word_classify.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "word classify.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/yanghuaizhi/tf_learning/blob/master/word_classify.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "MFio6s4BgY04",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "360b3db0-c8fc-46bf-853c-dfa873d43b85"
      },
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-alpha0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WVx2GqcBgkwb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6462882f-33e2-47b5-8e02-0ec87251b8e7"
      },
      "cell_type": "code",
      "source": [
        "imdb = keras.datasets.imdb\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "08StOZwggkvM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7cfdb35c-1a49-42ba-e8f4-ee6c6bf6e703"
      },
      "cell_type": "code",
      "source": [
        "print(\"Training entries: {}, labels: {}\".format(len(train_data), len(train_labels)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training entries: 25000, labels: 25000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qYc8Iyvpgx25",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9223ca1d-edbd-47b6-ddf3-3fe2a8fb44b7"
      },
      "cell_type": "code",
      "source": [
        "print(train_data[0])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VSckc5XchGRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1812581f-3e38-4bf5-8819-985b4761fbe8"
      },
      "cell_type": "code",
      "source": [
        "len(train_data[0]), len(train_data[1])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(218, 189)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "edvNMu0ZhPEF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9920e335-21d1-4ad0-b86d-05f66809f839"
      },
      "cell_type": "code",
      "source": [
        "# A dictionary mapping words to an integer index\n",
        "word_index = imdb.get_word_index()\n",
        "\n",
        "# The first indices are reserved\n",
        "word_index = {k:(v+3) for k,v in word_index.items()} \n",
        "word_index[\"<PAD>\"] = 0\n",
        "word_index[\"<START>\"] = 1\n",
        "word_index[\"<UNK>\"] = 2  # unknown\n",
        "word_index[\"<UNUSED>\"] = 3\n",
        "\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1646592/1641221 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "eBLzEUMchPGb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "c6825556-6907-4aba-8264-50769fdaaccd"
      },
      "cell_type": "code",
      "source": [
        "decode_review(train_data[0])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "iQ8lj71RhdAA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n",
        "                                                        value=word_index[\"<PAD>\"],\n",
        "                                                        padding='post',\n",
        "                                                        maxlen=256)\n",
        "\n",
        "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n",
        "                                                       value=word_index[\"<PAD>\"],\n",
        "                                                       padding='post',\n",
        "                                                       maxlen=256)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ANouuhD6hiBe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8248579f-1fe2-41f1-c9ba-c4a35be3c8f8"
      },
      "cell_type": "code",
      "source": [
        "len(train_data[0]), len(train_data[1])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "h_ng0gbJhlI6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "c1eeb462-0a6a-486b-8225-4491a0efa411"
      },
      "cell_type": "code",
      "source": [
        "print(train_data[0])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   1   14   22   16   43  530  973 1622 1385   65  458 4468   66 3941\n",
            "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
            "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
            "  172 4536 1111   17  546   38   13  447    4  192   50   16    6  147\n",
            " 2025   19   14   22    4 1920 4613  469    4   22   71   87   12   16\n",
