Course3Week2Lesson1.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Course 3 - Week 2 - Lesson 1.ipynb",
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      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zX4Kg8DUTKWO",
        "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": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%203%20-%20NLP/Course%203%20-%20Week%202%20-%20Lesson%201.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "P-AhVYeBWgQ3",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "# !pip install -q tensorflow-datasets"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_IoM4VFxWpMR",
        "colab": {}
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wHQ2Ko0zl7M4",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "train_data, test_data = imdb['train'], imdb['test']\n",
        "\n",
        "training_sentences = []\n",
        "training_labels = []\n",
        "\n",
        "testing_sentences = []\n",
        "testing_labels = []\n",
        "\n",
        "# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()\n",
        "for s,l in train_data:\n",
        "  training_sentences.append(s.numpy().decode('utf8'))\n",
        "  training_labels.append(l.numpy())\n",
        "  \n",
        "for s,l in test_data:\n",
        "  testing_sentences.append(s.numpy().decode('utf8'))\n",
        "  testing_labels.append(l.numpy())\n",
        "  \n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7n15yyMdmoH1",
        "colab": {}
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen=max_length)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9axf0uIXVMhO",
        "colab": {}
      },
      "source": [
        "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",
        "print(decode_review(padded[3]))\n",
        "print(training_sentences[3])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5NEpdhb8AxID",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V5LLrXC-uNX6",
        "colab": {}
      },
      "source": [
        "num_epochs = 10\n",
        "model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yAmjJqEyCOF_",
        "colab": {}
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape) # shape: (vocab_size, embedding_dim)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jmB0Uxk0ycP6",
        "colab": {}
      },
      "source": [
        "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, vocab_size):\n",
        "  word = reverse_word_index[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()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VDeqpOCVydtq",
        "colab": {}
      },
      "source": [
        "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YRxoxc2apscY",
        "colab": {}
      },
      "source": [
        "sentence = \"I really think this is amazing. honest.\"\n",
        "sequence = tokenizer.texts_to_sequences([sentence])\n",
        "print(sequence)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}