QuickDraw kaggle.ipynb

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  "metadata": {
    "colab": {
      "name": "AbramovPavelSergeevich_iad2_notebook.ipynb",
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    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "-5VTh7_K7wNv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:08:15.388798Z",
          "start_time": "2018-11-07T16:08:15.081241Z"
        },
        "id": "CkzI7g3e7wN1",
        "colab_type": "code",
        "outputId": "f1a53154-2048-4f56-88b9-af7a1b128db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import tqdm\n",
        "import matplotlib \n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import csv\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "J2B-7_lZ6Nm2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Отчет\n"
      ]
    },
    {
      "metadata": {
        "id": "9Y3-g-o26v0c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Эксперименты с архитектурой\n"
      ]
    },
    {
      "metadata": {
        "id": "nEEitGoTGjIV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Резюме:\n",
        "- We need to go deeper. Не смотря на то, что лучшей оказалась модель с 43 слоями, модель с 49 получала лучший результат на нетронутой выборке. (при top3 accuracy в **0.862 **loss в **1.200** и category accuarcy в **0.701** на 50 эпохах результат был **0.850** (разница ***0.012***) \\\\\n",
        "- При увеличении количества эпох до 90, top3 accuracy возрос до **0.892**, cat accuracy - до **0.8** loss упал до **0.9**, однако на нетронутой выборке модель получила **0.852** (разница в ***0.040***), в то время обученная на 90 эпохах нейронная сеть глубже давала результат **0.850** на нетронутой выборке при значении top3 accuracy в **0.852**.\n",
        "- При увеличении размера картинки, обучение происходило хуже, так как был выбран неправильный learning_rate (см loss графики пункта \"Эксперименты с  исходными данными\") \n",
        "Экспирименты с агументацией данных дали понять что, таким образом можно существенно расширить исходную выборку данных, однако она [агументация] может как улучшить, так и ухудшить качество обучения, конкретные параметры подбираются исходя из задачи."
      ]
    },
    {
      "metadata": {
        "id": "5tJsYVL4gmSN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Первая модель"
      ]
    },
    {
      "metadata": {
        "id": "kQLvall0gpwg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 256\n",
        "STEPS_PER_EPOCH = 100\n",
        "EPOCHS = 5\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  ### YOUR CODE HERE: replace with a better model! Batch normalization really helps!\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model\n",
        "  \n",
        "\"\"\"\n",
        "Результат обучения:\n",
        "Epoch 1/5\n",
        "100/100 [==============================] - 288s 3s/step - loss: 5.6091 - categorical_accuracy: 0.0115 - top_3_accuracy: 0.0335\n",
        "Model saved in model_0\n",
        "Epoch 2/5\n",
        "100/100 [==============================] - 287s 3s/step - loss: 4.9129 - categorical_accuracy: 0.0475 - top_3_accuracy: 0.1118\n",
        "Model saved in model_1\n",
        "Epoch 3/5\n",
        "100/100 [==============================] - 285s 3s/step - loss: 4.6153 - categorical_accuracy: 0.0724 - top_3_accuracy: 0.1623\n",
        "Model saved in model_2\n",
        "Epoch 4/5\n",
        "100/100 [==============================] - 287s 3s/step - loss: 4.4057 - categorical_accuracy: 0.1001 - top_3_accuracy: 0.2075\n",
        "Model saved in model_3\n",
        "Epoch 5/5\n",
        "100/100 [==============================] - 285s 3s/step - loss: 4.2042 - categorical_accuracy: 0.1257 - top_3_accuracy: 0.2546\n",
        "Model saved in model_4\n",
        "\"\"\"\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mTfYcHgAhDH6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Вторая модель\n",
        "Строим более глубокую сетку, для классификации более конкретных признаков (уменьшаем размер изображения) большая разница top3acc 0.8153 и catAcc 0.6351."
      ]
    },
    {
      "metadata": {
        "id": "6HkhtViNhTJC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        " \n",
        "BATCH_SIZE = 512\n",
        "STEPS_PER_EPOCH = 256\n",
        "EPOCHS = 10\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model\n",
        "  \n",
        "\"\"\"\n",
        "Результат обучения\n",
        "  Epoch 1/10\n",
        "256/256 [==============================] - 100s 391ms/step - loss: 3.9279 - categorical_accuracy: 0.2151 - top_3_accuracy: 0.3514\n",
        "Model saved in model_0\n",
        "Epoch 2/10\n",
        "256/256 [==============================] - 99s 385ms/step - loss: 2.3598 - categorical_accuracy: 0.4576 - top_3_accuracy: 0.6570\n",
        "Model saved in model_1\n",
        "Epoch 3/10\n",
        "256/256 [==============================] - 100s 392ms/step - loss: 2.0194 - categorical_accuracy: 0.5258 - top_3_accuracy: 0.7236\n",
        "Model saved in model_2\n",
        "Epoch 4/10\n",
        "256/256 [==============================] - 100s 392ms/step - loss: 1.8320 - categorical_accuracy: 0.5650 - top_3_accuracy: 0.7585\n",
        "Model saved in model_3\n",
        "Epoch 5/10\n",
        "256/256 [==============================] - 101s 394ms/step - loss: 1.7511 - categorical_accuracy: 0.5824 - top_3_accuracy: 0.7730\n",
        "Model saved in model_4\n",
        "Epoch 6/10\n",
        "256/256 [==============================] - 101s 395ms/step - loss: 1.6781 - categorical_accuracy: 0.5971 - top_3_accuracy: 0.7844\n",
        "Model saved in model_5\n",
        "Epoch 7/10\n",
        "256/256 [==============================] - 100s 389ms/step - loss: 1.6190 - categorical_accuracy: 0.6123 - top_3_accuracy: 0.7942\n",
        "Model saved in model_6\n",
        "Epoch 8/10\n",
        "256/256 [==============================] - 101s 394ms/step - loss: 1.5659 - categorical_accuracy: 0.6214 - top_3_accuracy: 0.8036\n",
        "Model saved in model_7\n",
        "Epoch 9/10\n",
        "256/256 [==============================] - 100s 391ms/step - loss: 1.5404 - categorical_accuracy: 0.6286 - top_3_accuracy: 0.8082\n",
        "Model saved in model_8\n",
        "Epoch 10/10\n",
        "256/256 [==============================] - 100s 390ms/step - loss: 1.5013 - categorical_accuracy: 0.6351 - top_3_accuracy: 0.8153\n",
        "Model saved in model_9\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9_pAxMCe7AuI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Лучшая модель с score 0.852"
      ]
    },
    {
      "metadata": {
        "id": "IU1z9ZuHeEVI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Модель"
      ]
    },
    {
      "metadata": {
        "id": "WJa4lAPn7Gtr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 512\n",
        "STEPS_PER_EPOCH = 64\n",
        "EPOCHS = 55\n",
        "\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "  \n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RmVToEhi90Zv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "KJVpjOH36RXi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.02459716796875,\n",
        "   0.14129638671875,\n",
        "   0.27490234375,\n",
        "   0.36834716796875,\n",
        "   0.43341064453125,\n",
        "   0.47271728515625,\n",
        "   0.51068115234375,\n",
        "   0.53546142578125,\n",
        "   0.554534912109375,\n",
        "   0.563995361328125,\n",
        "   0.578826904296875,\n",
        "   0.59417724609375,\n",
        "   0.60186767578125,\n",
        "   0.61236572265625,\n",
        "   0.61376953125,\n",
        "   0.625152587890625,\n",
        "   0.62774658203125,\n",
        "   0.636383056640625,\n",
        "   0.633331298828125,\n",
        "   0.642120361328125,\n",
        "   0.643218994140625,\n",
        "   0.64752197265625,\n",
        "   0.651336669921875,\n",
        "   0.65277099609375,\n",
        "   0.6575927734375,\n",
        "   0.657135009765625,\n",
        "   0.6650390625,\n",
        "   0.6683349609375,\n",
        "   0.664764404296875,\n",
        "   0.673492431640625,\n",
        "   0.673736572265625,\n",
        "   0.671356201171875,\n",
        "   0.67547607421875,\n",
        "   0.679840087890625,\n",
        "   0.677520751953125,\n",
        "   0.684844970703125,\n",
        "   0.681060791015625,\n",
        "   0.682159423828125,\n",
        "   0.687103271484375,\n",
        "   0.6888427734375,\n",
        "   0.690673828125,\n",
        "   0.690338134765625,\n",
        "   0.68975830078125,\n",
        "   0.694549560546875,\n",
        "   0.695404052734375,\n",
        "   0.693389892578125,\n",
        "   0.69659423828125,\n",
        "   0.702484130859375,\n",
        "   0.700042724609375,\n",
        "   0.70599365234375,\n",
        "   0.70404052734375,\n",
        "   0.699371337890625,\n",
        "   0.701995849609375,\n",
        "   0.70574951171875,\n",
        "   0.700927734375]\n",
        "loss = [5.542688682675362,\n",
        "   4.233328811824322,\n",
        "   3.307997390627861,\n",
        "   2.7816352657973766,\n",
        "   2.4551088102161884,\n",
        "   2.2626824118196964,\n",
        "   2.082345824688673,\n",
        "   1.9592841360718012,\n",
        "   1.8636007402092218,\n",
        "   1.836651200428605,\n",
        "   1.7532304544001818,\n",
        "   1.6748966928571463,\n",
        "   1.6483177337795496,\n",
        "   1.5930236987769604,\n",
        "   1.5922278054058552,\n",
        "   1.5504786856472492,\n",
        "   1.5343169439584017,\n",
        "   1.4951753057539463,\n",
        "   1.4979023542255163,\n",
        "   1.4679305367171764,\n",
        "   1.4549662824720144,\n",
        "   1.4383777976036072,\n",
        "   1.4292017817497253,\n",
        "   1.4072934202849865,\n",
        "   1.396900339052081,\n",
        "   1.3854043930768967,\n",
        "   1.35893720574677,\n",
        "   1.3464753739535809,\n",
        "   1.3663739021867514,\n",
        "   1.3320483546704054,\n",
        "   1.3206079099327326,\n",
        "   1.3210703376680613,\n",
        "   1.3026241939514875,\n",
        "   1.2984652742743492,\n",
        "   1.3045106679201126,\n",
        "   1.2835522796958685,\n",
        "   1.290334451943636,\n",
        "   1.2650095354765654,\n",
        "   1.262090740725398,\n",
        "   1.2531720250844955,\n",
        "   1.234130598604679,\n",
        "   1.258774783462286,\n",
        "   1.2484610080718994,\n",
        "   1.2329241335391998,\n",
        "   1.2272200137376785,\n",
        "   1.2281939964741468,\n",
        "   1.210948308929801,\n",
        "   1.2027747016400099,\n",
        "   1.2107891459017992,\n",
        "   1.1851924248039722,\n",
        "   1.1899655982851982,\n",
        "   1.1956732096150517,\n",
        "   1.1891954150050879,\n",
        "   1.1841240115463734,\n",
        "   1.1997005455195904]\n",
        "\n",
        "top3_acc =  [0.053375244140625,\n",
        "   0.266448974609375,\n",
        "   0.464813232421875,\n",
        "   0.57330322265625,\n",
        "   0.639862060546875,\n",
        "   0.6798095703125,\n",
        "   0.71368408203125,\n",
        "   0.73297119140625,\n",
        "   0.75054931640625,\n",
        "   0.758270263671875,\n",
        "   0.773468017578125,\n",
        "   0.78619384765625,\n",
        "   0.79052734375,\n",
        "   0.802886962890625,\n",
        "   0.799713134765625,\n",
        "   0.80828857421875,\n",
        "   0.811767578125,\n",
        "   0.8175048828125,\n",
        "   0.817138671875,\n",
        "   0.823394775390625,\n",
        "   0.824462890625,\n",
        "   0.826904296875,\n",
        "   0.828521728515625,\n",
        "   0.83319091796875,\n",
        "   0.83221435546875,\n",
        "   0.833038330078125,\n",
        "   0.841094970703125,\n",
        "   0.839080810546875,\n",
        "   0.837738037109375,\n",
        "   0.84307861328125,\n",
        "   0.84521484375,\n",
        "   0.844451904296875,\n",
        "   0.8489990234375,\n",
        "   0.849273681640625,\n",
        "   0.846649169921875,\n",
        "   0.850494384765625,\n",
        "   0.849578857421875,\n",
        "   0.853851318359375,\n",
        "   0.855224609375,\n",
        "   0.85418701171875,\n",
        "   0.857391357421875,\n",
        "   0.85211181640625,\n",
        "   0.856964111328125,\n",
        "   0.85870361328125,\n",
        "   0.860137939453125,\n",
        "   0.859893798828125,\n",
        "   0.86212158203125,\n",
        "   0.8634033203125,\n",
        "   0.860931396484375,\n",
        "   0.866363525390625,\n",
        "   0.864837646484375,\n",
        "   0.8660888671875,\n",
        "   0.865509033203125,\n",
        "   0.867767333984375,\n",
        "   0.862274169921875]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JsYdiwqx94MX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики"
      ]
    },
    {
      "metadata": {
        "id": "Nw4AC67Q9HUx",
        "colab_type": "code",
        "outputId": "86f70710-cee1-4247-8d7f-f41997228bc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='lower right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
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d7C6uxmoxM/KEdkwY2JGk+APr6X2GgdfrwxZIvOVl1YTrW5S/4dFPyhceDSWlUCaAXPx3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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3d6c809910>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "v3KfSUPg-MOh",
        "colab_type": "code",
        "outputId": "6eba2a60-81ea-4459-d066-304cd84073cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ojzZTGpzNsvDJVOXPO+LQy3B0mKbeJvZ21LC3s5a9nbUc6NpP92B3/Ct20Lgn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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3d6c8feb10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yq1rJc0LESvP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Эксперименты с BATCH_SIZE, EPOCHS, STEPS_PER_EPOCH\n",
        "\n",
        " - При увеличении кол-ва эпох, модель давала лучший результат (важно не переобучить)\n",
        "\n",
        " - При фиксированном кол-ве эпох (25) и шаге градиента (256) увеличение размера батча c 512 до 1024, улучшило результат: \\\\\n",
        "    <b>158s</b> 0.6s/step  - loss:<b> 1.2354</b> - categorical_accuracy: <b>0.6944</b> - top_3_accuracy: <b>0.8575</b> \\\\\n",
        "    <b>292s</b> __1s/step - loss: <b>1.0732</b> - categorical_accuracy: <b>0.7316</b> - top_3_accuracy: <b>0.8818</b>\n",
        " - Для изменения шага градиента лучше использовать callback\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "_T1xf2B1BJwA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Самая глубокая модель"
      ]
    },
    {
      "metadata": {
        "id": "-nDTMFeLd_G0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Модель"
      ]
    },
    {
      "metadata": {
        "id": "R4RlSs3EBXTo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 512\n",
        "STEPS_PER_EPOCH = 64\n",