            "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
            "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
            " 5244   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
            "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
            "   52    5   14  407   16   82    2    8    4  107  117 5952   15  256\n",
            "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
            "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
            " 2071   56   26  141    6  194 7486   18    4  226   22   21  134  476\n",
            "   26  480    5  144   30 5535   18   51   36   28  224   92   25  104\n",
            "    4  226   65   16   38 1334   88   12   16  283    5   16 4472  113\n",
            "  103   32   15   16 5345   19  178   32    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ecdhMzkFhlHr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "7327987f-4524-4c70-841f-806533ab9c38"
      },
      "cell_type": "code",
      "source": [
        "# input shape is the vocabulary count used for the movie reviews (10,000 words)\n",
        "vocab_size = 10000\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(keras.layers.Embedding(vocab_size, 16))\n",
        "model.add(keras.layers.GlobalAveragePooling1D())\n",
        "model.add(keras.layers.Dense(16, activation='relu'))\n",
        "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 16)          160000    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 160,289\n",
            "Trainable params: 160,289\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h9af0rByh1Re",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rqn0JxsOh3xZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_val = train_data[:10000]\n",
        "partial_x_train = train_data[10000:]\n",
        "\n",
        "y_val = train_labels[:10000]\n",
        "partial_y_train = train_labels[10000:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o7ShXetNh6dU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "cb557539-900c-4fe9-a286-447a737aac43"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=40,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val),\n",
        "                    verbose=1)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 15000 samples, validate on 10000 samples\n",
            "Epoch 1/40\n",
            "15000/15000 [==============================] - 1s 85us/sample - loss: 0.6919 - accuracy: 0.5301 - val_loss: 0.6901 - val_accuracy: 0.5853\n",
            "Epoch 2/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.6865 - accuracy: 0.6693 - val_loss: 0.6828 - val_accuracy: 0.6964\n",
            "Epoch 3/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.6749 - accuracy: 0.7307 - val_loss: 0.6681 - val_accuracy: 0.7508\n",
            "Epoch 4/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.6545 - accuracy: 0.7461 - val_loss: 0.6447 - val_accuracy: 0.7617\n",
            "Epoch 5/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.6229 - accuracy: 0.7852 - val_loss: 0.6104 - val_accuracy: 0.7823\n",
            "Epoch 6/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.5811 - accuracy: 0.8056 - val_loss: 0.5698 - val_accuracy: 0.7972\n",
            "Epoch 7/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.5337 - accuracy: 0.8235 - val_loss: 0.5252 - val_accuracy: 0.8183\n",
            "Epoch 8/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.4859 - accuracy: 0.8428 - val_loss: 0.4836 - val_accuracy: 0.8308\n",
            "Epoch 9/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.4413 - accuracy: 0.8581 - val_loss: 0.4460 - val_accuracy: 0.8418\n",
            "Epoch 10/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.4018 - accuracy: 0.8713 - val_loss: 0.4150 - val_accuracy: 0.8501\n",
            "Epoch 11/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.3684 - accuracy: 0.8799 - val_loss: 0.3892 - val_accuracy: 0.8580\n",
            "Epoch 12/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.3403 - accuracy: 0.8876 - val_loss: 0.3696 - val_accuracy: 0.8615\n",
