Copy_of_Course_3_Week_3_Lesson_2c.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Copy of Course 3 - Week 3 - Lesson 2c.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_2c.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": "VDfVu1BnFhSJ",
        "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%202c.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": "e77ee2d1-bb8f-4509-98e3-e6b8f513d4b7"
      },
      "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.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()\n",
        "\n",
        "num_epochs = 50\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)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-02 07:32:53--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.20.128, 74.125.197.128, 74.125.28.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.20.128|: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   100%[===================>]   5.38M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2020-08-02 07:32:53 (63.3 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",
            "conv1d (Conv1D)              (None, 116, 128)          10368     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 24)                3096      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 25        \n",
            "=================================================================\n",
            "Total params: 29,489\n",
            "Trainable params: 29,489\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.4772 - accuracy: 0.7581 - val_loss: 0.4239 - val_accuracy: 0.8030\n",
            "Epoch 2/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.3605 - accuracy: 0.8377 - val_loss: 0.3923 - val_accuracy: 0.8182\n",
            "Epoch 3/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.3228 - accuracy: 0.8579 - val_loss: 0.3918 - val_accuracy: 0.8271\n",
            "Epoch 4/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.2872 - accuracy: 0.8758 - val_loss: 0.4086 - val_accuracy: 0.8192\n",
            "Epoch 5/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.2531 - accuracy: 0.8951 - val_loss: 0.4234 - val_accuracy: 0.8171\n",
            "Epoch 6/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.2200 - accuracy: 0.9113 - val_loss: 0.4717 - val_accuracy: 0.8190\n",
            "Epoch 7/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1885 - accuracy: 0.9249 - val_loss: 0.5136 - val_accuracy: 0.8155\n",
            "Epoch 8/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1556 - accuracy: 0.9407 - val_loss: 0.5927 - val_accuracy: 0.8074\n",
