Tensorflow - IMDB Sentiments Classification Using RNN-LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sahug/ds-tensorflow-colab/blob/master/Tensorflow%20-%20IMDB%20Sentiments%20Classification%20Using%20RNN-LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tjWp_iocNX21"
      },
      "source": [
        "**Tensorflow - IMDB Sentiments Classification Using RNN-LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ZKegetpaNxo5"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2wYE9BQOa3Z",
        "outputId": "88633cb2-a378-4541-d4e4-b77febc355d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ClKpIBOQW1f"
      },
      "source": [
        "**Load Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0uPJHBjmQNwA"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 11s 1us/step\n",
            "17473536/17464789 [==============================] - 11s 1us/step\n"
          ]
        }
      ],
      "source": [
        "(X_train, Y_train), (X_test, Y_test) = keras.datasets.imdb.load_data(num_words=20000)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Apply Padding**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qnmzxfIBRErT"
      },
      "outputs": [],
      "source": [
        "# padding any reviews which has more that 100 length\n",
        "X_train = pad_sequences(X_train, maxlen=100)\n",
        "X_test = pad_sequences(X_test, maxlen=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "f71LVpb9Rw-X"
      },
      "outputs": [],
      "source": [
        "voacab_size = 20000\n",
        "embed_size = 128"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PxhmETMQR8MO"
      },
      "source": [
        "**Build LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "ctODIGqeR6Io"
      },
      "outputs": [],
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import LSTM, Dropout, Dense, Embedding, Activation \n",
        "# Embedding is advance version of converting words to vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "AnEP4PdTSgVE"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(voacab_size, embed_size, input_shape=(X_train.shape[1],)))\n",
        "model.add(LSTM(units=60, activation=keras.activations.tanh))\n",
        "model.add(Dense(units=1, activation=keras.activations.sigmoid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRBDST_7TlWu",
        "outputId": "e758e8c7-0592-4564-9951-9436a842ffa9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 100, 128)          2560000   \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 60)                45360     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 61        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,605,421\n",
            "Trainable params: 2,605,421\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "S7x92hf-Ts5B"
      },
      "outputs": [],
      "source": [
        "# model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001), \n",
        "    loss=keras.losses.BinaryCrossentropy(), \n",
        "    metrics=[\"accuracy\"]\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ar5BME0pT1-O",
        "outputId": "578f99cd-7d34-4cab-f846-36b518f298a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "Epoch 2/5\n",
            "Epoch 3/5\n",
            "Epoch 4/5\n",
            "Epoch 5/5\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, Y_train, epochs=5, batch_size=128, validation_data=(X_test, Y_test), verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "teVVodFFXx0J",
        "outputId": "3c5ef150-fbee-419f-edd6-d5f49a38c734"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation accuracy values\n",
        "# sparse_categorical_accuracy vs val_sparse_categorical_accuracy\n",
        "plt.plot(range(1, 6), history.history[\"accuracy\"])\n",
        "plt.plot(range(1, 6), history.history[\"val_accuracy\"])\n",
        "plt.title(\"Model Accuracy\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FlmIVtnCYBQ5",
        "outputId": "3a88c68e-a822-4b7c-e095-519051f45036"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot training and validation loss values\n",
        "# Loss vs Val_Loss\n",
        "plt.plot(range(1, 6), history.history[\"loss\"])\n",
        "plt.plot(range(1, 6), history.history[\"val_loss\"])\n",
        "plt.title(\"Model Loss\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend([\"Train\", \"Val\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "name": "Tensorflow - IMDB Sentiments Classification Using RNN-LSTM.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "0d7fd96aeadd740978789f2880dd43822d8681a39e327029c33ff72656207c4b"
    },
    "kernelspec": {
      "display_name": "Python 3.10.4 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}