            "Epoch 13/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.3173 - accuracy: 0.8940 - val_loss: 0.3522 - val_accuracy: 0.8674\n",
            "Epoch 14/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2967 - accuracy: 0.8991 - val_loss: 0.3395 - val_accuracy: 0.8720\n",
            "Epoch 15/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2795 - accuracy: 0.9047 - val_loss: 0.3288 - val_accuracy: 0.8733\n",
            "Epoch 16/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.2641 - accuracy: 0.9098 - val_loss: 0.3201 - val_accuracy: 0.8747\n",
            "Epoch 17/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2499 - accuracy: 0.9150 - val_loss: 0.3128 - val_accuracy: 0.8782\n",
            "Epoch 18/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2374 - accuracy: 0.9189 - val_loss: 0.3064 - val_accuracy: 0.8798\n",
            "Epoch 19/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.2261 - accuracy: 0.9214 - val_loss: 0.3012 - val_accuracy: 0.8807\n",
            "Epoch 20/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2159 - accuracy: 0.9250 - val_loss: 0.2976 - val_accuracy: 0.8815\n",
            "Epoch 21/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.2055 - accuracy: 0.9302 - val_loss: 0.2945 - val_accuracy: 0.8826\n",
            "Epoch 22/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1969 - accuracy: 0.9327 - val_loss: 0.2915 - val_accuracy: 0.8845\n",
            "Epoch 23/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1881 - accuracy: 0.9372 - val_loss: 0.2903 - val_accuracy: 0.8832\n",
            "Epoch 24/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1805 - accuracy: 0.9405 - val_loss: 0.2885 - val_accuracy: 0.8846\n",
            "Epoch 25/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1728 - accuracy: 0.9446 - val_loss: 0.2867 - val_accuracy: 0.8843\n",
            "Epoch 26/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1659 - accuracy: 0.9469 - val_loss: 0.2871 - val_accuracy: 0.8833\n",
            "Epoch 27/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1593 - accuracy: 0.9499 - val_loss: 0.2864 - val_accuracy: 0.8844\n",
            "Epoch 28/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1531 - accuracy: 0.9526 - val_loss: 0.2867 - val_accuracy: 0.8850\n",
            "Epoch 29/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1476 - accuracy: 0.9559 - val_loss: 0.2879 - val_accuracy: 0.8840\n",
            "Epoch 30/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1419 - accuracy: 0.9570 - val_loss: 0.2872 - val_accuracy: 0.8862\n",
            "Epoch 31/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1360 - accuracy: 0.9603 - val_loss: 0.2880 - val_accuracy: 0.8868\n",
            "Epoch 32/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1308 - accuracy: 0.9620 - val_loss: 0.2892 - val_accuracy: 0.8866\n",
            "Epoch 33/40\n",
            "15000/15000 [==============================] - 1s 56us/sample - loss: 0.1257 - accuracy: 0.9638 - val_loss: 0.2913 - val_accuracy: 0.8862\n",
            "Epoch 34/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1212 - accuracy: 0.9655 - val_loss: 0.2932 - val_accuracy: 0.8858\n",
            "Epoch 35/40\n",
            "15000/15000 [==============================] - 1s 56us/sample - loss: 0.1171 - accuracy: 0.9663 - val_loss: 0.2953 - val_accuracy: 0.8864\n",
            "Epoch 36/40\n",
            "15000/15000 [==============================] - 1s 56us/sample - loss: 0.1125 - accuracy: 0.9691 - val_loss: 0.2969 - val_accuracy: 0.8850\n",
            "Epoch 37/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1082 - accuracy: 0.9699 - val_loss: 0.2994 - val_accuracy: 0.8847\n",
            "Epoch 38/40\n",
            "15000/15000 [==============================] - 1s 57us/sample - loss: 0.1042 - accuracy: 0.9713 - val_loss: 0.3026 - val_accuracy: 0.8827\n",
            "Epoch 39/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.1010 - accuracy: 0.9724 - val_loss: 0.3060 - val_accuracy: 0.8827\n",
            "Epoch 40/40\n",