            "Epoch 9/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1327 - accuracy: 0.9493 - val_loss: 0.6514 - val_accuracy: 0.8050\n",
            "Epoch 10/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.1080 - accuracy: 0.9594 - val_loss: 0.7516 - val_accuracy: 0.7964\n",
            "Epoch 11/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0877 - accuracy: 0.9689 - val_loss: 0.8045 - val_accuracy: 0.7979\n",
            "Epoch 12/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0747 - accuracy: 0.9739 - val_loss: 0.9320 - val_accuracy: 0.7928\n",
            "Epoch 13/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0616 - accuracy: 0.9787 - val_loss: 1.0054 - val_accuracy: 0.7962\n",
            "Epoch 14/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0556 - accuracy: 0.9793 - val_loss: 1.1983 - val_accuracy: 0.7897\n",
            "Epoch 15/50\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0519 - accuracy: 0.9804 - val_loss: 1.2057 - val_accuracy: 0.7936\n",
            "Epoch 16/50\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0497 - accuracy: 0.9815 - val_loss: 1.2924 - val_accuracy: 0.7880\n",
            "Epoch 17/50\n",
            "625/625 [==============================] - 3s 5ms/step - loss: 0.0440 - accuracy: 0.9836 - val_loss: 1.3394 - val_accuracy: 0.7886\n",
            "Epoch 18/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0401 - accuracy: 0.9851 - val_loss: 1.4117 - val_accuracy: 0.7879\n",
            "Epoch 19/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0390 - accuracy: 0.9847 - val_loss: 1.5363 - val_accuracy: 0.7852\n",
            "Epoch 20/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0361 - accuracy: 0.9865 - val_loss: 1.5577 - val_accuracy: 0.7846\n",
            "Epoch 21/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0354 - accuracy: 0.9862 - val_loss: 1.9795 - val_accuracy: 0.7664\n",
            "Epoch 22/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0366 - accuracy: 0.9862 - val_loss: 1.7200 - val_accuracy: 0.7874\n",
            "Epoch 23/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0312 - accuracy: 0.9878 - val_loss: 1.7415 - val_accuracy: 0.7852\n",
            "Epoch 24/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0271 - accuracy: 0.9894 - val_loss: 1.9743 - val_accuracy: 0.7834\n",
            "Epoch 25/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0269 - accuracy: 0.9896 - val_loss: 1.8971 - val_accuracy: 0.7815\n",
            "Epoch 26/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0296 - accuracy: 0.9884 - val_loss: 1.9806 - val_accuracy: 0.7737\n",
