dsc_imdb.ipynb


    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shahkv95/Deep-learning---tf/blob/master/dsc_imdb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "R8a0w84uslEp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2043
        },
        "outputId": "a1e646ca-651d-4731-d927-84a544320ee1"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "imdb = keras.datasets.imdb\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)\n",
        "\n",
        "print(\"Training entries: {}, labels: {}\".format(len(train_data), len(train_labels)))\n",
        "print(train_data[0])\n",
        "word_index = imdb.get_word_index()\n",
        "\n",
        "# The first indices are reserved\n",
        "word_index = {k:(v+3) for k,v in word_index.items()}\n",
        "word_index[\"<PAD>\"] = 0\n",
        "word_index[\"<START>\"] = 1\n",
        "word_index[\"<UNK>\"] = 2  # unknown\n",
        "word_index[\"<UNUSED>\"] = 3\n",
        "\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])\n",
        "\n",
        "decode_review(train_data[0])\n",
        "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n",
        "                                                        value=word_index[\"<PAD>\"],\n",
        "                                                        padding='post'w,\n",
        "                                                        maxlen=256)\n",
        "\n",
        "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n",
        "                                                       value=word_index[\"<PAD>\"],\n",
        "                                                       padding='post',\n",
        "                                                       maxlen=256)\n",
        "vocab_size = 10000\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Embedding(vocab_size, 16))\n",
        "model.add(keras.layers.GlobalAveragePooling1D())\n",
        "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n",
        "model.add(keras.layers.Dropout(0.3))\n",
        "model.add(keras.layers.Dense(32, activation=tf.nn.relu))\n",
        "model.add(keras.layers.Dropout(0.3))\n",
        "model.add(keras.layers.Dense(64, activation=tf.nn.relu))\n",
        "model.add(keras.layers.Dropout(0.3))\n",
        "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
        "\n",
        "model.summary()\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['acc'])\n",
        "# x_val = train_data[:10000]\n",
        "# partial_x_train = train_data[10000:]\n",
        "#\n",
        "# y_val = train_labels[:10000]\n",
        "# partial_y_train = train_labels[10000:]\n",
        "\n",
        "history = model.fit(train_data,\n",
        "                    train_labels,\n",
        "                    epochs=40,\n",
        "                    batch_size=512,\n",
        "                    verbose=1)\n",
        "results = model.evaluate(test_data, test_labels)\n",
        "print(results)\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training entries: 25000, labels: 25000\n",
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: 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",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 16)          160000    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                544       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 162,993\n",
            "Trainable params: 162,993\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "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",
            "Epoch 1/40\n",
            "25000/25000 [==============================] - 1s 56us/sample - loss: 0.6918 - acc: 0.5336\n",
            "Epoch 2/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.6463 - acc: 0.6981\n",
            "Epoch 3/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.4443 - acc: 0.8417\n",
            "Epoch 4/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.3010 - acc: 0.8914\n",
            "Epoch 5/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.2457 - acc: 0.9146\n",
            "Epoch 6/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.2060 - acc: 0.9307\n",
            "Epoch 7/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.1797 - acc: 0.9421\n",
            "Epoch 8/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.1561 - acc: 0.9532\n",
            "Epoch 9/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.1355 - acc: 0.9610\n",
            "Epoch 10/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.1232 - acc: 0.9662\n",
            "Epoch 11/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.1114 - acc: 0.9697\n",
            "Epoch 12/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.1017 - acc: 0.9738\n",
            "Epoch 13/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0923 - acc: 0.9786\n",
            "Epoch 14/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0826 - acc: 0.9819\n",
            "Epoch 15/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0795 - acc: 0.9827\n",
            "Epoch 16/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0735 - acc: 0.9840\n",
            "Epoch 17/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0684 - acc: 0.9856\n",
            "Epoch 18/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0646 - acc: 0.9868\n",
            "Epoch 19/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0657 - acc: 0.9855\n",
            "Epoch 20/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0630 - acc: 0.9856\n",
            "Epoch 21/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0559 - acc: 0.9889\n",
            "Epoch 22/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0528 - acc: 0.9896\n",
            "Epoch 23/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0504 - acc: 0.9898\n",
            "Epoch 24/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0523 - acc: 0.9889\n",
            "Epoch 25/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0527 - acc: 0.9882\n",
            "Epoch 26/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0465 - acc: 0.9910\n",
            "Epoch 27/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0444 - acc: 0.9911\n",
            "Epoch 28/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0434 - acc: 0.9914\n",
            "Epoch 29/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0407 - acc: 0.9920\n",
            "Epoch 30/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0401 - acc: 0.9922\n",
            "Epoch 31/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0373 - acc: 0.9926\n",
            "Epoch 32/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0401 - acc: 0.9916\n",
            "Epoch 33/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0392 - acc: 0.9916\n",
            "Epoch 34/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0379 - acc: 0.9919\n",
            "Epoch 35/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0379 - acc: 0.9924\n",
            "Epoch 36/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0382 - acc: 0.9915\n",
            "Epoch 37/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0366 - acc: 0.9925\n",
            "Epoch 38/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0353 - acc: 0.9931\n",
            "Epoch 39/40\n",
            "25000/25000 [==============================] - 0s 17us/sample - loss: 0.0335 - acc: 0.9931\n",
            "Epoch 40/40\n",
            "25000/25000 [==============================] - 0s 18us/sample - loss: 0.0340 - acc: 0.9933\n",
            "25000/25000 [==============================] - 2s 68us/sample - loss: 0.9630 - acc: 0.8455\n",
            "[0.9630127204018831, 0.84548]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}