            "15000/15000 [==============================] - 1s 58us/sample - loss: 0.0968 - accuracy: 0.9745 - val_loss: 0.3085 - val_accuracy: 0.8834\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "aqPqrDnMiIxE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a07fdba4-8c0a-4f7d-9e3b-b35c9ed68c4a"
      },
      "cell_type": "code",
      "source": [
        "results = model.evaluate(test_data, test_labels)\n",
        "\n",
        "print(results)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25000/25000 [==============================] - 1s 37us/sample - loss: 0.3292 - accuracy: 0.8718\n",
            "[0.3291623979330063, 0.87184]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mnnYIlweiOWm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2751a5bd-bd41-4e96-a51b-25f6df47f827"
      },
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "UEqieUO3iOU9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "70ed9b24-6b06-4b21-8be7-2eb29a2e4a2c"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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duhmAHTu207Jla/bt+53XXlvBBRe0YdSoseTm5jrXtWvXzrnO03xmPu/q4CgxFzBv3omq\n8DFjiunY0crKlWbefTeQTz818+mnFb8tnqomEhHxVhV9d3q6FvL88+MIDQ3jnnvu4MILO3LNNQN5\n4omZdOjwtyofMyoqmt69Exk2bAjnnNOS+Ph2FZbeb7zxJqZPf5jRo+/GZrMxbtwDNGoUw44d2/jw\nw/cJDAykf/+rneuSk5OBAPr3v7rK6XOXYXfVh8sLudvC8kxbY+7bZ7BypZlp04LJzy9fnxIfb2X9\n+pqbPF6tZb2b8uPdlB/vVlv5WbnyHXr3TsRkMjFkSDJz5jxFbGzjMz6uJ/JT463NfVXTpnbuvLOE\nJ58sdPn6DTeUr7LJzDTTrVsYTZuG061bGJmZdapCQ0TEpxw8eJDhw2/j7rvv4MorE6slcNc0RZkK\nnFxN9N13AdSrZ+fo0QAeeyyYggKD0aOLCQpSwzYREV8zePBQBg8eWtvJOCMqeZ9CUpKF9evz2bcv\nj++/P86LLxYQFWVn1qxgEhLC2Lw5QH3HRUSkxil4V0LfvhY+/fQ4Q4cW8+23Jvr3D+Pbb2u2/6OI\niIgiTCXVrw+zZhXx9tv5tGplx2533UnQU/0fRUREFLyr6LLLrKxbd5y+fV33OfR0/0cREam7FLzP\nQEgIvPhiIZMnFxIS4uhxV6+enaefVmM1EZFTGTHidnbs2FFm3XPPPc3y5ctcbr9ly2Yeeuh+ACZN\nGlfu9TfeSGfx4gUVnu/773fz8897AUhNfZCiItc9itxx/fUDyM+vuS7Drih4V4N//auE3bvzSEiw\ncPy4wTvvBFKsgreISIV69+7DqlWryqxbv34tCQlXnnbfGTPmVPp8H320ll9++RmAhx9+jODgisdD\n9wXqKlZNgoNhyZICbr01lNWrzdx9dwgLFxZi1jssIlJOr15XMmrUMIYOvRuAb7/dSUxMDDExsXz5\n5Uaef/45AgMDiYiI4JFHZpTZt3//Xrz77ods3ryJ+fOfICoqmujoRs4pPtPSppKdfYCCggLuuGM4\nTZo05a23Mvjoo7VERkYyZcqDvPRSOnl5x3jssUcoKSkhICCASZMmYxgGaWlTadasOd9/v5u4uAuY\nNGmyyzwcOPBHmf1nzZqB2RzOI49M5uDBHIqLi7nzzhFcfPGl5dZddtk/z+j9U2ipRiEh8NJLBdx8\ncyj//W8go0bBM88UcvLIe5qpTES8zdSpwbzzTvWGgwEDLEydWlTh65GRUbRo0YJvvtlBfHx71q79\ngN69EwE4duwYqamP0qxZc6ZNm8LGjRsICwsrd4wFC55m8uRpnH9+HBMmjKZZs+YcO3aUSy+9jL59\nr+K3335l8uRJLFmyjH/8ozPdu/ciPr69c//nn3+Oq666hl69rmTdujUsWbKQO+8cwXff7eThh6cT\nGRlFUlI/jh07RkRE+ZHO/rr/008/zYAB15Obe4RnnlnEsWPH2LDhM/bs+b7cujOlavNqFhYGy5YV\ncMklVjIyArnvvhD+nIjGOaDLzp0mrFbDOaCLRmQTkbroqquu4sMPPwDgs88+pnv3XgA0bNiQmTMf\nZdSo4Wzd+hVHj7qe6GPfvn2cf34cAB07dgIgIqI+O3dmcc89d5CWNrXCfQG++24nf//7RQB06nQx\nu3d/B0Dz5i2Ijm5EQEAAjRrFlJtDvKL9v/nmG84551zy848zbdpktmz5koSEK12uO1OKGh4QHg7L\nl+dzww1hrFgRSGCgndmzi045oItK3yJSW6ZOLTplKdlTevfuzTPP/IfevfvQosXZ1K9fH4DHHpvG\n44/P5dxzWzJnzswK9z95as/SaTo++OA9jh49yjPPPM/Ro0e5667Bp0jBiSk/S0osGIbjeH+dqKTi\nKUDK7h8QEEBISAgLFvwf27d/zapV7/DZZ5+QkpLqct2ZUMnbQ+rXh/T0fNq3t7J0aRD//ncw332n\nAV1EREqFh4fTuvX5vPTSC84qc4Djx/No3LgJx44dY8uWryqcBrRRoxh+/vkn7HY7W7d+BTimEW3a\ntBkBAQF89NFa576GYWC1Wsvs37ZtPFu2OKb8/N//vqJNm7aVSv9f92/fvr1znu+//a0jEyY8yE8/\n/ehy3ZlSyduDGjaE114rYODAUJ5/PoioKBuHDpUf1EUDuohIXdW7dyKPPppKauo057qBA2/gnnvu\npEWLs7nlliEsWbKQ4cPvLbfv8OH38tBDD9CkSVPn5CLdu/dk0qRxfPPNDvr3v5rY2FheeGERf/vb\n35k79/Ey987vuutuHntsGu+88yZmcyAPPjgZi8X9WtC/7j979kzy8iwsWPAMb72VQUBAADffPJim\nTZuVW3emNCVoDThwwODaa0P5/nvXc8YuWFBxv3BvzM+ZUH68m/Lj3ZQf76YpQf1MbKydN94o4Nxz\nHSXsmBgbZrOd+HjrKQO3iIiIKwreNaRpUzsZGfm0aGEjOzuAtLQi1q/PV+AWEZFKU/CuQWedZeeN\nN/KJirIxdWowP/zgelITERGRU1HwrmHnnmtnxowiCgoMxow50QdcRETEXQreteCaayz071/Cxo1m\nFi8OrO3kiIiIj1HwrgWGATNnFhEVZePRR1V9LiIilaPgXUtiY+089pij+vzkIVRFREROR8G7Fl17\nrYV+/UrYsMHMkiWqPhcREfcoeNei0urzyEg7jz4azI8/qvpcREROT8G7ljVubGf69ELy841yM5B1\n6xaG2QzduoVp5jEREXFSRPACAwdaePvtElatCuSFFwKJirIzYkSo8/XSqUNBo7GJiIhK3l7BMGDW\nrCIaNrQzbVows2ZVPHWoiIiIgreXOLn6fM8eTR0qIiIVUzTwItddZyExsQRw3XBNU4eKiAgoeHsV\nw4DHHy8iLMz1LK1jxhTXcIpERMQbKXh7mcaN7Tz+eCEAYWF2TCY0daiIiJSh4O2Frr/eQp8+FvLz\nDZ5+Gk0dKiIiZSh4eyFH9XkhDRrYmTgR/vhDg7eIiMgJCt5eqkkTO//+dxF5eVTYdUxEROomjwbv\n6dOnM2jQIJKTk/n666/LvLZv3z5uuukmrr/+eqZMmeLJZPisW28tIT4eXn45kG++0e8sERFx8FhE\n2LRpE3v37iU9PZ20tDTS0tLKvD