            "Epoch 27/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0318 - accuracy: 0.9872 - val_loss: 2.0600 - val_accuracy: 0.7810\n",
            "Epoch 28/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0317 - accuracy: 0.9877 - val_loss: 2.1362 - val_accuracy: 0.7860\n",
            "Epoch 29/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0267 - accuracy: 0.9893 - val_loss: 2.0650 - val_accuracy: 0.7852\n",
            "Epoch 30/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0282 - accuracy: 0.9880 - val_loss: 2.1344 - val_accuracy: 0.7842\n",
            "Epoch 31/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0253 - accuracy: 0.9895 - val_loss: 2.1938 - val_accuracy: 0.7813\n",
            "Epoch 32/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0223 - accuracy: 0.9908 - val_loss: 2.3948 - val_accuracy: 0.7831\n",
            "Epoch 33/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0268 - accuracy: 0.9886 - val_loss: 2.3258 - val_accuracy: 0.7825\n",
            "Epoch 34/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0262 - accuracy: 0.9889 - val_loss: 2.2826 - val_accuracy: 0.7819\n",
            "Epoch 35/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0298 - accuracy: 0.9883 - val_loss: 2.2242 - val_accuracy: 0.7834\n",
            "Epoch 36/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0275 - accuracy: 0.9884 - val_loss: 2.3458 - val_accuracy: 0.7872\n",
            "Epoch 37/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0221 - accuracy: 0.9908 - val_loss: 2.5004 - val_accuracy: 0.7889\n",
            "Epoch 38/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0217 - accuracy: 0.9912 - val_loss: 2.3779 - val_accuracy: 0.7809\n",
            "Epoch 39/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0234 - accuracy: 0.9904 - val_loss: 2.5010 - val_accuracy: 0.7798\n",
            "Epoch 40/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0223 - accuracy: 0.9902 - val_loss: 2.4890 - val_accuracy: 0.7849\n",
            "Epoch 41/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0235 - accuracy: 0.9905 - val_loss: 2.5329 - val_accuracy: 0.7795\n",
            "Epoch 42/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0262 - accuracy: 0.9895 - val_loss: 2.4809 - val_accuracy: 0.7904\n",
            "Epoch 43/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0238 - accuracy: 0.9901 - val_loss: 2.3868 - val_accuracy: 0.7800\n",
            "Epoch 44/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0217 - accuracy: 0.9911 - val_loss: 2.4988 - val_accuracy: 0.7757\n",
