IMDb_sentiment_in_Keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IMDb sentiment in Keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mihirahlawat/NLP_basics/blob/master/IMDb_sentiment_in_Keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "15aMtZPpJcho",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# A Beginner’s Guide on Sentiment Analysis with RNN\n",
        "This code is practice implementation of a blog by Susan Li.\n",
        "https://towardsdatascience.com/@actsusanli"
      ]
    },
    {
      "metadata": {
        "id": "EBC_98dgWMfF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import imdb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1tG5Fe8eWcCS",
        "colab_type": "code",
        "outputId": "d6ecebbf-9e53-4dc8-b531-bcf2f198dc6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "vocabulary_size = 5000\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words = vocabulary_size)\n",
        "print('Loaded dataset with {} training samples, {} test samples'.format(len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded dataset with 25000 training samples, 25000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ePyPYBU2Wo8L",
        "colab_type": "code",
        "outputId": "a8d3161f-dcbe-4359-bd2e-d7875f5597f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "cell_type": "code",
      "source": [
        "print('---review---')\n",
        "print(X_train[6])\n",
        "print('---label---')\n",
        "print(y_train[6])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---review---\n",
            "[1, 2, 365, 1234, 5, 1156, 354, 11, 14, 2, 2, 7, 1016, 2, 2, 356, 44, 4, 1349, 500, 746, 5, 200, 4, 4132, 11, 2, 2, 1117, 1831, 2, 5, 4831, 26, 6, 2, 4183, 17, 369, 37, 215, 1345, 143, 2, 5, 1838, 8, 1974, 15, 36, 119, 257, 85, 52, 486, 9, 6, 2, 2, 63, 271, 6, 196, 96, 949, 4121, 4, 2, 7, 4, 2212, 2436, 819, 63, 47, 77, 2, 180, 6, 227, 11, 94, 2494, 2, 13, 423, 4, 168, 7, 4, 22, 5, 89, 665, 71, 270, 56, 5, 13, 197, 12, 161, 2, 99, 76, 23, 2, 7, 419, 665, 40, 91, 85, 108, 7, 4, 2084, 5, 4773, 81, 55, 52, 1901]\n",
            "---label---\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "O6jzRo3JKowv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here each number represents a word from the vocabulary of 5000 words."
      ]
    },
    {
      "metadata": {
        "id": "KJe3ehQ1WxBN",
        "colab_type": "code",
        "outputId": "2a1987e8-91e6-4c5c-d911-cedd231e2bfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "cell_type": "code",
      "source": [
        "word2id = imdb.get_word_index()\n",
        "id2word = {i: word for word, i in word2id.items()}\n",
        "print('---review with words---')\n",
        "print([id2word.get(i) for i in X_train[6]])\n",
        "print('---label---')\n",
        "print(y_train[6])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---review with words---\n",
            "['the', 'and', 'full', 'involving', 'to', 'impressive', 'boring', 'this', 'as', 'and', 'and', 'br', 'villain', 'and', 'and', 'need', 'has', 'of', 'costumes', 'b', 'message', 'to', 'may', 'of', 'props', 'this', 'and', 'and', 'concept', 'issue', 'and', 'to', \"god's\", 'he', 'is', 'and', 'unfolds', 'movie', 'women', 'like', \"isn't\", 'surely', \"i'm\", 'and', 'to', 'toward', 'in', \"here's\", 'for', 'from', 'did', 'having', 'because', 'very', 'quality', 'it', 'is', 'and', 'and', 'really', 'book', 'is', 'both', 'too', 'worked', 'carl', 'of', 'and', 'br', 'of', 'reviewer', 'closer', 'figure', 'really', 'there', 'will', 'and', 'things', 'is', 'far', 'this', 'make', 'mistakes', 'and', 'was', \"couldn't\", 'of', 'few', 'br', 'of', 'you', 'to', \"don't\", 'female', 'than', 'place', 'she', 'to', 'was', 'between', 'that', 'nothing', 'and', 'movies', 'get', 'are', 'and', 'br', 'yes', 'female', 'just', 'its', 'because', 'many', 'br', 'of', 'overly', 'to', 'descent', 'people', 'time', 'very', 'bland']\n",
            "---label---\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tv1FweE8KxVI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we map each number above with its actual word from the word_index."
      ]
    },
    {
      "metadata": {
        "id": "GC_O5XpPXOWR",
        "colab_type": "code",
        "outputId": "ad39a6c6-1bcf-4055-dc7d-8c13609e38e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print('Maximum review length: {}'.format(\n",
        "len(max((X_train + X_test), key=len))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum review length: 2697\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ky16vSMVYUMQ",
        "colab_type": "code",
        "outputId": "c2f16a92-9d20-4ff8-a633-caed4761a54d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print('Minimum review length: {}'.format(\n",
        "len(min((X_test + X_test), key=len))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Minimum review length: 14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "jUVQLQQTLG5l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We now set the size of each input sequence as 500.\n",
        "Those below 500 will be filled with a null value (0) and those above 500 would be truncated using the pad_sequences method."
      ]
    },
    {
      "metadata": {
        "id": "ixHkSAvJYd22",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.preprocessing import sequence\n",
        "max_words = 500\n",
        "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
        "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9QQbZTPlM58I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Now we set the size of embedding vectors as 32.\n",
        "And create a model using Keras."
      ]
    },
    {
      "metadata": {
        "id": "mQh1D3ueYr9I",
        "colab_type": "code",
        "outputId": "4fdbde56-91de-4673-8b7b-c5984c2c859c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "cell_type": "code",
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Embedding, LSTM, Dense, Dropout\n",
        "embedding_size=32\n",
        "model=Sequential()\n",
        "model.add(Embedding(vocabulary_size, embedding_size, input_length=max_words))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 500, 32)           160000    \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 100)               53200     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 213,301\n",
            "Trainable params: 213,301\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "p67jvzAjBmxd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy', \n",
        "             optimizer='adam', \n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AQLMczcSB9_k",
        "colab_type": "code",
        "outputId": "8526b683-1e9c-4945-e009-9187738bea46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "num_epochs = 3\n",
        "X_valid, y_valid = X_train[:batch_size], y_train[:batch_size]\n",
        "X_train2, y_train2 = X_train[batch_size:], y_train[batch_size:]\n",
        "model.fit(X_train2, y_train2, validation_data=(X_valid, y_valid), batch_size=batch_size, epochs=num_epochs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Train on 24936 samples, validate on 64 samples\n",
            "Epoch 1/3\n",
            "24936/24936 [==============================] - 399s 16ms/step - loss: 0.4810 - acc: 0.7633 - val_loss: 0.2460 - val_acc: 0.9531\n",
            "Epoch 2/3\n",
            "24936/24936 [==============================] - 398s 16ms/step - loss: 0.3430 - acc: 0.8497 - val_loss: 0.3023 - val_acc: 0.8750\n",
            "Epoch 3/3\n",
            "24936/24936 [==============================] - 398s 16ms/step - loss: 0.3991 - acc: 0.8323 - val_loss: 0.2732 - val_acc: 0.9062\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f33c3051ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "RRLxT6Q8CTmB",
        "colab_type": "code",
        "outputId": "28325121-5583-4fb7-d1b8-96e1b0451f16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test accuracy:', scores[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.84748\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}