TensorFlow3_Sarcasm_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TensorFlow3_Sarcasm-dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMl3A1tBAW1DTwMkxm/EXId",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/utkarsh0702/Tensorflow/blob/master/TensorFlow3_Sarcasm_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrTt6lsHYJae",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-37LHLraJeL",
        "colab_type": "code",
        "outputId": "d4438a9c-aada-4584-da5d-a0cc6162d04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget --no-check-certificate https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json -O /content/sarcasm.json"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-06 06:08:24--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.139.128, 2607:f8b0:400c:c15::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.139.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5643545 (5.4M) [application/json]\n",
            "Saving to: ‘/content/sarcasm.json’\n",
            "\n",
            "\r/content/sarcasm.js   0%[                    ]       0  --.-KB/s               \r/content/sarcasm.js 100%[===================>]   5.38M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2020-04-06 06:08:24 (71.3 MB/s) - ‘/content/sarcasm.json’ saved [5643545/5643545]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUDwnrUCaRXF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "#Hyperparameters\n",
        "vocab_size= 1000\n",
        "embedding_dim=16\n",
        "max_length=16\n",
        "trunc_type='post'\n",
        "oov_tok= '<OOV>'\n",
        "training_size=20000\n",
        "\n",
        "with open('/content/sarcasm.json') as f:\n",
        "  data= json.load(f)\n",
        "sentences=[]\n",
        "labels=[]\n",
        "\n",
        "for items in data:\n",
        "  sentences.append(items['headline'])\n",
        "  labels.append(items['is_sarcastic'])\n",
        "\n",
        "train_sentence= sentences[0:training_size]\n",
        "test_sentence= sentences[training_size:]\n",
        "train_label= labels[0:training_size]\n",
        "test_label= labels[training_size:]\n",
        "train_label_final= np.array(train_label)\n",
        "test_label_final= np.array(test_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_uFtiW5bnOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "token= Tokenizer(num_words=vocab_size, oov_token= oov_tok)\n",
        "token.fit_on_texts(train_sentence)\n",
        "word_index= token.word_index\n",
        "#for training data\n",
        "sequences= token.texts_to_sequences(train_sentence)\n",
        "padded= pad_sequences(sequences, maxlen=max_length,padding='post', truncating=trunc_type)\n",
        "#for testing data\n",
        "test_sequences= token.texts_to_sequences(test_sentence)\n",
        "test_padded= pad_sequences(test_sequences,padding='post', maxlen=max_length)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mO-0hH0khxR",
        "colab_type": "code",
        "outputId": "ef435a4e-2566-4f0b-a2e9-c3553de80699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "#Model building\n",
        "model= tf.keras.Sequential([\n",
        "                            tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length= max_length),\n",
        "                            tf.keras.layers.GlobalAveragePooling1D(),\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()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 16, 16)            16000     \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 102       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 16,109\n",
            "Trainable params: 16,109\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCAkaArInUhE",
        "colab_type": "code",
        "outputId": "1d93977b-428a-4069-f0c6-07b20265de90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "history=model.fit(padded,\n",
        "          train_label_final,\n",
        "          epochs=10,\n",
        "          validation_data=(test_padded, test_label_final))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.5601 - accuracy: 0.7125 - val_loss: 0.4295 - val_accuracy: 0.8037\n",
            "Epoch 2/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3888 - accuracy: 0.8242 - val_loss: 0.4010 - val_accuracy: 0.8126\n",
            "Epoch 3/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3649 - accuracy: 0.8356 - val_loss: 0.3994 - val_accuracy: 0.8144\n",
            "Epoch 4/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3572 - accuracy: 0.8382 - val_loss: 0.4013 - val_accuracy: 0.8098\n",
            "Epoch 5/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3537 - accuracy: 0.8409 - val_loss: 0.4032 - val_accuracy: 0.8113\n",
            "Epoch 6/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3509 - accuracy: 0.8411 - val_loss: 0.4037 - val_accuracy: 0.8135\n",
            "Epoch 7/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3487 - accuracy: 0.8419 - val_loss: 0.4045 - val_accuracy: 0.8123\n",
            "Epoch 8/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3476 - accuracy: 0.8413 - val_loss: 0.4065 - val_accuracy: 0.8088\n",
            "Epoch 9/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3459 - accuracy: 0.8425 - val_loss: 0.4048 - val_accuracy: 0.8107\n",
            "Epoch 10/10\n",
            "625/625 [==============================] - 1s 2ms/step - loss: 0.3448 - accuracy: 0.8436 - val_loss: 0.4064 - val_accuracy: 0.8141\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUQD72FznyX9",
        "colab_type": "code",
        "outputId": "d382c37e-d114-4907-ad18-3d1b05283772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graph(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_graph(history, \"accuracy\")\n",
        "plot_graph(history, \"loss\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Cy2vnpyXKY/TGrg4OqEAtIjksnQniRWCmmc0wsxKCJPDo0I3M7CxgHMGtOxL3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\nEZGklCBERCQpJQgREUnq/wMjpbtNxQ/OrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/ROSgKCAK0JFjy3jkc+/h9k/W84oG/xORg6SAKFBmxgVz6njuhvfzv47W4H8i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\nERFJSQEhIiIpKSBERCSl/w/PefQ8GVvbhQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ix-O17g5tnja",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}