        "EPOCHS = 55\n",
        "\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "  \n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J7lB1-A8BhSt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "clOMvPlDBvgU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.159149169921875,\n",
        "   0.3843269348144531,\n",
        "   0.4716339111328125,\n",
        "   0.5218505859375,\n",
        "   0.5577659606933594,\n",
        "   0.5794448852539062,\n",
        "   0.5978736877441406,\n",
        "   0.6089973449707031,\n",
        "   0.6197357177734375,\n",
        "   0.6290931701660156,\n",
        "   0.6366119384765625,\n",
        "   0.6433563232421875,\n",
        "   0.6487960815429688,\n",
        "   0.6540565490722656,\n",
        "   0.6579818725585938,\n",
        "   0.6621551513671875,\n",
        "   0.6679801940917969,\n",
        "   0.6680870056152344,\n",
        "   0.6756019592285156,\n",
        "   0.6771240234375,\n",
        "   0.6785888671875,\n",
        "   0.6824607849121094,\n",
        "   0.6859893798828125,\n",
        "   0.6885643005371094,\n",
        "   0.6894912719726562,\n",
        "   0.6920700073242188,\n",
        "   0.6945266723632812,\n",
        "   0.6961326599121094,\n",
        "   0.6990318298339844,\n",
        "   0.6999969482421875,\n",
        "   0.7014389038085938,\n",
        "   0.7027168273925781,\n",
        "   0.7037811279296875,\n",
        "   0.7050628662109375,\n",
        "   0.7072410583496094,\n",
        "   0.709075927734375,\n",
        "   0.7099456787109375,\n",
        "   0.711883544921875,\n",
        "   0.7129020690917969,\n",
        "   0.7135200500488281,\n",
        "   0.7141990661621094,\n",
        "   0.7173957824707031,\n",
        "   0.7170257568359375,\n",
        "   0.7168617248535156,\n",
        "   0.7189559936523438,\n",
        "   0.7193794250488281,\n",
        "   0.7203598022460938,\n",
        "   0.7223587036132812,\n",
        "   0.7230873107910156,\n",
        "   0.72296142578125]\n",
        "\n",
        "loss = [4.0954949129372835,\n",
        "   2.677185446023941,\n",
        "   2.254581238143146,\n",
        "   2.0164448134601116,\n",
        "   1.8597372327931225,\n",
        "   1.7590281628072262,\n",
        "   1.6808841531164944,\n",
        "   1.6359022175893188,\n",
        "   1.5910216085612774,\n",
        "   1.5478389784693718,\n",
        "   1.519457880873233,\n",
        "   1.4851983631961048,\n",
        "   1.460995335597545,\n",
        "   1.4397781323641539,\n",
        "   1.4296412440016866,\n",
        "   1.4054083214141428,\n",
        "   1.3797236778773367,\n",
        "   1.3754981234669685,\n",
        "   1.348359804134816,\n",
        "   1.341183066368103,\n",
        "   1.3333026729524136,\n",
        "   1.312041469849646,\n",
        "   1.3015994490124285,\n",
        "   1.2872980409301817,\n",
        "   1.2868137331679463,\n",
        "   1.2820721827447414,\n",
        "   1.2643222636543214,\n",
        "   1.251788608264178,\n",
        "   1.2456228993833065,\n",
        "   1.2394259781576693,\n",
        "   1.234804779291153,\n",
        "   1.2311858078464866,\n",
        "   1.223948482889682,\n",
        "   1.2188177374191582,\n",
        "   1.206835761666298,\n",
        "   1.2018101126886904,\n",
        "   1.1940142489038408,\n",
        "   1.1934803337790072,\n",
        "   1.1842816765420139,\n",
        "   1.1809150977060199,\n",
        "   1.1766377189196646,\n",
        "   1.1684965032618493,\n",
        "   1.169741126243025,\n",
        "   1.1696757595054805,\n",
        "   1.1596050402149558,\n",
        "   1.1559627600945532,\n",
        "   1.1563646218273789,\n",
        "   1.1451386036351323,\n",
        "   1.1392276713158935,\n",
        "   1.1423916555941105]\n",
        "\n",
        "top3_acc = [0.29581451416015625,\n",
        "   0.591796875,\n",
        "   0.6758384704589844,\n",
        "   0.7200736999511719,\n",
        "   0.7474327087402344,\n",
        "   0.7657089233398438,\n",
        "   0.7796707153320312,\n",
        "   0.7864456176757812,\n",
        "   0.7957267761230469,\n",
        "   0.8027305603027344,\n",
        "   0.8082809448242188,\n",
        "   0.8124961853027344,\n",
        "   0.8173065185546875,\n",
        "   0.8204994201660156,\n",
        "   0.8218040466308594,\n",
        "   0.8258018493652344,\n",
        "   0.83056640625,\n",
        "   0.8313522338867188,\n",
        "   0.8349227905273438,\n",
        "   0.83636474609375,\n",
        "   0.8369255065917969,\n",
        "   0.8407554626464844,\n",
        "   0.8429794311523438,\n",
        "   0.8443756103515625,\n",
        "   0.8451576232910156,\n",
        "   0.8458900451660156,\n",
        "   0.8488960266113281,\n",
        "   0.8508872985839844,\n",
        "   0.8522148132324219,\n",
        "   0.8523750305175781,\n",
        "   0.853851318359375,\n",
        "   0.8535041809082031,\n",
        "   0.8550643920898438,\n",
        "   0.8568572998046875,\n",
        "   0.8579826354980469,\n",
        "   0.8580970764160156,\n",
        "   0.8598098754882812,\n",
        "   0.8599586486816406,\n",
        "   0.86199951171875,\n",
        "   0.8613357543945312,\n",
        "   0.8622856140136719,\n",
        "   0.8641853332519531,\n",
        "   0.8631210327148438,\n",
        "   0.8643264770507812,\n",
        "   0.8657379150390625,\n",
        "   0.8660888671875,\n",
        "   0.864593505859375,\n",
        "   0.8675956726074219,\n",
        "   0.868621826171875,\n",
        "   0.8673553466796875]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o6Bmp-9OBezH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики"
      ]
    },
    {
      "metadata": {
        "id": "zu4Z4ghMBcIY",
        "colab_type": "code",
        "outputId": "119e66a0-d7b5-419e-bd5e-f0960db40f2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='lower right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UQsQVSQARy9cXkOiy0ymrFUwqEQySetnFOBfMA7+fBCDUPgvfxZcSrNapqnLs\nec0XoxCi2UkCAPKLfOQXVXBMl0xsVuvev9DSlJbimjeXUKfDCB5zLNjtWIqLCB1+BPax51B4yulm\nx6qDsWxCiJiRBACs+MNs3niwtf+3/7iUxOeewfXh++YkJX8+H+8/nwag+NW3IDkZjyeFYBzOlCSE\n2DtJABx89f/ODz/A/c+ZOH78AYBQp8OoGHMulWeP3bVRcnIzRSeEOFjEfQIIGwbL1xeSkeIiq427\nucOpWyBgTgIOOH7+EftPP1J55jB8l11JYMBpUr0jhNhncZ8ANm0vpdQX4OReWVha4OBj1rW/437C\nvNovnP8V2GyUXzUR3/hLCXeU+QqEEI0X9wmgavTPllb/b1uxHPfjj+B6/10s4TDBI7pg3ZJHOPcQ\nDI8Ho7kDFEIc9CQBVNX/t5Dhny35+aTcfB2uOR8BEOzRi7IbbsY/4iywxUEPZSFEk4lpAlBKzQRO\nBAzgOq31d9XWHQK8DjiBH7TWV8cyltoEgmFWbSwix5NEWnPP/WsYYLFgpKdjX/YzgWP7Un7DLfjP\nGCrj4gshYiJmTw6VUgOALlrrfsDlwKzdNnkUeFRrfTwQUkp1jFUsdfl9czH+YLhZJ3+x//A9qReM\nxf34o+YCh4OiOfMpmj0f/5BhcvIXQsRMLO8ATgfeB9Bar1BKZSilUrXWJUopK3AKcH5k/cQYxlGn\n5X9UNf9s+vp/+49LcT88Dddnn5oLnLvuQMLts5o8HiFE/IllAsgCllb7nB9ZVgJ4AC8wUynVB/hK\na31bfTvLyHBjtze+Dtzj2XOIh9WbSrBaLZx8TC7uBEej971PDAMeeACmTDE/n3oqTJ2Ka+BAYjXe\nZm1ljwdS7vgi5d53TfkQ2LLb+xzgcWA98LFSaoTW+uO6vlxYWN7oA3s8KeTv1hu2vCLAqo2FHJ6T\nRpm3gjJvRaP3vy8ci74ifcoUQjm5eGf9i8ApA8wVMeqtW1vZ44GUO75Iuevfpi6xTAB5mFf8VbKB\nLZH3O4A/tNa/Ayil5gM9gDoTwIH2x7ZSDAPUIU07+Uvg5FPwPjSDyhFnYVSbEF0IIZpaLLuPfgqM\nBYhU8+Rprb0AWusgsFYp1SWy7bGAjmEse/CW+wFIb4LWP/YfvidpymSz+geouPQKOfkLIZpdzBKA\n1noxsFQptRizBdBEpdQEpdTZkU2uB16IrC8GPoxVLLUp8wUASEqMbS2Y6503SR89jMRnn8b+w/cx\nPZYQQuyLmJ79tNa37rbo52rr1gD9Y3n8+pRGEkBKojNmx0icNZPk++4inJKK98VXCVabaUsIIZpb\n3PYE9kYSQHJiDFr/GAbuRx/Hg/bgAAAgAElEQVQiafoDhHJyKX7jPUJd1YE/jhBC7Ie4TQCxrAJy\nfL7APPl3PJSidz+SQduEEC1S3I4hXOoLArG5AwgMHETplKkUfTBHTv5CiBYrjhOAH7vNistxgAZY\nMwyc8+aa7y0WfNfeQDgn98DsWwghYiCOE0CA5ET7gZkDIBwm+ebrSbvwzyS8+Nz+708IIZpAHCeA\n4AGr/km6fyqJL79AoOdRVI760wHZpxBCxFpcJoBgKIyv8sAkANuyX0h8ahahTodR/O6HGG3bHoAI\nhRAi9vbaBEYplQHcAWRprS9SSo0CvtFa58c8uhgpqzhAD4BDIVL+fj2WUAjv9JkY6S1jUhkhhGiI\nhtwBPAtsAA6LfHYB/4lZRE2g9AD1AXB+/D8cS7+nYsxYAgMHHYjQhBCiyTQkAXi01rMAP4DW+m3A\nHdOoYmxXH4D9SwD+kaPxzniC0qnTDkRYQgjRpBrUC0op5cCc1hGlVHsgKZZBxdquYSD2swrIaqXi\noksOQERCCNH0GnIH8E/gO6CHUup/mOP5PBLTqGKsdD/vABwL55N0521QWnogwxJCiCa11wSgtX4T\nGAlcg/k84Bit9RuxDiyW9usZgM9Hyt9vIPHZf2Nbv+4ARyaEEE1nrwlAKXUZMARIATKBYZFlB61o\nAnDvewJwP/Ywtj/W47vyb4R69jrQoQkhRJNpyDOAU6q9dwInAIuA52MSURMoLW/cHYBtlcb9z8cJ\n5eRSdku9UxgLIUSLt9cEoLW+tPpnpZQbeCFmETWBxlYBJd/+dyyBAKXTHoHk5FiEJoQQTWafewJr\nrcuBI2IQS5MprQhgtVhIdDV8KGjb76txfrkQ/6mn4R86PIbRCSFE02hIT+CviDQBjcgBfolZRE2g\nzBcgKdGOdR8Gggsd3oWC+V/HMCohhGhaDbkEnlLtvQGUUG1qx4ORtzxASiMeAId6HRWDaIQQonnU\nmQCUUnWNbdAGOA1YsLedK6VmAidiJo7rtNbfVVu3HtgIhCKLLtRab25Q1PshbBiUVQTIatvwzszO\n/71H+NBOBI8+JoaRCSFE06rvDuDOetYZ7CUBKKUGAF201v2UUt0wWw31222zYVrrJu1N5asMYhj7\n0Au4rIyUm68Dm42dP60Elyu2AQohRBOpMwForU+ra51S6pwG7Pt04P3IvlYopTKUUqla65J9D/PA\nqWoC2tBewAlvvo61qIiymybLyV8I0ao05CFwR8xewJmRRS5gEPDOXr6aBSyt9jk/sqx6Avi3UqoT\n8DVwm9a6+sPmGjIy3NjtjZ++0eNJAWBnJAG0a5MUXVancBie/Rc4nSTdcgNJe9u+hdprOVspKXd8\nkXLvu4Y8BH4ZmAOMwhwXaDRwcSOOtXuTm38Ac4ECzDuFc4C36/pyYWF5Iw5p8nhSyM/3ArAprxgA\nG0Z0WV2cn8whbfVqfOdfRKnVDXvZviWqXvZ4IuWOL1Lu+repS0P6AQS11g8C27TWTwJnARMb8L08\nzCv+KtnAlqoPWuuXtNbbtdZBYDbQJOMq7MtAcIlPPwmA76qGFFcIIQ4uDUkAiUqpXCCslOoMBIBO\nDfjep8BYAKVUHyBPa+2NfE5TSn2ilHJGth0A/LqvwTdGg3sBB4MEe/SicsRZhLr3aILIhBCiadXX\nDDQn0ixzOjAYeBj4CbPZ5mt727HWerFSaqlSajEQBiYqpSYAxVrr95RSs4FvlFI+4Efqqf45kBqc\nAOx2yu6dBkadjyWEEOKgVt8zgGVKqSXAc8ArWuugUqoNkKK1LmzIzrXWt+626Odq6x4HHt/XgPdX\nWUMSQEWF2eLHYjH/hBCiFaqvCigbeAX4C7BBKfUIcERDT/4tlbcBCcA9YzoZA/thW7O6qcISQogm\nV18/gArgdeB1pVQH4ELgDaVUGfCs1vqgHA5613zAdRTd7yfxP8+BxUIoJ7cJIxNCiKbVoNFAtdZb\ntNaPAOcB64AnYxpVDJX6ArhddmzW2otu//EHrIWFVI4eA4mJTRydEEI0nYZ0BMsAzgcmYHYCew64\nNrZhxU6pL1Bv9Y/z6y8A8Pcf0FQhCSFEs6ivFdAozJN+f+BdYGL1wdwORoZhUOoLcEi7hDq3cSz6\nCoDAyf2bKiwhhGgW9d0B3Ix5tX+R1trXRPHEVGUgRDBk1H0HUFGB47tvCfbohdGmbdMGJ4QQTay+\nh8Ctrg6kIX0AvP98GkOafgoh4kDD50RsBfaaABISzIe/QggRB/Z5TuCD2a4EUEfeq6howmiEEKJ5\nxWcCcDv3XFlWRuaRnUi+4ZomjkoIIZpHXCWAMl8QqL0KyPF/32ApL8fIaNPUYQkhRLOIqwTgLfcD\nkJywZxWQM9L809//lCaNSQghmktcJYCqO4Da5gJwLPoSw24ncPzu0xYLIUTrFFcJoLTCfAaQstsz\nAIu3BPtPPxI85lhITm6O0IQQosnFVwKooxWQ45vFWEIhqf4RQsSV+OoHUB7A6bDi2G1y+cBRx+B9\ndBaBY49rpsiEEKLpxVcC8AVIqaX+32jfnoqLJzR9QEII0YziqwqoIrDnA2C/HwKB5glICCGaUdwk\ngEAwTKU/tEcfAOe8T2jb9VBcb7/RTJEJIUTziGkVkFJqJnAiYADX1TactFJqGtBPaz0wlrHUNQ6Q\n8+svsJaVEsrtGMvDCyFEixOzOwCl1ACgi9a6H3A5MKuWbboDp8YqhurqmgzesegrDLebYJ9jmyIM\nIYRoMWJZBXQ68D6A1noFkKGUSt1tm0eBO2IYQ1RtdwCW7duxr1xB4LgTwFnL+EBCCNGKxbIKKAtY\nWu1zfmRZCYBSagLwBbC+ITvLyHBj36355r6wOsyitvck4/GkmAsXzAbAOXTIrmWtUGsuW32k3PFF\nyr3vmrIZaHSWFaVUG+BSYDCQ05AvFxaWN/rAHk8KedtKzCBCYfLzvQAkz/6URKCw9/EEI8taG48n\nJVreeCLlji9S7vq3qUssE0Ae5hV/lWxgS+T9IMADfIU50fzhSqmZWusbYhVMbVVAvsv+QqjTYQSP\nPiZWhxVCiBYrlgngU2Aq8LRSqg+Qp7X2Amit3wbeBlBKdQJejOXJH3YlgOr9AELde+Dr3iOWhxVC\niBYrZg+BtdaLgaVKqcWYLYAmKqUmKKXOjtUx69OQ+YCFECKexPQZgNb61t0W/VzLNuuBgbGMA/ZM\nAK7/vkryPXfifWQW/uEjY314IYRoceKmJ3CZL4DNaiHBabYksuVtxrpjB0ZCQjNHJoQQzSNuEoDX\nFyA50YHFYjZGsm7bCkC4fVZ9XxNCiFYrbhJAmS9AsntX/b912zZAEoAQIn7FRQIIhcKUVwRJTqie\nALZg2O0YbWQSeCFEfIqLBFDqC2BQswWQdds28+rfGhc/gRBC7CEuJoQpKfMDNfsAVIy/FMNiqesr\nQgjR6sVFAvCWmwkgpdozgPLrb26ucIQQokWIi/oPb9UdQIJ0AhNCiCpxkQCqqoCqngHYl35H6vhx\nOD/7pDnDEkKIZhUXCaCqCqiqGaht9Spcc2dHm4IKIUQ8iosEsPsdwK5OYO2bLSYhhGhucZEAvOU1\nxwGSXsBCCBEnCaCkrBLYlQBsW80EEGrfodliEkKI5hYXCcBbHsBiAbfLbPVq3bYVw2bDyMxs5siE\nEKL5xEU/gJIyP0kJDqxWs+NX8IguGIlu6QUshIhrcZEAvGX+Gr2ASx9/qhmjEUKIlqHVXwIbhoG3\n3E+KzAQmhBA1tPoE4KsMEQobu1oAbd1C4qwZ2L/7tpkjE0KI5tXqE0BpRdVk8GZtl02vJPm+u3F+\nvqD5ghJCiBYgps8AlFIzgRMBA7hOa/1dtXV/AS4HQphzBU/UWhsHOoZS6QMghBC1itkdgFJqANBF\na90P80Q/q9o6NzAOOEVrfTJwJNAvFnHsPhm8NdIHIJwlCUAIEd9iWQV0OvA+gNZ6BZChlEqNfC7X\nWp+utQ5EkkEasDUWQZTtngC2yx2AEEJAbKuAsoCl1T7nR5aVVC1QSt0KXAc8prVeW9/OMjLc2O22\nfQ7CYs8HILt9Kh5PChTtNPfX/QjwpOzz/g5Gnjgp5+6k3PFFyr3vmrIfwB7Tb2mtH1RKPQ7MVkp9\nrbVeVNeXCwvLG3XQw9olccbxHcnJSCQ/30ta/k4cVis7LImQ723UPg8mHk8K+XFQzt1JueOLlLv+\nbeoSywSQh3nFXyUb2AKglGoD9NRaf6m19iml5gAnA3UmgMZq38bNtecdE/2Rit94D3w+sMdFHzgR\np7ZsyeM//3mOb79dQnFxMSkpKfTufQzjx1/O4Ycf0aB9eL1e5s2by5gx58Y4WtFcYvkM4FNgLIBS\nqg+Qp7WuSlUO4EWlVHLk8/GAjmEsNSUmNtmhhGhq69at5fLLL8ZisfCvfz3H/Plf8+9/P09KSipX\nXTWBFSt+a9B+li79P957760YRyuaU8wSgNZ6MbBUKbUYswXQRKXUBKXU2VrrbcA9wEKl1BJgB/C/\nWMUSVVmJY9FXWPM2x/xQQjSXGTMeoksXxeTJU8jK6oDFYqFDh2xuvvk2zjnnPHbuNJ+DBQIBZs6c\nztlnD+eMM07hkkvOZ8mSrwGYO/dj7rrrdtavX8egQSexbNnPAHz00Qdccsk4Bg/uz9ixo3jttZej\nxw2FQjzxxAyGDBnAqFFDeO21l5k8+QZmzHgous2cOR8xfvx5nH76yZxzzkhefvkFDMNs/f3cc09z\nzTVXMn36/Qwe3J85cz5i6NDT8Pv9NY4xcuQZzJ79Ya1lf++9tzn//DGcccYpnHPOSF599T811i9c\n+BkXX/xnBg/uz/jx57Fo0VfRdb//voZrrrmSM844hTFjRvDyyy9E1/Xv35eFCz+Lfl65cjn9+/dl\ny5Y8AMaOHcWLLz7LRRedyy23XAfAmjWrue66vzJs2CCGDRvE7bffws6dO6L72Lp1K5Mn38CQIQMY\nPfpMZs16lGAwyPPPP8NFF9W86youLmLAgBP44Yfva/9Hb6SY1oNorW/dbdHP1da9CLwYy+Pvzrbh\nD9LPHoHvoksonfFEUx5aiCZRUFDAjz8uZfr0x2pd/9e/Toq+f/31l1myZBHPPvsS6ekZvPnm6/zj\nH7fx3ntzGDp0BJs3b+Lzz+fz8stvArB48dfMmjWD6dNn0qvX0axYsZxbbrmO7OxsBg48nbff/i8f\nffQBjz/+FJ06debxxx/hl19+5owzzJrgb75ZzMMPT2PatEc49tjj+PXXX/j7328gM9PDsGEjAVi7\n9ndOOOEk5s79PJKgHubrr79k0KDBAPz441IqKysYOPD0Pcq2bNnPzJw5naeeeo6ePXvxyy8/MWnS\nVSjVjb59j0frldx7713ce++DnHBCPxYsmMedd07m1VffJiOjDTfffC0jR47m0UdnsXHjRq655kqy\nsjpwxhlDG/Tbz5nzEQ888AidOx8OwJQpk+nX72QeffQJysvLmTz5ep566nHuvPNeAG6//Sa6dj2S\n99+fTUlJCZMmXU1GRhuGDx/FCy/8P1as+I1u3XoA8NVXn+PxtOOYY45tUCwNFVcV4dFOYO1kJjCx\n79oc27PW5eV/u5aKy68EIOVvf8Hx7ZI9tgkc2xfvMy8CkPDyi7gfe6TWfRUs+QGcTmyrV5E2bgwF\nS3/dpxg3btwIQMeOh+512wsuGM855/yZpCSzJvaMM87kyScfY/36dfTs2WuP7T/44B2GDBlG7959\nAOjZsxfDh49i9uwPGTjwdJYsWcSpp54WPWlde+2NzJ8/r9r332XgwEGccILZ5ad37z4MHDiI+fM/\njSaAQMDPuHEXYrfbsdvtDBo0mLlzP4omgIULP+PUU0/D7XbvEV/Pnkfx8cfzSUkxH3oedVRvOnTI\nYcWK5fTtezxz5nxEr15HcfLJpwAwZMgwbDbzON9+uwSvt4SLL74Up9PJEUd04YEHHo7uqyH69Olb\n4/nK88+/Ei1Hamoq/fr1Z+7cjwFYtWolq1Zppk17FLc7Cbc7ialT7ycYDJKV1YE+fY5jzpyPor/l\nggXzGTp0BBbLHm1p9kt8JYCtWwAIZ8lEMKJ1C4dDe92muLiIWbMeZenS7ykrK42eXPz+ylq337hx\nA998s5jZs3fV1hqGEU02O3fu4Oijj4muc7uT6NTpsOjnvLzNnH76GTX2mZt7SLR6CaBtWw8Ox66B\nG4cPH8WkSVdRULCTtLR0vvzyc+666746yhzm5ZdfYP78TyksLAQMAoFAtDx5eZvo0CG7xneq4tm8\n+RPats3E6XRG1/Xp07fW49Rl933/9NMPvPDCM/zxxx8EgwFCoRAeT7vI8TZhs9loX60/Uvfuuy4w\nRowYxWOPPcKkSTfi8/n44YfvuPHGv+9TPA0RXwkgMgm8dAITjdGQq3HvU/9vr9tUXDyBiosn1LtN\nqEvXfb76B+jUqRMWi4V169bSsWOnere9667bCQaDPP30C2Rn51BYWMBZZ51Z5/Yul4vx4y/j8suv\nqnW9YRjY7TVH3bVWm3MjEPDv/hWAGle1DkfNU9JRR/UmJyeXzz77hMMP74LD4ajzxPzii88ye/aH\nPPDAw/To0QubzcaFF46tcZxwOFzrd61WC+Fww0eiCYX23E/1sm/Y8Ad33HELl1xyOY8/fj5udxKv\nvPIi77//TuR45u9iGEatV/UDBpzGjBkPsWTJIkpKiunWrTu5uYc0OL6GavWDwVUnk8GL1i4tLY3j\njjuBV175T/ThanX33383L774LADLl//KqFF/IicnF4vFgtlhv265uR1ZvbpmY70dO/IJBMze9unp\nGeRVa2Dh8/lYv35X/86cnFx+/31Nje+vXfs7ubkd6z3u8OGjWLjwM+bNm8uZZw6vkVSq++23Xznx\nxJM46qje2Gw2SkqKow9pq46/ceMfNb7z/vvvsHbtGnJyctmxYzvl5bv6Gy1Z8jVLlpgt051OFxUV\nFdF1mzdvqjfmVatWEg6HueiiCbjdSQBovTK6Pjs7l1AoxKZNG6PLfv75Jz777BMAXK4EBg8+M1Lu\nTxg6dGS9x2usOEsAUgUkWr/rr7+ZvLxN3HTTJDZs+APDMNiyJY+HHrqfJUsWRR+gduiQza+/LiMY\nDPLrr8v4+OMPsVqt7Nhh9p53uVwUFhZSVFREZWUFY8acy5Ili5g3by7BYJB169YyceJfok1F+/Y9\nni++WMCaNaupqKjgyScfx+HYVaUyYsRZfPnlQr777luCwSDfffctX365kBEjzqq3PEOHjmTFiuUs\nXPgZw4aNqHO77Oxsfv99NWVlpWzZksfDD0+jffusaHlGjBjN8uW/8dlnnxAMBvnii4XMmjUDlyuB\nE088mfT0DJ599l+RxLWOadPupaSkGDCfqXz11Rf4/X62bMnjf/97t96YO3TIIRQK8dtvyygvL+ft\nt//L1q15eL0lVFRU0KVLV448sjvPPPMUXq+X7du38fDDD9RICMOHj+Lrr79k+fJlDBp0Rj1Ha7y4\nqgIqu+NuKi6aIA+BRavWsWMnnn32ZZ5//hmuvfZqSkpKSE9P5/jjT+TZZ18iK3IBdOONk3n44QcY\nOnQgPXr04rbb7iIhIYEHH7wPlyuBU089jffff4cxY0Ywder9nHLKQG655Taee+4Zpk27h7ZtMxkx\n4izOPfd8AM4//2I2bdrIVVdNIC0tnUsv/Qvr16+NXrEPHHg6O3bk8/jjj7Bt2zY6dOjA5MlTGDDg\ntHrLk5mZyfHHn0hRUVG91VoXX3wpU6dOYfTooWRlZXPDDbewadNGnnhiBm53EpMm3cADDzzCE0/M\nZNq0e8jN7ci99z5ITk4uALNm/ZsHHpjKiBGDadOmDeeeO44zzxwOmA+0H3lkGsOGnUbnzkdwxRVX\n89NPP9QZS48ePRk37iImT74Ru93GqFFnc889DzJp0lWMHTuSjz76jOnTzTjGjBmO253E4MFnctFF\nE6L76N69J1lZWXTufPg+PYzeF5babhNbovx8b6MDjddu4hC/ZZdyN4/KykpcLlf083nn/Ymzzjqb\nCy+8ZL/2e9llFzJmzJ8ZOXJ0reubu9yx4Pf7OffcUfzjH/dx7LHH1bpNA4eCqLPpUFxVAVGtDk8I\ncWDNmzeX0aPPZPXqVYRCIWbP/pCtW7fQr9/Jjd5nKBTipZeep7S0tMHt8VsDv9/PrFmPkp2dW+fJ\n/0CImyogS6mXzM45VI76EyXPvdTc4QjR6gwefCYbN25g8uQb8HpL6NAhm7vuup/OnRs29tDutm7d\nygUXjOHQQztx//0P17izaM1+/vknbrjhb3Tv3rPOJq8HStwkgGgLoBjVpQkR7ywWC5dddiWXXXbl\nAdlfVlYWCxYsPiD7OpgcfXTvJit33FQB7eoDIA+AhRAC4ioBVA0DIZ3AhBAC4ikBbJWpIIUQorr4\nSQDbZDJ4IYSoLm4eAvuHDMVISyN02OHNHYoQQrQIcZMAAif1J3BS/+YOQwghWoy4qQISQghRU9wk\ngPRRZ5J8603NHYYQQrQYMa0CUkrNBE4EDOA6rfV31dadBkwDQpgTwl+hta59sO79VV6O49slGDIZ\nvGjlpkyZwgcffACYY80HAgEcDkd0zPnx4y9jwoQrDvhxlyxZxPPPP8369etxuxPp0+c4Jk68nszM\nzAN+LHHgxOwOQCk1AOiite4HXI45MXx1zwBjtdYnAylA7Ab62BIZBlqagIpW7r777mPBgsUsWLCY\nf/3rOQBee+2d6LJYnPzXrVvL7bffzDnnnMecOQt47rlX2Lo1j+nT7z/gxxIHViyrgE4H3gfQ5kwT\nGUqp1Grrj9VaV82qkA+0jVkkkgCEqOGPP9Zzww0TGT78dIYOHciUKX9n584dAGzatJH+/fvy+efz\nufTSCxg06GQuueR81q1bW+u+7HY7d9xxN0OHjsBut5OZ6WHAgEGsWbOqzuN///3/8Ze/XMKZZw5g\n1KghTJt2T40JV1avXsU111zJGWecwpgxI3j11f9E1xUXFzF16hSGDj2NkSMH88ADU6MTufz1r5fz\n+OOP1jjWoEEn8cknswG45547uf/+u7nllusYNmwQACUlxdx99x2cddaZDBkygKuvvoyVK5dHv19Z\nWcGMGQ8xcuQZDB16GrfffgsFBTv5/vv/Y8CAEygo2FnjeJdcMi466U5LF8sqoCxgabXP+ZFlJQBa\n6xIApVQHYAhwZ8wiyTNnBQpJHwDRSG8uWMN3K7c3+XGPO7Idfx7UuMHU6uL3+7n++r8xYMBpPPDA\nI/h85dx11+3cd99dzJz5ZHS7N954lfvum05GRgbTpz/A7bffzOuv7zkRyiGHdOSQQ8xZvQzDYN26\ntcyZ83Gdo3f6fD5uu+1m/vKXqxk7dhw7duQzceKVvPbaS1x22ZWUl5dz883XcvbZY3n00SfYsGE9\n11xzJR065DBo0GDuv38qYPDmm+8TCoW55ZbrePjhh/nb325sUPkXLfqKm26azEMPzQTgn/98jO3b\nt/H66+/gdLp44okZ3Hnnrbz1ljn38VNPzWL58t948cXXcLvd3H33HUybdg/Tpz+Gx9OOefPmct55\nFwKwYcN61q79Pbrvlq4pm4HuMSa1Uqod8CHwN631zj2/sktGhhu73da4I0fuAFK6HEaKJ/4Gg/PE\nYZnhwJY70e3EZqtzWPWYSXQ797kcVdtv3eoGoE2bpBr7+OSTTygt9TJlym0kRp6LXXfdJC699FLs\n9iBt2phTGF500YX07t0NgEmT/sbo0aMpLt7GEUfUnpC++eYbLr/8cgzDYNy4cUyZcmsd0zem8NVX\nX+J2uyMTo6fRr98JrF27Co8nhTlzvsbnK+e66ybidDrJzc3kySefJCMjA6vVz5IlX/P6669z+OHm\nRC4PP/wQ+fn5eDwpOBw2EhMde/xmKSkJeDwpuFx2MjPbMm7cOdF1Dz30AMFgELfb/L3GjBnNu+++\nhc0WIC0tjU8+mc3UqVPp1q0zAPfcczdaa9q1S2Xs2HOYN28O11xzNQBvvfUVxx9/PL16dW34P9h+\n2p//zmOZAPIwr/irZANbqj5EqoPmAHdorT/d284KC8v3tkmdPLm5VJ5xJmVtswm1skkj9qY1TpTR\nEAe63KNO7MioE+ufuzZW9qUc1ctdVGT+P1NQUIbLtWsfK1euoX37LEpLg5SWmsuTkzMxDINlyzQp\nKWZNbUZG++i+EhPTAVi1ah1pabUPqHj44T1YuHAJ69evY/r0+/jrX6/hvvseqnXbjz76gDfeeJUt\nW/IIh8OEQiF69z6W/HwvK1asITPTQ3FxJVAJwBFH9ATgl1+WYRgGiYnp0dgyMjrQtWtX8vO9BAIh\nfL7AHr+Z11tBfr6Xysog7dpl1Vi/fv06/vnPmSxf/hs+367zzJYtBWzfXkxpaSlJSW2i30lISOfo\no08gP9/LgAFDePLJJ1m8eCldunTlo49mM27chU32/1wDJ4Spc10snwF8CowFUEr1AfK01tUjfRSY\nqbWeG8MYTOecQ8mrbxHq2SvmhxKipfP7/XWuq2otBBAO72qUt2vmwPrvgiwWC4cd1plrr72Jzz+f\nz/bt2/bY5rvvvmX69Pu54ILxfPzxfBYsWMzw4aOi661WC+Fw7RMAWq2W3eLZu+rlAHA4HNH3oVCI\nm2++loSERF566Q0WLlzC44//q9rxrJHj1d5AsUOHbPr06csnn8xmw4Y/yMvbxIABgxocW3OLWQLQ\nWi8GliqlFmO2AJqolJqglDpbKeUGxgNXKKU+j/wdmEHEhRD1ysnJZcuWvOiDU4B169ZgtVrJzs6N\nLtu8eVP0fV7kOVr7WoZTf+21l7nxxkk1lvn9AcB8QLy7335bRlZWB4YNGxmd5MVsJ7Irvu3bt9V4\nKLxo0Vd8++0SOnTIwWq1smHDH9F1q1ev4q23zInpXS5Xje9t3bqVYDBY52+xc+cOtm7dwrnnjos2\nWdV6ZXR9enoGbndSjeNt2ZLHG2+8Gk1CI0acxeefz2fevLmceupp0aqkg0FMO4JprW/VWp+kte6v\ntf5Za/2i1vo9rXW51sknLnUAAAiDSURBVNqltR5Y7e+ZmAVy7/9v7+6Dq6jOOI5/UwYkIC9JRV4E\nyrTDPBTtVHkZFQwJbwJNhGGAIqLQgK2D0lZKAafQKUIpLagwKKLUALYCVqpQGDrVsY7t8GaFoUDa\n+rQCdUQUAkHebEII6R+7QEi5FEhuLu7+PjOZu3tyszlPNvc+95yze85M0hc9k7TDi3yedO+eRXp6\nAxYvXkhpaQlFRQdZtqyArKxsGjc+f6HemjWvcuDAJ3z22UmWL3+Rdu2+fNFF2Tt37sq2bX9h9erf\ncurUKQ4dOsSSJYu5+eavkZn5vxf3tWp1E8XFh9m370OOHTvKs88uIC3tCxQXH6K8vJxu3bJo1Oh6\nCgqep6SkhD17djN79gyOHz9GRkYG3bv3YMmSxRw5UszRo58yf/5cdu3aBUDbtl9i+/atHDlSzMmT\nJygoeO6SK4llZGSSnp7Ozp07KCsrY/PmjWzevAGAoqIi0tLSyMsbyMqVL51LmosWPc0772w511rK\nzu7JiRPHWbVqJQMG5FXn1NS6eNwJPG8e9VdoGUgRgAYNGvLEEwvYvft9Bg0awEMP5dO+vTF16uMX\nPC83dyCPPfYD8vL6snfvHmbNmnPR45l1YM6c+axbt5r+/XMYO3YkGRkZCZ/fq1dfsrJyyM8fyejR\nI2jevAUTJ07hyJFiHn74QerVq8eCBc9TWLiD3NzeTJkygREj7qdPn34ATJs2nRtuaMbw4YMZOXIY\nbdq0ZfLkyQDcd99oMjO/yNCh9zB27ANkZ/ekSZOmCf8WdevWZdKkH/Haa6+Qm9uH9evXMn36LG69\ntROPPjoO9/cYN+57dOnSlTFj7mfIkDzOnCln6tSfnDvGddfVp3fvu2nQoCGdOnW5onORamlX0peW\nSkVFx6+uoiUlNGt7I6eycjj66toartW1T4PA8VITce/b9yH33juYpUuX07691VDNkivV53vKlAl0\n6NCR/Pxv1+rvvcxB4IQDN5FvAWgdABFJloqKCtavX0th4U4GDx6W6upcschPB31+LWAlABGpOeXl\n5fTtm0WLFi2ZOfMXNG2auKvpWhWDBHB2KUgtBi9yOVq3bsOGDVtTXY1rXp06dXjrrU2prka1RL4L\nKK20BDIzOdOiZaqrIiJyTYl8AigdOhwOH6Z04OBUV0VE5JoS+QRwTlrtz+MiInIti08CEBGRCygB\niIjElBKAiEhMKQGIiMSUEoCISEwpAYiIxJQSgIhITCkBiIjE1OdmOmgREalZagGIiMSUEoCISEwp\nAYiIxJQSgIhITCkBiIjElBKAiEhMKQGIiMRU5NcENrN5wB1ABfB9d383xVVKKjO7BfgdMM/dnzGz\nNsCvgTrAx8AD7l6ayjomg5nNAbII/qdnA+8S4bjNrAGwDGgO1AdmAjuIcMxVmVk6UEgQ+x+JeOxm\nlgOsAv4WFu0C5lCNuCPdAjCzbKC9u98JjAUWpLhKSWVmDYGnCV4MZ80AFrp7FvA+MCYVdUsmM+sJ\n3BKe5/7AfKIf9z3AVnfPBr4JPEX0Y65qGlAcbscl9j+5e0749V2qGXekEwDQG1gD4O7/ADLMrHFq\nq5RUpcA3gP2VynKAteH2OqBPLdepNvwZGBZufwo0JOJxu/tv3H1OuNsG2EfEY67MzDoAHYH1YVEO\nMYm9ihyqEXfUu4BaANsq7ReFZcdSU53kcvfTwGkzq1zcsFKT8CDQstYrlmTuXg6cDHfHAr8H+kU9\nbgAz2wS0BvKAN+MQc+hJYDwwOtyP/P95qKOZrQUygcepZtxRbwFUFfeV4SMdv5kNIkgA46t8K7Jx\nu3s3YCDwEhfGGdmYzWwUsNnd9yZ4SlRj/xfBm/4ggsRXwIUf4q847qgngP0En/jPakUwUBInJ8LB\nMoCbuLB7KDLMrB8wFRjg7keJeNxm1jkc4Mfd/0rwRnA8yjFXkgsMMrMtwIPAj4n4+QZw94/Crr8K\nd98NfELQrX3VcUc9AbwBDAUws07Afnc/ntoq1bo3gSHh9hDgDymsS1KYWRNgLpDn7mcHBaMedw9g\nIoCZNQeuJ/oxA+Duw929q7vfAbxAcBVQ5GM3s5Fm9sNwuwXBFWBLqUbckZ8O2sx+TvBiOQM84u47\nUlylpDGzzgR9o+2AMuAjYCTB5YL1gQ+AfHcvS1EVk8LMvgNMB/5ZqXg0wZtDJOMOP/UVEAwApxN0\nDWwFfkVEY74YM5sO/Bt4nYjHbmaNgBVAU6AewTnfTjXijnwCEBGRi4t6F5CIiCSgBCAiElNKACIi\nMaUEICISU0oAIiIxFfWpIEQuyczaAQ5srvKt9e4+twaOnwP81N3vqu6xRGqaEoAIFLl7TqorIVLb\nlABEEjCz0wR3mfYkuNP2W+5eaGa3E9xwV0awzsR4d/+7mbUHfknQtVoC5IeHqmNmi4DbCGZszQ3L\nVwAZQF1gnbvPqp3IRAIaAxBJrA5QGLYOFhHMvQ7BnZcT3L0nwTz8C8Py54C57t4DWML5Kaq/CkwP\npy4oA/oBfYG64Tzu3QjmstHrUWqVWgAi0MzM3q5SNjl8fD183AhMMrOmQPNKK8u9Dbwcbt8e7uPu\nL8O5MYD33P1A+Jx9BLfyrwNmmNkrBNNXv+DuZ2ouJJH/TwlAJMEYQLiuwtlP5WkE3T1V505Jq1RW\nwcVb1aer/oy7HzSzrwN3Ekzvu9XMOrn7f64qApGroCanyKX1Ch/vAnaGU01/HI4DQLAC05ZwexPB\nkpSY2XAz+1mig5rZ3UCuu29098nACeDGZAQgkohaACIX7wI6u9jIbWY2jmCwdlRYNgp4yszKgXJg\nXFg+HlhsZo8Q9PWPAb6S4Hc68KKZTQ6P8Ya7f1ATwYhcLs0GKpKAmVUQDNRW7cIRiQR1AYmIxJRa\nACIiMaUWgIhITCkBiIjElBKAiEhMKQGIiMSUEoCISEz9F3t2ARA3E0lfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3d6c705790>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7b0G2VhiBcw-",
        "colab_type": "code",
        "outputId": "65451c0b-34c3-4fb6-ff6e-35eb8c18bef4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3d6c6977d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eWQ8jINI_Sdl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###  Эксперименты с размерами картинки"
      ]
    },
    {
      "metadata": {
        "id": "ptp9NEted6qo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Модель"
      ]
    },
    {
      "metadata": {
        "id": "1XDiKto6BCRO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "IMAGE_SIZE=128\n",
        "\n",
        "BATCH_SIZE = 128\n",
        "STEPS_PER_EPOCH = 64\n",
        "EPOCHS = 150\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "    \n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(1024, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model\n",
        "\n",
        "def step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):\n",
        "    '''\n",
        "    Wrapper function to create a LearningRateScheduler with step decay schedule.\n",
        "    '''\n",
        "    def schedule(epoch):\n",
        "        return initial_lr * (decay_factor ** np.floor(epoch/step_size))\n",
        "    \n",
        "    return LearningRateScheduler(schedule)\n",
        "\n",
        "lr_sched = step_decay_schedule(initial_lr=1e-4, decay_factor=0.75, step_size=2)\n",
        "\n",
        "#    callbacks=[ModelSaveCallback(\"model_{}\"),\n",
        "#              ReduceLROnPlateau(model='loss', factor=0.2,verbose=1,\n",
        "#              patience=1, min_lr=0.0001),\n",
        "#              lr_sched],\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KMF7Donf_kfe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "Bps16fEN_qUv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.4322509765625,\n",
        "  0.4434814453125,\n",
        "  0.436767578125,\n",
        "  0.4447021484375,\n",
        "  0.4498291015625,\n",
        "  0.4664306640625,\n",
        "  0.48291015625,\n",
        "  0.47607421875,\n",
        "  0.48095703125,\n",
        "  0.480712890625,\n",
        "  0.4840087890625,\n",
        "  0.484375,\n",
        "  0.4879150390625,\n",
        "  0.49267578125,\n",
        "  0.4970703125,\n",
        "  0.48876953125,\n",
        "  0.4755859375,\n",
        "  0.4801025390625,\n",
        "  0.469482421875,\n",
        "  0.46484375,\n",
        "  0.4666748046875,\n",
        "  0.4674072265625,\n",
        "  0.4541015625,\n",
        "  0.453125,\n",
        "  0.4449462890625,\n",
        "  0.4541015625,\n",
        "  0.449951171875,\n",
        "  0.453857421875,\n",
        "  0.4384765625,\n",
        "  0.44384765625,\n",
        "  0.4490966796875,\n",
        "  0.4451904296875,\n",
        "  0.43798828125,\n",
        "  0.442138671875,\n",
        "  0.4393310546875,\n",
        "  0.43408203125,\n",
        "  0.4383544921875,\n",
        "  0.4381103515625,\n",
        "  0.4400634765625,\n",
        "  0.4375,\n",
        "  0.4268798828125,\n",
        "  0.43359375,\n",
        "  0.44189453125,\n",
        "  0.4356689453125,\n",
        "  0.4388427734375,\n",
        "  0.43505859375,\n",
        "  0.4385986328125,\n",
        "  0.439453125,\n",
        "  0.4415283203125,\n",
        "  0.42578125,\n",
        "  0.444091796875,\n",
        "  0.4322509765625,\n",
        "  0.4296875,\n",
        "  0.434326171875,\n",
        "  0.4384765625,\n",
        "  0.427001953125,\n",
        "  0.4395751953125,\n",
        "  0.434814453125,\n",
        "  0.4351806640625,\n",
        "  0.425048828125,\n",
        "  0.4368896484375,\n",
        "  0.4407958984375,\n",
        "  0.421630859375,\n",
        "  0.4371337890625,\n",
        "  0.44384765625,\n",
        "  0.422607421875,\n",
        "  0.4244384765625,\n",
        "  0.435791015625,\n",
        "  0.438232421875,\n",
        "  0.4288330078125,\n",
        "  0.4344482421875,\n",
        "  0.4315185546875,\n",
        "  0.435302734375,\n",
        "  0.4268798828125,\n",
        "  0.435791015625,\n",
        "  0.4356689453125,\n",
        "  0.434326171875,\n",
        "  0.43212890625,\n",
        "  0.42578125,\n",
        "  0.4210205078125,\n",
        "  0.43701171875,\n",
        "  0.4339599609375,\n",
        "  0.4381103515625,\n",
        "  0.43359375,\n",
        "  0.4232177734375,\n",
        "  0.4354248046875,\n",
        "  0.4495849609375,\n",
        "  0.434814453125,\n",
        "  0.43603515625,\n",
        "  0.434326171875,\n",
        "  0.4393310546875,\n",
        "  0.427734375,\n",
        "  0.43798828125,\n",
        "  0.432861328125,\n",
        "  0.423583984375,\n",
        "  0.4324951171875,\n",
        "  0.433349609375,\n",
        "  0.4346923828125,\n",
        "  0.4305419921875,\n",
        "  0.42822265625,\n",
        "  0.4276123046875]\n",
        "loss = [2.4680885784327984,\n",
        "  2.472873527556658,\n",
        "  2.4965452067553997,\n",
        "  2.4268768951296806,\n",
        "  2.4164752513170242,\n",
        "  2.359053226187825,\n",
        "  2.3030606023967266,\n",
        "  2.3121595233678818,\n",
        "  2.2988932579755783,\n",
        "  2.273866046220064,\n",
        "  2.2664893195033073,\n",
        "  2.2634465489536524,\n",
        "  2.29483843781054,\n",
        "  2.292356889694929,\n",
        "  2.261128695681691,\n",
        "  2.3013865780085325,\n",
        "  2.331161566078663,\n",
        "  2.3209762051701546,\n",
        "  2.3425158616155386,\n",
        "  2.382807033136487,\n",
        "  2.3598240986466408,\n",
        "  2.3897702768445015,\n",
        "  2.4268571883440018,\n",
        "  2.4405363239347935,\n",
        "  2.442212674766779,\n",
        "  2.446333918720484,\n",
        "  2.4333897791802883,\n",
        "  2.436490997672081,\n",
        "  2.473171040415764,\n",
        "  2.4825322180986404,\n",
        "  2.4522971492260695,\n",
        "  2.456740587949753,\n",
        "  2.461005538702011,\n",
        "  2.4704791717231274,\n",
        "  2.461165402084589,\n",
        "  2.4972593560814857,\n",
        "  2.478463299572468,\n",
        "  2.499819088727236,\n",
        "  2.5023694150149822,\n",
        "  2.490478605031967,\n",
        "  2.517062198370695,\n",
        "  2.5062113143503666,\n",
        "  2.4742205403745174,\n",
        "  2.495595920830965,\n",
        "  2.4766996167600155,\n",
        "  2.497177306562662,\n",
        "  2.5080887600779533,\n",
        "  2.474413773044944,\n",
        "  2.481488808989525,\n",
        "  2.506655767560005,\n",
        "  2.460327574983239,\n",
        "  2.544426079839468,\n",
        "  2.508283633738756,\n",
        "  2.515280596911907,\n",
        "  2.495601087808609,\n",
        "  2.5242313779890537,\n",
        "  2.491091188043356,\n",
        "  2.503649730235338,\n",
        "  2.498815592378378,\n",
        "  2.5347756147384644,\n",
        "  2.509564932435751,\n",
        "  2.471431352198124,\n",
        "  2.52050119638443,\n",
        "  2.5140980444848537,\n",
        "  2.4882354140281677,\n",
        "  2.561452727764845,\n",
        "  2.514063909649849,\n",
        "  2.5178940519690514,\n",
        "  2.490605767816305,\n",
        "  2.5121261924505234,\n",
        "  2.5227228552103043,\n",
        "  2.5112877301871777,\n",
        "  2.5397618897259235,\n",
        "  2.5135655775666237,\n",
        "  2.4897408299148083,\n",
        "  2.5168511047959328,\n",
        "  2.5253648720681667,\n",
        "  2.5061205588281155,\n",
        "  2.5294235106557608,\n",
        "  2.5274696983397007,\n",
        "  2.51240024715662,\n",
        "  2.509125415235758,\n",
        "  2.5197943933308125,\n",
        "  2.503511391580105,\n",
        "  2.540722291916609,\n",
        "  2.492282163351774,\n",
        "  2.4659560546278954,\n",
        "  2.543206974864006,\n",
        "  2.528570022433996,\n",
        "  2.5130017958581448,\n",
        "  2.5036022551357746,\n",
        "  2.514274623245001,\n",
        "  2.516697980463505,\n",
        "  2.5066333562135696,\n",
        "  2.537329461425543,\n",
        "  2.5336676351726055,\n",
        "  2.502676784992218,\n",
        "  2.520548664033413,\n",
        "  2.5340559370815754,\n",
        "  2.5276586897671223,\n",
        "  2.5231171511113644]\n",
        "lr = [1.0033913e-07,\n",
        "  7.525435e-08,\n",
        "  7.525435e-08,\n",
        "  5.6440758e-08,\n",
        "  5.6440758e-08,\n",
        "  4.233057e-08,\n",
