"y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = tf.keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"# Loading the ResNet50 model with pre-trained ImageNet weights\n",
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"big_model = ResNet50(weights='imagenet', include_top=False,\n",
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" input_shape=(X_train.shape[1], X_train.shape[2], 3))\n",
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"\n",
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"# Add new last layer\n",
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"model = tf.keras.models.Sequential()\n",
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"model.add(big_model)\n",
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"model.add(tf.keras.layers.Flatten())\n",
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"model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
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"\n",
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"# Make the big first layer frozen for speed\n",
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"model.layers[0].trainable = False\n",
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"\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer=Adam(config.learn_rate),\n",
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" metrics=['accuracy'])\n",
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"# log the number of total parameters\n",
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"config.total_params = model.count_params()\n",
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"print(\"Total params: \", config.total_params)\n",
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"model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),\n",
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" callbacks=[WandbCallback(data_type=\"image\", labels=class_names, save_model=False)])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/keras-cifar/cifar.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import wandb\n",
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"\n",
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"wandb.init()\n",
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"\n",
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"config = wandb.config\n",
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"config.batch_size = 128\n",
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"config.epochs = 10\n",
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"config.learn_rate = 1000\n",
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"\n",
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"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
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" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
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"num_classes = len(class_names)\n",
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"\n",
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"(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
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"\n",
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"# Convert class vectors to binary class matrices.\n",
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"y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
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"y_test = tf.keras.utils.to_categorical(y_test, num_classes)\n",
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"\n",
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"model = tf.keras.models.Sequential()\n",
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"model.add(tf.keras.layers.Flatten(input_shape=X_train.shape[1:]))\n",
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"model.add(tf.keras.layers.Dense(num_classes))\n",
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"model.compile(loss='mse',\n",
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" optimizer=tf.keras.optimizers.Adam(config.learn_rate),\n",
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" metrics=['accuracy'])\n",
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"# log the number of total parameters\n",
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"config.total_params = model.count_params()\n",
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"print(\"Total params: \", config.total_params)\n",
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"model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_test, y_test),\n",
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" callbacks=[wandb.keras.WandbCallback(data_type=\"image\", labels=class_names, save_model=False)])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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