imdb_classification_GRU_LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2VQo4bajwUU"
      },
      "source": [
        "# setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z682XYsrjkY9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "\n",
        "tfds.disable_progress_bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mp1Z7P9pYRSK"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, metric):\n",
        "  plt.plot(history.history[metric])\n",
        "  plt.plot(history.history['val_'+metric], '')\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(metric)\n",
        "  plt.legend([metric, 'val_'+metric])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SHRwRoP2nVHX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af348795-ff10-4280-81d9-dce88c99d71c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\n",
            "Dataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorSpec(shape=(), dtype=tf.string, name=None),\n",
              " TensorSpec(shape=(), dtype=tf.int64, name=None))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "dataset, info = tfds.load('imdb_reviews', with_info=True,\n",
        "                          as_supervised=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']\n",
        "\n",
        "train_dataset.element_spec"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWA4c2ir7g6p"
      },
      "source": [
        "Initially this returns a dataset of (text, label pairs):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vd4_BGKyurao",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79217928-e141-4b03-f9f3-3acfd4f2df3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text:  b\"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\"\n",
            "label:  0\n"
          ]
        }
      ],
      "source": [
        "for example, label in train_dataset.take(1):\n",
        "  print('text: ', example.numpy())\n",
        "  print('label: ', label.numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2qVJzcEluH_"
      },
      "source": [
        "Next shuffle the data for training and create batches of these `(text, label)` pairs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dDsCaZCDYZgm"
      },
      "outputs": [],
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VznrltNOnUc5"
      },
      "outputs": [],
      "source": [
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jqkvdcFv41wC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7ca61c7-5da0-4619-a277-fc9a41d042ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "texts:  [b\"What a fascinating film. Even if it wasn't based on real life, Forbidden Lies was a fascinating portrait of a con artist in her element. And it is the kind of film psychology students could study to learn about compulsive liars.<br /><br />The author of Forbidden Love, Norma, was revealed as a fraud in the media but this move really does give her ample opportunity to clear her name.<br /><br />But the twists and turns she takes the documentary maker through are amazing. What a patient woman! I loved this movie. I have not read the book but simply heard good reviews and went to see it on boring rainy afternoon. The journey this film takes you on is clever, interesting and totally engrossing.\"\n",
            " b'Watching \"Kroko\" I would have liked to leave the cinema very much for the first time in my life. I would not recommend to watch this movie: flat main characters - absolutely no development e.g. Kroko the metaphoric German problem child remains a pure metaphor without any capability of positive involvement despite several plot-wise chances to do so. Uninspired actors, non-evolving plot. I guess the movie attempted an environmental survey but did not succeed: camera appeared shaky rather than motivated. Pictures were low - contrast, gray and dark - i am sure deliberately but the components did not add up to a convincing impression of the social milieu. The story had certain potential though, it could have made a good short story.'\n",
            " b'I only hope that no classicists/ancient historians saw \"Cleopatra\", or, if they did, that they took it as a laugh. The movie is horrendously inaccurate, more laughably, even, than \"Gladiator\" (which is at least a well-written script whose historical errors are articulate and correspond well to the story). Most blatant is Octavius, Caesar\\'s heir, in the Senate before Caesar\\'s assassination: at the ripe old age of 19!<br /><br />Besides this, the acting is mediocre. Timothy Dalton has more than a hint of James Bond in him when he says, \"Caesar. Julius Caesar.\" Billy Zane is a laughably dense Marc Antony. And Leonor Varela tries her best to be the seductive Pharaoh (who in real life was not good-looking at all) but comes off as unbelievable.<br /><br />So this is a warning for all historians--this movie is not true to life!']\n",
            "\n",
            "labels:  [1 0 0]\n"
          ]
        }
      ],
      "source": [
        "for example, label in train_dataset.take(1):\n",
        "  print('texts: ', example.numpy()[:3])\n",