            "Epoch 45/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0191 - accuracy: 0.9919 - val_loss: 2.6225 - val_accuracy: 0.7860\n",
            "Epoch 46/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0200 - accuracy: 0.9910 - val_loss: 2.5571 - val_accuracy: 0.7836\n",
            "Epoch 47/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0241 - accuracy: 0.9904 - val_loss: 2.4985 - val_accuracy: 0.7855\n",
            "Epoch 48/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0226 - accuracy: 0.9906 - val_loss: 2.5334 - val_accuracy: 0.7787\n",
            "Epoch 49/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0202 - accuracy: 0.9913 - val_loss: 2.6321 - val_accuracy: 0.7800\n",
            "Epoch 50/50\n",
            "625/625 [==============================] - 3s 4ms/step - loss: 0.0168 - accuracy: 0.9925 - val_loss: 2.7043 - val_accuracy: 0.7857\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g9DC6dmLF8DC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "d8e7be65-73c3-4643-f93c-24159811283c"
      },
      "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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IXmknUqtMD552/eCO7+00ELOfhLEXQPYvdubQKb+CpCYw4Kmqi1u5IiraCJRSh5n/ql3SsetVlT9WQl07HUSnS+Hj38DL/exArO2r7OC0xPqV/wzlKi0RKBVt9mTaqZVPvbHsuX6OR5fBcMePtpSw4Ts4fYydSVRFPC0RKBVtFrwOJmTHDlS15CYwYrJtH2h2StUfX7lCE4FS0WTdHDt2oOMlzs2oKXLkMpMqomnVkFLRIFBg1wB483JIbGAnfVOqgrREoFSky/oZPrwVti611UEXPV72ADClSqGJQKlIZQzMewW+/BPEJcG1E23PHqWOkSYCpWqChW9B9s9w3iPgr8DEgPt2wJTRdj2BDhfbtX6TGjsfp4pKmgiUcpMxMPNxO+EbwKZ5cM075c8NtG05vHsN5GyDy/5lJ4jTaZ9VJWhjsVJuCQZg2q9tEjj1Rrj6Ndi6xK4ctm1F6e9Z+Yld5zdYBDd/bmf71CSgKkkTgVJuKMyDSdfDoreh3+/h8mft4u2jPjs4t8//vjy4vzHw7VMw6Tpo3NmuMKbLLKoqoolAqeqWtxPGD7ZLSF72LzjvoYO/6lucBrfNtGMA3hkGP71sk8b7o2DW49B9ONz0KdRp5uopqOiibQRKVac9GfDWVbBrHQx7007bcLi6LeHm6fDhbfD572HOvyA3y44NOOturQpSVU5LBEpVl+xVtsonZwvcMKX0JFAsPgmGvw197rHTRYyYZB9rElAO0BKBUtVhy2JbEhCxVTvNuh/9PR6vLQVc8BdNAMpRjpUIRKSViMwSkRUislxE7illHxGRZ0VktYgsEZFTnYpHKdds/BHeuBx8CTBqesWSQEmaBJTDnCwRBID7jDELRSQZWCAiXxpjSvaLuxToEL6dDrwYvlcqOqz+2vYOqtMcbphq1wZWqoZxrERgjNlijFkYfpwDrARaHLbbYGC8sX4E6omIdodQ0WHFR/DOcGhwAoz6XJOAqrGqpbFYRNoCPYGfDnupBbCpxPMMjkwWiMhoEUkTkbTs7GynwlTRJmeb7X/vhvR34L2boHlPuOljnf5B1WiOJwIRSQI+AH5jjNl7PMcwxrxijEk1xqSmpJQz9F6pYjnb4N9dYeH4yh9rww8w6QbYu6Vi+6eNg6ljoN05cONUXcpR1XiOJgIR8WOTwARjzIel7JIJlCwvtwxvU6pyMtMgWAhL36v8sWY/CSunwbiLYMea8vedPxY+uRc6XGRnA9XpoFUEcLLXkACvASuNMU+Xsds04MZw76EzgD3GmAr+7FKqHJvT7f2G7yC3EtWJOVth7Tdw0iAo3AfjLj547MPNexU+vQ86XmrHAFRkFlGlagAnSwR9gBuA80QkPXwbICK3i8jt4X