5jxgzuuOMOXn/9dUwmE7///runkuKzzGaYPRtsNoOpU4NrOzki\nIuIlPBa8N2zYQEJCAgCtW7cmNzeXvLw8AGw2G1999RU9e/YEIDU1lWbNmnkqKT4tMRG6d7ewfr2Z\ntWtNtZ0cERHxAh4b2zwnJ4d27do5l6OiosjOziY8PJxDhw5Rr149HnvsMbKysrj44osZP378KY8X\nGRmG2exe8IqJiTijtHub+fPNdOwIjzwSxnXXOUrkvszfro/y492UH++m/FRNjYUBu91e5vkff/zB\nkCFDaN68OcOHD2f9+vV07969wv0PH8536zwxMRFkZx870+R6jZiYCJo0OcYttwSzdGkQTz5ZyNCh\nJbWdrCrzx+uj/Hgv5ce7KT/uHdMVj1Wbx8bGkpOT41w+cOAAMTExAERGRtKsWTPOPvtsTCYTnTt3\nZvfu3Z5Kil+4//5i6tWzM2tWEMf857MuIiJV4LHg3aVLF1avXg1AVlYWsbGxhIeHA2A2m2nRogU/\n/fST8/WWLVt6Kil+oXFjO6NHF5OTE6DZxURE6jiPVZt36tSJdu3akZycjGEYpKamkpGRQUREBL17\n9yYlJYVJkyZht9uJi4tzNl6Tit19dzEvvRTIggVBDBlSwtlnux4DXURE/JtH73lPmDChzHKbNm2c\nz8855xyWL1/uydP7ndBQSEkpYuTIUKZPD+a55wprO0kiIlILNPKHj7nuOgsdO1rJyAhk82bH5cvM\nNNOtWxhNm4bTrVsYmZk+3hxdREROScHbxwQEwCOPFAEwZUoIGRlmRowIZedOE1arwc6dJkaMCFUA\nFxHxYwrePuiyy6z071/C5s0mpk1z3XhNjdpERPyXgrePmjy5iMBAO7/95voS7tqlSysi4q/0De+j\nWrWyc8cdJYDr6ULj4mw1myAREakxCt4+bPz4IurVc91dbMyY4hpOjYiI1BQFbx/WsKGj6xhAZKQN\ns9lOfLyVBQsKSEqy1HLqRETEU9Qk2cfddlsJixcHsXevwccf53P++aouFxHxdyp5+7igIJgypQir\n1WDmTLUwFxGpCxS8/UDfvo6BW95+O5CsLF1SERF/p296P2AY8MADjnvfs2ap9C0i4u8UvP1Ez55W\nLr7YyqpVgWzbpssqIuLP9C3vJwwDJk0qLX0H13JqRETEkxS8/cjll1vp3NnCBx+YnZOWiIiI/9E3\nvB9xlL4dg7PMnKnSt4iIv1Lw9jOdO1u54goLH31k5osvTLWdHBER8QAFbz9U2vJc/b5FRPyTgrcf\nuuQSG716WfjsMzOffKLSt4iIv1Hw9lOlpe8ZM4Kxu567REREfJSCt5/q2NFGYmIJX35pYt06lb5F\nRPyJgrcfu//+Ey3PVfoWEfEfCt5+rH17GwMGlLB1q4n331fpW0TEXyh4+7mJE4sxDDszZwaTkWGm\nW7cwmjYNp1u3MDIzNSOsiIgv0re3n2vTxkZSkoWMjEDuvjvUuX7nThMjRoQCBSQlWWovgSIiUmkq\nedcBEyYUAa5ves+bp77gIiK+RsG7DjjvPDuG4fq1Xbv0ERAR8TX65q4jWrWyuVwfF+d6vYiIeC8F\n7zqitNvYX40Z43q9iIh4LwXvOiIpycL06YU47n3badvWyoIFaqwmIuKLFLzrkLvuKmHYsBLA4JZb\nShS4RUR8lIJ3HTNuXDEREXbmzAkiN7e2UyMiIlWh4F3HREfbGTOmmEOHApg/X93ERER8kYJ3HTRs\nWDHNmtlYuDCIX3+toA+ZiIh4LQXvOig0FCZNKqKoyGDmzODaTo6IiFSSgncddcMNFuLjrbz6qpkd\nO/QxEBHxJfrWrqNMJpgypQi73eCRR1T6FhHxJR6dmGT69Ols27YNwzBISUmhQ4cOztd69uxJkyZN\nMJkcU1XOnj2bxo0bezI58hc9e1rp1s3C+vVm1q0z0aOHtbaTJCIibvBY8N60aRN79+4lPT2dPXv2\nkJKSQnp6epltFi1aRL169TyVBHHDlClFJCSYeOSRYK64Ih+Tpv0WEfF6Hqs237BhAwkJCQC0bt2a\n3Nxc8vLyPHU6qaILL7Rx/fUWsrJMvP66ZogVEfEFHgveOTk5REZGOpejoqLIzs4us01qaio33XQT\ns2fPxm53PWWleN6DDxYRHGxnxoxgCgpqOzUiInI6NVbU+mtwHj16NJdffjkNGjRg5MiRrF69msTE\nxAr3j4wMw2x2r043JibijNLqbTydn5gYGDMGZs0yWL48ggce8OjpdH28nPLj3ZQf71ZT+fFY8I6N\njSUnJ8e5fODAAWJiYpzL1157rfP5FVdcwa5du04ZvA8fznfrvDExEWRnH6tCir1TTeVn2DBYtCic\ntDS45prjREd7piZE18e7KT/eTfnxbp7IT0U/BjxWbd6lSxdWr14NQFZWFrGxsYSHhwNw7Ngx7rzz\nToqLHdNRfvnll5x//vmeSoq4oUEDGDeuiGPHDJ58UsOmioh4M4+VvDt16kS7du1ITk7GMAxSU1PJ\nyMggIiKC3r17c8UVVzBo0CCCg4OJj48/ZalbasbQoSUsWhTE4sWBfPihiZ9+CiAuzsbYscWagUxE\nxIsYdh9pKeZuVYSqYc5MSkowzz9fvuRdXXN/6/p4N+XHuyk/3s0vqs3FN336qetGgfPmqSpdRMRb\nKHhLGbt3u/5I7Nqlj4qIiLfQN7KUERdnq9R6ERGpeQreUsbYscUu148Z43q9iIjUPAVvKSMpycKC\nBQXEx1sxDEdbxiuvLFFrcxERL6LgLeUkJVlYvz6fH3/Mo0ULG2vXmvn2W31URES8hb6RpUJhYZCW\nVojFYjBpUjC+0alQRMT/KXjLKSUmWrnySguff27mjTc065iIiDdQ8JbTSksrJCTETmpqMEeP1nZq\nREREwVtO65xz7IwZU0x2dgAzZwbXdnJEROo8BW9xy8iRxbRqZWPx4kC2b9fHRkSkNulbWNwSEgKP\nPVaIzWbwwAMh2DRmi4hIrVHwFrf16GFlwIASNm82sWKFGq+JiNQWBW+plGnTiggLszNtWjCHD9d2\nakRE6iYFb6mUZs3sTJhQxMGDAaSlqfGaiEhtUPCWShsxooQLLrCydGkgW7fqIyQiUtP0zSuVFhgI\nM2YUYbcb3H9/CFZrbadIRKQRGrF2AAAgAElEQVRuUfCWKunSxcp115WwbZuJl14KrO3kiIjUKQre\nUmVTpxYREWFn+vRgsrON2k6OiEidoeAtVda4sZ1+/UrIzTVo374eV1wRRmamupCJiHiagrdUWWam\nmfT0IADsdoNvvzUxYkSoAriIiIcpeEuVzZ0b5HL9vHmu14uISPVQ8JYq27XL9cfnu+/0sRIR8SR9\ny0qVxcW5HuC8fn17DadERKRuUfCWKhs7ttjl+sOHA3jnHd33FhHxFAVvqbKkJAsLFhQQH2/FbLYT\nH29l6tRCwsLs3HdfCD//rO5jIiKeoOKRnJGkJAtJSZYy6xo2tDN2bCh33x3KW2/lE6gxXEREqpVK\n3lLtbrrJwsCBjqlDZ81Sy3MRkeqm4C3VzjDg8ccLOeccG/PnB7F+vam2kyQi4lcUvMUjIiJg0aIC\nzGYYOTKEAwd0/1tEpLooeIvHdOxo46GHisjODuBf/wrB5rpnmYiIVJKCt3jUiBElJCRYWLfOzH/+\no5ZrIiLVQcFbPCogAObPL6RxYxvTpwezcWNtp0hExPcpeIvHNWpk59lnC7FaYdAgdP9bROQMuRW8\nd+zYwbp16wB48sknue2229i8ebNHEyb+pWtXKxMnFrN3L9x6ayjHj9d2ikREfJdbwfvRRx+lZcuW\nbN68me3btzN58mTmz5/v6bSJnxk/vpihQ+F//zMxbFgoFstpdxERERfcCt7BwcGce+65fPjhh9x4\n442cd955BASoxl0qxzBg4ULo0cPCmjVm7r8/GLvmMBERqTS3InBBQQGrVq1izZo1dO3alSNHjnD0\n6NHT7jd9+nQGDRpEcnIyX3/9tcttnnjiCQYPHly5VIvPycw0061bGKGh8NtvBi1a2Fi2LIg5czQC\nm4hIZbkVvMeNG8c777zDfffdR3h4OEuXLmXo0KGn3GfTpk3s3buX9PR00tLSSEtLK7fN999/z5df\nflmlhIvvyMw0M2JEKDt3mrBaYdcuE7/8EkB0tI2ZM4NZvlxD7IuIVIZb35qXXXYZ7du3Jzw8nJyc\nHDp37kynTp1Ouc+GDRtISEgAoHXr1uTm5pKXl0d4eLhzmxkzZnDffffx9NNPn0EWxNvNneu6dN2w\noR2bzc64cSE0blxAz57WGk6ZiIhvcqvkPW3aNFatWsWRI0dITk5m2bJlTJ069ZT75OTkEBkZ6VyO\niooiOzvbuZyRkcGll15K8+bNq5Zy8Rm7drn+mO3dG8BLLzmGUL3jjlC+/lrtKERE3OFWyfubb75h\n8uTJLF++nKSkJEaOHMltt91WqRPZT2qZdOTIETIyMnjhhRf4448/3No/MjIMs9m9CS5iYiIqlTZv\n5+v5iY+H7dtdrTe46qowXnkFrr8ebrmlHhs2QMuWNZ/GM+Hr1+evlB/vpvx4t5rKj1vBuzTwrl+/\nnrFjxwJQXFx8yn1iY2PJyclxLh84cICYmBgAvvjiCw4dOsQtt9xCcXExP//8M9OnTyclJaXC4x0+\nnO9OUomJiSA7+5hb2/oCf8jPqFGOe95/NXJkAdnZFi6/HNLSAklJCeHKK63897/5REXVQkKrwB+u\nz8mUH++m/Hg3T+Snoh8DbtVTtmzZkn79+nH8+HHatm3Lm2++SYMGDU65T5cuXVi9ejUAWVlZxMbG\nOu93JyYmsnLlSl599VWefvpp2rVrd8rALb4tKcnCggUFxMdbMZshPt7KggUFJCWd6Oh9110ljBxZ\nzPffmxg8OIyCglpMsIiIl3Or5P3oo4+ya9cuWrduDcB5553HrFmzTrlPp06daNeuHcnJyRiGQWpq\nKhkZGURERNC7d+8zT7n4lKQkC0lJlj9/mbquRZk8uYjffzfIzAzknntCWLy4EJOmAhcRKcet4F1Y\nWMjatWuZN28ehmHQsWNHzjvvvNPuN2HChDLLbdq0KbfNWWedxdKlS91Mrviz0klMsrMNVq4MZPhw\nePrpQkLL17iLiNRpblWbT548mby8PJKTk7nxxhvJycnhoYce8nTapA4KDob/+78COne28M47gQwc\nGEZ2tiYyERE5mVsl75ycHObMmeNc7tGjh0ZFE4+pXx9efbWAceNCeO21QPr2DeOVVwqIi7PVdtJE\nRLyC28OjFpzUgig/P5+ioiKPJUokONhRZT5xYhE//xxA//5hfPqpboCLiICbJe9BgwbRt29f2rdv\nDzhaj48ZM8ajCRMxDJg4sZhzz7UxdmwIN94Yypw5hSQnazoyEanb3Are119/PV26dCErKwvDMJg8\nebIamUmNueEGC82bFzB0aCijR4fy009FPPBAMYZuhYtIHeX2jBBNmzaladOmzuWKZgkT8YR//tPK\nypXHufnmMObMCeannwKYO7eQkJDaTpmISM2r8mDSdk3ELB5SOn1o06bhdOsWRmam4zfmeefZWbUq\nn0susZKREcgNN4Ry8KCK3yJS91Q5eBuqsxQPKDt9qMHOnSZGjAh1BvDoaDtvvJHPtdeWsHGjmX79\nwtizR59FEalbTllt3q1bN5dB2m63c/jwYY8lSuquiqYPnTcvyDmcakgIPPdcIeeea2Pu3GD69KnH\nU08V0revGrKJSN1wyuD9yiuv1FQ6RICKpw/96/qAAEhJKea882xMnBjCbbeFMnJkMf/+dxFmt1ty\niIj4plN+zWmubalpcXE2du4s35+7ogFabrzRQvv2+dxxRyjPPBPEV18FsHBhIU2aqE2GiPivKt/z\nFvGEsWNdTzU7ZkzFU9DGx9v44IPjXH11CV98YaZnTw3oIiL+TcFbvErZ6UPtLqcPdSUiAhYtKiQt\nrZAjRwyuvz6UuXODsGlEVRHxQ7o7KF6ndPrQyjIMGDashI4drQwbFsr06cFs2mTimWcKiIz0QEJF\nRGqJSt7idy65xMaHH+bTvbuFNWvMJCTUY+tWfdRFxH/oG038UnS0neXLC5g4sYhffzUYMCCMxYsD\n0dhCIuIPFLzFb5lMjolN0tMLiIiw8+CDIfTtG8YXX6gxm4j4NgVv8Xvdu1v58MN8rrmmhC1bTFx9\ndRi33x7CDz9oZDYR8U0K3lInNGtmZ9GiQt599zgXX2zl3XcDufzyejz0UDAaLFBEfI2Ct/isiiYw\nOZVLLrHx7rv5PP98AU2b2lm4MIhLLw3n2WcDKSqqgUSLiFQDBW/xSaebwORUDAOuvtrCZ58d5+GH\nCwFITQ2ha9d6vP22WY3aRMTrKXiLTzrVBCbuCg6Ge+4pYdOmPEaMKOb33w3uuiuU/v0dI7QpiIuI\nt1LwFp/k7gQm7oiMhGnTivjkk+NcdVUJmzebGDgwjH79wli50qxR2kTE6yh4i0+qaKKSita7o1Ur\nO0uWFLJq1XH69i3hq69MDB0ayuWXh7FihZniiodXFxGpUQre4pOqMoGJuy66yMaLLxbyySfHSU4u\n4ccfAxg9OpR//KMeCxcGcvz4GZ9CROSMKHiLT6rqBCaVccEFNubPL2TTpuMMH17M4cMGDz0UQqdO\n4Tz+eBCHDlXbqUREKsWw232jWU529jG3touJiXB7W1+g/HiPgwcNFi8OZPHiIA4fNggLs3PnnQbX\nXXec+Hj/uDHuy9fHFeXHuyk/7h3TFZW8RdwUHW3n/vuL+eqrPKZNK6RhQztPPQXdu9fjyivDeOGF\nQHJzazuVIlIXKHiLVFJ4OIwYUcKmTcfJyIArr7Tw9dcBPPBACBdeGM7dd4fw8ccmtVIXEY/RfN4i\nVRQUBElJ0LVrAfv3G7z6aiDLlweSkeH4a9HCxqBBJSQnl3D22T5xd0pEfIRK3uL3qjKMamU1aWJn\n9OhiPv/8OO+8k8/NNxdz6JDB7NnBXHxxONddF8qyZYHk5GgyFBE5cwre4tfOZBjVqjAM+Mc/rMyd\nW8T27XnMn1/AZZdZ+OQTM+PGhdC+fT2SkkJ5/vlAfv9dgVxEqkbBW/xadQyjWlXh4ZCcbOHttwv4\n8ss8pk4t5KKLbHz2mZmUlBA6dgwnMTGMp54K0vSkIlIpuuctfq06h1E9E+ecY+fee0u4994S9u83\nWLXKzH//a+bzz01s2RLMtGnBtG1rpX9/C337WmjXzkaAflqLSAX09SB+zRPDqJ6pJk3s3H57CW+8\nUUBWlqNqvU8fCz/8EMDs2cH06lWPCy+sx8iRIbzxhln3yUWkHJW8xa+NHVvMiBGh5dZXxzCq1SEq\nylG1npxsIS8P1qwxs2aNmXXrTLz2WiCvvRaIYdj5299s9OhhoUcPKxdfbMWs/1wRr5CXB7//HsBv\nvxk0bgzx8TVzXn0FiF9zDJdawLx5QezaFUBcnI0xY4qrdRjV6hIeDtdea+Haay3YbJCVFcC6dY5A\nvnGjif/9L5gnn4SICDtXXGGhe3crl11m5fzzVcUu4glWK/zyi8GvvzqCc2mQPvkxN7dszVhWlkFM\njOe7hno0eE+fPp1t27ZhGAYpKSl06NDB+dqrr77K66+/TkBAAG3atCE1NRXDUPWgVL+kJItXButT\nCQiACy+0ceGFxYwe7fh1/+mnJtauNbN2rZl33w3k3XcDAWjY0M7FF1u55BIrl15qpWNHK/Xq1XIG\nRHyEzQb79xv88EMAe/YE8MMPpX8GP/0UQEmJ67gUHm6neXMbnTo5Hps1s9OlSzCNGtXMmA4eC96b\nNm1i7969pKens2fPHlJSUkhPTwegoKCAd999l5dffpnAwECGDBnC1q1b6dSpk6eSI+LTwsMhMdFK\nYqIVu72IH380+PhjM19+aWLTJpOzuh3AZLLTvr2NSy89EdCbNdMgMVJ32e1w4IAjQP/4o3FSgA7g\nxx8DKCgoH6AbNLBz4YU2Wra0cc45juBcGqSbN7dRv37588TEBJOdXQMZwoPBe8OGDSQkJADQunVr\ncnNzycvLIzw8nNDQUF588UXAEcjz8vKIiYnxVFJE3JaZaWbu3BNV7GPHel8Vu2E45h5v1aqEoUNL\nAPjjD4PNmx2B/MsvTXz9dQDbtplYtMixT1SUjfPPtxEXV/axeXO7qtzFL1gsjgD988+uA3R+fvkA\nHRZmp3VrG61b22jVyhGoHc/tREXZ8ebKYI8F75ycHNq1a+dcjoqKIjs7m/