        "  print()\n",
        "  print('labels: ', label.numpy()[:3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5eWCo88voPY"
      },
      "source": [
        "## Create the text encoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uC25Lu1Yvuqy"
      },
      "outputs": [],
      "source": [
        "VOCAB_SIZE = 1000\n",
        "encoder = tf.keras.layers.TextVectorization(\n",
        "    max_tokens=VOCAB_SIZE)\n",
        "encoder.adapt(train_dataset.map(lambda text, label: text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tBoyjjWg0Ac9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21fb14a1-2cbd-4d5d-db63-1d6bdc4f8087"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['', '[UNK]', 'the', 'and', 'a', 'of', 'to', 'is', 'in', 'it', 'i',\n",
              "       'this', 'that', 'br', 'was', 'as', 'for', 'with', 'movie', 'but'],\n",
              "      dtype='<U14')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "vocab = np.array(encoder.get_vocabulary())\n",
        "vocab[:20]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RGc7C9WiwRWs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "295e32ab-98ac-4ee9-e046-25e1f98cde6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 49,   4,   1, ...,   0,   0,   0],\n",
              "       [147,   1,  10, ...,   0,   0,   0],\n",
              "       [ 10,  61, 428, ...,   0,   0,   0]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "encoded_example = encoder(example)[:3].numpy()\n",
        "encoded_example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N_tD0QY5wXaK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fee9e469-7264-45fe-bcfb-991566a21921"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:  b\"What a fascinating film. Even if it wasn't based on real life, Forbidden Lies was a fascinating portrait of a con artist in her element. And it is the kind of film psychology students could study to learn about compulsive liars.<br /><br />The author of Forbidden Love, Norma, was revealed as a fraud in the media but this move really does give her ample opportunity to clear her name.<br /><br />But the twists and turns she takes the documentary maker through are amazing. What a patient woman! I loved this movie. I have not read the book but simply heard good reviews and went to see it on boring rainy afternoon. The journey this film takes you on is clever, interesting and totally engrossing.\"\n",
            "Round-trip:  what a [UNK] film even if it wasnt based on real life [UNK] [UNK] was a [UNK] [UNK] of a [UNK] [UNK] in her [UNK] and it is the kind of film [UNK] [UNK] could [UNK] to learn about [UNK] [UNK] br the [UNK] of [UNK] love [UNK] was [UNK] as a [UNK] in the [UNK] but this move really does give her [UNK] [UNK] to clear her [UNK] br but the [UNK] and turns she takes the documentary [UNK] through are amazing what a [UNK] woman i loved this movie i have not read the book but simply heard good reviews and went to see it on boring [UNK] [UNK] the [UNK] this film takes you on is [UNK] interesting and totally [UNK]                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               \n",
            "\n",
            "Original:  b'Watching \"Kroko\" I would have liked to leave the cinema very much for the first time in my life. I would not recommend to watch this movie: flat main characters - absolutely no development e.g. Kroko the metaphoric German problem child remains a pure metaphor without any capability of positive involvement despite several plot-wise chances to do so. Uninspired actors, non-evolving plot. I guess the movie attempted an environmental survey but did not succeed: camera appeared shaky rather than motivated. Pictures were low - contrast, gray and dark - i am sure deliberately but the components did not add up to a convincing impression of the social milieu. The story had certain potential though, it could have made a good short story.'\n",
            "Round-trip:  watching [UNK] i would have liked to leave the cinema very much for the first time in my life i would not recommend to watch this movie [UNK] main characters absolutely no development [UNK] [UNK] the [UNK] [UNK] problem child [UNK] a [UNK] [UNK] without any [UNK] of [UNK] [UNK] despite several [UNK] [UNK] to do so [UNK] actors [UNK] plot i guess the movie [UNK] an [UNK] [UNK] but did not [UNK] camera [UNK] [UNK] rather than [UNK] [UNK] were low [UNK] [UNK] and dark i am sure [UNK] but the [UNK] did not add up to a [UNK] [UNK] of the [UNK] [UNK] the story had certain potential though it could have made a good short story                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n",
            "\n",