0+A9YCq4FXgTscjEfFki3pEF/XDsb6+ZPjP87S9+wxzv8T3PwF+BLhjYGwbvah+/34Enz2O+h0GQwbD774ysWvVDVyrPuoMWYuUG4HaGOMAe50KgYVo4yBzYvgpIGw4XtbrZM66viOtXgSND8VGnWwz2+ZYQeGvX21nS20yyD44QWY8SB0HghDXgdfXNWdi1LVQKeYUNFn72bYlw3NetgL9brZdqK3Y7VtOWxbCqdcc3BbneZ2htDmPeG9kbZn0IwHbdXR0Dc0CaiIpIlARZ8t4Tr85j3sfD6hAPzy+bEfZ8kk8Pig69WHbq/VwA4OO/ECWD4FulwBQ8aB11/52JVygc41pKLP5kUgXmjSFfyJULeVrR7qeV3FjxEKwpL37MW+dqMjX4+rZVcSWz8X2vYFr/5XUpFLSwQq+mxOh5TO9mItAiddDmtmQv4xDGNZPwdyNtv5/8vi9cMJ/TUJqIiniUBFF2Ns1VDzHge3dRlsxxT874uKH2fxJIivA50urfoYlaphNBGo6FKyobhYy96Q1BRWTK3YMQrzbFVSl0G2akmpKKeJQEWXzYvsffOeB7d5PLYr6f++soPCjubnT6EwF7pfc/R9lYoCmghUdNmSbhuKm3Y9dHuXwRDYD6u/Ovoxlky0Dcxt+jgTo1I1jCYCFV2KG4oPr9JpfRbUaminhi5PzjbbsNxtqC1JKBUD9JuuokdpDcXFvD7ofBmsmgFF+WUfY9kHdkqJU7RaSMUOTQQqeuzNtA3FJdsHSuoy2Nb9r51V9jGWTLQNzSmdnIlRqRpIE4GKHsWzgjYrpUQA0LYfJNSFFdNKfz3rZ7vIfHljB5SKQjoSRkWPshqKi/ni7Oygv3wKgcKD8wLl7bTTVae9bt/fbUj1xaxUDaCJQEWPzYtKbyguqcsgWPwOfPdv2L8b1s+GrcsAA/5acPa9uqykijmaCFR0MMZWDXW8pPz92ve3I4ZnPQG+BGjVG/o/BG3Phhan6eyhKiZpIlDRYW8m5G0vvcdQSf4EGDnNDixrkaqriCmFJgIVLY7WUFxSWb2KlIpR2mtIRYfiqafLaihWSpVJE4GKDlvSofFJOkmcUsdBE4GKfMUNxRWpFlJKHUETgYp8FW0oVkqVShOBinzFU09riUCp46KJQEW+zUcZUayUKpcmAhX5tKFYqUrRRKAimzG2akirhZQ6bpoIVGTbkwF5O7ShWKlK0JHFKrKEgnaq6PVzYf0c2PCD3d7iVHfjUiqCaSJQkWHLEpj1f7DheyjYY7c16gjdh8IJ50NzTQRKHS/HEoGIjAMGAlnGmCO6c4jIucBHwLrwpg+NMY85FY+KYAU5MOk6O1HcyVdAu352ttDkpm5HplRUcLJE8AbwPDC+nH3mGGMGOhiDigZfPAK7N8HN06H1GW5Ho1TUcayx2BgzG9jp1PFVjFj9FSx4Hc66S5OAUg5xu9fQmSKyWEQ+F5GTy9pJREaLSJqIpGVnZ1dnfMpN+3fDR3fZVcf6P+R2NEpFLTcTwUKgjTHmFOA5YGpZOxpjXjHGpBpjUlNSUqotQOWy6Q9A7ja48iVdQEYpB7mWCIwxe40xueHHnwF+EWnkVjyqhln5CSx+F/r9TheSUcphriUCEWkqIhJ+3Dscyw634lE1yL4d8MlvoGl36Ps7t6NRKupVKBGIyD0iUkes10RkoYhcdJT3vAv8AHQSkQwRuUVEbheR28O7DAGWichi4FngGmOMqczJqChgDHx6r20fuPIlXUxeqWpQ0e6jNxtj/iMiFwP1gRuAt4AvynqDMeba8g5ojHke271UqYOWvg8rPoLz/wxNyuw/oJSqQhWtGpLw/QDgLWPM8hLblKoaC8fD1Nuh1elw1t1uR6NUzKhoIlggIl9gE8EMEUkGQs6FpWJKKARfPQrT7rKjhq97D7w6+4lS1aWi/9tuAXoAa40xeSLSABjlXFgqZhTthym3w4qpcNooGPAUeP1uR6VUTKloIjgTSDfG7BOR64FTgf84F5aKCbnZMPFayEiDC/9qRw+L1jgqVd0qWjX0IpAnIqcA9wFrKH8OIaXKl/UzjD0Pti6DYeOhz92aBJRySUUTQSDctXMw8Lwx5gUg2bmwVNQqyLXTSb/aH4ryYdSn0GWQ21EpFdMqWjWUIyJ/xHYb7SsiHkArclXFhYKw6G2Y9YSdNuLkK+Gix6FuS7cjUyrmVTQRDAdGYMcTbBWR1sBTzoWlosrqr+xU0lkroGVvGP42tOrtdlRKqbAKJYLwxX8C0EtEBgLzjDHaRqDKl7cTPhwNq7+E+m1h6JvQZbC2BShVw1QoEYjIMGwJ4BvsQLLnROR+Y8z7DsamItn+3fDWlbYUcNET0Ps28MW7HZVSqhQVrRp6COhljMkCEJEU4CtAE4E6UkEOTBgC25bDNROg48VuR6SUKkdFE4GnOAmE7cD9RW1UTVS4DyYMhc2LbFWQJgGlaryKJoLpIjIDeDf8fDjwmTMhqYhVmAfvDIdNP8GQcXCSLketVCSoaGPx/SJyNdAnvOkVY8wU58JSEacoHyZdB+vnwlWv2O6hSqmIUOGZvYwxHwAfOBiLijRF+bAvG/ZlwTf/gDUzYdDz0H2Y25EppY5BuYlARHKA0haLEcAYY+o4EpWqmdbMgm/+bi/8+7ZDwd5DX7/saTj1BndiU0odt3ITgTFGp5FQVigEn9wLgQJocybUTjl4S2oMDdpDSie3o1RKHQed9F1VzP9mwK51MOR16HqV29EopaqQdgFVFfPjf6FOSzhJJ4hTKtpoIlBHt3UprJttRwfrymFKRR1NBOrofnwJ/LXg1BvdjkQp5QBNBKp8udmwdDKcci3UauB2NEopB2giUOVLGwfBQjhjjNuRKKUcoolAlS1QAPPHQoeLoFEHt6NRSjlEE4Eq27IP7eAxLQ0oFdU0EajSGWO7jKZ0hvb93Y5GKeUgTQSqdBu+g61LbGlAVxRTKqo5lghEZJyIZInIsjJeFxF5VkRWi8gSETnVqVjUcfjxRUhsAN2Hux2JUsphTpYI3gAuKef1S4EO4dto4EUHY1HHYuc6+PlTSB0F/kS3o1FKOcyxRGCMmQ3sLGeXwcB4Y/0I1BORZk7Fo47BD8+Dxwu9bnU7EqVUNXCzjaAFsKnE84zwNuWWUAi+/LPtMtrzeqjT3O2IlFLVICImjhGR0djqI1q3bu1yNFGqKB+mjoHlH0LqzXDpU25HpJSqJm6WCDKBViWetwxvO4Ix5hVjTKoxJjUlJaVagospeTvhrStsErjwMbvAjE4up1TMcDMRTANuDPceOgPYY4zZ4mI8sWnnWhh7AWQutGsN9LlHu4sqFWMc+9knIu8C5wKNRCQD+DPgBzDGvAR8BgwAVgN5wCinYlFl2DQf3h1uB4+NnAatz3A7IqWUCxxLBMaYa4/yugHudOrz1VHsXAfjB0NyE7jufWh4gtsRKaVcohXBsSgUgml32S6iIz+ButpZS6lYpokgFqW9BuvnwKDnNQkopXSuoZiza70dK3DC+XasgFIq5mkiiCXG2Coh8cDl/9HeQUopQKuGYsuC1+0i9AP/DfVaHX1/pVRM0BJBrNi9Eb54BNqdA6fd5HY0SqkaRBNBLDAGpt1t7wc9p1VCSqlDaNVQLFg4HtbOggH/hPpt3I5GKVXDaIkg2q2ZBTMegrZ9IfUWt6NRStVAWiKIVvu2w4wHYckkaHACDH4ePJr3lVJH0kQQbYyBRW/Dl49AQS70+z30vQ/8CW5HppSqoTQRRJPsVfDJb+zC863PtN1EG3d2OyqlVA2niSBaLPsAptxu1xi+/FnoeYNWBSmlKkQTQTTYNA+mjIEWp8Gw8ZDU2O2IlFIRRBNBpNu1ASaOsOsLX/MO1GrgdkRKqQijdQeRLH8vvHsNBAthxGRNAkqp46IlgkgVDMD7N0P2L3DDh5DS0e2IlFIRShNBpPriYVj9pe0Z1P5ct6NRSkUwrRqKRPPHwk8vwhl3Qqou9ayUqhxNBJHml8/hs99Dh4vhor+6HY1SKgpo1VCkKNoPXz8GP/4XmnaDIa/ZNYeVUqqSNBFEgi2L4cPRkP0z9B4NF/wF4mq5HZVSKkpoIqjJQkH47t8w629QqyFc/wGceIHbUSmloowmgppq5zqY8ivY9BN0uQIGPqPjBJRSjtBEUBNlpMHbV9uZRK96FboN1VXFlFKO0URQ06z9Ft69FpJS4Iap0KCd2xEppaKcdh+tSX7+FCYMtctJ3jxDk4BSqlpoIqgpFk+CSTdA065w06eQ3NTtiJRSMcLRRCAil4jILyKyWkQeKOX1m0QkW0TSw7dbnYynxpr3KkwZDW37wI0faaOwUqpaOdZGICJe4AXgQiADmC8i04wxKw7bdZIx5tdOxVGjBQMw51/wzf9BpwEw5HVdUlIpVe2cbCzuDaw2xqwFEJGJwGDg8EQQe4IBu6j8nH/CzrXQfTgMfgG8frcjU0rFICcTQQtgU4nnGcDppex3tYj0A1YB9xpjNh2+g4iMBkYDtG7d2oFQq0mwCBZPtAlg13po2t0uJtNpgHYPVUq5xu3G4o+BtsaY7sCXwJul7WSMecUYk2qMSU1JSTmuD0rftJvhL//Anryi44/2eAUDsOBNeO40mPZrSKgH106EX82GzpdpElBKucrJRJAJtCrxvGV42wHGmB3GmILw07HAaQ7Gw4INu3hwylKMMU5+zKFWfwUv9YGP77bTRIyYDKO/gU6XagJQStUITiaC+UAHEWknInHANcC0kjuISLMSTwcBK96mSC8AABAhSURBVJ0Kpkerevz2oo58unQLk+YfUftU9bb/DyYMsyOEAwUw/G24bSZ0vFgTgFKqRnGsjcAYExCRXwMzAC8wzhizXEQeA9KMMdOAu0VkEBAAdgI3ORUPwO39TuC71dv5y8crSG3bgBMbJ1X9h+zfBd8+CfNeAX8tuPCvcPqvwBdf9Z+llFJVQKq1mqQKpKammrS0tGN/Y+YC+OS37O08jEHfNCWxXmOm3HEWCf5KzOlvDORmQdZy2LYCslbCL59B/m449Ubo/7CdKkIppVwmIguMMamlvRY7cw0V7gMTos6sB5np8TN9e0+mTBzCtSNurli3zVDQVvdsSYfN6bBtGWxbDvt3Htyndgq0PhP6/9EuHqOUUhEgdkoExbYuhfR32Zc2gdqB3RTGNySuxzCo2wLEA4i9L67H37nOXvy3LIGifXabLxGanAxNukDjEjf99