        "  4.233057e-08,\n",
        "  3.1747927e-08,\n",
        "  3.1747927e-08,\n",
        "  2.3810946e-08,\n",
        "  2.3810946e-08,\n",
        "  1.785821e-08,\n",
        "  1.785821e-08,\n",
        "  1.3393657e-08,\n",
        "  1.3393657e-08,\n",
        "  1.0045243e-08,\n",
        "  1.0045243e-08,\n",
        "  7.533932e-09,\n",
        "  7.533932e-09,\n",
        "  5.650449e-09,\n",
        "  5.650449e-09,\n",
        "  4.2378367e-09,\n",
        "  4.2378367e-09,\n",
        "  3.1783776e-09,\n",
        "  3.1783776e-09,\n",
        "  2.3837832e-09,\n",
        "  2.3837832e-09,\n",
        "  1.7878373e-09,\n",
        "  1.7878373e-09,\n",
        "  1.3408781e-09,\n",
        "  1.3408781e-09,\n",
        "  1.0056586e-09,\n",
        "  1.0056586e-09,\n",
        "  7.542439e-10,\n",
        "  7.542439e-10,\n",
        "  5.656829e-10,\n",
        "  5.656829e-10,\n",
        "  4.2426218e-10,\n",
        "  4.2426218e-10,\n",
        "  3.1819664e-10,\n",
        "  3.1819664e-10,\n",
        "  2.386475e-10,\n",
        "  2.386475e-10,\n",
        "  1.789856e-10,\n",
        "  1.789856e-10,\n",
        "  1.342392e-10,\n",
        "  1.342392e-10,\n",
        "  1.006794e-10,\n",
        "  1.006794e-10,\n",
        "  7.550956e-11,\n",
        "  7.550956e-11,\n",
        "  5.6632164e-11,\n",
        "  5.6632164e-11,\n",
        "  4.2474125e-11,\n",
        "  4.2474125e-11,\n",
        "  3.1855595e-11,\n",
        "  3.1855595e-11,\n",
        "  2.3891694e-11,\n",
        "  2.3891694e-11,\n",
        "  1.791877e-11,\n",
        "  1.791877e-11,\n",
        "  1.3439078e-11,\n",
        "  1.3439078e-11,\n",
        "  1.0079309e-11,\n",
        "  1.0079309e-11,\n",
        "  7.559482e-12,\n",
        "  7.559482e-12,\n",
        "  5.669611e-12,\n",
        "  5.669611e-12,\n",
        "  4.2522084e-12,\n",
        "  4.2522084e-12,\n",
        "  3.1891562e-12,\n",
        "  3.1891562e-12,\n",
        "  2.3918673e-12,\n",
        "  2.3918673e-12,\n",
        "  1.7939004e-12,\n",
        "  1.7939004e-12,\n",
        "  1.3454253e-12,\n",
        "  1.3454253e-12,\n",
        "  1.009069e-12,\n",
        "  1.009069e-12,\n",
        "  7.5680174e-13,\n",
        "  7.5680174e-13,\n",
        "  5.676013e-13,\n",
        "  5.676013e-13,\n",
        "  4.2570098e-13,\n",
        "  4.2570098e-13,\n",
        "  3.1927572e-13,\n",
        "  3.1927572e-13,\n",
        "  2.394568e-13,\n",
        "  2.394568e-13,\n",
        "  1.795926e-13,\n",
        "  1.795926e-13,\n",
        "  1.3469445e-13,\n",
        "  1.3469445e-13,\n",
        "  1.01020836e-13,\n",
        "  1.01020836e-13,\n",
        "  7.576563e-14,\n",
        "  7.576563e-14,\n",
        "  5.6824222e-14,\n",
        "  5.6824222e-14]\n",
        "top3_acc = [0.6414794921875,\n",
        "  0.6322021484375,\n",
        "  0.63427734375,\n",
        "  0.647216796875,\n",
        "  0.6480712890625,\n",
        "  0.656982421875,\n",
        "  0.674560546875,\n",
        "  0.6707763671875,\n",
        "  0.6785888671875,\n",
        "  0.6783447265625,\n",
        "  0.6820068359375,\n",
        "  0.678466796875,\n",
        "  0.676513671875,\n",
        "  0.6834716796875,\n",
        "  0.6839599609375,\n",
        "  0.674560546875,\n",
        "  0.66796875,\n",
        "  0.6739501953125,\n",
        "  0.6624755859375,\n",
        "  0.6624755859375,\n",
        "  0.6602783203125,\n",
        "  0.659912109375,\n",
        "  0.653564453125,\n",
        "  0.6474609375,\n",
        "  0.642822265625,\n",
        "  0.6455078125,\n",
        "  0.6448974609375,\n",
        "  0.6507568359375,\n",
        "  0.637451171875,\n",
        "  0.6373291015625,\n",
        "  0.6500244140625,\n",
        "  0.6380615234375,\n",
        "  0.6402587890625,\n",
        "  0.641357421875,\n",
        "  0.640869140625,\n",
        "  0.634033203125,\n",
        "  0.6416015625,\n",
        "  0.64453125,\n",
        "  0.6337890625,\n",
        "  0.6346435546875,\n",
        "  0.6318359375,\n",
        "  0.63037109375,\n",
        "  0.6392822265625,\n",
        "  0.636962890625,\n",
        "  0.642822265625,\n",
        "  0.6280517578125,\n",
        "  0.6337890625,\n",
        "  0.6441650390625,\n",
        "  0.6434326171875,\n",
        "  0.6358642578125,\n",
        "  0.6461181640625,\n",
        "  0.6221923828125,\n",
        "  0.6307373046875,\n",
        "  0.6312255859375,\n",
        "  0.63037109375,\n",
        "  0.6279296875,\n",
        "  0.63720703125,\n",
        "  0.638427734375,\n",
        "  0.637939453125,\n",
        "  0.6236572265625,\n",
        "  0.6328125,\n",
        "  0.6409912109375,\n",
        "  0.6314697265625,\n",
        "  0.6318359375,\n",
        "  0.6461181640625,\n",
        "  0.6236572265625,\n",
        "  0.63623046875,\n",
        "  0.626953125,\n",
        "  0.6407470703125,\n",
        "  0.63525390625,\n",
        "  0.6226806640625,\n",
        "  0.6326904296875,\n",
        "  0.6275634765625,\n",
        "  0.6309814453125,\n",
        "  0.6373291015625,\n",
        "  0.6312255859375,\n",
        "  0.63232421875,\n",
        "  0.6358642578125,\n",
        "  0.6282958984375,\n",
        "  0.6300048828125,\n",
        "  0.6304931640625,\n",
        "  0.6396484375,\n",
        "  0.626220703125,\n",
        "  0.632080078125,\n",
        "  0.62451171875,\n",
        "  0.63623046875,\n",
        "  0.6419677734375,\n",
        "  0.6273193359375,\n",
        "  0.632080078125,\n",
        "  0.6304931640625,\n",
        "  0.6387939453125,\n",
        "  0.6322021484375,\n",
        "  0.63134765625,\n",
        "  0.6351318359375,\n",
        "  0.6298828125,\n",
        "  0.6319580078125,\n",
        "  0.63232421875,\n",
        "  0.6265869140625,\n",
        "  0.6221923828125,\n",
        "  0.63330078125,\n",
        "  0.62646484375]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lnub97dbAFib",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики "
      ]
    },
    {
      "metadata": {
        "id": "DHWoJ7T8_doP",
        "colab_type": "code",
        "outputId": "a1046de6-8c79-4a53-f1a0-405ed8d6ac0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='center right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJZXGhw47uBDo7cCm/ansjznN0fhcHGwtCfY7M/23R7A7OgstB2Kz+csw48B8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\n2CAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b371d9c10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rj2l0DJxAUyL",
        "colab_type": "code",
        "outputId": "667a32b0-8385-497e-bd76-62a338a959a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+lVrwJp3ktnELA8tNcTPLLn0Dh9BTwXStnLPfKHBwkeZcXOItkdzuPYwOyv9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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b34699bd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Xihq_DIdAXfY",
        "colab_type": "code",
        "outputId": "8ee39c2b-065f-4375-9716-8c753f2ae2cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(lr, 'y', label='loss')\n",
        "ax.set_title('Learning rate curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4KpYGstkDyGY79kgw27e3MDLSQCazKEoclYHsTJRMmgiCpqrygCBoiMobxtU3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\nACIiKaUEICKSUkoAIiIppQQgIpJS/x8S56Byx3SxiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b38f81250>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Jyj1GITcbQjr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "PZvMj3_Sbbqb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.4322509765625,\n",
        "  0.4434814453125,\n",
        "  0.436767578125,\n",
        "  0.4447021484375,\n",
        "  0.4498291015625,\n",
        "  0.4664306640625,\n",
        "  0.48291015625,\n",
        "  0.47607421875,\n",
        "  0.48095703125,\n",
        "  0.480712890625,\n",
        "  0.4840087890625,\n",
        "  0.484375,\n",
        "  0.4879150390625,\n",
        "  0.49267578125,\n",
        "  0.4970703125,\n",
        "  0.48876953125,\n",
        "  0.4755859375,\n",
        "  0.4801025390625,\n",
        "  0.469482421875,\n",
        "  0.46484375,\n",
        "  0.4666748046875,\n",
        "  0.4674072265625,\n",
        "  0.4541015625,\n",
        "  0.453125,\n",
        "  0.4449462890625,\n",
        "  0.4541015625,\n",
        "  0.449951171875,\n",
        "  0.453857421875,\n",
        "  0.4384765625,\n",
        "  0.44384765625,\n",
        "  0.4490966796875,\n",
        "  0.4451904296875,\n",
        "  0.43798828125,\n",
        "  0.442138671875,\n",
        "  0.4393310546875,\n",
        "  0.43408203125,\n",
        "  0.4383544921875,\n",
        "  0.4381103515625,\n",
        "  0.4400634765625,\n",
        "  0.4375,\n",
        "  0.4268798828125,\n",
        "  0.43359375,\n",
        "  0.44189453125,\n",
        "  0.4356689453125,\n",
        "  0.4388427734375,\n",
        "  0.43505859375,\n",
        "  0.4385986328125,\n",
        "  0.439453125,\n",
        "  0.4415283203125,\n",
        "  0.42578125,\n",
        "  0.444091796875,\n",
        "  0.4322509765625,\n",
        "  0.4296875,\n",
        "  0.434326171875,\n",
        "  0.4384765625,\n",
        "  0.427001953125,\n",
        "  0.4395751953125,\n",
        "  0.434814453125,\n",
        "  0.4351806640625,\n",
        "  0.425048828125,\n",
        "  0.4368896484375,\n",
        "  0.4407958984375,\n",
        "  0.421630859375,\n",
        "  0.4371337890625,\n",
        "  0.44384765625,\n",
        "  0.422607421875,\n",
        "  0.4244384765625,\n",
        "  0.435791015625,\n",
        "  0.438232421875,\n",
        "  0.4288330078125,\n",
        "  0.4344482421875,\n",
        "  0.4315185546875,\n",
        "  0.435302734375,\n",
        "  0.4268798828125,\n",
        "  0.435791015625,\n",
        "  0.4356689453125,\n",
        "  0.434326171875,\n",
        "  0.43212890625,\n",
        "  0.42578125,\n",
        "  0.4210205078125,\n",
        "  0.43701171875,\n",
        "  0.4339599609375,\n",
        "  0.4381103515625,\n",
        "  0.43359375,\n",
        "  0.4232177734375,\n",
        "  0.4354248046875,\n",
        "  0.4495849609375,\n",
        "  0.434814453125,\n",
        "  0.43603515625,\n",
        "  0.434326171875,\n",
        "  0.4393310546875,\n",
        "  0.427734375,\n",
        "  0.43798828125,\n",
        "  0.432861328125,\n",
        "  0.423583984375,\n",
        "  0.4324951171875,\n",
        "  0.433349609375,\n",
        "  0.4346923828125,\n",
        "  0.4305419921875,\n",
        "  0.42822265625,\n",
        "  0.4276123046875]\n",
        "loss = [2.4680885784327984,\n",
        "  2.472873527556658,\n",
        "  2.4965452067553997,\n",
        "  2.4268768951296806,\n",
        "  2.4164752513170242,\n",
        "  2.359053226187825,\n",
        "  2.3030606023967266,\n",
        "  2.3121595233678818,\n",
        "  2.2988932579755783,\n",
        "  2.273866046220064,\n",
        "  2.2664893195033073,\n",
        "  2.2634465489536524,\n",
        "  2.29483843781054,\n",
        "  2.292356889694929,\n",
        "  2.261128695681691,\n",
        "  2.3013865780085325,\n",
        "  2.331161566078663,\n",
        "  2.3209762051701546,\n",
        "  2.3425158616155386,\n",
        "  2.382807033136487,\n",
        "  2.3598240986466408,\n",
        "  2.3897702768445015,\n",
        "  2.4268571883440018,\n",
        "  2.4405363239347935,\n",
        "  2.442212674766779,\n",
        "  2.446333918720484,\n",
        "  2.4333897791802883,\n",
        "  2.436490997672081,\n",
        "  2.473171040415764,\n",
        "  2.4825322180986404,\n",
        "  2.4522971492260695,\n",
        "  2.456740587949753,\n",
        "  2.461005538702011,\n",
        "  2.4704791717231274,\n",
        "  2.461165402084589,\n",
        "  2.4972593560814857,\n",
        "  2.478463299572468,\n",
        "  2.499819088727236,\n",
        "  2.5023694150149822,\n",
        "  2.490478605031967,\n",
        "  2.517062198370695,\n",
        "  2.5062113143503666,\n",
        "  2.4742205403745174,\n",
        "  2.495595920830965,\n",
        "  2.4766996167600155,\n",
        "  2.497177306562662,\n",
        "  2.5080887600779533,\n",
        "  2.474413773044944,\n",
        "  2.481488808989525,\n",
        "  2.506655767560005,\n",
        "  2.460327574983239,\n",
        "  2.544426079839468,\n",
        "  2.508283633738756,\n",
        "  2.515280596911907,\n",
        "  2.495601087808609,\n",
        "  2.5242313779890537,\n",
        "  2.491091188043356,\n",
        "  2.503649730235338,\n",
        "  2.498815592378378,\n",
        "  2.5347756147384644,\n",
        "  2.509564932435751,\n",
        "  2.471431352198124,\n",
        "  2.52050119638443,\n",
        "  2.5140980444848537,\n",
        "  2.4882354140281677,\n",
        "  2.561452727764845,\n",
        "  2.514063909649849,\n",
        "  2.5178940519690514,\n",
        "  2.490605767816305,\n",
        "  2.5121261924505234,\n",
        "  2.5227228552103043,\n",
        "  2.5112877301871777,\n",
        "  2.5397618897259235,\n",
        "  2.5135655775666237,\n",
        "  2.4897408299148083,\n",
        "  2.5168511047959328,\n",
        "  2.5253648720681667,\n",
        "  2.5061205588281155,\n",
        "  2.5294235106557608,\n",
        "  2.5274696983397007,\n",
        "  2.51240024715662,\n",
        "  2.509125415235758,\n",
        "  2.5197943933308125,\n",
        "  2.503511391580105,\n",
        "  2.540722291916609,\n",
        "  2.492282163351774,\n",
        "  2.4659560546278954,\n",
        "  2.543206974864006,\n",
        "  2.528570022433996,\n",
        "  2.5130017958581448,\n",
        "  2.5036022551357746,\n",
        "  2.514274623245001,\n",
        "  2.516697980463505,\n",
        "  2.5066333562135696,\n",
        "  2.537329461425543,\n",
        "  2.5336676351726055,\n",
        "  2.502676784992218,\n",
        "  2.520548664033413,\n",
        "  2.5340559370815754,\n",
        "  2.5276586897671223,\n",
        "  2.5231171511113644]\n",
        "\n",
        "lr = [1.0033913e-07,\n",
        "  7.525435e-08,\n",
        "  7.525435e-08,\n",
        "  5.6440758e-08,\n",
        "  5.6440758e-08,\n",
        "  4.233057e-08,\n",
        "  4.233057e-08,\n",
        "  3.1747927e-08,\n",
        "  3.1747927e-08,\n",
        "  2.3810946e-08,\n",
        "  2.3810946e-08,\n",
        "  1.785821e-08,\n",
        "  1.785821e-08,\n",
        "  1.3393657e-08,\n",
        "  1.3393657e-08,\n",
        "  1.0045243e-08,\n",
        "  1.0045243e-08,\n",
        "  7.533932e-09,\n",
        "  7.533932e-09,\n",
        "  5.650449e-09,\n",
        "  5.650449e-09,\n",
        "  4.2378367e-09,\n",
        "  4.2378367e-09,\n",
        "  3.1783776e-09,\n",
        "  3.1783776e-09,\n",
        "  2.3837832e-09,\n",
        "  2.3837832e-09,\n",
        "  1.7878373e-09,\n",
        "  1.7878373e-09,\n",
        "  1.3408781e-09,\n",
        "  1.3408781e-09,\n",
        "  1.0056586e-09,\n",
