Lab9_Using_the_sequencing_apis.ipynb

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      "name": "Course 3 - Week 1 - Lesson 2.ipynb",
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        "<a href=\"https://colab.research.google.com/github/vidyesh95/tensorFlowTutorial/blob/main/Lab9-TensorFlow%20In%20Practice-Course%203%20-%20NLP-Course%203%20-%20Week%201%20-%20Lesson%202.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "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": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%203%20-%20NLP/Course%203%20-%20Week%201%20-%20Lesson%202.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rX8mhOLljYeM"
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      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZSlp3DAjdYf"
      },
      "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": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "ArOPfBwyZtln",
        "outputId": "d3daad61-804a-4b63-b05e-2c99631e463c"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "sentences = [\n",
        "    'I love my dog',\n",
        "    'I love my cat',\n",
        "    'You love my dog!',\n",
        "    'Do you think my dog is amazing?'\n",
        "]\n",
        "\n",
        "tokenizer = Tokenizer(num_words = 100, oov_token=\"<OOV>\")\n",
        "tokenizer.fit_on_texts(sentences)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentences)\n",
        "\n",
        "padded = pad_sequences(sequences, maxlen=5)\n",
        "print(\"\\nWord Index = \" , word_index)\n",
        "print(\"\\nSequences = \" , sequences)\n",
        "print(\"\\nPadded Sequences:\")\n",
        "print(padded)\n",
        "\n",
        "\n",
        "# Try with words that the tokenizer wasn't fit to\n",
        "test_data = [\n",
        "    'i really love my dog',\n",
        "    'my dog loves my manatee'\n",
        "]\n",
        "\n",
        "test_seq = tokenizer.texts_to_sequences(test_data)\n",
        "print(\"\\nTest Sequence = \", test_seq)\n",
        "\n",
        "padded = pad_sequences(test_seq, maxlen=10)\n",
        "print(\"\\nPadded Test Sequence: \")\n",
        "print(padded)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Word Index =  {'<OOV>': 1, 'my': 2, 'love': 3, 'dog': 4, 'i': 5, 'you': 6, 'cat': 7, 'do': 8, 'think': 9, 'is': 10, 'amazing': 11}\n",
            "\n",
            "Sequences =  [[5, 3, 2, 4], [5, 3, 2, 7], [6, 3, 2, 4], [8, 6, 9, 2, 4, 10, 11]]\n",
            "\n",
            "Padded Sequences:\n",
            "[[ 0  5  3  2  4]\n",
            " [ 0  5  3  2  7]\n",
            " [ 0  6  3  2  4]\n",
            " [ 9  2  4 10 11]]\n",
            "\n",
            "Test Sequence =  [[5, 1, 3, 2, 4], [2, 4, 1, 2, 1]]\n",
            "\n",
            "Padded Test Sequence: \n",
            "[[0 0 0 0 0 5 1 3 2 4]\n",
            " [0 0 0 0 0 2 4 1 2 1]]\n"
          ]
        }
      ]
    }
  ]
}