            "Original:  b'I only hope that no classicists/ancient historians saw \"Cleopatra\", or, if they did, that they took it as a laugh. The movie is horrendously inaccurate, more laughably, even, than \"Gladiator\" (which is at least a well-written script whose historical errors are articulate and correspond well to the story). Most blatant is Octavius, Caesar\\'s heir, in the Senate before Caesar\\'s assassination: at the ripe old age of 19!<br /><br />Besides this, the acting is mediocre. Timothy Dalton has more than a hint of James Bond in him when he says, \"Caesar. Julius Caesar.\" Billy Zane is a laughably dense Marc Antony. And Leonor Varela tries her best to be the seductive Pharaoh (who in real life was not good-looking at all) but comes off as unbelievable.<br /><br />So this is a warning for all historians--this movie is not true to life!'\n",
            "Round-trip:  i only hope that no [UNK] [UNK] saw [UNK] or if they did that they took it as a laugh the movie is [UNK] [UNK] more [UNK] even than [UNK] which is at least a [UNK] script whose [UNK] [UNK] are [UNK] and [UNK] well to the story most [UNK] is [UNK] [UNK] [UNK] in the [UNK] before [UNK] [UNK] at the [UNK] old age of [UNK] br [UNK] this the acting is [UNK] [UNK] [UNK] has more than a [UNK] of james [UNK] in him when he says [UNK] [UNK] [UNK] [UNK] [UNK] is a [UNK] [UNK] [UNK] [UNK] and [UNK] [UNK] tries her best to be the [UNK] [UNK] who in real life was not [UNK] at all but comes off as [UNK] br so this is a [UNK] for all [UNK] movie is not true to life                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               \n",
            "\n"
          ]
        }
      ],
      "source": [
        "for n in range(3):\n",
        "  print(\"Original: \", example[n].numpy())\n",
        "  print(\"Round-trip: \", \" \".join(vocab[encoded_example[n]]))\n",
        "  print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjUqGVBxGw-t"
      },
      "source": [
        "# LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7zsmInBOCPO"
      },
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LwfoBkmRYcP3"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.Embedding(\n",
        "        input_dim=len(encoder.get_vocabulary()),\n",
        "        output_dim=64,\n",
        "        # Use masking to handle the variable sequence lengths\n",
        "        mask_zero=True),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87a8-CwfKebw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e76daefa-e154-43d7-a1d7-de95e5ac93c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False, True, True, True, True]\n"
          ]
        }
      ],
      "source": [
        "print([layer.supports_masking for layer in model.layers])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O41gw3KfWHus",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e02dc2b3-f714-4ecd-c629-ebccafa98622"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 6s 6s/step\n",
            "[0.00871077]\n"
          ]
        }
      ],
      "source": [
        "# predict on a sample text without padding.\n",
        "\n",
        "sample_text = ('The movie was cool. The animation and the graphics '\n",
        "               'were out of this world. I would recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))\n",
        "print(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0VQmGnEWcuz"
      },
      "source": [
        "Now, evaluate it again in a batch with a longer sentence. The result should be identical:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIgpuTeFNDzq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c6e645a-9854-4f07-9f53-b5cee21e39eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 66ms/step\n",
            "[0.00871077]\n"
          ]
        }
      ],
      "source": [
        "# predict on a sample text with padding\n",
        "\n",
        "padding = \"the \" * 2000\n",
        "predictions = model.predict(np.array([sample_text, padding]))\n",
        "print(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRI776ZcH3Tf"
      },
      "source": [
        "Compile the Keras model to configure the training process:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kj2xei41YZjC"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIwH3nto596k"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hw86wWS4YgR2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dc63c8e-cd61-41b7-e7ad-2061662eef5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 59s 109ms/step - loss: 0.6615 - accuracy: 0.5504 - val_loss: 0.6381 - val_accuracy: 0.6214\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 26s 67ms/step - loss: 0.4656 - accuracy: 0.7590 - val_loss: 0.3967 - val_accuracy: 0.8224\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.3612 - accuracy: 0.8384 - val_loss: 0.3789 - val_accuracy: 0.8151\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 24s 61ms/step - loss: 0.3333 - accuracy: 0.8542 - val_loss: 0.3573 - val_accuracy: 0.8318\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 24s 61ms/step - loss: 0.3218 - accuracy: 0.8599 - val_loss: 0.3399 - val_accuracy: 0.8396\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.3132 - accuracy: 0.8654 - val_loss: 0.3367 - val_accuracy: 0.8417\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 25s 62ms/step - loss: 0.3074 - accuracy: 0.8674 - val_loss: 0.3382 - val_accuracy: 0.8594\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.3029 - accuracy: 0.8686 - val_loss: 0.3318 - val_accuracy: 0.8594\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.3016 - accuracy: 0.8696 - val_loss: 0.3280 - val_accuracy: 0.8573\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2987 - accuracy: 0.8724 - val_loss: 0.3249 - val_accuracy: 0.8542\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 25s 62ms/step - loss: 0.2956 - accuracy: 0.8728 - val_loss: 0.3281 - val_accuracy: 0.8469\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2943 - accuracy: 0.8738 - val_loss: 0.3207 - val_accuracy: 0.8562\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2918 - accuracy: 0.8739 - val_loss: 0.3227 - val_accuracy: 0.8526\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.2895 - accuracy: 0.8760 - val_loss: 0.3230 - val_accuracy: 0.8547\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2879 - accuracy: 0.8764 - val_loss: 0.3241 - val_accuracy: 0.8583\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2855 - accuracy: 0.8762 - val_loss: 0.3329 - val_accuracy: 0.8594\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2825 - accuracy: 0.8780 - val_loss: 0.3211 - val_accuracy: 0.8536\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2803 - accuracy: 0.8795 - val_loss: 0.3293 - val_accuracy: 0.8375\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2762 - accuracy: 0.8807 - val_loss: 0.3244 - val_accuracy: 0.8505\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.2741 - accuracy: 0.8818 - val_loss: 0.3243 - val_accuracy: 0.8630\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_dataset, epochs=20,\n",
        "                    validation_data=test_dataset,\n",
        "                    validation_steps=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BaNbXi43YgUT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5be22515-e5da-41b3-8826-a7e5937eb729"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391/391 [==============================] - 12s 30ms/step - loss: 0.3238 - accuracy: 0.8611\n",
            "Test Loss: 0.3238326609134674\n",
            "Test Accuracy: 0.8611199855804443\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss:', test_loss)\n",
        "print('Test Accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OZmwt_mzaQJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "ec8cb29f-fefa-4652-85c9-5561ac663443"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 0.6808266043663025)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_graphs(history, 'accuracy')\n",
        "plt.ylim(None, 1)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_graphs(history, 'loss')\n",
        "plt.ylim(0, None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZXgfQSgRW6zU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1fff305-509c-4506-e865-8bf951be53ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ],
      "source": [
        "sample_text = ('The movie was cool. The animation and the graphics '\n",
        "               'were out of this world. I would recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7g1evcaRpTKm"
      },
      "source": [
        "## Stack two or more LSTM layers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jo1jjO3vn0jo"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.Embedding(len(encoder.get_vocabulary()), 64, mask_zero=True),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,  return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEPV5jVGp-is"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LeSE-YjdqAeN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3508e7f4-b986-4da2-bcc4-19f34275778a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 78s 154ms/step - loss: 0.6276 - accuracy: 0.5856 - val_loss: 0.4517 - val_accuracy: 0.7547\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.3886 - accuracy: 0.8278 - val_loss: 0.3523 - val_accuracy: 0.8464\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3379 - accuracy: 0.8543 - val_loss: 0.3343 - val_accuracy: 0.8526\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.3191 - accuracy: 0.8634 - val_loss: 0.3241 - val_accuracy: 0.8516\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.3112 - accuracy: 0.8668 - val_loss: 0.3319 - val_accuracy: 0.8620\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.3086 - accuracy: 0.8658 - val_loss: 0.3276 - val_accuracy: 0.8609\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.3074 - accuracy: 0.8676 - val_loss: 0.3367 - val_accuracy: 0.8396\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 46s 116ms/step - loss: 0.3020 - accuracy: 0.8683 - val_loss: 0.3244 - val_accuracy: 0.8443\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2988 - accuracy: 0.8690 - val_loss: 0.3254 - val_accuracy: 0.8380\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 44s 111ms/step - loss: 0.2964 - accuracy: 0.8726 - val_loss: 0.3135 - val_accuracy: 0.8557\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2947 - accuracy: 0.8718 - val_loss: 0.3179 - val_accuracy: 0.8547\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2938 - accuracy: 0.8714 - val_loss: 0.3176 - val_accuracy: 0.8578\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.2908 - accuracy: 0.8736 - val_loss: 0.3309 - val_accuracy: 0.8375\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.2903 - accuracy: 0.8744 - val_loss: 0.3240 - val_accuracy: 0.8385\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.2878 - accuracy: 0.8776 - val_loss: 0.3228 - val_accuracy: 0.8547\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.2832 - accuracy: 0.8764 - val_loss: 0.3182 - val_accuracy: 0.8474\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2844 - accuracy: 0.8755 - val_loss: 0.3221 - val_accuracy: 0.8510\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.2812 - accuracy: 0.8780 - val_loss: 0.3292 - val_accuracy: 0.8609\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2789 - accuracy: 0.8785 - val_loss: 0.3372 - val_accuracy: 0.8620\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.2741 - accuracy: 0.8808 - val_loss: 0.3263 - val_accuracy: 0.8432\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_dataset, epochs=20,\n",
        "                    validation_data=test_dataset,\n",
        "                    validation_steps=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_LdwilM1qPM3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "112a6575-6816-439a-bf79-5027e4991d0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391/391 [==============================] - 18s 46ms/step - loss: 0.3282 - accuracy: 0.8449\n",
            "Test Loss: 0.32819825410842896\n",
            "Test Accuracy: 0.8449199795722961\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss:', test_loss)\n",
        "print('Test Accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ykUKnAoqbycW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f600877-176c-4950-83a1-9b0690be2e34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n",
            "[[-2.312]]\n"
          ]
        }
      ],
      "source": [
        "# predict on a sample text without padding.\n",
        "\n",
        "sample_text = ('The movie was not good. The animation and the graphics '\n",
        "               'were terrible. I would not recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))\n",
        "print(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_YYub0EDtwCu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "outputId": "d5f56ea9-1d74-4c8b-a915-1c0e77e3b929"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_graphs(history, 'accuracy')\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_graphs(history, 'loss')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GRU"
      ],
      "metadata": {
        "id": "E2oaTHv1Rg3M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GRU"
      ],
      "metadata": {
        "id": "2D8oL06lVBfu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.Embedding(\n",
        "        input_dim=len(encoder.get_vocabulary()),\n",
        "        output_dim=64,\n",
        "        # Use masking to handle the variable sequence lengths\n",
        "        mask_zero=True),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "metadata": {
        "id": "zKbcLOeBR3YB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print([layer.supports_masking for layer in model.layers])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoGJyi2hR3Tm",
        "outputId": "b0a5380a-bad7-4ed0-eaa2-5a785f98dfc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False, True, True, True, True]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on a sample text without padding.\n",
        "\n",
        "sample_text = ('The movie was cool. The animation and the graphics '\n",
        "               'were out of this world. I would recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))\n",
        "print(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9sshT0pR3QH",
        "outputId": "7f396565-e18c-48c1-c45d-21a03ccbd507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 4s 4s/step\n",
            "[-0.00188887]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "5k2KLY-9R3M-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_dataset, epochs=20,\n",
        "                    validation_data=test_dataset,\n",
        "                    validation_steps=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYV0cahVR3Kl",
        "outputId": "35b732b8-8c4b-4b0b-dcad-9a0723b7e103"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 65s 130ms/step - loss: 0.6889 - accuracy: 0.5000 - val_loss: 0.6754 - val_accuracy: 0.4917\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 34s 87ms/step - loss: 0.4622 - accuracy: 0.7466 - val_loss: 0.3590 - val_accuracy: 0.8448\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 27s 70ms/step - loss: 0.3325 - accuracy: 0.8540 - val_loss: 0.3462 - val_accuracy: 0.8432\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 29s 75ms/step - loss: 0.3129 - accuracy: 0.8653 - val_loss: 0.3263 - val_accuracy: 0.8521\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 27s 69ms/step - loss: 0.3033 - accuracy: 0.8713 - val_loss: 0.3228 - val_accuracy: 0.8604\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 31s 79ms/step - loss: 