        "  1.0056586e-09,\n",
        "  7.542439e-10,\n",
        "  7.542439e-10,\n",
        "  5.656829e-10,\n",
        "  5.656829e-10,\n",
        "  4.2426218e-10,\n",
        "  4.2426218e-10,\n",
        "  3.1819664e-10,\n",
        "  3.1819664e-10,\n",
        "  2.386475e-10,\n",
        "  2.386475e-10,\n",
        "  1.789856e-10,\n",
        "  1.789856e-10,\n",
        "  1.342392e-10,\n",
        "  1.342392e-10,\n",
        "  1.006794e-10,\n",
        "  1.006794e-10,\n",
        "  7.550956e-11,\n",
        "  7.550956e-11,\n",
        "  5.6632164e-11,\n",
        "  5.6632164e-11,\n",
        "  4.2474125e-11,\n",
        "  4.2474125e-11,\n",
        "  3.1855595e-11,\n",
        "  3.1855595e-11,\n",
        "  2.3891694e-11,\n",
        "  2.3891694e-11,\n",
        "  1.791877e-11,\n",
        "  1.791877e-11,\n",
        "  1.3439078e-11,\n",
        "  1.3439078e-11,\n",
        "  1.0079309e-11,\n",
        "  1.0079309e-11,\n",
        "  7.559482e-12,\n",
        "  7.559482e-12,\n",
        "  5.669611e-12,\n",
        "  5.669611e-12,\n",
        "  4.2522084e-12,\n",
        "  4.2522084e-12,\n",
        "  3.1891562e-12,\n",
        "  3.1891562e-12,\n",
        "  2.3918673e-12,\n",
        "  2.3918673e-12,\n",
        "  1.7939004e-12,\n",
        "  1.7939004e-12,\n",
        "  1.3454253e-12,\n",
        "  1.3454253e-12,\n",
        "  1.009069e-12,\n",
        "  1.009069e-12,\n",
        "  7.5680174e-13,\n",
        "  7.5680174e-13,\n",
        "  5.676013e-13,\n",
        "  5.676013e-13,\n",
        "  4.2570098e-13,\n",
        "  4.2570098e-13,\n",
        "  3.1927572e-13,\n",
        "  3.1927572e-13,\n",
        "  2.394568e-13,\n",
        "  2.394568e-13,\n",
        "  1.795926e-13,\n",
        "  1.795926e-13,\n",
        "  1.3469445e-13,\n",
        "  1.3469445e-13,\n",
        "  1.01020836e-13,\n",
        "  1.01020836e-13,\n",
        "  7.576563e-14,\n",
        "  7.576563e-14,\n",
        "  5.6824222e-14,\n",
        "  5.6824222e-14]\n",
        "\n",
        "top3_acc = [0.6414794921875,\n",
        "  0.6322021484375,\n",
        "  0.63427734375,\n",
        "  0.647216796875,\n",
        "  0.6480712890625,\n",
        "  0.656982421875,\n",
        "  0.674560546875,\n",
        "  0.6707763671875,\n",
        "  0.6785888671875,\n",
        "  0.6783447265625,\n",
        "  0.6820068359375,\n",
        "  0.678466796875,\n",
        "  0.676513671875,\n",
        "  0.6834716796875,\n",
        "  0.6839599609375,\n",
        "  0.674560546875,\n",
        "  0.66796875,\n",
        "  0.6739501953125,\n",
        "  0.6624755859375,\n",
        "  0.6624755859375,\n",
        "  0.6602783203125,\n",
        "  0.659912109375,\n",
        "  0.653564453125,\n",
        "  0.6474609375,\n",
        "  0.642822265625,\n",
        "  0.6455078125,\n",
        "  0.6448974609375,\n",
        "  0.6507568359375,\n",
        "  0.637451171875,\n",
        "  0.6373291015625,\n",
        "  0.6500244140625,\n",
        "  0.6380615234375,\n",
        "  0.6402587890625,\n",
        "  0.641357421875,\n",
        "  0.640869140625,\n",
        "  0.634033203125,\n",
        "  0.6416015625,\n",
        "  0.64453125,\n",
        "  0.6337890625,\n",
        "  0.6346435546875,\n",
        "  0.6318359375,\n",
        "  0.63037109375,\n",
        "  0.6392822265625,\n",
        "  0.636962890625,\n",
        "  0.642822265625,\n",
        "  0.6280517578125,\n",
        "  0.6337890625,\n",
        "  0.6441650390625,\n",
        "  0.6434326171875,\n",
        "  0.6358642578125,\n",
        "  0.6461181640625,\n",
        "  0.6221923828125,\n",
        "  0.6307373046875,\n",
        "  0.6312255859375,\n",
        "  0.63037109375,\n",
        "  0.6279296875,\n",
        "  0.63720703125,\n",
        "  0.638427734375,\n",
        "  0.637939453125,\n",
        "  0.6236572265625,\n",
        "  0.6328125,\n",
        "  0.6409912109375,\n",
        "  0.6314697265625,\n",
        "  0.6318359375,\n",
        "  0.6461181640625,\n",
        "  0.6236572265625,\n",
        "  0.63623046875,\n",
        "  0.626953125,\n",
        "  0.6407470703125,\n",
        "  0.63525390625,\n",
        "  0.6226806640625,\n",
        "  0.6326904296875,\n",
        "  0.6275634765625,\n",
        "  0.6309814453125,\n",
        "  0.6373291015625,\n",
        "  0.6312255859375,\n",
        "  0.63232421875,\n",
        "  0.6358642578125,\n",
        "  0.6282958984375,\n",
        "  0.6300048828125,\n",
        "  0.6304931640625,\n",
        "  0.6396484375,\n",
        "  0.626220703125,\n",
        "  0.632080078125,\n",
        "  0.62451171875,\n",
        "  0.63623046875,\n",
        "  0.6419677734375,\n",
        "  0.6273193359375,\n",
        "  0.632080078125,\n",
        "  0.6304931640625,\n",
        "  0.6387939453125,\n",
        "  0.6322021484375,\n",
        "  0.63134765625,\n",
        "  0.6351318359375,\n",
        "  0.6298828125,\n",
        "  0.6319580078125,\n",
        "  0.63232421875,\n",
        "  0.6265869140625,\n",
        "  0.6221923828125,\n",
        "  0.63330078125,\n",
        "  0.62646484375]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eRUQ-w2abytE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики"
      ]
    },
    {
      "metadata": {
        "id": "jl_RDjswb1ws",
        "colab_type": "code",
        "outputId": "dceaa75d-3285-4d24-d3b5-e8465f4aff92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='center right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJZXGhw47uBDo7cCm/ansjznN0fhcHGwtCfY7M/23R7A7OgstB2Kz+csw48B8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\n2CAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b38f20a10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ywfo_YwQb73d",
        "colab_type": "code",
        "outputId": "e13b7eab-96d5-46f3-f4d6-3afd65ff0fe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+lVrwJp3ktnELA8tNcTPLLn0Dh9BTwXStnLPfKHBwkeZcXOItkdzuPYwOyv9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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b38f42a10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zAZmca0ib-nE",
        "colab_type": "code",
        "outputId": "507f2c91-cb49-4ede-bd8f-cb9613497793",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(lr, 'y', label='loss')\n",
        "ax.set_title('Learning rate curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4KpYGstkDyGY79kgw27e3MDLSQCazKEoclYHsTJRMmgiCpqrygCBoiMobxtU3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\nACIiKaUEICKSUkoAIiIppQQgIpJS/x8S56Byx3SxiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b38f42310>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "G3cW2JsNkYN-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "nWJzRlPfkel-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.4322509765625,\n",
        "  0.4434814453125,\n",
        "  0.436767578125,\n",
        "  0.4447021484375,\n",
        "  0.4498291015625,\n",
        "  0.4664306640625,\n",
        "  0.48291015625,\n",
        "  0.47607421875,\n",
        "  0.48095703125,\n",
        "  0.480712890625,\n",
        "  0.4840087890625,\n",
        "  0.484375,\n",
        "  0.4879150390625,\n",
        "  0.49267578125,\n",
        "  0.4970703125,\n",
        "  0.48876953125,\n",
        "  0.4755859375,\n",
        "  0.4801025390625,\n",
        "  0.469482421875,\n",
        "  0.46484375,\n",
        "  0.4666748046875,\n",
        "  0.4674072265625,\n",
        "  0.4541015625,\n",
        "  0.453125,\n",
        "  0.4449462890625,\n",
        "  0.4541015625,\n",
        "  0.449951171875,\n",
        "  0.453857421875,\n",
        "  0.4384765625,\n",
        "  0.44384765625,\n",
        "  0.4490966796875,\n",
        "  0.4451904296875,\n",
        "  0.43798828125,\n",
        "  0.442138671875,\n",
        "  0.4393310546875,\n",
        "  0.43408203125,\n",
        "  0.4383544921875,\n",
        "  0.4381103515625,\n",
        "  0.4400634765625,\n",
        "  0.4375,\n",
        "  0.4268798828125,\n",
        "  0.43359375,\n",
        "  0.44189453125,\n",
        "  0.4356689453125,\n",
        "  0.4388427734375,\n",
        "  0.43505859375,\n",
        "  0.4385986328125,\n",
        "  0.439453125,\n",
        "  0.4415283203125,\n",
        "  0.42578125,\n",
        "  0.444091796875,\n",
        "  0.4322509765625,\n",
        "  0.4296875,\n",
        "  0.434326171875,\n",
        "  0.4384765625,\n",
        "  0.427001953125,\n",
        "  0.4395751953125,\n",
        "  0.434814453125,\n",
        "  0.4351806640625,\n",
        "  0.425048828125,\n",
        "  0.4368896484375,\n",
        "  0.4407958984375,\n",
        "  0.421630859375,\n",
        "  0.4371337890625,\n",
        "  0.44384765625,\n",
        "  0.422607421875,\n",
        "  0.4244384765625,\n",
        "  0.435791015625,\n",
        "  0.438232421875,\n",
        "  0.4288330078125,\n",
        "  0.4344482421875,\n",
        "  0.4315185546875,\n",
        "  0.435302734375,\n",
        "  0.4268798828125,\n",
        "  0.435791015625,\n",
        "  0.4356689453125,\n",
        "  0.434326171875,\n",
        "  0.43212890625,\n",
        "  0.42578125,\n",
        "  0.4210205078125,\n",
        "  0.43701171875,\n",
        "  0.4339599609375,\n",
        "  0.4381103515625,\n",
        "  0.43359375,\n",
        "  0.4232177734375,\n",
        "  0.4354248046875,\n",
        "  0.4495849609375,\n",
        "  0.434814453125,\n",
        "  0.43603515625,\n",
        "  0.434326171875,\n",
        "  0.4393310546875,\n",
        "  0.427734375,\n",
        "  0.43798828125,\n",
        "  0.432861328125,\n",
        "  0.423583984375,\n",
        "  0.4324951171875,\n",
        "  0.433349609375,\n",
        "  0.4346923828125,\n",
        "  0.4305419921875,\n",
        "  0.42822265625,\n",
        "  0.4276123046875]\n",
        "\n",
        "loss = [2.4680885784327984,\n",
        "  2.472873527556658,\n",
        "  2.4965452067553997,\n",
        "  2.4268768951296806,\n",
        "  2.4164752513170242,\n",
        "  2.359053226187825,\n",
        "  2.3030606023967266,\n",
        "  2.3121595233678818,\n",
        "  2.2988932579755783,\n",
        "  2.273866046220064,\n",
        "  2.2664893195033073,\n",
        "  2.2634465489536524,\n",
        "  2.29483843781054,\n",
        "  2.292356889694929,\n",
        "  2.261128695681691,\n",
        "  2.3013865780085325,\n",
        "  2.331161566078663,\n",
        "  2.3209762051701546,\n",
        "  2.3425158616155386,\n",
        "  2.382807033136487,\n",
        "  2.3598240986466408,\n",
        "  2.3897702768445015,\n",
        "  2.4268571883440018,\n",
        "  2.4405363239347935,\n",
        "  2.442212674766779,\n",
        "  2.446333918720484,\n",
        "  2.4333897791802883,\n",
        "  2.436490997672081,\n",
        "  2.473171040415764,\n",
        "  2.4825322180986404,\n",
        "  2.4522971492260695,\n",
        "  2.456740587949753,\n",
        "  2.461005538702011,\n",
        "  2.4704791717231274,\n",
        "  2.461165402084589,\n",
        "  2.4972593560814857,\n",
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        "\n",
        "\n",
        "[1.0033913e-07,\n",
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        "  1.009069e-12,\n",
        "  7.5680174e-13,\n",
        "  7.5680174e-13,\n",
        "  5.676013e-13,\n",
        "  5.676013e-13,\n",
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        "  4.2570098e-13,\n",
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        "  3.1927572e-13,\n",
        "  2.394568e-13,\n",
        "  2.394568e-13,\n",
        "  1.795926e-13,\n",
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        "  1.3469445e-13,\n",
        "  1.3469445e-13,\n",
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        "  1.01020836e-13,\n",
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        "  7.576563e-14,\n",
        "  5.6824222e-14,\n",
        "  5.6824222e-14]\n",
        "\n",
        "top3_acc = [0.6414794921875,\n",
        "  0.6322021484375,\n",
        "  0.63427734375,\n",
        "  0.647216796875,\n",
        "  0.6480712890625,\n",
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        "  0.6358642578125,\n",
        "  0.6282958984375,\n",
        "  0.6300048828125,\n",
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        "  0.62451171875,\n",
        "  0.63623046875,\n",
        "  0.6419677734375,\n",
        "  0.6273193359375,\n",
        "  0.632080078125,\n",
        "  0.6304931640625,\n",
        "  0.6387939453125,\n",
        "  0.6322021484375,\n",
        "  0.63134765625,\n",
        "  0.6351318359375,\n",
        "  0.6298828125,\n",
        "  0.6319580078125,\n",
        "  0.63232421875,\n",
        "  0.6265869140625,\n",
        "  0.6221923828125,\n",
        "  0.63330078125,\n",
        "  0.62646484375]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e4mZ1bCZkx4j",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики"
      ]
    },
    {
      "metadata": {
        "id": "WYlkIpBrk0Kb",
        "colab_type": "code",
        "outputId": "dad2e232-a5d3-4653-c81e-2752ab2594f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='center right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FJZXGhw47uBDo7cCm/ansjznN0fhcHGwtCfY7M/23R7A7OgstB2Kz+csw48B8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\n2CAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b31646d50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bSaswaAzk6D-",
        "colab_type": "code",
        "outputId": "fc01dd85-9fd0-4dc7-f7ea-e818c95e8148",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z+lVrwJp3ktnELA8tNcTPLLn0Dh9BTwXStnLPfKHBwkeZcXOItkdzuPYwOyv9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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5b343910d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Cg1R88gdkOkT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Вывод\n",
        "При анализе изменямых параметров становится ясно, что проблема в том, что изначально выбран неправильный learning_rate (loss-графики)\n"
      ]
    },
    {
      "metadata": {
        "id": "ViekbrJjNA2l",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Эксперименты с агументацией данных:"
      ]
    },
    {
      "metadata": {
        "id": "fKD2B4XwNF6P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Резюме \n",
        "\n",
        "На лучшей модели проверяется гипотеза:\n",
        "\n",
        " - При увеличении картинки и поворотах можно будет выбрать некие фичи, что улучшить качество.\n",
        " Добавляем по одной картинке для каждого объекта => batch_size увеличивается в два раза. (Оставляем его таким, тк если мы уменьшим его в 2 раза, то потеряем в уникальных изображениях из исходных данных)"
      ]
    },
    {
      "metadata": {
        "id": "7_8VanX1fE-4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###  Модель без агументации данных.\n"
      ]
    },
    {
      "metadata": {
