TF 2 Tutorials/text_classification_rnn.ipynb

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      "name": "text_classification_rnn.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshildarji/ML-Practise/blob/master/TF%202%20Tutorials/text_classification_rnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYNsZJMVcLLp",
        "colab_type": "text"
      },
      "source": [
        "### [Text classification with an RNN](https://www.tensorflow.org/beta/tutorials/text/text_classification_rnn)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vk_gY2EGcLLr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "46a389d5-cf3f-4eb7-c975-ecc61c4d2476"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "tfds.disable_progress_bar()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5JfEZhdcLLx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ffb3e996-9321-4137-bf08-35f978dca848"
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-rc0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuWEBoa2cLL8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-GRWDzWcLMB",
        "colab_type": "text"
      },
      "source": [
        "Just a small helper function to plot graphs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wjlhPcscLMH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot(history, string):\n",
        "    plt.plot(history.history[string], label='Training '+string)\n",
        "    plt.plot(history.history['val_'+string], label='Validation '+string)\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel(string.title())\n",
        "    plt.legend();"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b75AP4wwcLML",
        "colab_type": "text"
      },
      "source": [
        "#### Load and prepare the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPbrygLpcLMM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD8QGjs-cLMR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrFyPvQmcLMY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twLWA69LcLMc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6b38e92-32fc-4c04-9c31-81e4487bcf62"
      },
      "source": [
        "print('Vocab size: {}'.format(tokenizer.vocab_size))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocab size: 8185\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxEu5k_lcLMi",
        "colab_type": "text"
      },
      "source": [
        "An example!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fZwJmVTcLMr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_string = 'Hello World'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgrXp4ABcLM4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f3611e94-6dd7-49e3-be91-1b7f6ac0bdc6"
      },
      "source": [
        "tokenized_string = tokenizer.encode(sample_string)\n",
        "print('Tokenized string: {}'.format(tokenized_string))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenized string: [4025, 222, 2307, 1829]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yc206qAocLND",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1defd861-2699-4bc9-9c13-dad29ec66bd2"
      },
      "source": [
        "original_string = tokenizer.decode(tokenized_string)\n",
        "print('Original string: {}'.format(original_string))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original string: Hello World\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xPFo4u8pcLNL",
        "colab_type": "text"
      },
      "source": [
        "This tokenizer encodes the string by breaking it into subwords if the word is not in its dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDLxEuu9cLNO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "45d81d0d-cac4-40d9-96b1-76a7c43028ee"
      },
      "source": [
        "for ts in tokenized_string:\n",
        "    print('{}\\t-->\\t{}'.format(ts, tokenizer.decode([ts])))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4025\t-->\tHell\n",
            "222\t-->\to \n",
            "2307\t-->\tWor\n",
            "1829\t-->\tld\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6cHpCCjcLNW",
        "colab_type": "text"
      },
      "source": [
        "Back to dataset preparation!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7e82G7FcLNY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9PTgKe6cLNj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n",
        "train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-td2PqXcLNp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4kWc75acLNw",
        "colab_type": "text"
      },
      "source": [
        "#### Define the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnkduTSpcLNy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nd6kYxFmcLN2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "             loss='binary_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7nR57arcLN7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "2673e4fb-48ec-41fc-a669-d3451e5ec21b"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 64)          523840    \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 128)               66048     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 598,209\n",
            "Trainable params: 598,209\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzXwQQiFcLOK",
        "colab_type": "text"
      },
      "source": [