0.2999 - accuracy: 0.8719 - val_loss: 0.3234 - val_accuracy: 0.8557\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.2963 - accuracy: 0.8747 - val_loss: 0.3243 - val_accuracy: 0.8562\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 25s 62ms/step - loss: 0.2926 - accuracy: 0.8755 - val_loss: 0.3254 - val_accuracy: 0.8562\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 26s 66ms/step - loss: 0.2898 - accuracy: 0.8778 - val_loss: 0.3182 - val_accuracy: 0.8578\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2906 - accuracy: 0.8766 - val_loss: 0.3207 - val_accuracy: 0.8630\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 24s 61ms/step - loss: 0.2892 - accuracy: 0.8776 - val_loss: 0.3202 - val_accuracy: 0.8635\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.2846 - accuracy: 0.8812 - val_loss: 0.3194 - val_accuracy: 0.8583\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2828 - accuracy: 0.8836 - val_loss: 0.3528 - val_accuracy: 0.8260\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2802 - accuracy: 0.8816 - val_loss: 0.3272 - val_accuracy: 0.8651\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 25s 62ms/step - loss: 0.2787 - accuracy: 0.8815 - val_loss: 0.3210 - val_accuracy: 0.8599\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2785 - accuracy: 0.8830 - val_loss: 0.3240 - val_accuracy: 0.8620\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 24s 62ms/step - loss: 0.2767 - accuracy: 0.8839 - val_loss: 0.3216 - val_accuracy: 0.8594\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 25s 63ms/step - loss: 0.2747 - accuracy: 0.8842 - val_loss: 0.3268 - val_accuracy: 0.8516\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 25s 64ms/step - loss: 0.2727 - accuracy: 0.8865 - val_loss: 0.3412 - val_accuracy: 0.8531\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 28s 72ms/step - loss: 0.2727 - accuracy: 0.8865 - val_loss: 0.3255 - val_accuracy: 0.8594\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss:', test_loss)\n",
        "print('Test Accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lacFEJ7KR3E6",
        "outputId": "76ef6a86-d798-4887-9dff-f02f52897e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391/391 [==============================] - 10s 27ms/step - loss: 0.3246 - accuracy: 0.8594\n",
            "Test Loss: 0.3245905041694641\n",
            "Test Accuracy: 0.8593999743461609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_graphs(history, 'accuracy')\n",
        "plt.ylim(None, 1)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_graphs(history, 'loss')\n",
        "plt.ylim(0, None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "yz1KEeZ8R3BH",
        "outputId": "162cb150-b8ff-4f78-e68a-148006533096"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 0.7097344264388085)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text = ('The movie was cool. The animation and the graphics '\n",
        "               'were out of this world. I would recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "065Tek0qUZX_",
        "outputId": "a6cbc7f6-360d-4eeb-b64e-5750f9212461"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 7 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f51d1c85f30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xML4diHcXTKb",
        "outputId": "3ec53a4d-a6bb-4319-813c-66ce36a27dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.78783536]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stack two or more GRU layers"
      ],
      "metadata": {
        "id": "Tg9-RKwHUecS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.Embedding(len(encoder.get_vocabulary()), 64, mask_zero=True),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64,  return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "rLabQ_IiUeMe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0CLmcc5JUaaI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_dataset, epochs=20,\n",
        "                    validation_data=test_dataset,\n",
        "                    validation_steps=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AEwav5vuUaXw",
        "outputId": "691de3b7-0370-479c-ce8f-1bb7686fc4f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 79s 159ms/step - loss: 0.6890 - accuracy: 0.5000 - val_loss: 0.6655 - val_accuracy: 0.4917\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 46s 118ms/step - loss: 0.4579 - accuracy: 0.7638 - val_loss: 0.3799 - val_accuracy: 0.8427\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 45s 116ms/step - loss: 0.3425 - accuracy: 0.8556 - val_loss: 0.3404 - val_accuracy: 0.8500\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3222 - accuracy: 0.8640 - val_loss: 0.3256 - val_accuracy: 0.8547\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3111 - accuracy: 0.8685 - val_loss: 0.3225 - val_accuracy: 0.8615\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3069 - accuracy: 0.8690 - val_loss: 0.3195 - val_accuracy: 0.8583\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.3022 - accuracy: 0.8738 - val_loss: 0.3171 - val_accuracy: 