Copy_of_Course_3_Week_3_Lesson_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of Course 3 - Week 3 - Lesson 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RhuthuHegde/Tensorflow-coursera/blob/master/Copy_of_Course_3_Week_3_Lesson_2.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": "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIjKmk036tPP",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%203%20-%20NLP/Course%203%20-%20Week%203%20-%20Lesson%202.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": "jGwXGIXvFhXW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "80171a20-3746-451a-a756-81054fefa9ad"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "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)\n",
        "\n",
        "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()\n",
        "\n",
        "num_epochs = 50\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",
        "history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-02 06:50:27--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.206.128, 64.233.167.128, 74.125.133.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.206.128|: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    37%[======>             ]   2.01M  4.95MB/s               \r/tmp/sarcasm.json   100%[===================>]   5.38M  12.3MB/s    in 0.4s    \n",
            "\n",
            "2020-08-02 06:50:28 (12.3 MB/s) - ‘/tmp/sarcasm.json’ saved [5643545/5643545]\n",
            "\n",
            "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",
            "Epoch 1/50\n",
            "625/625 [==============================] - 38s 61ms/step - loss: 0.4572 - accuracy: 0.7663 - val_loss: 0.3871 - val_accuracy: 0.8208\n",
            "Epoch 2/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.3539 - accuracy: 0.8361 - val_loss: 0.3705 - val_accuracy: 0.8278\n",
            "Epoch 3/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.3273 - accuracy: 0.8527 - val_loss: 0.3681 - val_accuracy: 0.8338\n",
            "Epoch 4/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.3100 - accuracy: 0.8613 - val_loss: 0.3758 - val_accuracy: 0.8357\n",
            "Epoch 5/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2977 - accuracy: 0.8687 - val_loss: 0.3780 - val_accuracy: 0.8317\n",
            "Epoch 6/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2879 - accuracy: 0.8741 - val_loss: 0.3817 - val_accuracy: 0.8319\n",
            "Epoch 7/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2810 - accuracy: 0.8763 - val_loss: 0.3872 - val_accuracy: 0.8296\n",
            "Epoch 8/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2740 - accuracy: 0.8785 - val_loss: 0.3838 - val_accuracy: 0.8329\n",
            "Epoch 9/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2673 - accuracy: 0.8824 - val_loss: 0.3890 - val_accuracy: 0.8268\n",
            "Epoch 10/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2607 - accuracy: 0.8856 - val_loss: 0.4037 - val_accuracy: 0.8265\n",
            "Epoch 11/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2522 - accuracy: 0.8913 - val_loss: 0.4192 - val_accuracy: 0.8225\n",
            "Epoch 12/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2456 - accuracy: 0.8951 - val_loss: 0.4391 - val_accuracy: 0.8249\n",
            "Epoch 13/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2395 - accuracy: 0.8959 - val_loss: 0.4175 - val_accuracy: 0.8243\n",
            "Epoch 14/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2320 - accuracy: 0.9012 - val_loss: 0.4290 - val_accuracy: 0.8283\n",
            "Epoch 15/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2249 - accuracy: 0.9029 - val_loss: 0.4479 - val_accuracy: 0.8237\n",
            "Epoch 16/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2163 - accuracy: 0.9085 - val_loss: 0.4707 - val_accuracy: 0.8223\n",
            "Epoch 17/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2072 - accuracy: 0.9130 - val_loss: 0.5244 - val_accuracy: 0.8179\n",
            "Epoch 18/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.2018 - accuracy: 0.9162 - val_loss: 0.4915 - val_accuracy: 0.8177\n",
            "Epoch 19/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1968 - accuracy: 0.9180 - val_loss: 0.5249 - val_accuracy: 0.8173\n",
            "Epoch 20/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1879 - accuracy: 0.9223 - val_loss: 0.5574 - val_accuracy: 0.8180\n",
            "Epoch 21/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1780 - accuracy: 0.9258 - val_loss: 0.6101 - val_accuracy: 0.8167\n",
            "Epoch 22/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1743 - accuracy: 0.9289 - val_loss: 0.5299 - val_accuracy: 0.8132\n",
