pretrained_embeddings.ipynb

{
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  "metadata": {
    "colab": {
      "name": "pretrained-embeddings.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sanuj/tf-basics/blob/master/pretrained_embeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FhKq3qdKjNrG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aab8cbdc-dd3e-4ad7-9fd5-6008a43105dd"
      },
      "cell_type": "code",
      "source": [
        "! wget http://s3.amazonaws.com/text-datasets/aclImdb.zip\n",
        "! unzip -q aclImdb.zip\n",
        "! ls"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "aclImdb  aclImdb.zip  __MACOSX\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bDhPzM9Xkerb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Processing the labels of the raw IMDB data\n",
        "import os\n",
        "\n",
        "imdb_dir = './aclImdb'\n",
        "train_dir = os.path.join(imdb_dir, 'train')\n",
        "\n",
        "labels = []\n",
        "texts = []\n",
        "\n",
        "for label_type in ['neg', 'pos']:\n",
        "  dir_name = os.path.join(train_dir, label_type)\n",
        "\n",
        "  for fname in os.listdir(dir_name):\n",
        "    if fname[-4:] == '.txt':\n",
        "      f = open(os.path.join(dir_name, fname))\n",
        "      texts.append(f.read())\n",
        "      f.close()\n",
        "      if label_type == 'neg':\n",
        "        labels.append(0)\n",
        "      else:\n",
        "        labels.append(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WTtWq-1jogs5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "121b9149-6c17-41e0-f4f3-e123255893d5"
      },
      "cell_type": "code",
      "source": [
        "# Tokenizing the text of the raw IMDB data\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "import numpy as np\n",
        "\n",
        "# Cuts off reviews after 100 words\n",
        "maxlen = 100\n",
        "# Trains on 200 samples\n",
        "training_samples = 200\n",
        "# Validates on 10,000 samples\n",
        "validation_samples = 10000\n",
        "# Considers only the top 10,000 words in the dataset\n",
        "max_words = 10000\n",
        "\n",
        "tokenizer = Tokenizer(num_words=max_words)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "print('Found %s unique tokens.' % len(word_index))\n",
        "\n",
        "data = pad_sequences(sequences, maxlen=maxlen)\n",
        "labels = np.asarray(labels)\n",
        "print('Shape of data tensor:', data.shape)\n",
        "print('Shape of label tensor:', labels.shape)\n",
        "\n",
        "# Splits the data into a training set and a validation set, but first shuffles the data,\n",
        "# because you’re starting with data in which samples are ordered (all negative first, then\n",
        "# all positive)\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]\n",
        "\n",
        "x_train = data[:training_samples]\n",
        "y_train = labels[:training_samples]\n",
        "x_val = data[training_samples: training_samples + validation_samples]\n",
        "y_val = labels[training_samples: training_samples + validation_samples]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Found 88582 unique tokens.\n",
            "Shape of data tensor: (25000, 100)\n",
            "Shape of label tensor: (25000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hwnJ0GiCok3a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "63a0e7bf-610a-4fc6-90b8-082fadec987a"
      },
      "cell_type": "code",
      "source": [
        "# Downloading Glove\n",
        "! wget http://nlp.stanford.edu/data/glove.6B.zip\n",
        "! unzip -q glove.6B.zip"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "aclImdb      glove.6B.100d.txt\tglove.6B.300d.txt  glove.6B.zip  sample_data\n",
            "aclImdb.zip  glove.6B.200d.txt\tglove.6B.50d.txt   __MACOSX\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iiSxAciyrsfG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1d38c34d-98b6-47cd-8ff0-9a987066499d"
      },
      "cell_type": "code",
      "source": [
        "# Parsing the GloVe word-embeddings file\n",
        "glove_dir = './'\n",
        "\n",
        "embeddings_index = {}\n",
        "f = open(os.path.join(glove_dir, 'glove.6B.100d.txt'))\n",
        "for line in f:\n",
        "  values = line.split()\n",
        "  word = values[0]\n",
        "  coefs = np.asarray(values[1:], dtype='float32')\n",
        "  embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "print('Found %s word vectors.' % len(embeddings_index))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "q4fR4pCUvXVU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Preparing the GloVe word-embeddings matrix\n",
        "embedding_dim = 100\n",
        "\n",
        "embedding_matrix = np.zeros((max_words, embedding_dim))\n",
        "for word, i in word_index.items():\n",
        "  if i < max_words:\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    # Words not found in the embedding index will be all zeros.\n",
        "    if embedding_vector is not None:\n",
        "      embedding_matrix[i] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "398aQh3Dvps1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "13ae8edd-5019-4e10-fe7b-d29adf6ce21d"
      },
      "cell_type": "code",
      "source": [
        "# Model definition\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding, Flatten, Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 17,
      "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, 100, 100)          1000000   \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                320032    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 1,320,065\n",
            "Trainable params: 1,320,065\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VbKjOCY0wJDq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1135
        },
        "outputId": "880bb46c-7bd6-41a8-e365-b6877ec3ff13"
      },
      "cell_type": "code",
      "source": [
        "# Loading pretrained word embeddings into the Embedding layer\n",
        "model.layers[0].set_weights([embedding_matrix])\n",
        "model.layers[0].trainable = False\n",
        "\n",
        "# Training and evaluation\n",
        "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
        "history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))\n",
        "model.save_weights('pre_trained_glove_model.h5')\n",
        "\n",
        "# Plotting the results\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "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 200 samples, validate on 10000 samples\n",
            "Epoch 1/10\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 1.6981 - acc: 0.4700 - val_loss: 0.7173 - val_acc: 0.5052\n",
            "Epoch 2/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.5101 - acc: 0.8000 - val_loss: 0.9753 - val_acc: 0.5031\n",
            "Epoch 3/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4398 - acc: 0.8500 - val_loss: 0.6804 - val_acc: 0.5718\n",
            "Epoch 4/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.2884 - acc: 0.9700 - val_loss: 0.9993 - val_acc: 0.4979\n",
            "Epoch 5/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3011 - acc: 0.8650 - val_loss: 1.0281 - val_acc: 0.5065\n",
            "Epoch 6/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4359 - acc: 0.7350 - val_loss: 1.2814 - val_acc: 0.5034\n",
            "Epoch 7/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.1663 - acc: 0.9450 - val_loss: 0.7927 - val_acc: 0.5380\n",
            "Epoch 8/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.0939 - acc: 1.0000 - val_loss: 0.6962 - val_acc: 0.5802\n",
            "Epoch 9/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.0626 - acc: 1.0000 - val_loss: 0.7215 - val_acc: 0.5765\n",
            "Epoch 10/10\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.0498 - acc: 1.0000 - val_loss: 0.7870 - val_acc: 0.5650\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lKYRF5Irs2uV+81Fau4iUpL8WEbkizZrZL6tdREpSCIvIFRk+vMBt+7Bh7ttF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\nZXrTgLgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j8vKAmJgcDB4s/Zk+Zs7MhZ2diDlz7JGba+3SEFkHw5nIRm3ZosDbbztALge+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\nJIbhTEREJDH/D6ItOkEqehlzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4J45oCd4wiea",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}