        "id": "6_WJwCYhfLUk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 512\n",
        "STEPS_PER_EPOCH = 128\n",
        "EPOCHS = 50\n",
        "\n",
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zsSr_cSGfckJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Массивы:"
      ]
    },
    {
      "metadata": {
        "id": "eOAbNkYTfiqH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc =[0.110992431640625,\n",
        "  0.18963623046875,\n",
        "  0.263763427734375,\n",
        "  0.3127899169921875,\n",
        "  0.3480987548828125,\n",
        "  0.3790130615234375,\n",
        "  0.4034271240234375,\n",
        "  0.4199981689453125,\n",
        "  0.4349212646484375,\n",
        "  0.4438629150390625,\n",
        "  0.458953857421875,\n",
        "  0.4643096923828125,\n",
        "  0.4718780517578125,\n",
        "  0.4775238037109375,\n",
        "  0.4791717529296875,\n",
        "  0.483062744140625,\n",
        "  0.4871063232421875,\n",
        "  0.491668701171875,\n",
        "  0.4888153076171875,\n",
        "  0.5007171630859375,\n",
        "  0.49688720703125,\n",
        "  0.499908447265625,\n",
        "  0.498504638671875,\n",
        "  0.502593994140625,\n",
        "  0.5036468505859375,\n",
        "  0.5051116943359375,\n",
        "  0.50262451171875,\n",
        "  0.5066375732421875,\n",
        "  0.505859375,\n",
        "  0.5013427734375,\n",
        "  0.5063934326171875,\n",
        "  0.50311279296875,\n",
        "  0.5091552734375,\n",
        "  0.5047607421875,\n",
        "  0.5095672607421875,\n",
        "  0.505645751953125,\n",
        "  0.5090179443359375,\n",
        "  0.506683349609375,\n",
        "  0.5061798095703125,\n",
        "  0.506500244140625,\n",
        "  0.5070343017578125,\n",
        "  0.511474609375,\n",
        "  0.5070037841796875,\n",
        "  0.507232666015625,\n",
        "  0.510009765625,\n",
        "  0.5093536376953125,\n",
        "  0.5079498291015625,\n",
        "  0.5080413818359375,\n",
        "  0.50836181640625,\n",
        "  0.506500244140625]\n",
        "\n",
        "loss =  [4.759915500879288,\n",
        "  4.119159534573555,\n",
        "  3.668934267014265,\n",
        "  3.344358215108514,\n",
        "  3.122873591259122,\n",
        "  2.949145160615444,\n",
        "  2.809198673814535,\n",
        "  2.72814286313951,\n",
        "  2.650218477472663,\n",
        "  2.593439484015107,\n",
        "  2.5187066569924355,\n",
        "  2.4914218112826347,\n",
        "  2.449601475149393,\n",
        "  2.41269388794899,\n",
        "  2.4114991184324026,\n",
        "  2.374601664021611,\n",
        "  2.3659047223627567,\n",
        "  2.3453390300273895,\n",
        "  2.343330467119813,\n",
        "  2.3026546854525805,\n",
        "  2.3030626606196165,\n",
        "  2.2994032129645348,\n",
        "  2.297671416774392,\n",
        "  2.282514115795493,\n",
        "  2.278309663757682,\n",
        "  2.2696991711854935,\n",
        "  2.276376763358712,\n",
        "  2.259805638343096,\n",
        "  2.268813209608197,\n",
        "  2.2780629005283117,\n",
        "  2.264872604981065,\n",
        "  2.2716403640806675,\n",
        "  2.2652347832918167,\n",
        "  2.262054679915309,\n",
        "  2.26261237449944,\n",
        "  2.271057326346636,\n",
        "  2.2498729666695,\n",
        "  2.2595823351293802,\n",
        "  2.26347921974957,\n",
        "  2.250196225941181,\n",
        "  2.250006066635251,\n",
        "  2.239623885601759,\n",
        "  2.2500197663903236,\n",
        "  2.2571550710126758,\n",
        "  2.248923072591424,\n",
        "  2.2539265109226108,\n",
        "  2.2499399902299047,\n",
        "  2.246763882227242,\n",
        "  2.2393512036651373,\n",
        "  2.2581502310931683]\n",
        "\n",
        "lr = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc = [0.2039642333984375,\n",
        "  0.3274078369140625,\n",
        "  0.419036865234375,\n",
        "  0.4839324951171875,\n",
        "  0.5266265869140625,\n",
        "  0.5580596923828125,\n",
        "  0.58770751953125,\n",
        "  0.6025543212890625,\n",
        "  0.615478515625,\n",
        "  0.627197265625,\n",
        "  0.6404266357421875,\n",
        "  0.6468048095703125,\n",
        "  0.654083251953125,\n",
        "  0.659637451171875,\n",
        "  0.660675048828125,\n",
        "  0.667572021484375,\n",
        "  0.66827392578125,\n",
        "  0.6712188720703125,\n",
        "  0.673370361328125,\n",
        "  0.6812896728515625,\n",
        "  0.6812744140625,\n",
        "  0.6820526123046875,\n",
        "  0.681671142578125,\n",
        "  0.682464599609375,\n",
        "  0.6859893798828125,\n",
        "  0.686248779296875,\n",
        "  0.6841583251953125,\n",
        "  0.68798828125,\n",
        "  0.68609619140625,\n",
        "  0.6839599609375,\n",
        "  0.688507080078125,\n",
        "  0.683197021484375,\n",
        "  0.68731689453125,\n",
        "  0.6858367919921875,\n",
        "  0.6888580322265625,\n",
        "  0.6849517822265625,\n",
        "  0.6927032470703125,\n",
        "  0.6875762939453125,\n",
        "  0.6843414306640625,\n",
        "  0.6898040771484375,\n",
        "  0.6904296875,\n",
        "  0.6919097900390625,\n",
        "  0.690185546875,\n",
        "  0.687591552734375,\n",
        "  0.69171142578125,\n",
        "  0.6894683837890625,\n",
        "  0.69219970703125,\n",
        "  0.689666748046875,\n",
        "  0.6897430419921875,\n",
        "  0.68902587890625]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T63wHykvf8on",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Графики"
      ]
    },
    {
      "metadata": {
        "id": "68zhDYtkgAJ2",
        "colab_type": "code",
        "outputId": "4f0c2111-fcc1-4b5e-d972-d08891011c25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='center right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F0Qm4up2Fs0tqzWafqp/HgiXgdqMrLkIzmiA8/MQyzZExHPLKal3VIn7fAaDO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\n479vAHd/Ix76y7v7a8C/iYa1u5131guAnkoGzwH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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6090860590>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "40hIriwRgKim",
        "colab_type": "code",
        "outputId": "fe5c176c-c2ae-4959-9277-723a80ba8181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qmyoYn1HEtJzpTM+egeVMx3JmMGX0VPLzM1m7ZSNVTZVUNVVS2VRJVVMVAQLk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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6090860810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yjO3s6p_gf2Z",
        "colab_type": "code",
        "outputId": "228fda98-486f-4adb-e5da-1defced76633",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(lr, 'y', label='loss')\n",
        "ax.set_title('Learning rate curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NSWxx0p93ZVG/x66XS5xTYQ8CpwGY2UHAGnfvBnD3VUCzmS2KruI6Maqfr81D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\nERGRglJiERGRglJiERGRglJiERGRgvr/YyYc7o4fddAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60908604d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qkZ9L3xGGDqB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Модель с агументацией данных"
      ]
    },
    {
      "metadata": {
        "id": "UGBjUdmeGMt4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def images_and_labels_generator_with_aug(batch_size):\n",
        "  image_gen = ImageDataGenerator(\n",
        "    rotation_range=45,\n",
        "    rescale=3.2,\n",
        "    zoom_range=1.2\n",
        "    \n",
        "  )\n",
        "\n",
        "  for batch in raw_batch_generator(batch_size):\n",
        "    batch_images = []\n",
        "    batch_labels = []\n",
        "    for e in batch:\n",
        "      image = draw_it(e[0])\n",
        "      batch_images.append(image)\n",
        "      image = np.tile(np.expand_dims(image, axis=2), (1, 1, 3))\n",
        "      batch_labels.append(e[1])\n",
        "      batch_images.append(image_gen.random_transform(image)[:,:,0])\n",
        "      batch_labels.append(e[1])\n",
        "        \n",
        "      \n",
        "    batch_images = np.stack(batch_images, axis=0)\n",
        "    yield batch_images, batch_labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tKZosDQxGb5P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "QbZPIgJAGjsY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.007415771484375,\n",
        "  0.022796630859375,\n",
        "  0.0433349609375,\n",
        "  0.071258544921875,\n",
        "  0.096160888671875,\n",
        "  0.1148834228515625,\n",
        "  0.1322021484375,\n",
        "  0.14642333984375,\n",
        "  0.1559906005859375,\n",
        "  0.1679229736328125,\n",
        "  0.1779937744140625,\n",
        "  0.1855926513671875,\n",
        "  0.191070556640625,\n",
        "  0.197540283203125,\n",
        "  0.201904296875,\n",
        "  0.2020721435546875,\n",
        "  0.2059326171875,\n",
        "  0.209808349609375,\n",
        "  0.2117462158203125,\n",
        "  0.21240234375,\n",
        "  0.216522216796875,\n",
        "  0.216644287109375,\n",
        "  0.218475341796875,\n",
        "  0.2166595458984375,\n",
        "  0.22039794921875,\n",
        "  0.222259521484375,\n",
        "  0.2225799560546875,\n",
        "  0.2215118408203125,\n",
        "  0.2217864990234375,\n",
        "  0.225616455078125,\n",
        "  0.2236328125,\n",
        "  0.224639892578125,\n",
        "  0.2237701416015625,\n",
        "  0.226165771484375,\n",
        "  0.22308349609375,\n",
        "  0.22564697265625,\n",
        "  0.2223663330078125,\n",
        "  0.2261199951171875,\n",
        "  0.2242279052734375,\n",
        "  0.2274017333984375,\n",
        "  0.224029541015625,\n",
        "  0.226104736328125,\n",
        "  0.225830078125,\n",
        "  0.2285308837890625,\n",
        "  0.225006103515625,\n",
        "  0.2252655029296875,\n",
        "  0.22607421875,\n",
        "  0.22503662109375,\n",
        "  0.2281494140625,\n",
        "  0.2252655029296875]\n",
        "\n",
        "loss = [5.7739698104560375,\n",
        "  5.552871033549309,\n",
        "  5.285460088402033,\n",
        "  5.049863412976265,\n",
        "  4.86707342043519,\n",
        "  4.724752277135849,\n",
        "  4.608510885387659,\n",
        "  4.499989453703165,\n",
        "  4.429089799523354,\n",
        "  4.3647153079509735,\n",
        "  4.294530261307955,\n",
        "  4.239372041076422,\n",
        "  4.203357718884945,\n",
        "  4.171484369784594,\n",
        "  4.149225004017353,\n",
        "  4.129408471286297,\n",
        "  4.111062694340944,\n",
        "  4.074572619050741,\n",
        "  4.071458769962192,\n",
        "  4.067548392340541,\n",
        "  4.043001951649785,\n",
        "  4.033365316689014,\n",
        "  4.026085656136274,\n",
        "  4.0362971387803555,\n",
        "  4.0240539107471704,\n",
        "  4.009893884882331,\n",
        "  4.005370466038585,\n",
        "  4.013318680226803,\n",
        "  4.006745036691427,\n",
        "  3.9872453436255455,\n",
        "  3.994716826826334,\n",
        "  3.9803254026919603,\n",
        "  4.002521056681871,\n",
        "  3.982033709064126,\n",
        "  4.000495778396726,\n",
        "  3.9995923656970263,\n",
        "  3.9951315131038427,\n",
        "  3.9814962670207024,\n",
        "  3.9901318419724703,\n",
        "  3.9776270277798176,\n",
        "  3.986276650801301,\n",
        "  3.9780593495815992,\n",
        "  3.981793038547039,\n",
        "  3.9751983247697353,\n",
        "  3.9906250424683094,\n",
        "  3.9913211911916733,\n",
        "  3.9747091848403215,\n",
        "  3.9860664550215006,\n",
        "  3.9766838401556015,\n",
        "  3.9835875891149044]\n",
        "\n",
        "lr = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc = [0.02093505859375,\n",
        "  0.0516204833984375,\n",
        "  0.093505859375,\n",
        "  0.140472412109375,\n",
        "  0.1768646240234375,\n",
        "  0.2042999267578125,\n",
        "  0.227630615234375,\n",
        "  0.25,\n",
        "  0.2613372802734375,\n",
        "  0.2771759033203125,\n",
        "  0.2894744873046875,\n",
        "  0.3008880615234375,\n",
        "  0.306671142578125,\n",
        "  0.3150177001953125,\n",
        "  0.3198394775390625,\n",
        "  0.322174072265625,\n",
        "  0.328460693359375,\n",
        "  0.3322906494140625,\n",
        "  0.3319854736328125,\n",
        "  0.3352203369140625,\n",
        "  0.3384857177734375,\n",
        "  0.33831787109375,\n",
        "  0.3433685302734375,\n",
        "  0.339630126953125,\n",
        "  0.342987060546875,\n",
        "  0.3454742431640625,\n",
        "  0.3440399169921875,\n",
        "  0.3446502685546875,\n",
        "  0.3451080322265625,\n",
        "  0.348846435546875,\n",
        "  0.3479461669921875,\n",
        "  0.347869873046875,\n",
        "  0.3454132080078125,\n",
        "  0.351226806640625,\n",
        "  0.3463134765625,\n",
        "  0.3502960205078125,\n",
        "  0.3480377197265625,\n",
        "  0.350341796875,\n",
        "  0.348358154296875,\n",
        "  0.35003662109375,\n",
        "  0.3525238037109375,\n",
        "  0.34930419921875,\n",
        "  0.3510589599609375,\n",
        "  0.3522796630859375,\n",
        "  0.3479766845703125,\n",
        "  0.3480072021484375,\n",
        "  0.349853515625,\n",
        "  0.3491668701171875,\n",
        "  0.352203369140625,\n",
        "  0.35040283203125]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Wmy-0pikIyg7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Графики"
      ]
    },
    {
      "metadata": {
        "id": "TeQD5xWuIwPP",
        "colab_type": "code",
        "outputId": "61931283-079e-4873-ef03-8762bf5beb1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='Category accuracy')\n",
        "ax.plot(top3_acc, label='Top 3 accuracy')\n",
        "ax.legend(loc='center right', fontsize='x-large')\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k4GzcGGdkNM5o15mjZdyVWIePAsD82ssYt2xCZ7Hgs3ULuoJ81+7+eS257/zP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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60754d6050>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xHoOzIisI9ZR",
        "colab_type": "code",
