chapter11_part01_introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "# Deep learning for text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Natural Language Processing: the bird's eye view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Preparing text data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Text standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Text splitting (tokenization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Vocabulary indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Using the `TextVectorization` layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "class Vectorizer:\n",
    "    def standardize(self, text):\n",
    "        text = text.lower()\n",
    "        return \"\".join(char for char in text if char not in string.punctuation)\n",
    "\n",
    "    def tokenize(self, text):\n",
    "        text = self.standardize(text)\n",
    "        return text.split()\n",
    "\n",
    "    def make_vocabulary(self, dataset):\n",
    "        self.vocabulary = {\"\": 0, \"[UNK]\": 1}\n",
    "        for text in dataset:\n",
    "            text = self.standardize(text)\n",
    "            tokens = self.tokenize(text)\n",
    "            for token in tokens:\n",
    "                if token not in self.vocabulary:\n",
    "                    self.vocabulary[token] = len(self.vocabulary)\n",
    "        self.inverse_vocabulary = dict(\n",
    "            (v, k) for k, v in self.vocabulary.items())\n",
    "\n",
    "    def encode(self, text):\n",
    "        text = self.standardize(text)\n",
    "        tokens = self.tokenize(text)\n",
    "        return [self.vocabulary.get(token, 1) for token in tokens]\n",
    "\n",
    "    def decode(self, int_sequence):\n",
    "        return \" \".join(\n",
    "            self.inverse_vocabulary.get(i, \"[UNK]\") for i in int_sequence)\n",
    "\n",
    "vectorizer = Vectorizer()\n",
    "dataset = [\n",
    "    \"I write, erase, rewrite\",\n",
    "    \"Erase again, and then\",\n",
    "    \"A poppy blooms.\",\n",
    "]\n",
    "vectorizer.make_vocabulary(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_sentence = \"I write, rewrite, and still rewrite again\"\n",
    "encoded_sentence = vectorizer.encode(test_sentence)\n",
    "print(encoded_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "decoded_sentence = vectorizer.decode(encoded_sentence)\n",
    "print(decoded_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import TextVectorization\n",
    "text_vectorization = TextVectorization(\n",
    "    output_mode=\"int\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import string\n",
    "import tensorflow as tf\n",
    "\n",
    "def custom_standardization_fn(string_tensor):\n",
    "    lowercase_string = tf.strings.lower(string_tensor)\n",
    "    return tf.strings.regex_replace(\n",
    "        lowercase_string, f\"[{re.escape(string.punctuation)}]\", \"\")\n",
    "\n",
    "def custom_split_fn(string_tensor):\n",
    "    return tf.strings.split(string_tensor)\n",
    "\n",
    "text_vectorization = TextVectorization(\n",
    "    output_mode=\"int\",\n",
    "    standardize=custom_standardization_fn,\n",
    "    split=custom_split_fn,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "dataset = [\n",
    "    \"I write, erase, rewrite\",\n",
    "    \"Erase again, and then\",\n",
    "    \"A poppy blooms.\",\n",
    "]\n",
    "text_vectorization.adapt(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying the vocabulary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "vocabulary = text_vectorization.get_vocabulary()\n",
    "test_sentence = \"I write, rewrite, and still rewrite again\"\n",
    "encoded_sentence = text_vectorization(test_sentence)\n",
    "print(encoded_sentence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inverse_vocab = dict(enumerate(vocabulary))\n",
    "decoded_sentence = \" \".join(inverse_vocab[int(i)] for i in encoded_sentence)\n",
    "print(decoded_sentence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Two approaches for representing groups of words: sets and sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Preparing the IMDB movie reviews data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
    "!tar -xf aclImdb_v1.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!rm -r aclImdb/train/unsup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!cat aclImdb/train/pos/4077_10.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import os, pathlib, shutil, random\n",
    "\n",
    "base_dir = pathlib.Path(\"aclImdb\")\n",
    "val_dir = base_dir / \"val\"\n",
    "train_dir = base_dir / \"train\"\n",
    "for category in (\"neg\", \"pos\"):\n",
    "    os.makedirs(val_dir / category)\n",
    "    files = os.listdir(train_dir / category)\n",
    "    random.Random(1337).shuffle(files)\n",
    "    num_val_samples = int(0.2 * len(files))\n",
    "    val_files = files[-num_val_samples:]\n",
    "    for fname in val_files:\n",
    "        shutil.move(train_dir / category / fname,\n",
    "                    val_dir / category / fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "batch_size = 32\n",
    "\n",
    "train_ds = keras.utils.text_dataset_from_directory(\n",
    "    \"aclImdb/train\", batch_size=batch_size\n",
    ")\n",
    "val_ds = keras.utils.text_dataset_from_directory(\n",
    "    \"aclImdb/val\", batch_size=batch_size\n",
    ")\n",
    "test_ds = keras.utils.text_dataset_from_directory(\n",
    "    \"aclImdb/test\", batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying the shapes and dtypes of the first batch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "for inputs, targets in train_ds:\n",
    "    print(\"inputs.shape:\", inputs.shape)\n",
    "    print(\"inputs.dtype:\", inputs.dtype)\n",
    "    print(\"targets.shape:\", targets.shape)\n",
    "    print(\"targets.dtype:\", targets.dtype)\n",
    "    print(\"inputs[0]:\", inputs[0])\n",
    "    print(\"targets[0]:\", targets[0])\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Processing words as a set: the bag-of-words approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Single words (unigrams) with binary encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Preprocessing our datasets with a `TextVectorization` layer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization = TextVectorization(\n",
    "    max_tokens=20000,\n",
    "    output_mode=\"binary\",\n",
    ")\n",
    "text_only_train_ds = train_ds.map(lambda x, y: x)\n",
    "text_vectorization.adapt(text_only_train_ds)\n",
    "\n",
    "binary_1gram_train_ds = train_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "binary_1gram_val_ds = val_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "binary_1gram_test_ds = test_ds.map(lambda x, y: (text_vectorization(x), y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Inspecting the output of our binary unigram dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "for inputs, targets in binary_1gram_train_ds:\n",
    "    print(\"inputs.shape:\", inputs.shape)\n",
    "    print(\"inputs.dtype:\", inputs.dtype)\n",
    "    print(\"targets.shape:\", targets.shape)\n",
    "    print(\"targets.dtype:\", targets.dtype)\n",
    "    print(\"inputs[0]:\", inputs[0])\n",
    "    print(\"targets[0]:\", targets[0])\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Our model-building utility**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "def get_model(max_tokens=20000, hidden_dim=16):\n",
    "    inputs = keras.Input(shape=(max_tokens,))\n",
    "    x = layers.Dense(hidden_dim, activation=\"relu\")(inputs)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "    model = keras.Model(inputs, outputs)\n",
    "    model.compile(optimizer=\"rmsprop\",\n",
    "                  loss=\"binary_crossentropy\",\n",
    "                  metrics=[\"accuracy\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training and testing the binary unigram model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model = get_model()\n",
    "model.summary()\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\"binary_1gram.keras\",\n",
    "                                    save_best_only=True)\n",
    "]\n",
    "model.fit(binary_1gram_train_ds.cache(),\n",
    "          validation_data=binary_1gram_val_ds.cache(),\n",
    "          epochs=10,\n",
    "          callbacks=callbacks)\n",
    "model = keras.models.load_model(\"binary_1gram.keras\")\n",
    "print(f\"Test acc: {model.evaluate(binary_1gram_test_ds)[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Bigrams with binary encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Configuring the `TextVectorization` layer to return bigrams**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization = TextVectorization(\n",
    "    ngrams=2,\n",
    "    max_tokens=20000,\n",
    "    output_mode=\"binary\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training and testing the binary bigram model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization.adapt(text_only_train_ds)\n",
    "binary_2gram_train_ds = train_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "binary_2gram_val_ds = val_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "binary_2gram_test_ds = test_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "\n",
    "model = get_model()\n",
    "model.summary()\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\"binary_2gram.keras\",\n",
    "                                    save_best_only=True)\n",
    "]\n",
    "model.fit(binary_2gram_train_ds.cache(),\n",
    "          validation_data=binary_2gram_val_ds.cache(),\n",
    "          epochs=10,\n",
    "          callbacks=callbacks)\n",
    "model = keras.models.load_model(\"binary_2gram.keras\")\n",
    "print(f\"Test acc: {model.evaluate(binary_2gram_test_ds)[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Bigrams with TF-IDF encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Configuring the `TextVectorization` layer to return token counts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization = TextVectorization(\n",
    "    ngrams=2,\n",
    "    max_tokens=20000,\n",
    "    output_mode=\"count\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Configuring the `TextVectorization` layer to return TF-IDF-weighted outputs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization = TextVectorization(\n",
    "    ngrams=2,\n",
    "    max_tokens=20000,\n",
    "    output_mode=\"tf_idf\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training and testing the TF-IDF bigram model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "text_vectorization.adapt(text_only_train_ds)\n",
    "\n",
    "tfidf_2gram_train_ds = train_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "tfidf_2gram_val_ds = val_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "tfidf_2gram_test_ds = test_ds.map(lambda x, y: (text_vectorization(x), y))\n",
    "\n",
    "model = get_model()\n",
    "model.summary()\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\"tfidf_2gram.keras\",\n",
    "                                    save_best_only=True)\n",
    "]\n",
    "model.fit(tfidf_2gram_train_ds.cache(),\n",
    "          validation_data=tfidf_2gram_val_ds.cache(),\n",
    "          epochs=10,\n",
    "          callbacks=callbacks)\n",
    "model = keras.models.load_model(\"tfidf_2gram.keras\")\n",
    "print(f\"Test acc: {model.evaluate(tfidf_2gram_test_ds)[1]:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(1,), dtype=\"string\")\n",
    "processed_inputs = text_vectorization(inputs)\n",
    "outputs = model(processed_inputs)\n",
    "inference_model = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "raw_text_data = tf.convert_to_tensor([\n",
    "    [\"That was an excellent movie, I loved it.\"],\n",
    "])\n",
    "predictions = inference_model(raw_text_data)\n",
    "print(f\"{float(predictions[0] * 100):.2f} percent positive\")"
   ]
  }
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