learning_embedding.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "learning-embedding.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/learning_embedding.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "awkPfuENS0vL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "8dba50b3-90e1-4f6a-91f8-09277cd4f46b"
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import imdb\n",
        "from keras import preprocessing\n",
        "\n",
        "# hyper-params\n",
        "max_features = 10000\n",
        "maxlen = 20\n",
        "\n",
        "# loading and padding data\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
        "x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)\n",
        "x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)\n",
        "                                               \n",
        "from keras.models import Sequential\n",
        "from keras.layers import Flatten, Dense\n",
        "from keras.layers import Embedding\n",
        "\n",
        "# building model\n",
        "model = Sequential()\n",
        "model.add(Embedding(10000, 8, input_length=maxlen))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# compile model\n",
        "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
        "model.summary()\n",
        "\n",
        "# train the model\n",
        "history = model.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "print(history)\n",
        "\n",
        "# results on test data\n",
        "results = model.evaluate(x_test, y_test)\n",
        "print(results)"
      ],
      "execution_count": 2,
      "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, 20, 8)             80000     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 160)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 161       \n",
            "=================================================================\n",
            "Total params: 80,161\n",
            "Trainable params: 80,161\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",
            "Train on 20000 samples, validate on 5000 samples\n",
            "Epoch 1/10\n",
            "20000/20000 [==============================] - 2s 87us/step - loss: 0.6759 - acc: 0.6043 - val_loss: 0.6398 - val_acc: 0.6810\n",
            "Epoch 2/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.5657 - acc: 0.7428 - val_loss: 0.5467 - val_acc: 0.7206\n",
            "Epoch 3/10\n",
            "20000/20000 [==============================] - 1s 54us/step - loss: 0.4752 - acc: 0.7808 - val_loss: 0.5113 - val_acc: 0.7384\n",
            "Epoch 4/10\n",
            "20000/20000 [==============================] - 1s 54us/step - loss: 0.4263 - acc: 0.8079 - val_loss: 0.5008 - val_acc: 0.7454\n",
            "Epoch 5/10\n",
            "20000/20000 [==============================] - 1s 54us/step - loss: 0.3930 - acc: 0.8257 - val_loss: 0.4981 - val_acc: 0.7540\n",
            "Epoch 6/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.3668 - acc: 0.8395 - val_loss: 0.5013 - val_acc: 0.7534\n",
            "Epoch 7/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.3435 - acc: 0.8534 - val_loss: 0.5051 - val_acc: 0.7518\n",
            "Epoch 8/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.3223 - acc: 0.8658 - val_loss: 0.5132 - val_acc: 0.7486\n",
            "Epoch 9/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.3022 - acc: 0.8765 - val_loss: 0.5213 - val_acc: 0.7492\n",
            "Epoch 10/10\n",
            "20000/20000 [==============================] - 1s 55us/step - loss: 0.2839 - acc: 0.8860 - val_loss: 0.5302 - val_acc: 0.7466\n",
            "<keras.callbacks.History object at 0x7f37a8d9ab00>\n",
            "25000/25000 [==============================] - 0s 14us/step\n",
            "[0.5214665159606934, 0.75592]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}