        "outputId": "5cffd31c-baf4-4866-c432-d83a7d40f7ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss')\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iEKnuoxnAxWa2hNAg8vfN7OyamWbWHfgbcJe7zwVw93x3f87dg+6+EdgF5EYo\nPjnC+NyJLLxqKdcOu5G1RR/yuRfP5nvzv6UnuInEmIgUBXe/0t1Hu/tY4BFCZx/VvYz218B97v5a\nTYOZXWNmd4Tf5wDZQH4k4pP69UhO59dT/4eXP/83BqafwCOr/sCkZ8cwd8vfoh2aiLSRiI0p1Kjp\nPgpPlgCvA8VA3RPknwaeCf9MB5IIjSnMOdr21X3UfE3Ju6yyjN+8/yt++/59VFRXMD3vc/xg/D2c\nkD64jaI89vR5x55Yzb013UcRLwqRpqLQfM3Je13RWr7z9jdYXLCQhLgEvnjyl/jm6d8hIyUzwlEe\ne/q8Y0+s5t4exxSkkxiSOZSXLp7Dn6Y/Rb9u/Xnog98z5qnTeGjl/1Je1eg5ACLSAakoyFEFAgFm\nDLyQBVct5e7xP6E6GOSuhXcy+dkxPLfuaT28R6QTUVGQJkuKT+KW027jnWtWcPPwr7B13xZu//tX\nGf7Yidz59jdZ/fGqaIcoIq2koiDN1rNLT34y6ZcsuWY5/zHqDrokdOHR1Q9z5qwJnPv8FJ5Y8ydK\ny/dFO0wRaQENNGsQqtUqqyuZt3UuMz98jHnb5lIdrCYlPoVz887n0sFXcNbx55Acn3zM9tdS+rxj\nT6zm3pqB5ojeJVViQ0JcAtMHfI7pAz7Hzv0FPLNuJi989ByvbPwLr2z8C92TenDBwIu49MQrmNB3\nEvFxui2JSHulIwX9FRERwWCQ1R9/wIvrn+cv619g54GC0L679GbGwAu54ISLGd93Iglxbfd3iT7v\n2BOrues6hRbSL0zbqA5Ws6RgES+uf57Zm16mqCx0N/XMlEym583gwhMuZlK/qRG/CZ8+79gTq7mr\nKLSQfmHaXmV1JYsLFvLXTS8ze9Or7Dm4G4D05HS+c8Zd3HjyzcQFInP+gz7v2BOrueviNekwEuIS\nmNRvCj+ffC8fXO+8eslcvnLqrQSB786/g4tfOp/1xR9FO0yRmKWiIFETF4hjTJ+x3DPhpyz413e5\nYODFvLNzMdOeG89vlv2KiqqKaIcoEnNUFKRdyO6aw6PTn+RP058iPSWDn7zz35z7wlRW7lke7dBE\nYopOSZV2ZcbAC5mYO4m7F32fmWsf57wXp9Gna1/SktLolphGWlIaaUndSUtKo3tSDzJSMuiRnE5G\ncgbpKRmkJ6eT130A6SkZ0U5FpENSUZB2p0dyOvdO+y2XDL6cn7/7Y3Yd3MWeg7vZUL6eyurKo66f\nHJ/MZYP/hS+dcgsn9Tq5DSIW6TxUFKTdmtRvCpP6TamdDgaDHK46TGl5KaXlJZQcLqH4cDElhz+h\n+HAxn5QVU1xWxNytr/H0uidNtswvAAAN8klEQVR5et2TTOg7iS+dcgvn5Z0fxUxEOg4VBekwAoEA\nKQkppCSkkJWa1eByd0/4Cf+3dS5/+OD3vL3jTRYWzOe47nl89fQvM7jrSQzJHErv1GwCgXrPyBOJ\naSoK0unEBeI4J2865+RNZ13RWh7+4EFe+OhZvvf379Uuk5GcgWUOZUjmUCxzKHnd8ziuex79044j\nJSHlU9sLBoNsL93Gh3vXsGbvKtbu/ZCuiV0ZmX06o7JHMyRzaJtemS0SSbp4TRe2xITisiKWlyzh\n3S3LWFe0jnVFH7Jl32aqg9WfWTY7NYfjuh9PbrdcCvYXsLbow0bv+pqakMppvUcyMvt0Ts8+g3F9\nx7erJ9PF4uddI1Zzb7dXNJtZF2A1cI+7P1an/WzgJ0AVMMfd7wm33weMBYLA19196dH2oaLQfMo7\n5FDlITYUf8RHxc7WfVvYtm8r20q3sm3fVvL376AqWEV8IJ5B6YMZ1vMkhvU8mWE9T2Joz5PYV76P\n93e/x/u732PZ7qWsK1pLkNCvYoAAw3qezITciYzvO6nZReJgxUG2lW6la2JXclL7kBifeEzzjiWx\nmnt7vkvqXUBRPe33A+cB+cBbZvYikAUMdvdxZjYUeBQYF+H4JIZ1SejC8KxTGZ516mfmVVRVsPvg\nLnp1yfpMd1KNYT1P4gvDrgegtHwfK/Ys552di1lUsIClu95hzd5VPPTB7wkQYHDGiWSn5tCzS08y\nU3rSs0svMlN6kpaUxs79BWwu2cTmfZvYXLKJXQd21u4jLhBHdmoOud360S+tH7nd+nNK1qmcm3c+\nXRO7RuYfRmJaxIqCmQ0BhgGzj2gfCBS5+/bw9BzgLEJF4SUAd19rZhlm1t3d9bQWaXOJ8Yn0S+vf\n5OXTkrp/6mypssoylu9ZxsL8+SwqWMAHhSv5qNgb3UaAAP3S+jOp31SOTzueQ5WHyN+/g/z9O1hR\n+D7v7X63dtnUhFTOzZvO5wdd3ujzKvaXl3KopJiUYPpRB9Y/3LuGpz58nL9seJGsLr253K7k8sH/\nQp9ufetdvqKqggX5b/PXTS+z5uNVxAXiSYhLICEugfjw+8T4JDKTM8msKYYpPenZJVQULXMo3RK7\nNRqTtL1IHin8GrgNuP6I9hygsM70HuAEoBewrE57YXjZRotCRkYqCQktvz9/VlZai9ftyJR3pKXR\nv890Ljp1em1LeVU5Hx/8mMIDhaGfBwspKSsht3sugzIHMSB9AMkJ9X+5V1VXsWv/LrZ8soW5G+fy\nzOpneGnDn3lpw5/pkdyDS4ZewpjcMWwv2c6mTzaxqTj0+vjgxwDkdMthWt600GvANE7IOIFAIMD+\n8v08u/pZHnn/Ed7JfweAXqm92PDJR9yz+L/40eIfcOaAM7n2lGu5dOilJMUnMW/TPF5Y+wIvr3uZ\n4rJiABLjQl1cFdVNvzVJfCCeUX1HMfm4yUw+fjITjptAZpdQN1t1sJrtJdvxvY5/7PjeUEEdkzuG\ncf3H1cZfn2AwyJ4De9ixbweVpX3J6ZVzTM80q6iqYMmOJczdOJflu5ZTXlVOVbCKquoqqoJVVFZX\nEheIY2zuWC448QImHjex0S7AYDDI1pKtVAeryUvPO2Y3hGzp73pExhTM7DrgOHf/kZn9ENhSM6Zg\nZuOBb7n7JeHpm4GBhIrCbHd/Ody+APiiuzd6dzSNKTSf8u74gsEgqz5eyZ/Xv8DLG/5M/v4dn5qf\nGJdI/7TjOL57HhndejB/ywIKD+2pnd+3ay4n9xrOwoIFHKjYT1wgjjP7n80Xht3AOcefx/6KUl7Z\n+BLP+7O8u2sJEOpuS4hLrB1079O1LzMGXsiFJ3yeM3LG1j48qaq6ispgJZXVlZRXHaa4rIi9ZXsp\nKiui6NBe9pbtZfeBnSzb/R4rCt//1AWJQzOHEReIZ1PJBg5VHmow/54pPRmVPZrTc87g+O55bN23\nhQ2frGdD8Uds+GQD+8pLapftmtiNgT1OCL3SB5LXfSDVwWpKyksoOVzMJ4c/oeTwJ5QcLiE5PqW2\nmy70M/TaX1HKP7a/yVvb/86C/Pnsr6j/9yhAgPi4eKqD1bUnMaQldWdq/zM55/jzOPO4cyirPMTK\nwuWs3LOClYXL+aBwBcWHi2v/jQeln4hlDsEyhmCZQ8lM6Un+/u1sL93OjtJtbC/dxo7S7VRUV/Dn\ni/9K3265n4mj3Q00m9lzhL7oq4B+wGHgK+4+z8zygGfcfVx42R8AewkVhZ3u/odw+ybgVHdvNDMV\nheZT3p1LdbCapbveZXPJRo5LO57ju+eR07VP7Zd0VlYae/bsY33xRywoeJuF+fNZmP82RWVF9OvW\nn6uHXstVQ75Ablq/ere/pWQzL66fxZ8/ep7y6nLOH3ABF55wMaOyR7f6r9qDFQdZtnspS3YuYknB\nIt7b/S4BApyQPphB6YNCPzMGMyh9MBXVFSzbtZRlu5fy3u6lbC/d9pntJcYlMqDHQE5IH0xut1yK\nKgtZu9vZsm9To0WmOQb2OIGp/c9kav+zOKPPGFITutZ2mdUckZRVlrGoYD5vbH2dN7bOZdu+LQ1u\nL6/7AE7NGkFCXAIfFTvri52yqrJGY0hPTmdI5jAenT6TXl16fWZ+uysKdR15pBBuWwPMAHYAi4Fr\nCBWFu939HDMbCdzv7hOPtn0VheZT3rGlvryrg9UU7M+nT9e+7erxqFXVVQQCgSYVm90HdvHe7qXk\nl24nr8cABqUP5rjueZ+6ZqQm9+pgNbsO7GRTyUa2lGwmIS6BHsnppCenf+rnocpD5JduZ8f+Hf/8\nuX8H8YE4JuZOYUr/aRzfPa9ZOQWDQdYXf8Tcra/x9o436ZGUzim9T+O0rBGcknUqPZLTP/NvsK10\nK160jo+K11FcVky/tP70T+tPv7Tj6J/Wn7Sk7o3us0MUhfBkibv/xcwmAz8Pt73o7r8KL/szYDJQ\nDdzq7iuPtn0VheZT3rElVvOG2M29PZ+Sirv/sJ62t6nndFN3vzPS8YiISMP0PAUREamloiAiIrVU\nFEREpJaKgoiI1FJREBGRWioKIiJSS0VBRERqdfiH7IiIyLGjIwUREamloiAiIrVUFEREpJaKgoiI\n1FJREBGRWioKIiJSS0VBRERqRfx5Cu2Vmd0HjAWCwNfdfWmUQ4oYMzsZeBm4z91/Z2b9gSeBeGAn\ncK27H45mjJFgZr8AJhH6Pf8psJROnreZpQKPAdlACnAPsJJOnncNM+sCrCaU9//RyfM2s6nA88Ca\ncNMq4Be0Iu+YPFIwsynA4PBzom8C7o9ySBFjZl2B3xL6D1Ljv4EH3H0SsAH4YjRiiyQzmwacHP6M\npwO/IQbyBi4E3nP3KcC/APcSG3nXuAsoCr+Plbzfcvep4dfttDLvmCwKwFnASwDuvhbIMLPGH3ra\ncR0GPgcU1GmbCrwSfv8qcHYbx9QW3gauCL//BOhKDOTt7s+5+y/Ck/0JPQd9Kp08bwAzGwIMA2aH\nm6YSA3nXYyqtyDtWu49ygGV1pgvDbfuiE07kuHslUGlmdZu71jmc3AP0afPAIszdq4AD4cmbgDnA\neZ097xpmtgjoB1wAzIuRvH8N3AZcH57u9L/nYcPM7BUgE7ibVuYdq0cKR6r3AdYxolPnbmYXEyoK\ntx0xq1Pn7e7jgYuAmXw6106Zt5ldByx2980NLNIp8wbWEyoEFxMqhn/k03/sNzvvWC0KBYSODGr0\nJTQgEyv2hwfkAHL5dNdSp2Fm5wH/CZzv7iXEQN5mNip8IgHuvoLQF0RpZ88bmAFcbGZLgJuB7xMD\nn7e754e7DIPuvhHYRag7vMV5x2pRmAtcDmBmI4ECdy+Nbkhtah5wWfj9ZcBrUYwlIsysB/BL4AJ3\nrxl47PR5A5OBbwKYWTbQjRjI292vdPfR7j4WeITQ2UedPm8zu8bM7gi/zyF01tmfaEXeMXvrbDP7\nGaH/QNXAre6+MsohRYSZjSLU15oHVAD5wDWETltMAbYCN7p7RZRCjAgz+zLwQ+CjOs3XE/rC6Mx5\ndyHUhdAf6EKoa+E94Ak6cd51mdkPgS3A63TyvM0sDXgaSAeSCH3ey2lF3jFbFERE5LNitftIRETq\noaIgIiK1VBRERKSWioKIiNRSURARkVqxepsLkUaZWR7gwOIjZs12918eg+1PBX7k7hNbuy2RY0lF\nQaRhhe4+NdpBiLQlFQWRZjKzSkJXzE4jdMXwDe6+2szGELpQsILQczpuc/cPzWww8DCh7toy4Mbw\npuLN7PfACEJ3s50Rbn8ayAASgVfd/cdtk5mIxhREWiIeWB0+ivg9ofvXQ+gq0v9w92mEnmPwQLj9\nQeCX7j4ZeJR/3tJ7KPDD8K0ZKoDzgHOAxPC98McTun+P/p9Km9GRgkjDsszsH0e0fTv88/Xwz4XA\nt8wsHciu8wS/fwDPht+PCU/j7s9C7ZjCOnffHV5mB6FbFbwK/LeZzSJ0u+9H3L362KUk0jgVBZGG\n1TumEH42Rc1f7wFCXUVH3i8mUKctSP1H5ZVHruPue8zsVGAcodshv2dmI939UIsyEGkmHZaKtMyZ\n4Z8TgQ/Ct+beGR5XgNDTrpaE3y8i9EhQzOxKM/tJQxs1s3OBGe6+0N2/DewHekciAZH66EhBpGH1\ndR/VPMRlhJndQmhA+Lpw23XAvWZWBVQBt4TbbwMeMrNbCY0dfBE4oYF9OvC4mX07vI257r71WCQj\n0hS6S6pIM5lZkNBg8JHdPyIdnrqPRESklo4URESklo4URESkloqCiIjUUlEQEZFaKgoiIlJLRUFE\nRGr9PyyHdeRWjOnhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60754ebc50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZUxDjqqZJBXJ",
        "colab_type": "code",
        "outputId": "994ab5cc-58cf-4585-81bb-1b3f69c31e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(lr, 'y', label='loss')\n",
        "ax.set_title('Learning rate curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NSWxx0p93ZVG/x66XS5xTYQ8CpwGY2UHAGnfvBnD3VUCzmS2KruI6Maqfr81D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\nERGRglJiERGRglJiERGRglJiERGRgvr/YyYc7o4fddAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f609062b790>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xdc6l1MidnrJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Сравнение"
      ]
    },
    {
      "metadata": {
        "id": "0-oEGUTLduV_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "-19n-jtGdS_K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.007415771484375,\n",
        "  0.022796630859375,\n",
        "  0.0433349609375,\n",
        "  0.071258544921875,\n",
        "  0.096160888671875,\n",
        "  0.1148834228515625,\n",
        "  0.1322021484375,\n",
        "  0.14642333984375,\n",
        "  0.1559906005859375,\n",
        "  0.1679229736328125,\n",
        "  0.1779937744140625,\n",
        "  0.1855926513671875,\n",
        "  0.191070556640625,\n",
        "  0.197540283203125,\n",
        "  0.201904296875,\n",
        "  0.2020721435546875,\n",
        "  0.2059326171875,\n",
        "  0.209808349609375,\n",
        "  0.2117462158203125,\n",
        "  0.21240234375,\n",
        "  0.216522216796875,\n",
        "  0.216644287109375,\n",
        "  0.218475341796875,\n",
        "  0.2166595458984375,\n",
        "  0.22039794921875,\n",
        "  0.222259521484375,\n",
        "  0.2225799560546875,\n",
        "  0.2215118408203125,\n",
        "  0.2217864990234375,\n",
        "  0.225616455078125,\n",
        "  0.2236328125,\n",
        "  0.224639892578125,\n",
        "  0.2237701416015625,\n",
        "  0.226165771484375,\n",
        "  0.22308349609375,\n",
        "  0.22564697265625,\n",
        "  0.2223663330078125,\n",
        "  0.2261199951171875,\n",
        "  0.2242279052734375,\n",
        "  0.2274017333984375,\n",
        "  0.224029541015625,\n",
        "  0.226104736328125,\n",
        "  0.225830078125,\n",
        "  0.2285308837890625,\n",
        "  0.225006103515625,\n",
        "  0.2252655029296875,\n",
        "  0.22607421875,\n",
        "  0.22503662109375,\n",
        "  0.2281494140625,\n",
        "  0.2252655029296875]\n",
        "\n",
        "loss = [5.7739698104560375,\n",
        "  5.552871033549309,\n",
        "  5.285460088402033,\n",
        "  5.049863412976265,\n",
        "  4.86707342043519,\n",
        "  4.724752277135849,\n",
        "  4.608510885387659,\n",
        "  4.499989453703165,\n",
        "  4.429089799523354,\n",
        "  4.3647153079509735,\n",
        "  4.294530261307955,\n",
        "  4.239372041076422,\n",
        "  4.203357718884945,\n",
        "  4.171484369784594,\n",
        "  4.149225004017353,\n",
        "  4.129408471286297,\n",
        "  4.111062694340944,\n",
        "  4.074572619050741,\n",
        "  4.071458769962192,\n",
        "  4.067548392340541,\n",
        "  4.043001951649785,\n",
        "  4.033365316689014,\n",
        "  4.026085656136274,\n",
        "  4.0362971387803555,\n",
        "  4.0240539107471704,\n",
        "  4.009893884882331,\n",
        "  4.005370466038585,\n",
        "  4.013318680226803,\n",
        "  4.006745036691427,\n",
        "  3.9872453436255455,\n",
        "  3.994716826826334,\n",
        "  3.9803254026919603,\n",
        "  4.002521056681871,\n",
        "  3.982033709064126,\n",
        "  4.000495778396726,\n",
        "  3.9995923656970263,\n",
        "  3.9951315131038427,\n",
        "  3.9814962670207024,\n",
        "  3.9901318419724703,\n",
        "  3.9776270277798176,\n",
        "  3.986276650801301,\n",
        "  3.9780593495815992,\n",
        "  3.981793038547039,\n",
        "  3.9751983247697353,\n",
        "  3.9906250424683094,\n",
        "  3.9913211911916733,\n",
        "  3.9747091848403215,\n",
        "  3.9860664550215006,\n",
        "  3.9766838401556015,\n",
        "  3.9835875891149044]\n",
        "\n",
        "lr = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc = [0.02093505859375,\n",
        "  0.0516204833984375,\n",
        "  0.093505859375,\n",
        "  0.140472412109375,\n",
        "  0.1768646240234375,\n",
        "  0.2042999267578125,\n",
        "  0.227630615234375,\n",
        "  0.25,\n",
        "  0.2613372802734375,\n",
        "  0.2771759033203125,\n",
        "  0.2894744873046875,\n",
        "  0.3008880615234375,\n",
        "  0.306671142578125,\n",
        "  0.3150177001953125,\n",
        "  0.3198394775390625,\n",
        "  0.322174072265625,\n",
        "  0.328460693359375,\n",
        "  0.3322906494140625,\n",
        "  0.3319854736328125,\n",
        "  0.3352203369140625,\n",
        "  0.3384857177734375,\n",
        "  0.33831787109375,\n",
        "  0.3433685302734375,\n",
        "  0.339630126953125,\n",
        "  0.342987060546875,\n",
        "  0.3454742431640625,\n",
        "  0.3440399169921875,\n",
        "  0.3446502685546875,\n",
        "  0.3451080322265625,\n",
        "  0.348846435546875,\n",
        "  0.3479461669921875,\n",
        "  0.347869873046875,\n",
        "  0.3454132080078125,\n",
        "  0.351226806640625,\n",
        "  0.3463134765625,\n",
        "  0.3502960205078125,\n",
        "  0.3480377197265625,\n",
        "  0.350341796875,\n",
        "  0.348358154296875,\n",
        "  0.35003662109375,\n",
        "  0.3525238037109375,\n",
        "  0.34930419921875,\n",
        "  0.3510589599609375,\n",
        "  0.3522796630859375,\n",
        "  0.3479766845703125,\n",
        "  0.3480072021484375,\n",
        "  0.349853515625,\n",
        "  0.3491668701171875,\n",
        "  0.352203369140625,\n",
        "  0.35040283203125]\n",
        "\n",
        "#====================\n",
        "\n",
        "cat_acc2 =[0.110992431640625,\n",
        "  0.18963623046875,\n",
        "  0.263763427734375,\n",
        "  0.3127899169921875,\n",
        "  0.3480987548828125,\n",
        "  0.3790130615234375,\n",
        "  0.4034271240234375,\n",
        "  0.4199981689453125,\n",
        "  0.4349212646484375,\n",
        "  0.4438629150390625,\n",
        "  0.458953857421875,\n",
        "  0.4643096923828125,\n",
        "  0.4718780517578125,\n",
        "  0.4775238037109375,\n",
        "  0.4791717529296875,\n",
        "  0.483062744140625,\n",
        "  0.4871063232421875,\n",
        "  0.491668701171875,\n",
        "  0.4888153076171875,\n",
        "  0.5007171630859375,\n",
        "  0.49688720703125,\n",
        "  0.499908447265625,\n",
        "  0.498504638671875,\n",
        "  0.502593994140625,\n",
        "  0.5036468505859375,\n",
        "  0.5051116943359375,\n",
        "  0.50262451171875,\n",
        "  0.5066375732421875,\n",
        "  0.505859375,\n",
        "  0.5013427734375,\n",
        "  0.5063934326171875,\n",
        "  0.50311279296875,\n",
        "  0.5091552734375,\n",
        "  0.5047607421875,\n",
        "  0.5095672607421875,\n",
        "  0.505645751953125,\n",
        "  0.5090179443359375,\n",
        "  0.506683349609375,\n",
        "  0.5061798095703125,\n",
        "  0.506500244140625,\n",
        "  0.5070343017578125,\n",
        "  0.511474609375,\n",
        "  0.5070037841796875,\n",
        "  0.507232666015625,\n",
        "  0.510009765625,\n",
        "  0.5093536376953125,\n",
        "  0.5079498291015625,\n",
        "  0.5080413818359375,\n",
        "  0.50836181640625,\n",
        "  0.506500244140625]\n",
        "\n",
        "loss2 =  [4.759915500879288,\n",
        "  4.119159534573555,\n",
        "  3.668934267014265,\n",
        "  3.344358215108514,\n",
        "  3.122873591259122,\n",
        "  2.949145160615444,\n",
        "  2.809198673814535,\n",
        "  2.72814286313951,\n",
        "  2.650218477472663,\n",
        "  2.593439484015107,\n",
        "  2.5187066569924355,\n",
        "  2.4914218112826347,\n",
        "  2.449601475149393,\n",
        "  2.41269388794899,\n",
        "  2.4114991184324026,\n",
        "  2.374601664021611,\n",
        "  2.3659047223627567,\n",
        "  2.3453390300273895,\n",
        "  2.343330467119813,\n",
        "  2.3026546854525805,\n",
        "  2.3030626606196165,\n",
        "  2.2994032129645348,\n",
        "  2.297671416774392,\n",
        "  2.282514115795493,\n",
        "  2.278309663757682,\n",
        "  2.2696991711854935,\n",
        "  2.276376763358712,\n",
        "  2.259805638343096,\n",
        "  2.268813209608197,\n",
        "  2.2780629005283117,\n",
        "  2.264872604981065,\n",
        "  2.2716403640806675,\n",
        "  2.2652347832918167,\n",
        "  2.262054679915309,\n",
        "  2.26261237449944,\n",
        "  2.271057326346636,\n",
        "  2.2498729666695,\n",
        "  2.2595823351293802,\n",
        "  2.26347921974957,\n",
        "  2.250196225941181,\n",
        "  2.250006066635251,\n",
        "  2.239623885601759,\n",