0.8568\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2992 - accuracy: 0.8717 - val_loss: 0.3175 - val_accuracy: 0.8651\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2947 - accuracy: 0.8754 - val_loss: 0.3183 - val_accuracy: 0.8630\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.2900 - accuracy: 0.8774 - val_loss: 0.3264 - val_accuracy: 0.8490\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 46s 116ms/step - loss: 0.2851 - accuracy: 0.8805 - val_loss: 0.3269 - val_accuracy: 0.8500\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 44s 112ms/step - loss: 0.2810 - accuracy: 0.8826 - val_loss: 0.3278 - val_accuracy: 0.8557\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2794 - accuracy: 0.8831 - val_loss: 0.3301 - val_accuracy: 0.8578\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.2768 - accuracy: 0.8833 - val_loss: 0.3319 - val_accuracy: 0.8542\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2705 - accuracy: 0.8868 - val_loss: 0.3391 - val_accuracy: 0.8411\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.2692 - accuracy: 0.8877 - val_loss: 0.3452 - val_accuracy: 0.8557\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.2638 - accuracy: 0.8905 - val_loss: 0.3535 - val_accuracy: 0.8443\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2634 - accuracy: 0.8894 - val_loss: 0.3597 - val_accuracy: 0.8557\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 45s 116ms/step - loss: 0.2590 - accuracy: 0.8917 - val_loss: 0.3507 - val_accuracy: 0.8422\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2589 - accuracy: 0.8906 - val_loss: 0.3572 - val_accuracy: 0.8370\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss:', test_loss)\n",
        "print('Test Accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h203aJ2XUaS9",
        "outputId": "8d750a9b-90f1-4e81-a211-a9c60c36445a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391/391 [==============================] - 28s 71ms/step - loss: 0.3417 - accuracy: 0.8434\n",
            "Test Loss: 0.3416501581668854\n",
            "Test Accuracy: 0.8433600068092346\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on a sample text without padding.\n",
        "\n",
        "sample_text = ('The movie was not good. The animation and the graphics '\n",
        "               'were terrible. I would not recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHhzN49qUaQG",
        "outputId": "553b44f5-c40c-4d34-89f5-caf5c0aa1d52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 8 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f51d36f3760> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 6s 6s/step\n",
            "[[-3.345282]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_graphs(history, 'accuracy')\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_graphs(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "STPYYV-mUaMs",
        "outputId": "9f6d4d0d-b418-42e0-afe5-0a3223bc3997"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "meUlEOyIUyz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qxqX8EYKUyw2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "omhCQm_EUyuY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LSTM + GRU\n"
      ],
      "metadata": {
        "id": "nyjsTqD7e9XO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    encoder,\n",
        "    tf.keras.layers.Embedding(len(encoder.get_vocabulary()), 64, mask_zero=True),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,  return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1)\n",
        "])\n",
        "model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              optimizer=tf.keras.optimizers.Adam(1e-4),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "QQ1mCB5jUyqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_dataset, epochs=20,\n",
        "                    validation_data=test_dataset,\n",
        "                    validation_steps=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAht5f5vUyoH",
        "outputId": "7a1832f1-e53b-4b7e-a661-ed1845ea6d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "391/391 [==============================] - 77s 155ms/step - loss: 0.6526 - accuracy: 0.5528 - val_loss: 0.5073 - val_accuracy: 0.7542\n",
            "Epoch 2/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.4277 - accuracy: 0.8066 - val_loss: 0.3730 - val_accuracy: 0.8349\n",
            "Epoch 3/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.3577 - accuracy: 0.8484 - val_loss: 0.3467 - val_accuracy: 0.8464\n",
            "Epoch 4/20\n",
            "391/391 [==============================] - 46s 118ms/step - loss: 0.3357 - accuracy: 0.8596 - val_loss: 0.3363 - val_accuracy: 0.8484\n",
            "Epoch 5/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3227 - accuracy: 0.8660 - val_loss: 0.3255 - val_accuracy: 0.8557\n",
            "Epoch 6/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.3191 - accuracy: 0.8662 - val_loss: 0.3211 - val_accuracy: 0.8536\n",
            "Epoch 7/20\n",
            "391/391 [==============================] - 45s 115ms/step - loss: 0.3109 - accuracy: 0.8697 - val_loss: 0.3194 - val_accuracy: 0.8562\n",