a+UqqHKKxHEXiIIy8/P5+/PPUe/fV/Q37MICZXRrdRfy/66b9YDmvew9406gjd2ClNKqcinVUOlSEhI4Lobb+fy57tyRoskxl3fHY8AJmTr/o0BDCTW17WBlVJRze0BZa7q0CSZPw08mW/W7GXQ2CV89HMuRXF17aRvtRtC7UaaBJRSUS+mEwHAtb1b8eTV3ckrDHLPxHT6PTmLl79dw579LoxAVkopF8RsG8HhQiHDN6uyeHX2On5Yu4NacV6GpbZiVJ+2tGlYu8o/TymlqpM2Fh+jZZl7GDd3HdMWbyZoDOd2TOHGs9pyTocUPB4dFayUijyaCI7Ttr35vPPTRt6Zt5HsnALaNqzF9We0YWhqK+om6pTRSqnIoYmgkgoDIaYv38r479eTtmEXiX4vV/RszojebejWsm61xqKUUsdDE0EVWpa5h/E/rGfa4s3kF4Xo1qIuI05vzaBTmlM7PmZ74yqlajhNBA7Ys7+IqYsyeeenjfyyLYekeB+DezTnutPb0KV5HbfDU0qpQ2gicJAxhoUbdzHhx418snQLhYEQp7Ssy/BerRnUozlJWkpQStUAmgiqye68QqYsymTivE38si2HWnFeBnZvxvBerTm1dT1E1yFQSrlEE0E1M8awaNNuJs3bxMdLNpNXGKRjkySGntaKwT2b0zg5we0QlVIxRhOBi3ILAny8eDOT5m8ifdNuvB7hnI4pXH1qS84/qXHl1kNQSqkK0kRQQ6zOyuWDhRlMWZjJ1r351E30c/kpzRiW2opuLepq1ZFSyjGaCGqYYMjw3ertfLAwg+nLtlIQCHFSszpc06sVV/RoQd1aOlhNKVW1NBHUYHv2FzFt8WYmzd/Issy9xPk8XNq1KcN7teKMdg11SgulVJXQRBAhlmXuYdL8TUxNzyQnP0BKcjxnn9iIs09sRN8OjWhcRxuZlVLHRxNBhNlfGGTG8q3M/DmL71ZvZ8e+QgA6NUnm7A6N6NcxhdPbNdCGZqVUhWkiiGChkGHFlr3MXb2dOf/LZv76XRQGQiT6vfQ5sSH9Ozemf6fGNK+X6HaoSqkaTBNBFNlfGOTHtTuY9UsWM3/OImPXfgA6N03mnE4pnNG+Ialt6pOcoA3OSqmDNBFEKWMMq7NymfmzTQoLNuwiEDJ4BLo0r0Pvtg3p3a4BvdrWp2FSvNvhKqVcpIkgRuQVBli0cTc/rdvJvHU7WLRxNwWBEACNkuJon5LECSm1ad8oiRMa2/uW9RPxeWN+xVKlol55iUBnRIsiteJ89DmxEX1ObARAQSDIssw9LNiwi9VZuazN3seM5dvYuW/TgffEeT20aViLE1IOJocTGifRvF4C9RLjiPNpklAq2mkiiGLxPi+ntWnAaW0aHLJ9175C1m7PZU32PtZm72NNdi6rsnL4auU2AqFDS4i147zUqxVHvVp+6tXyUyfBT2Kcl1pxXmrF+Uj0Fz/2EufzEOfz4Pd6iPPax/E+L8kJPuom+qlby09yvE9HUCtVw2giiEH1a8dxWu0jE0RRMMTGnXmsycplW04Bu/cVsnt/EbvzitidZx9n7c0lrzDI/qIgeYUB8otCx/TZHoE6iX7qJvpJivdRO85HrXivvY/zUjveR7zPg8cj+DyCRwSvx978XiHe5yXB7znkvnh/jwge4ZDHwZAhZAyBoCFoDMGQvcX7vNRJtAmqTqKfpDifDt5TMcvRRCAilwD/AbzAWGPM3w97PR4YD5wG7ACGG2PWOxmTKpvf67FVRClJFX5PMGTYXxRkf2GQwmCIokCIwmCIwkCIgkCIgkCQnPwAe/YXsXd/EXvCt915ReQVBthXEGTnvkI27cwjrzBIbkGAwkCIUPiiHaqmJiwRSI73kZzgp3a8Le0kxdvklBTvIyHOi4T3AxAEETCG8LkGKQiEyC8Khs/b/g0KAyGKguHnQfs4VMZJxfk8JMX7qJPoJznBR3K8va8V5z2QxIqCxff2b+T3ekgIJ8UEv5d4f/ixz0ti3MHH9jUPoRDkFhSRWxAkN7+I3IIAuQVB8ouCiHAggYrY88Nw4N83ryhIfmGQvMIg+YEgXpEDpcDiEuCBc0jwh5NsONkm+Ev8DcOfUeJvWBQKEQja87I3QzAUOvBDoPiHgVfs45AxhEIQCIUOJPqQMQf+9gWBEAXF/xZFQUKGcAnVc0TM8eFSrN97aIk23m9fK/6xEe+32wFChhLfURtLQcB+f4u/x3mF9m9bFAjh8wo+jwef1/6gKX4c7/MQ5/UeOHZxPIlxXhJ83mr7ceJYIhARL/ACcCGQAcwXkWnGmBUldrsF2GWMOVFErgH+AQx3KiZV9bweISne59gCPKb4V7yxF8Hi/9z5h92HjMGYgyUAE/6PWrJE4S0uZXiE/KIge/cH2Lu/iL35ReH7AHvzi8grCLKvMMC+ggDZOQXsKwyQXxSkuF+FCcdVLN7nPeKikeD3UCfBF77oeMOlGXuR8ZRRNVYYDJGTHyAnv4ic/ADZObnk5NsLi+9Aqchjz8NrSz2F4b9BflGQ/HDiOVbFFx+MPbeQOfg3BEiM85Lo9x6oEkz02+QYDBkKA6EDybs4+e8rtH/X6kriFeERalQ8FVWcFBL99jbi