DwcOe6hQsX8tJLLzFk\nyBBatGhxyuNFRoZhNpvcOndFs7D4KuWnZqxYASNGnFguHdClfn1ITq54P2/IT0wMtG8PQ4c6lgsL\n4auv4PPPHX87dgTw5ZcBbNxYdr+wMGjTBtq2dTy2aQOtW0fQujUuSxa+yBuuT3Wqi/kpKIDff4df\nf3X8/fZb+ef79+NyPoGwMDjvPDj//BOPpc+bNjUwDBPgXmyprvxUhxprsOZq5tHhw4czZMgQhg0b\nxkUXXcRFF11U4f6HD+e7dR5NMefdvDk/jzwShqt/4mnTrPTq5frz5835iYtz/J0c0H/4IYDduwPY\ntevEY1ZWAFu2lC9iREXZOPdcO+ee66g2dDw6lps08Y0Suzdfn6rwh/zk5TlKyNnZAZSUhLF3byGH\nDhkcOQJHjhh/Pj/xeOSI4bJau1RQkJ0mTexccsmJKm1HzZSjJN2kScUl6Jyc6s1bTU4J6rHgHRsb\nS85J78yBAwecVeNHjhxh9+7dXHLJJYSEhHDFFVewZcuWUwZvEU/zlgFdPCUkBOLjbeXmHrda4eef\nDXbvDuCPP8LIyirmp58C2Ls3gO3bA9iypfwPmuBgO2efbePss+2cc47tzz/HunPPtXFSBZv4Obsd\ncnMd891nZwf8+Wj8GaANZ6DOznYsl6++DnF53Pr17URG2rngAhsNGzoCdNOmNpo2dTw2a2anaVNH\n9bYv/JCsbh4L3l26dOGpp54iOTmZrKwsYmNjnVXmFouFSZMm8fbbb1OvXj22b9/O1Vdf7amkiLgl\nLs7Gzp3lA1VtDuhSE0wmaNnSTsuWVmJiIDu7yPma1Qr79jla3ToC+onnP/8cwO7dros00dEngnnj\nxnYiIhx/4eGUeR4efuK1iAjUf91LFBRAdrZBTo7xZ9ANICfHKPN38KDj8dAho8IW2aXMZjuNGtk5\n7zwbMTF2YmPtxMTYOPfcYAIDC4iKstOwoSNYR0Y6Govps3BqHnt7OnXqRLt27UhOTsYwDFJTU8nI\nyCAiIoLevXszcuRIhgwZgtls5oILLqBXr16eSoqIW7x9QJfaYDLBWWfZOessK127Wsu9fvQo7N0b\n8OefcdLzikvtp1Ja2oqOdpSoIiMdj9HRZZ9HRZ3405d8eXY7FBXBsWMGx45BXp7hfO54NMjLM8jL\ng0OHjD8DdYAzYOflnb6lVni4IyB36GAjJsZGdLRjOTraflKAthMba6NhQ1yWjh2ts72rQaivMOyu\nbkZ7IXfvI/jDPaGTKT81KzPTXKkBXbw9P5VVnfkpLbWXBoOTA4ljuXxQOXToxF9xsXtNfRs2PBHU\no6NtZQJ806YhHD5ciMUCJSVgsRh/Pp5Ytlgcy0FBEBJiJzjYcYshNNROSIhj3cmPZjPOfazW0ucG\nNlvZ9cXFjuBYmseTn/912TBOnDs0tDQNfz0/1KsXyOHDJRQWQkGBUeYxP9+goAAKCx2PFkvlmkqb\nTI7g26iRI+jGxJQ+t5VZ53ifHWk6U/r/ce+Yrug3q8hJfHFAF291otRe+fKB3Q7Hj1MmmJ/8d/Bg\n2cecHEep32p1VdKvhihTzUwmx22CiAjH/Vs4EYRzcwOcQdm1wDJLYWF2QkLshIY6egg0bmwjJIQ/\nb02cuCVx8vOTb100aOAIypGRdfPesa9S8BapghP9wSEuLswr+4P7MsPAeU/87LPdC/42m6Ma/+DB\nE0E9ICCMgoICzGYIDLT/+ej4M5vtfz46fmiUlOAsuRYWun4sKHCUqs3mE38mk/2k56Xr7ZhMVHif\nPzSU0/YhttuhuJgyaWjQIJz8/LyTagVOfxzxTwreIpWUmWkuc2+8tD84FCiA16KAAGjY0FGN3rq1\nI+A7GuD55jUxDAgOdvw1aHByfnziTqd4mCpJRCpp7twgl+vnzXO9XkSkuil4i1SSv/cHFxHvp28b\nkUqqqN+3v/cHFxHvoeAtUkljx7ru912X+4OLSM1S8BappKQkCwsWFBAfb8Vshvh4KwsWqLGaiNQc\nBW+RKkhKsrB+fT4lJbB+ff4pA3dmpplu3cJo2jScbt3CyMxUJw8ROTP6FhHxIHUrExFPUMlbxIPU\nrUxEPEHBW8SD1K1MRDxB3yAiHqRuZSLiCQreIh6kbmUi4gkK3iIeVLZbmf2U3crUKl1E3KVvBxEP\nc2eaUbVKF5HKUMlbxAuoVbqIVIaCt4gXUKt0EakMfTOIeAG1SheRylDwFvECapUuIpWh4C3iBdQq\nXUQqQ//1Il5CrdJFxF0qeYv4ELVKFxFQ8BbxKWqVLiKg4C3iU9QqXURAwVvEp1S2Vboat4n4J/0n\ni/gQR6O0AubNC2LXrgDi4myMGVNcYat0NW4T8U8K3iI+xp1W6XDqxm0K3iK+TdXmIn5KjdtE/Jf+\ni0X8lBq3ifgvBW8RP1WZxm2lDdvMZtSwTcQH6D9UxE+527hNDdtEfI+Ct4gfc6dxmxq2ifgeVZuL\n1HFq2Cbie/TfKVLHqWGbiO9R8Bap4zRqm4jv8eh/3fTp09m2bRuGYZCSkkKHDh2cr33xxRfMmTOH\ngIAAWrZsSVpaGgEB+i0hUtPKNmwzERdn1ahtIl7OY9Fy06ZN7N27l/T0dNLS0khLSyvz+pQpU5g/\nfz4rVqzg+PHjfPLJJ55KioicRlKShfXr8ykpgfXr8ysMxJqSVMQ7eCx4b9iwgYSEBABat25Nbm4u\neXl5ztczMjJo0qQJAFFRURw+fNhTSRGRaqLGbSLewWP/cTk5OURGRjqXo6KiyM7Odi6Hh4cDcODA\nAT777DO6devmqaSISDWpTOM23RsX8Zwa+2+y2+3l1h08eJC7776b1NTUMoHelcjIMMxmk1vniomJ\nqFIavZXy493qUn6mTIGbbiq/fvJkU5n9VqyAESNOvF56b7x+fUhOrs7Unl5duj6+SPmpGo8F79jY\nWHJycpzLBw4cICYmxrmcl5fHsGHDGDt2LF27dj3t8Q4fznfrvDExEWRnH6t8gr2U8uPd6lp+evWC\nBQvM5UZt69XLwkkVazzySBhQ/sf2tGlWevVy73+5OtS16+NrlB/3jumKx6rNu3TpwurVqwHIysoi\nNjbWWVUOMGPGDG677TauuOIKTyVBRDygtHHb77/nVdi4TffGRTzLYyXvTp060a5dO5KTkzEMg9TU\nVDIyMoiIiKBr1668+eab7N27l9dffx2Aq666ikGDBnkqOSJSg+LibOzcWb7kXdG98blzT5Tkx451\n3U1NRE7w6D3vCRMmlFlu06aN8/mOHTs8eWoRqUVjxxaX6Q9e6q8Dv6jfuEjVqA5LRKpdUpKFBQsK\niI+3YjbbiY+3smBB+YCsfuMiVaO+GyLiEe7MaFbZe+OqYhdxUMlbRGpNZfuNjxgRys6dJqxWw1nF\nrv7jUhcpeItIranMpCiqYhc5QcFbRGqNu/fGQd3PRE6mT72I1Cp3+o1D1YZmNZvR0KzilxS8RcQn\nuFvFXvbeOLo3Ln5JwVtEfIK6n4mcoJ+iIuIz1P1MxEElbxHxK+p+JnWBgreI+BV1P5O6QMFbRPxK\n2XvjVFv3s9IW7E2bhqsFu9Q6ffpExO+U3ht3zK9c8fzh7s5+pglUxNuo5C0idZa7VeyqXhdvo+At\nInWWu93PVL0u3kafKhGp09zpfqbqdfE2KnmLiJyGp6rXVUqXqtInRUTkNByl5gLmzTsxmMuYMeUH\nc6ls9bpK6VJVCt4iIm6ozup1OHUpXcFbTkfV5iIi1aQyA8RUpRGcZkmTUgreIiLVpDLzk7s7jKtm\nSRNXFLxFRKqRu/OTqxGcnAldVRGRWqBGcHImFLxFRGqJGsFJVanaXETEi3m6EZyq132TgreIiBer\nzCxpVWsEp3nMfZGCt4iIlyttBFdSQo03glMJ3TvpKoiI+InqbgSnBnDeSyVvERE/4k5XNXer19VN\nzXspeIuI1DHuVq9XpZua7qPXDAVvEZE6xt2R4NwtoUPV7qNruNeq0zsmIlIHudPHfOzY4jL3vEud\nSTc13UevHip5i4iIS54Yq1330auH3gUREamQOyV0cL+UruFeq4dK3iIicsa85T56XSmh+3fuRESk\nxug+es1RyVtERGqMJ4Z7rYsldI8G7+nTpzNo