            "Epoch 23/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1655 - accuracy: 0.9319 - val_loss: 0.5849 - val_accuracy: 0.8123\n",
            "Epoch 24/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1558 - accuracy: 0.9367 - val_loss: 0.6317 - val_accuracy: 0.8091\n",
            "Epoch 25/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1504 - accuracy: 0.9377 - val_loss: 0.6524 - val_accuracy: 0.8083\n",
            "Epoch 26/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1444 - accuracy: 0.9399 - val_loss: 0.7440 - val_accuracy: 0.8058\n",
            "Epoch 27/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1371 - accuracy: 0.9454 - val_loss: 0.7362 - val_accuracy: 0.8052\n",
            "Epoch 28/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1301 - accuracy: 0.9473 - val_loss: 0.8686 - val_accuracy: 0.8046\n",
            "Epoch 29/50\n",
            "625/625 [==============================] - 37s 58ms/step - loss: 0.1248 - accuracy: 0.9480 - val_loss: 0.9286 - val_accuracy: 0.8052\n",
            "Epoch 30/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1203 - accuracy: 0.9529 - val_loss: 0.8357 - val_accuracy: 0.8076\n",
            "Epoch 31/50\n",
            "625/625 [==============================] - 37s 58ms/step - loss: 0.1109 - accuracy: 0.9559 - val_loss: 1.0378 - val_accuracy: 0.8018\n",
            "Epoch 32/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1090 - accuracy: 0.9566 - val_loss: 1.0194 - val_accuracy: 0.8068\n",
            "Epoch 33/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.1100 - accuracy: 0.9557 - val_loss: 1.0390 - val_accuracy: 0.8018\n",
            "Epoch 34/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0959 - accuracy: 0.9628 - val_loss: 1.1069 - val_accuracy: 0.8027\n",
            "Epoch 35/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0908 - accuracy: 0.9638 - val_loss: 1.1829 - val_accuracy: 0.7983\n",
            "Epoch 36/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0907 - accuracy: 0.9645 - val_loss: 1.1445 - val_accuracy: 0.8001\n",
            "Epoch 37/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0865 - accuracy: 0.9653 - val_loss: 1.2606 - val_accuracy: 0.7992\n",
            "Epoch 38/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0853 - accuracy: 0.9676 - val_loss: 1.2742 - val_accuracy: 0.7961\n",
            "Epoch 39/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0785 - accuracy: 0.9689 - val_loss: 1.3411 - val_accuracy: 0.7970\n",
            "Epoch 40/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0840 - accuracy: 0.9669 - val_loss: 1.3059 - val_accuracy: 0.7959\n",
            "Epoch 41/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0748 - accuracy: 0.9692 - val_loss: 1.3877 - val_accuracy: 0.8019\n",
            "Epoch 42/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0686 - accuracy: 0.9729 - val_loss: 1.4547 - val_accuracy: 0.7958\n",
            "Epoch 43/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0645 - accuracy: 0.9743 - val_loss: 1.5710 - val_accuracy: 0.7930\n",
            "Epoch 44/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0623 - accuracy: 0.9745 - val_loss: 1.6274 - val_accuracy: 0.7961\n",
            "Epoch 45/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0639 - accuracy: 0.9741 - val_loss: 1.6527 - val_accuracy: 0.7952\n",
            "Epoch 46/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0778 - accuracy: 0.9693 - val_loss: 1.5580 - val_accuracy: 0.7930\n",
            "Epoch 47/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0662 - accuracy: 0.9732 - val_loss: 1.5809 - val_accuracy: 0.7973\n",
            "Epoch 48/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0550 - accuracy: 0.9774 - val_loss: 1.6797 - val_accuracy: 0.7946\n",
            "Epoch 49/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0538 - accuracy: 0.9775 - val_loss: 1.7241 - val_accuracy: 0.7898\n",
            "Epoch 50/50\n",
            "625/625 [==============================] - 37s 59ms/step - loss: 0.0635 - accuracy: 0.9740 - val_loss: 1.6588 - val_accuracy: 0.7898\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g9DC6dmLF8DC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "503f8896-aee6-4a16-8519-eb4cfc5a94be"
      },
      "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": null,
      "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": "7ZEZIUppGhdi",
        "colab": {}
      },
      "source": [
        "model.save(\"test.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgpIHoDeDd2I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}