9Nbc2rd9lX+OkyWC3sBqY8xaABGZCAwGSiaCwcCj4cfvA8+LiJhI68qkHCNiL3o+IN6Hrvh2FKGQOZAg8wNB8otKJIqiEB6B5ARb0qgd76N2vJd4X9WPUA+FDPsKi0uCNsHuLwqGk01xorYJtfjfOM7rwecR/D4Pfo9NeKESPwRCIUMgZO+L3+MROZAkPeESyoEqw3ByjvN6EBECQVsqKygqWWoNUhQ0B0puheHSyMHEFjy0dBFOtF6PLc14JVwN6bGJvmQpsla8j6R4L36vh0DIHCjx2MfhzwkeLDkWBoMHYssvCh6ogs0P3+8vCpGS7Ew3cCf/V7UANpV4ngGcXtY+xpiAiOwBGgLbS+4kIqOB0QCtW7d2Kl6lIp7HI/YXZJy70494PBJOOH6o72ooB/i8HnxeD7Xi3I6k5omIvoHGmFeMManGmNSUlBS3w1FKqajiZCLIBFqVeN4yvK3UfUTEB9TFNhorpZSqJk4mgvlABxFpJyJxwDXAtMP2mQaMDD8eAszU9gGllKpejrURhOv8fw3MwHYfHWeMWS4ijwFpxphpwGvAWyKyGtiJTRZKKaWqkaNdMIwxnwGfHbbtTyUe5wNDnYxBKaVU+SKisVgppZRzNBEopVSM00SglFIxLuLWIxCRbGDDcb69EYcNVoshsXruet6xRc+7bG2MMaUOxIq4RFAZIpJW1sIM0S5Wz13PO7boeR8frRpSSqkYp4lAKaViXKwlglfcDsBFsXruet6xRc/7OMRUG4FSSqkjxVqJQCml1GE0ESilVIyLmUQgIpeIyC8islpEHnA7HqeIyDgRyRKRZSW2NRCRL0Xkf+H7GrJUSNURkVYiMktEVojIchG5J7w9qs9dRBJEZJ6ILA6f91/C29uJyE/h7/uk8AzAUUdEvCKySEQ+CT+P+vMWkfUislRE0kUkLbytUt/zmEgEJdZPvhToAlwrIl3cjcoxbwCXHLbtAeBrY0wH4Ovw82gTAO4zxnQBzgDuDP8bR/u5FwDnGWNOAXoAl4jIGdj1v58xxpwI7MKuDx6N7gFWlngeK+fd3xjTo8TYgUp9z2MiEVBi/WRjTCFQvH5y1DHGzMZO6V3SYODN8OM3gSuqNahqYIzZYoxZGH6cg704tCDKz91YueGn/vDNAOdh1wGHKDxvABFpCVwGjA0/F2LgvMtQqe95rCSC0tZPbuFSLG5oYozZEn68FWjiZjBOE5G2QE/gJ2Lg3MPVI+lAFvAlsAbYbYwJhHeJ1u/7v4HfA6Hw84bExnkb4AsRWRBezx0q+T13dD0CVfMYY4yIRG2fYRFJAj4AfmOM2Wt/JFrReu7GmCDQQ0TqAVOAzi6H5DgRGQhkGWMWiMi5bsdTzc42xmSKSGPgSxH5ueSLx/M9j5USQUXWT45m20SkGUD4PsvleBwhIn5sEphgjPkwvDkmzh3AGLMbmAWcCdQLrwMO0fl97wMMEpH12Kre84D/EP3njTEmM3yfhU38vank9zxWEkFF1k+OZiXXhh4JfORiLI4I1w+/Bqw0xjxd4qWoPncRSQmXBBCRROBCbPvILOw64BCF522M+aMxpqUxpi32//NMY8x1RPl5i0htEUkufgxcBCyjkt/zmBlZLCIDsHWKxesnP+FySI4QkXeBc7HT0m4D/gxMBSYDrbFTeA8zxhzeoBzRRORsYA6wlIN1xg9i2wmi9txFpDu2cdCL/WE32RjzmIi0x/5SbgAsAq43xhS4F6lzwlVDvzPGDIz28w6f35TwUx/wjjHmCRFpSCW+5zGTCJRSSpUuVqqGlFJKlUETgVJKxThNBEopFeM0ESilVIzTRKCUUjFOE4FSYSISDM/oWHyrsgnqRKRtyRlhlapJdIoJpQ7ab4zp4XYQSlU3LREodRTh+d+fDM8BP09ETgxvbysiM0VkiYh8LSKtw9ubiMiU8BoBi0XkrPChvCLyanjdgC/CI4ERkbvD6ygsEZGJLp2mimGaCJQ6KPGwqqHhJV7bY4zpBjyPHaEO8BzwpjGmOzABeDa8/Vng2/AaAacCy8PbOwAvGGNOBnYDV4e3PwD0DB/ndqdOTqmy6MhipcJEJNcYk1TK9vXYxV/Whie222qMaSgi24Fmxpii8PYtxphGIpINtCw5tUF4auwvwwuHICJ/APzGmMdFZDqQi50KZGqJ9QWUqhZaIlCqYkwZj49FyTlvghxso7sMu4LeqcD8ErNnKlUtNBEoVTHDS9z/EH78PXbmS4DrsJPegV0qcAwcWDSmblkHFREP0MoYMwv4A1AXOKJUopST9JeHUgclhlf6KjbdGFPchbS+iCzB/qq/NrztLuB1EbkfyAZGhbffA7wiIrdgf/mPAbZQOi/wdjhZCPBseF0BpaqNthEodRThNoJUY8x2t2NRyglaNaSUUjFOSwRKKRXjtESglFIxThOBUkrFOE0ESikV4zQRKKVUjNNEoJRSMe7/AY9aTnd38w5ZAAAAAElFTkSuQmCC\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": "zIWbYZ24Hd7_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}