Course3Week2Lesson3.ipynb

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    "accelerator": "GPU",
    "colab": {
      "name": "Course 3 - Week 2 - Lesson 3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "P-AhVYeBWgQ3",
        "colab": {}
      },
      "source": [
        "# NOTE: PLEASE MAKE SURE YOU ARE RUNNING THIS IN A PYTHON3 ENVIRONMENT\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wm9S3T8-9H4q",
        "colab": {}
      },
      "source": [
        "# Double check TF 2.0x is installed. If you ran the above block, there was a \n",
        "# 'reset all runtimes' button at the bottom that you needed to press\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_IoM4VFxWpMR",
        "colab": {}
      },
      "source": [
        "# If the import fails, run this\n",
        "# !pip install -q tensorflow-datasets\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews/subwords8k\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wHQ2Ko0zl7M4",
        "colab": {}
      },
      "source": [
        "train_data, test_data = imdb['train'], imdb['test']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fqGRSe_eCdOz",
        "colab": {}
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F6b_EraCDLOh",
        "colab": {}
      },
      "source": [
        "print(tokenizer.subwords)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fPl2BXhYEHRP",
        "colab": {}
      },
      "source": [
        "sample_string = 'TensorFlow, from basics to mastery'\n",
        "\n",
        "tokenized_string = tokenizer.encode(sample_string)\n",
        "print ('Tokenized string is {}'.format(tokenized_string))\n",
        "\n",
        "original_string = tokenizer.decode(tokenized_string)\n",
        "print ('The original string: {}'.format(original_string))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_3t7vvNLEZml",
        "colab": {}
      },
      "source": [
        "for ts in tokenized_string:\n",
        "  print ('{} ----> {}'.format(ts, tokenizer.decode([ts])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5NEpdhb8AxID",
        "colab": {}
      },
      "source": [
        "embedding_dim = 64\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, embedding_dim),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fkt8c5dNuUlT",
        "colab": {}
      },
      "source": [
        "num_epochs = 10\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_data, epochs=num_epochs, validation_data=test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-_rMnm7WxQGT",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qACq5FLzTW4A",
        "colab": {}
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape) # shape: (vocab_size, embedding_dim)\n",
        "\n",
        "import io\n",
        "\n",
        "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n",
        "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n",
        "for word_num in range(1, tokenizer.vocab_size):\n",
        "  word = tokenizer.decode([word_num])\n",
        "  embeddings = weights[word_num]\n",
        "  out_m.write(word + \"\\n\")\n",
        "  out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\n",
        "out_v.close()\n",
        "out_m.close()\n",
        "\n",
        "\n",
        "try:\n",
        "  from google.colab import files\n",
        "except ImportError:\n",
        "  pass\n",
        "else:\n",
        "  files.download('vecs.tsv')\n",
        "  files.download('meta.tsv')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}