            "Epoch 8/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.3038 - accuracy: 0.8718 - val_loss: 0.3379 - val_accuracy: 0.8677\n",
            "Epoch 9/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3059 - accuracy: 0.8722 - val_loss: 0.3293 - val_accuracy: 0.8469\n",
            "Epoch 10/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.3027 - accuracy: 0.8738 - val_loss: 0.3170 - val_accuracy: 0.8583\n",
            "Epoch 11/20\n",
            "391/391 [==============================] - 47s 120ms/step - loss: 0.3011 - accuracy: 0.8725 - val_loss: 0.3184 - val_accuracy: 0.8609\n",
            "Epoch 12/20\n",
            "391/391 [==============================] - 46s 118ms/step - loss: 0.2958 - accuracy: 0.8749 - val_loss: 0.3199 - val_accuracy: 0.8646\n",
            "Epoch 13/20\n",
            "391/391 [==============================] - 47s 119ms/step - loss: 0.2945 - accuracy: 0.8755 - val_loss: 0.3179 - val_accuracy: 0.8562\n",
            "Epoch 14/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.2897 - accuracy: 0.8785 - val_loss: 0.3189 - val_accuracy: 0.8552\n",
            "Epoch 15/20\n",
            "391/391 [==============================] - 46s 117ms/step - loss: 0.2864 - accuracy: 0.8789 - val_loss: 0.3260 - val_accuracy: 0.8573\n",
            "Epoch 16/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2848 - accuracy: 0.8815 - val_loss: 0.3228 - val_accuracy: 0.8484\n",
            "Epoch 17/20\n",
            "391/391 [==============================] - 45s 114ms/step - loss: 0.2824 - accuracy: 0.8795 - val_loss: 0.3303 - val_accuracy: 0.8578\n",
            "Epoch 18/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2816 - accuracy: 0.8806 - val_loss: 0.3286 - val_accuracy: 0.8448\n",
            "Epoch 19/20\n",
            "391/391 [==============================] - 44s 113ms/step - loss: 0.2799 - accuracy: 0.8828 - val_loss: 0.3360 - val_accuracy: 0.8531\n",
            "Epoch 20/20\n",
            "391/391 [==============================] - 45s 113ms/step - loss: 0.2779 - accuracy: 0.8838 - val_loss: 0.3515 - val_accuracy: 0.8615\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "\n",
        "print('Test Loss:', test_loss)\n",
        "print('Test Accuracy:', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLx9X7VpfKtO",
        "outputId": "99ce5277-6aa3-4c61-f875-53edcccb2124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "391/391 [==============================] - 19s 48ms/step - loss: 0.3508 - accuracy: 0.8598\n",
            "Test Loss: 0.3508366346359253\n",
            "Test Accuracy: 0.8597999811172485\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict on a sample text without padding.\n",
        "\n",
        "sample_text = ('The movie was not good. The animation and the graphics '\n",
        "               'were terrible. I would not recommend this movie.')\n",
        "predictions = model.predict(np.array([sample_text]))\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYLCiJzffKoW",
        "outputId": "42a105f7-dac3-4b9d-aeff-6c34d402cb86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 4s 4s/step\n",
            "[[-2.710727]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_graphs(history, 'accuracy')\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_graphs(history, 'loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "cualeU0ufKl1",
        "outputId": "3ef3790a-91a0-4155-a56f-fa56d163c9d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hov0T1qqfKf-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TOTAL\n",
        "\n"
      ],
      "metadata": {
        "id": "wVrwTyE9VEa7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "В ходе выполнения лабораторной работы по обучению сетей со слоями GRU (Gated Recurrent Unit) и LSTM (Long Short-Term Memory) были исследованы и сравнены два популярных подхода к моделированию последовательностей. Оба слоя GRU и LSTM представляют собой разновидности рекуррентных нейронных сетей, способных обрабатывать информацию с учетом долгосрочных зависимостей в последовательностях.\n",
        "\n",
        "В результате эксперимента было установлено, что оба слоя GRU и LSTM способны эффективно обрабатывать и моделировать сложные последовательности. Однако было отмечено, что LSTM обычно имеет более высокую вычислительную сложность, чем GRU, за счет использования дополнительных вентилей и ячеек памяти.\n",
        "\n",
        "Выводы из данной лабораторной работы могут быть полезны при выборе подходящего типа рекуррентного слоя в зависимости от конкретной задачи и требований к производительности. GRU может быть предпочтительным выбором, если требуется более легкая модель, а LSTM может быть полезным, если необходимо более точное моделирование долгосрочных зависимостей в данных."
      ],
      "metadata": {
        "id": "WR3d4lsDVIIC"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": [
        "_2VQo4bajwUU",
        "bjUqGVBxGw-t",
        "E2oaTHv1Rg3M",
        "nyjsTqD7e9XO"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}