wehub-resource-sync
479 lines
13 KiB
Plaintext
479 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Processing words as a sequence: The sequence model approach"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### A first practical example"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Downloading the data**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
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"!tar -xf aclImdb_v1.tar.gz\n",
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"!rm -r aclImdb/train/unsup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Preparing the data**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import os, pathlib, shutil, random\n",
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"from tensorflow import keras\n",
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"batch_size = 32\n",
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"base_dir = pathlib.Path(\"aclImdb\")\n",
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"val_dir = base_dir / \"val\"\n",
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"train_dir = base_dir / \"train\"\n",
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"for category in (\"neg\", \"pos\"):\n",
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" os.makedirs(val_dir / category)\n",
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" files = os.listdir(train_dir / category)\n",
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" random.Random(1337).shuffle(files)\n",
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" num_val_samples = int(0.2 * len(files))\n",
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" val_files = files[-num_val_samples:]\n",
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" for fname in val_files:\n",
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" shutil.move(train_dir / category / fname,\n",
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" val_dir / category / fname)\n",
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"\n",
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"train_ds = keras.utils.text_dataset_from_directory(\n",
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" \"aclImdb/train\", batch_size=batch_size\n",
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")\n",
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"val_ds = keras.utils.text_dataset_from_directory(\n",
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" \"aclImdb/val\", batch_size=batch_size\n",
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")\n",
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"test_ds = keras.utils.text_dataset_from_directory(\n",
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" \"aclImdb/test\", batch_size=batch_size\n",
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")\n",
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"text_only_train_ds = train_ds.map(lambda x, y: x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Preparing integer sequence datasets**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras import layers\n",
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"\n",
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"max_length = 600\n",
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"max_tokens = 20000\n",
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"text_vectorization = layers.TextVectorization(\n",
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" max_tokens=max_tokens,\n",
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" output_mode=\"int\",\n",
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" output_sequence_length=max_length,\n",
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")\n",
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"text_vectorization.adapt(text_only_train_ds)\n",
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"\n",
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"int_train_ds = train_ds.map(\n",
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" lambda x, y: (text_vectorization(x), y),\n",
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" num_parallel_calls=4)\n",
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"int_val_ds = val_ds.map(\n",
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" lambda x, y: (text_vectorization(x), y),\n",
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" num_parallel_calls=4)\n",
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"int_test_ds = test_ds.map(\n",
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" lambda x, y: (text_vectorization(x), y),\n",
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" num_parallel_calls=4)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**A sequence model built on one-hot encoded vector sequences**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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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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"inputs = keras.Input(shape=(None,), dtype=\"int64\")\n",
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"embedded = tf.one_hot(inputs, depth=max_tokens)\n",
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"x = layers.Bidirectional(layers.LSTM(32))(embedded)\n",
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"x = layers.Dropout(0.5)(x)\n",
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"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Training a first basic sequence model**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"callbacks = [\n",
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" keras.callbacks.ModelCheckpoint(\"one_hot_bidir_lstm.keras\",\n",
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" save_best_only=True)\n",
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"]\n",
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"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\n",
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"model = keras.models.load_model(\"one_hot_bidir_lstm.keras\")\n",
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"print(f\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Understanding word embeddings"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Learning word embeddings with the Embedding layer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Instantiating an `Embedding` layer**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"embedding_layer = layers.Embedding(input_dim=max_tokens, output_dim=256)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Model that uses an `Embedding` layer trained from scratch**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(None,), dtype=\"int64\")\n",
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"embedded = layers.Embedding(input_dim=max_tokens, output_dim=256)(inputs)\n",
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"x = layers.Bidirectional(layers.LSTM(32))(embedded)\n",
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"x = layers.Dropout(0.5)(x)\n",
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"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.summary()\n",
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"\n",
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"callbacks = [\n",
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" keras.callbacks.ModelCheckpoint(\"embeddings_bidir_gru.keras\",\n",
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" save_best_only=True)\n",
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"]\n",
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"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\n",
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"model = keras.models.load_model(\"embeddings_bidir_gru.keras\")\n",
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"print(f\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Understanding padding and masking"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Using an `Embedding` layer with masking enabled**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(None,), dtype=\"int64\")\n",
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"embedded = layers.Embedding(\n",
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" input_dim=max_tokens, output_dim=256, mask_zero=True)(inputs)\n",
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"x = layers.Bidirectional(layers.LSTM(32))(embedded)\n",
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"x = layers.Dropout(0.5)(x)\n",
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"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.summary()\n",
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"\n",
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"callbacks = [\n",
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" keras.callbacks.ModelCheckpoint(\"embeddings_bidir_gru_with_masking.keras\",\n",
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" save_best_only=True)\n",
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"]\n",
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"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\n",
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"model = keras.models.load_model(\"embeddings_bidir_gru_with_masking.keras\")\n",
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"print(f\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Using pretrained word embeddings"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"!wget http://nlp.stanford.edu/data/glove.6B.zip\n",
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"!unzip -q glove.6B.zip"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Parsing the GloVe word-embeddings file**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"path_to_glove_file = \"glove.6B.100d.txt\"\n",
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"\n",
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"embeddings_index = {}\n",
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"with open(path_to_glove_file) as f:\n",
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" for line in f:\n",
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" word, coefs = line.split(maxsplit=1)\n",
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" coefs = np.fromstring(coefs, \"f\", sep=\" \")\n",
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" embeddings_index[word] = coefs\n",
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"\n",
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"print(f\"Found {len(embeddings_index)} word vectors.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Preparing the GloVe word-embeddings matrix**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"embedding_dim = 100\n",
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"\n",
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"vocabulary = text_vectorization.get_vocabulary()\n",
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"word_index = dict(zip(vocabulary, range(len(vocabulary))))\n",
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"\n",
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"embedding_matrix = np.zeros((max_tokens, embedding_dim))\n",
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"for word, i in word_index.items():\n",
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" if i < max_tokens:\n",
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" embedding_vector = embeddings_index.get(word)\n",
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" if embedding_vector is not None:\n",
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" embedding_matrix[i] = embedding_vector"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"embedding_layer = layers.Embedding(\n",
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" max_tokens,\n",
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" embedding_dim,\n",
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" embeddings_initializer=keras.initializers.Constant(embedding_matrix),\n",
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" trainable=False,\n",
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" mask_zero=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Model that uses a pretrained Embedding layer**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(None,), dtype=\"int64\")\n",
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"embedded = embedding_layer(inputs)\n",
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"x = layers.Bidirectional(layers.LSTM(32))(embedded)\n",
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"x = layers.Dropout(0.5)(x)\n",
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"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.summary()\n",
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"\n",
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"callbacks = [\n",
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" keras.callbacks.ModelCheckpoint(\"glove_embeddings_sequence_model.keras\",\n",
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" save_best_only=True)\n",
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"]\n",
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"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\n",
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"model = keras.models.load_model(\"glove_embeddings_sequence_model.keras\")\n",
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"print(f\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\")"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "chapter11_part02_sequence-models.i",
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"private_outputs": false,
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"provenance": [],
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"toc_visible": true
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},
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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": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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