        "#### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQRkuOiHcLON",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "afe4c287-3a64-4df4-fd28-e3f18d7522d6"
      },
      "source": [
        "history = model.fit(train_dataset, epochs=10, validation_data=test_dataset)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 182s 465ms/step - loss: 0.5408 - accuracy: 0.7284 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 176s 449ms/step - loss: 0.3825 - accuracy: 0.8454 - val_loss: 0.4324 - val_accuracy: 0.8204\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 173s 443ms/step - loss: 0.4309 - accuracy: 0.8188 - val_loss: 0.7105 - val_accuracy: 0.7592\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 173s 441ms/step - loss: 0.3809 - accuracy: 0.8384 - val_loss: 0.5806 - val_accuracy: 0.7278\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 172s 440ms/step - loss: 0.4397 - accuracy: 0.8091 - val_loss: 0.5589 - val_accuracy: 0.7636\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 172s 440ms/step - loss: 0.3294 - accuracy: 0.8648 - val_loss: 0.4506 - val_accuracy: 0.8133\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 173s 442ms/step - loss: 0.2697 - accuracy: 0.8968 - val_loss: 0.4971 - val_accuracy: 0.8213\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 172s 441ms/step - loss: 0.2112 - accuracy: 0.9206 - val_loss: 0.4724 - val_accuracy: 0.8261\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 173s 443ms/step - loss: 0.2045 - accuracy: 0.9232 - val_loss: 0.4855 - val_accuracy: 0.8250\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 173s 443ms/step - loss: 0.1813 - accuracy: 0.9350 - val_loss: 0.6313 - val_accuracy: 0.8008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QVLtMajcLOe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "d1670a0e-9ae7-4fad-8156-83c274b33bb0"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plot(history, 'accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plot(history, 'loss')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HI4TjWDLBJwhC7PxeO3kiVByvWeQthANVlsP2b4xiFw/7vRG3Z7K2H2hX7/OY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\ni/0/J6M8YRQSAAAAAklEQVQFBujv7MoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6JOISKckjnp",
        "colab_type": "text"
      },
      "source": [
        "#### Add two or more LSTM layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l28Thbz-k6jD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7d3IiIIlWfM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "             loss='binary_crossentropy',\n",
        "             metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqSpUQr2lgYd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "45658188-5bb1-4649-ad48-febd465d44b5"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, None, 64)          523840    \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, None, 128)         66048     \n",
            "_________________________________________________________________\n",
            "bidirectional_2 (Bidirection (None, 64)                41216     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 635,329\n",
            "Trainable params: 635,329\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXUtZd6llhqP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "4abf5c3b-f158-41bd-abcb-48f1f3c193ae"
      },
      "source": [
        "history = model.fit(train_dataset, epochs=10, validation_data=test_dataset)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 331s 846ms/step - loss: 0.5375 - accuracy: 0.7196 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 323s 826ms/step - loss: 0.4150 - accuracy: 0.8165 - val_loss: 0.7056 - val_accuracy: 0.6320\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 321s 820ms/step - loss: 0.4506 - accuracy: 0.7901 - val_loss: 0.4564 - val_accuracy: 0.8077\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 320s 818ms/step - loss: 0.3393 - accuracy: 0.8621 - val_loss: 0.3996 - val_accuracy: 0.8254\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 321s 821ms/step - loss: 0.2760 - accuracy: 0.8906 - val_loss: 0.3515 - val_accuracy: 0.8568\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 319s 817ms/step - loss: 0.2126 - accuracy: 0.9212 - val_loss: 0.3306 - val_accuracy: 0.8677\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 320s 818ms/step - loss: 0.1624 - accuracy: 0.9436 - val_loss: 0.3356 - val_accuracy: 0.8732\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 318s 814ms/step - loss: 0.1172 - accuracy: 0.9620 - val_loss: 0.4224 - val_accuracy: 0.8596\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 318s 814ms/step - loss: 0.0838 - accuracy: 0.9745 - val_loss: 0.4578 - val_accuracy: 0.8704\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 320s 817ms/step - loss: 0.0741 - accuracy: 0.9776 - val_loss: 0.4821 - val_accuracy: 0.8683\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-tjwkKXlplr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "ba82f0d9-df1b-4a20-f05b-87eacd5ba1a6"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plot(history, 'accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plot(history, 'loss')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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csfMUTDKIjQSvlhWPSwhRLGlZEzXa9cwcpq/Yx8srDxDm68G6ad0lURPVW8Hk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\nmoiIiEgU+/9n4g5VBDlswQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}