C3W3_4_5_Sarcasm_with_Bidirectional_LSTM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "C3W3-4-5-Sarcasm with Bidirectional LSTM",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/georgezoto/TensorFlow-in-Practice/blob/master/C3W3_4_5_Sarcasm_with_Bidirectional_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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": "markdown",
      "metadata": {
        "id": "WTmp5aHhzqQt",
        "colab_type": "text"
      },
      "source": [
        "# C3W3-4-Sarcasm with Bidirectional LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jGwXGIXvFhXW",
        "outputId": "1dc745de-4c3d-4c34-cb68-ef85170ff645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "import json\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json \\\n",
        "    -O /tmp/sarcasm.json\n",
        "\n",
        "vocab_size = 1000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "training_size = 20000\n",
        "\n",
        "\n",
        "with open(\"/tmp/sarcasm.json\", 'r') as f:\n",
        "    datastore = json.load(f)\n",
        "\n",
        "\n",
        "sentences = []\n",
        "labels = []\n",
        "urls = []\n",
        "for item in datastore:\n",
        "    sentences.append(item['headline'])\n",
        "    labels.append(item['is_sarcastic'])\n",
        "\n",
        "training_sentences = sentences[0:training_size]\n",
        "testing_sentences = sentences[training_size:]\n",
        "training_labels = labels[0:training_size]\n",
        "testing_labels = labels[training_size:]\n",
        "\n",
        "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "training_sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-23 16:17:44--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.194.128, 2404:6800:4003:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.194.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5643545 (5.4M) [application/json]\n",
            "Saving to: ‘/tmp/sarcasm.json’\n",
            "\n",
            "\r/tmp/sarcasm.json     0%[                    ]       0  --.-KB/s               \r/tmp/sarcasm.json    74%[=============>      ]   4.01M  12.3MB/s               \r/tmp/sarcasm.json   100%[===================>]   5.38M  16.2MB/s    in 0.3s    \n",
            "\n",
            "2020-04-23 16:17:45 (16.2 MB/s) - ‘/tmp/sarcasm.json’ saved [5643545/5643545]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nkm_ki940AAd",
        "colab_type": "code",
        "outputId": "ebbf88ad-e91f-4ac4-f1b8-bbdc6781b715",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 120, 16)           16000     \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 64)                12544     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 24)                1560      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 25        \n",
            "=================================================================\n",
            "Total params: 30,129\n",
            "Trainable params: 30,129\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77CYCG_j0AGI",
        "colab_type": "code",
        "outputId": "a9e84384-2e6f-44da-ef0e-7c3de03d2653",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "num_epochs = 10\n",
        "\n",
        "training_padded = np.array(training_padded)\n",
        "training_labels = np.array(training_labels)\n",
        "testing_padded = np.array(testing_padded)\n",
        "testing_labels = np.array(testing_labels)\n",
        "\n",
        "history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 9s 15ms/step - loss: 0.4547 - accuracy: 0.7683 - val_loss: 0.3891 - val_accuracy: 0.8165\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 8s 14ms/step - loss: 0.3610 - accuracy: 0.8360 - val_loss: 0.3925 - val_accuracy: 0.8186\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 8s 14ms/step - loss: 0.3383 - accuracy: 0.8482 - val_loss: 0.3960 - val_accuracy: 0.8129\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 8s 14ms/step - loss: 0.3240 - accuracy: 0.8547 - val_loss: 0.3796 - val_accuracy: 0.8298\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 9s 14ms/step - loss: 0.3120 - accuracy: 0.8613 - val_loss: 0.3768 - val_accuracy: 0.8317\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 9s 14ms/step - loss: 0.3035 - accuracy: 0.8663 - val_loss: 0.3716 - val_accuracy: 0.8350\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 9s 14ms/step - loss: 0.2969 - accuracy: 0.8677 - val_loss: 0.3815 - val_accuracy: 0.8241\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 8s 14ms/step - loss: 0.2904 - accuracy: 0.8727 - val_loss: 0.3783 - val_accuracy: 0.8325\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 9s 14ms/step - loss: 0.2816 - accuracy: 0.8758 - val_loss: 0.3862 - val_accuracy: 0.8307\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 8s 14ms/step - loss: 0.2745 - accuracy: 0.8787 - val_loss: 0.3903 - val_accuracy: 0.8304\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g9DC6dmLF8DC",
        "outputId": "31b1a870-1c64-441d-8307-2c928848e091",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "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": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7ZEZIUppGhdi",
        "colab": {}
      },
      "source": [
        "model.save(\"C3W3-4-Sarcasm_with_Bidirectional_LSTM.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiVMu_-D0Sn4",
        "colab_type": "text"
      },
      "source": [
        "# C3W3-5-Sarcasm with 1D Convolutional Layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsxSWzGKz4jI",
        "colab_type": "code",
        "outputId": "ab57e33a-5bca-4722-e473-3c6e52e1a7aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    tf.keras.layers.Dense(24, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 120, 16)           16000     \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 116, 128)          10368     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 24)                3096      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 25        \n",
            "=================================================================\n",
            "Total params: 29,489\n",
            "Trainable params: 29,489\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTOZaI5lz7W1",
        "colab_type": "code",
        "outputId": "b4731a1d-652e-434d-9d4a-e2ee25fd9936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "num_epochs = 10\n",
        "history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.4715 - accuracy: 0.7578 - val_loss: 0.4229 - val_accuracy: 0.8071\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.3579 - accuracy: 0.8384 - val_loss: 0.3917 - val_accuracy: 0.8229\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.3165 - accuracy: 0.8589 - val_loss: 0.3902 - val_accuracy: 0.8219\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2799 - accuracy: 0.8791 - val_loss: 0.4029 - val_accuracy: 0.8190\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2439 - accuracy: 0.8964 - val_loss: 0.4383 - val_accuracy: 0.8214\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.2101 - accuracy: 0.9145 - val_loss: 0.4600 - val_accuracy: 0.8135\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1717 - accuracy: 0.9337 - val_loss: 0.5334 - val_accuracy: 0.8111\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1438 - accuracy: 0.9444 - val_loss: 0.5863 - val_accuracy: 0.8070\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.1164 - accuracy: 0.9572 - val_loss: 0.6875 - val_accuracy: 0.8032\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 2s 4ms/step - loss: 0.0943 - accuracy: 0.9666 - val_loss: 0.7757 - val_accuracy: 0.8047\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q6xCGuLU0VSE",
        "outputId": "7d89683a-971a-46d4-adaf-37693e6b2697",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\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": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Q-ZynZHf0VSG",
        "colab": {}
      },
      "source": [
        "model.save(\"C3W3-5-Sarcasm_with_1D_Convolutional_Layer.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A09kj6pr3NlF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}