0CCSk5P5+uuvy7xWVFTEAw88wMCBAz2ZBBER8TLV\nPdxrZUvo7vZF9+ZA77HgvWnTJvbu3Ut6ejppaWmkpaWVeX3WrFm0bdvWU6cXEREfV9330StbQvfm\nQWc8Frw3bNhAQkICAK1btyY3N5e8vDzn6/fdd5/zdREREVfcGe7VEyPGefugMx4L3jk5OURGRjqX\no6KiyM7Odi6Hh4d76tQiIlKHeKKle9Wq4qmxEnqNlf/tdvsZ7R8ZGYbZbHJr25iYiDM6l7dRfryb\n8uPdlB/vVl35GT7c8edgAsq3aJ8yBW66qfy+kyebyqUjPh62by+/bXy8UWbbp592nZ5nngk9KT3V\nz2PBOzY2lpycHOfygQMHiImJqfLxDh/Od2u7mJgIsrOPVfk83kb58W7Kj3dTfrxbTeenVy9YsMBc\nbsrUXr0snFQxDMCoUWaXXdpGjiwgO/tEif6bb8IBo9x233xjJzs7r9z6yqrox43Hqs27dOnC6tWr\nAcjKyiI2NlZV5SIiUqvcuYdeul11V8VXJ4+VvDt16kS7du1ITk7GMAxSU1PJyMggIiKC3r17M3r0\naPbv38+PP/7I4MGDufHGGxkwYICnkiMiIlIp1T3oTHXy6D3vCRMmlFlu06aN8/n8+fM9eWoRERGP\ncwT3gj+r4k3ExVkZM6bY4yO7eUeHNRERER9VWkJ33MN3r33WmdLwqCIiIj5GwVtERMTHKHiLiIj4\nGAVvERERH6PgLSIi4mMUvEVERHyMgreIiIiPUfAWERHxMQreIiIiPsawn+lcnSIiIlKjVPIWERHx\nMQreIiIiPkbBW0RExMcoeIuIiPgYBW8REREfo+AtIiLiY8y1nYDqNH36dLZt24ZhGKSkpNChQ4fa\nTlKVbdy4kTFjxnD++ecDEBcXx+TJk2s5VZW3a9cu7r33XoYOHcqtt97Kvn37uP/++7FarcTExPD4\n448TFBRU28l021/zM2nSJLKysmjYsCEAd955J927d6/dRFbCrFmz+Oqrr7BYLIwYMYILL7zQp6/P\nX/Ozdu1an70+BQUFTJo0iYMHD1JUVMS9995LmzZtfPb6uMrP6tWrffb6lCosLOSqq67i3nvvpXPn\nzjV2ffwmeG/atIm9e/eSnp7Onj17SElJIT09vbaTdUYuvfRS5s+fX9vJqLL8/HymTZtG586dnevm\nz5/PzTffTN++fZkzZw6vv/46N998cy2m0n2u8gMwbtw4evToUUupqrovvviC3bt3k56ezuHDh0lK\nSqJz584+e31c5eeyyy7z2euzbt062rdvz7Bhw/jtt9+444476NSpk89eH1f5+fvf/+6z16fUs88+\nS4MGDYCa/X7zm2rzDRs2kJCQAEDr1q3Jzc0lLy+vllNVtwUFBbFo0SJiY2Od6zZu3EivXr0A6NGj\nBxs2bKit5FWaq/z4sksuuYR58+YBUL9+fQoKCnz6+rjKj9VqreVUVV2/fv0YNmwYAPv27aNx48Y+\nfX1c5cfX7dmzh++//95ZW1CT18dvgndOTg6RkZHO5aioKLKzs2sxRWfu+++/5+677+amm27is88+\nq+3kVJrZbCYkJKTMuoKCAmc1UnR0tE9dI1f5AVi2bBlDhgzhvvvu49ChQ7WQsqoxmUyEhYUB8Prr\nr3PFFVf49PVxlR+TyeSz16dUcnIyEyZMICUlxaevT6mT8wO++/8DMHPmTCZNmuRcrsnr4zfV5n/l\n66O+nnvuuYwaNYq+ffvyyy+/MGTIEN5//32fub/lDl+/RgDXXHMNDRs2pG3btixcuJCnn36aKVOm\n1HayKmXNmjW8/vrrLAR//RcAAAUvSURBVFmyhCuvvNK53levz8n52bFjh89fnxUrVrBz504mTpxY\n5pr46vU5OT8pKSk+e33efPNNOnbsSIsWLVy+7unr4zcl79jYWHJycpzLBw4cICYmphZTdGYaN25M\nv379MAyDs88+m0aNGvHHH3/UdrLOWFhYGIWFhQD88ccfPl8F3blzZ9q2bQtAz5492bVrVy2nqHI+\n+eQTnnvuORYtWkRERITPX5+/5seXr8+OHTvYt28fAG3btsVqtVKvXj2fvT6u8hMXF+ez12f9+vV8\n+OGH3Hjjjbz22mv85z//qdH/H78J3l26dGH16tUAZGVlERsbS3h4eC2nqurefvttFi9eDEB2djYH\nDx70i3tE//znP53X6f333+fyyy+v5RSdmX/961/88ssvgON+V2nvAF9w7NgxZs2axYIFC5ytfX35\n+rjKjy9fn82bN7NkyRLAcVswPz/fp6+Pq/xMmTLFZ6/P3LlzeeONN3j11Ve54YYbuPfee2v0+vjV\nrGKzZ89m8+bNGIZBamoqbdq0qe0kVVleXh4TJkzg6NGjlJSUMGrUKLp161bbyaqUHTt2MHPmTH77\n7TfMZjONGzdm9uzZTJo0iaKiIpo1a8Zjjz1GYGBgbSfVLa7yc+utt7Jw4UJCQ0MJCwvjscceIzo6\nuraT6pb09HSeeuopWrZs6Vw3Y8YMHnroIZ+8Pq7yM3DgQJYtW+aT16ewsJB///vf7Nu3j8LCQkaN\nGkX79u154IEHfPL6uMpPWFgYjz/+uE9en5M99dRTNG/enK5du9bY9fGr4C0iIlIX+E21uYiISF2h\n4C0iIuJjFLxFRER8jIK3iIiIj1HwFhER8TF+O8KaiMCvv/5KYmIif//738us79atG3fdddcZH3/j\nxo3MnTuX5cuXn/GxRMR9Ct4ifi4qKoqlS5fWdjJEpBopeIvUUfHx8dx7771s3LiR48ePM2PGDOLi\n4ti2bRszZszAbDZjGAZTpkzhvPPO46effmLy5MnYbDaCg4N57LHHALDZbKSmprJz506CgoJYsGAB\nAOPHj+fo0aNYLBZ69OjBPffcU5vZFfEruuctUkdZrVbOP/98li5dyk033eScO/7+++/nwQcfZOnS\npdx+++08/PDDAKSmpnLnnXfy8ssvc91117Fq1SrAMS3iv/71L1599VXMZjOffvopn3/+ORaLhVde\neYUVK1YQFhaGzWartbyK+BuVvEX83KFDhxg8eHCZdRMnTgSga9euAHTq1InFixdz9OhRDh48SIcO\nHQC49NJLGTduHABff/01l156KQD9+/cHHPe8W7VqRaNGjQBo0qQJR48epWfPnsyfP58xY8bQrVs3\nbrjhBgICVFYQqS4K3iJ+7lT3vE8eHdkwDAzDqPB1wGXp2WQylVsXHR3NW2+9xdatW/nwww+57rrr\nyMzMdDkfuohUnn4Ki9RhX3zxBQBfffUVF1xwAREREcTExLBt2zYANmzYQMeOHQFH6fyTTz4BYOXK\nlcyZM6fC43766aesX7+eiy66iPvvv5+wsDAOHjzo4dyI1B0qeYv8f3t3TAQhDARQ9A+JC2ioMIEw\nmsxQMcQDNSgCATjAwRmgvWLv/jOQSfWz2+THva3Nu64D4LoujuPgeR5qrQDUWlnXlZQSTdMwzzMA\npRRKKez7Ts6ZZVm47/v1zL7vmaaJbdtIKTGOI23bfu+S0p/xVzHpTw3DwHme5OwbXorGtbkkScE4\neUuSFIyTtyRJwRhvSZKCMd6SJAVjvCVJCsZ4S5IUjPGWJCmYD2B4ilLtJUghAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uxvnnV3FieET",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "73eb41cb-852e-451a-f9c8-f1334d6617cc"
      },
      "cell_type": "code",
      "source": [
        "plt.clf()   # clear figure\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/ENjtkJxsuEL96FEDu4cddUFBULOm84tZSEjRvy+7LIzU1FJU7CIovMWnBOq5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\nxM8ovEVERPzM/wM9zysmMVB5AAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}