Multi_layer_LSTM_Course_4_Week_3_Lesson_1b.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Multi layer LSTM Course 4 - Week 3 - Lesson 1b.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/neelsoumya/nlp_resources/blob/master/Multi_layer_LSTM_Course_4_Week_3_Lesson_1b.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rFiCyWQ-NC5D",
        "colab_type": "text"
      },
      "source": [
        "# Multiple Layer LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y20Lud2ZMBhW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aclov8NVMJml",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# If the tf.__version__ is 1.x, please run this cell\n",
        "!pip install tensorflow==2.0.0-beta0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAU8g7C0MPZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW-4Vo4TMUHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the data\n",
        "dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L11bIR6-PKvs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffvRUI0_McDS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n",
        "train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)\n",
        "test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jo1jjO3vn0jo",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\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.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKI5dfPgMioL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uip7QOVzMoMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mlgzaRDMtF6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NUM_EPOCHS = 10\n",
        "history = model.fit(train_dataset, epochs=NUM_EPOCHS, validation_data=test_dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Mp1Z7P9pYRSK",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_sX6ilIM515",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_graphs(history, 'accuracy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFEXtKtqNARB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}