        "  2.2500197663903236,\n",
        "  2.2571550710126758,\n",
        "  2.248923072591424,\n",
        "  2.2539265109226108,\n",
        "  2.2499399902299047,\n",
        "  2.246763882227242,\n",
        "  2.2393512036651373,\n",
        "  2.2581502310931683]\n",
        "\n",
        "lr2 = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc2 = [0.2039642333984375,\n",
        "  0.3274078369140625,\n",
        "  0.419036865234375,\n",
        "  0.4839324951171875,\n",
        "  0.5266265869140625,\n",
        "  0.5580596923828125,\n",
        "  0.58770751953125,\n",
        "  0.6025543212890625,\n",
        "  0.615478515625,\n",
        "  0.627197265625,\n",
        "  0.6404266357421875,\n",
        "  0.6468048095703125,\n",
        "  0.654083251953125,\n",
        "  0.659637451171875,\n",
        "  0.660675048828125,\n",
        "  0.667572021484375,\n",
        "  0.66827392578125,\n",
        "  0.6712188720703125,\n",
        "  0.673370361328125,\n",
        "  0.6812896728515625,\n",
        "  0.6812744140625,\n",
        "  0.6820526123046875,\n",
        "  0.681671142578125,\n",
        "  0.682464599609375,\n",
        "  0.6859893798828125,\n",
        "  0.686248779296875,\n",
        "  0.6841583251953125,\n",
        "  0.68798828125,\n",
        "  0.68609619140625,\n",
        "  0.6839599609375,\n",
        "  0.688507080078125,\n",
        "  0.683197021484375,\n",
        "  0.68731689453125,\n",
        "  0.6858367919921875,\n",
        "  0.6888580322265625,\n",
        "  0.6849517822265625,\n",
        "  0.6927032470703125,\n",
        "  0.6875762939453125,\n",
        "  0.6843414306640625,\n",
        "  0.6898040771484375,\n",
        "  0.6904296875,\n",
        "  0.6919097900390625,\n",
        "  0.690185546875,\n",
        "  0.687591552734375,\n",
        "  0.69171142578125,\n",
        "  0.6894683837890625,\n",
        "  0.69219970703125,\n",
        "  0.689666748046875,\n",
        "  0.6897430419921875,\n",
        "  0.68902587890625]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vPJuPZNTn6lq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Графики"
      ]
    },
    {
      "metadata": {
        "id": "4wqNbuvpoBGF",
        "colab_type": "code",
        "outputId": "59af1008-5f9a-4c7f-c953-1c241fde162c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='cat_acc with aug')\n",
        "ax.plot(top3_acc, label='top3_acc with aug')\n",
        "ax.plot(cat_acc2, '--', label='cat_acc w/o aug')\n",
        "ax.plot(top3_acc2, label='top3_acc w/o aug')\n",
        "ax.legend()\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DRu+U+oqB4sNGmSiKkl95PQcgpfzcEW9tGvLZt4Fv53P/kym5by/Z7i5Ca8+Z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\nwKUcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f6077cdaa50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "k1GkJiAno2N4",
        "colab_type": "code",
        "outputId": "6cc30f36-c3b5-410c-a381-25de65f45490",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss with aug')\n",
        "ax.plot(loss2, label='loss w/o aug')\n",
        "ax.legend()\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KSQCHyEkYwukDz+S7fStYUvB1qMMRQoigkQTQihvG3wzAkysfC3EkQggRPJIAWjEh/Rgm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\nIYSIUJIAhBAiQkkCEEKICPX/5ebf3JkI+80AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f607dcd7a90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XxFbD4AEeSyM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Вывод:\n",
        "Качество при такой агументации ухудшилось, lr слишком маленький для выборки с аугументацией данных."
      ]
    },
    {
      "metadata": {
        "id": "4qj8dBI2pO0K",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Агументация с уменьшением размера и небольшими поворотами изображения"
      ]
    },
    {
      "metadata": {
        "id": "A49DQaOkpuXS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Агументация."
      ]
    },
    {
      "metadata": {
        "id": "2epGAIOopoq7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def images_and_labels_generator_with_aug(batch_size):\n",
        "  image_gen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    rescale=3.2,\n",
        "    zoom_range=-0.3,\n",
        "    vertical_flip=True,\n",
        "    horizontal_flip=True\n",
        "  )\n",
        "\n",
        "  for batch in raw_batch_generator(batch_size):\n",
        "    batch_images = []\n",
        "    batch_labels = []\n",
        "    for e in batch:\n",
        "      image = draw_it(e[0])\n",
        "      batch_images.append(image)\n",
        "      image = np.tile(np.expand_dims(image, axis=2), (1, 1, 3))\n",
        "      batch_labels.append(e[1])\n",
        "      batch_images.append(image_gen.random_transform(image)[:,:,0])\n",
        "      batch_labels.append(e[1])\n",
        "        \n",
        "      \n",
        "    batch_images = np.stack(batch_images, axis=0)\n",
        "    yield batch_images, batch_labels\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UChl3SOSpxKi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Массивы"
      ]
    },
    {
      "metadata": {
        "id": "hzHb1iVhp0JY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "cat_acc = [0.0087127685546875,\n",
        "  0.0276947021484375,\n",
        "  0.06304931640625,\n",
        "  0.09869384765625,\n",
        "  0.124664306640625,\n",
        "  0.1497039794921875,\n",
        "  0.177001953125,\n",
        "  0.1910552978515625,\n",
        "  0.2088623046875,\n",
        "  0.2176971435546875,\n",
        "  0.2317352294921875,\n",
        "  0.24127197265625,\n",
        "  0.24749755859375,\n",
        "  0.2587738037109375,\n",
        "  0.2598419189453125,\n",
        "  0.26318359375,\n",
        "  0.2672119140625,\n",
        "  0.2713623046875,\n",
        "  0.2757568359375,\n",
        "  0.2765350341796875,\n",
        "  0.2818145751953125,\n",
        "  0.2821502685546875,\n",
        "  0.280731201171875,\n",
        "  0.2846221923828125,\n",
        "  0.282562255859375,\n",
        "  0.283599853515625,\n",
        "  0.290496826171875,\n",
        "  0.2921142578125,\n",
        "  0.2920684814453125,\n",
        "  0.2923431396484375,\n",
        "  0.29144287109375,\n",
        "  0.29083251953125,\n",
        "  0.29058837890625,\n",
        "  0.291778564453125,\n",
        "  0.2912139892578125,\n",
        "  0.2940673828125,\n",
        "  0.288909912109375,\n",
        "  0.29229736328125,\n",
        "  0.29193115234375,\n",
        "  0.2941741943359375,\n",
        "  0.2956695556640625,\n",
        "  0.2916107177734375,\n",
        "  0.294952392578125,\n",
        "  0.293853759765625,\n",
        "  0.29119873046875,\n",
        "  0.2921142578125,\n",
        "  0.293975830078125,\n",
        "  0.2911376953125,\n",
        "  0.29229736328125,\n",
        "  0.2939300537109375]\n",
        "\n",
        "loss = [5.752310052514076,\n",
        "  5.432461433112621,\n",
        "  5.052845023572445,\n",
        "  4.761567298322916,\n",
        "  4.522728066891432,\n",
        "  4.347186699509621,\n",
        "  4.1746551394462585,\n",
        "  4.056543312966824,\n",
        "  3.9502307195216417,\n",
        "  3.884231897071004,\n",
        "  3.807611260563135,\n",
        "  3.7380385622382164,\n",
        "  3.702265363186598,\n",
        "  3.6432966142892838,\n",
        "  3.6268637981265783,\n",
        "  3.6078698839992285,\n",
        "  3.580007104203105,\n",
        "  3.558967398479581,\n",
        "  3.5352901369333267,\n",
        "  3.5201967023313046,\n",
        "  3.4939307868480682,\n",
        "  3.500583516433835,\n",
        "  3.502577595412731,\n",
        "  3.4875891264528036,\n",
        "  3.4821457285434008,\n",
        "  3.478899395093322,\n",
        "  3.4470438733696938,\n",
        "  3.447694670408964,\n",
        "  3.4554072562605143,\n",
        "  3.4434306416660547,\n",
        "  3.430903224274516,\n",
        "  3.44375754147768,\n",
        "  3.440896589308977,\n",
        "  3.43197475746274,\n",
        "  3.443271204829216,\n",
        "  3.4251230377703905,\n",
        "  3.4521923083812,\n",
        "  3.4238597247749567,\n",
        "  3.4304174333810806,\n",
        "  3.4387022145092487,\n",
        "  3.431653991341591,\n",
        "  3.428588816896081,\n",
        "  3.422405270859599,\n",
        "  3.431295493617654,\n",
        "  3.4358621444553137,\n",
        "  3.430178115144372,\n",
        "  3.4246390108019114,\n",
        "  3.4308938942849636,\n",
        "  3.4271194357424974,\n",
        "  3.4163502026349306]\n",
        "\n",
        "lr = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc = [0.0234222412109375,\n",
        "  0.0661163330078125,\n",
        "  0.132171630859375,\n",
        "  0.189178466796875,\n",
        "  0.2332000732421875,\n",
        "  0.2710113525390625,\n",
        "  0.306976318359375,\n",
        "  0.327606201171875,\n",
        "  0.351043701171875,\n",
        "  0.3638916015625,\n",
        "  0.3812713623046875,\n",
        "  0.3938751220703125,\n",
        "  0.4019012451171875,\n",
        "  0.4155120849609375,\n",
        "  0.4175872802734375,\n",
        "  0.421966552734375,\n",
        "  0.4277496337890625,\n",
        "  0.4312286376953125,\n",
        "  0.44024658203125,\n",
        "  0.4413604736328125,\n",
        "  0.4432373046875,\n",
        "  0.4459228515625,\n",
        "  0.442474365234375,\n",
        "  0.4476470947265625,\n",
        "  0.4470977783203125,\n",
        "  0.446441650390625,\n",
        "  0.4565887451171875,\n",
        "  0.4555511474609375,\n",
        "  0.454071044921875,\n",
        "  0.456451416015625,\n",
        "  0.45697021484375,\n",
        "  0.455047607421875,\n",
        "  0.454010009765625,\n",
        "  0.459625244140625,\n",
        "  0.4550628662109375,\n",
        "  0.4581756591796875,\n",
        "  0.4505462646484375,\n",
        "  0.457977294921875,\n",
        "  0.4553070068359375,\n",
        "  0.458099365234375,\n",
        "  0.45916748046875,\n",
        "  0.4562225341796875,\n",
        "  0.4593353271484375,\n",
        "  0.459564208984375,\n",
        "  0.4541473388671875,\n",
        "  0.457489013671875,\n",
        "  0.4588623046875,\n",
        "  0.45745849609375,\n",
        "  0.4580078125,\n",
        "  0.461517333984375]\n",
        "\n",
        "#====================\n",
        "\n",
        "cat_acc2 =[0.110992431640625,\n",
        "  0.18963623046875,\n",
        "  0.263763427734375,\n",
        "  0.3127899169921875,\n",
        "  0.3480987548828125,\n",
        "  0.3790130615234375,\n",
        "  0.4034271240234375,\n",
        "  0.4199981689453125,\n",
        "  0.4349212646484375,\n",
        "  0.4438629150390625,\n",
        "  0.458953857421875,\n",
        "  0.4643096923828125,\n",
        "  0.4718780517578125,\n",
        "  0.4775238037109375,\n",
        "  0.4791717529296875,\n",
        "  0.483062744140625,\n",
        "  0.4871063232421875,\n",
        "  0.491668701171875,\n",
        "  0.4888153076171875,\n",
        "  0.5007171630859375,\n",
        "  0.49688720703125,\n",
        "  0.499908447265625,\n",
        "  0.498504638671875,\n",
        "  0.502593994140625,\n",
        "  0.5036468505859375,\n",
        "  0.5051116943359375,\n",
        "  0.50262451171875,\n",
        "  0.5066375732421875,\n",
        "  0.505859375,\n",
        "  0.5013427734375,\n",
        "  0.5063934326171875,\n",
        "  0.50311279296875,\n",
        "  0.5091552734375,\n",
        "  0.5047607421875,\n",
        "  0.5095672607421875,\n",
        "  0.505645751953125,\n",
        "  0.5090179443359375,\n",
        "  0.506683349609375,\n",
        "  0.5061798095703125,\n",
        "  0.506500244140625,\n",
        "  0.5070343017578125,\n",
        "  0.511474609375,\n",
        "  0.5070037841796875,\n",
        "  0.507232666015625,\n",
        "  0.510009765625,\n",
        "  0.5093536376953125,\n",
        "  0.5079498291015625,\n",
        "  0.5080413818359375,\n",
        "  0.50836181640625,\n",
        "  0.506500244140625]\n",
        "\n",
        "loss2 =  [4.759915500879288,\n",
        "  4.119159534573555,\n",
        "  3.668934267014265,\n",
        "  3.344358215108514,\n",
        "  3.122873591259122,\n",
        "  2.949145160615444,\n",
        "  2.809198673814535,\n",
        "  2.72814286313951,\n",
        "  2.650218477472663,\n",
        "  2.593439484015107,\n",
        "  2.5187066569924355,\n",
        "  2.4914218112826347,\n",
        "  2.449601475149393,\n",
        "  2.41269388794899,\n",
        "  2.4114991184324026,\n",
        "  2.374601664021611,\n",
        "  2.3659047223627567,\n",
        "  2.3453390300273895,\n",
        "  2.343330467119813,\n",
        "  2.3026546854525805,\n",
        "  2.3030626606196165,\n",
        "  2.2994032129645348,\n",
        "  2.297671416774392,\n",
        "  2.282514115795493,\n",
        "  2.278309663757682,\n",
        "  2.2696991711854935,\n",
        "  2.276376763358712,\n",
        "  2.259805638343096,\n",
        "  2.268813209608197,\n",
        "  2.2780629005283117,\n",
        "  2.264872604981065,\n",
        "  2.2716403640806675,\n",
        "  2.2652347832918167,\n",
        "  2.262054679915309,\n",
        "  2.26261237449944,\n",
        "  2.271057326346636,\n",
        "  2.2498729666695,\n",
        "  2.2595823351293802,\n",
        "  2.26347921974957,\n",
        "  2.250196225941181,\n",
        "  2.250006066635251,\n",
        "  2.239623885601759,\n",
        "  2.2500197663903236,\n",
        "  2.2571550710126758,\n",
        "  2.248923072591424,\n",
        "  2.2539265109226108,\n",
        "  2.2499399902299047,\n",
        "  2.246763882227242,\n",
        "  2.2393512036651373,\n",
        "  2.2581502310931683]\n",
        "\n",
        "lr2 = [1e-04,\n",
        "  1e-04,\n",
        "  7.5e-05,\n",
        "  7.5e-05,\n",
        "  5.625e-05,\n",
        "  5.625e-05,\n",
        "  4.21875e-05,\n",
        "  4.21875e-05,\n",
        "  3.1640626e-05,\n",
        "  3.1640626e-05,\n",
        "  2.3730468e-05,\n",
        "  2.3730468e-05,\n",
        "  1.779785e-05,\n",
        "  1.779785e-05,\n",
        "  1.3348389e-05,\n",
        "  1.3348389e-05,\n",
        "  1.0011291e-05,\n",
        "  1.0011291e-05,\n",
        "  7.5084686e-06,\n",
        "  7.5084686e-06,\n",
        "  5.6313515e-06,\n",
        "  5.6313515e-06,\n",
        "  4.2235138e-06,\n",
        "  4.2235138e-06,\n",
        "  3.1676352e-06,\n",
        "  3.1676352e-06,\n",
        "  2.3757264e-06,\n",
        "  2.3757264e-06,\n",
        "  1.7817948e-06,\n",
        "  1.7817948e-06,\n",
        "  1.3363461e-06,\n",
        "  1.3363461e-06,\n",
        "  1.0022595e-06,\n",
        "  1.0022595e-06,\n",
        "  7.516947e-07,\n",
        "  7.516947e-07,\n",
        "  5.6377104e-07,\n",
        "  5.6377104e-07,\n",
        "  4.2282826e-07,\n",
        "  4.2282826e-07,\n",
        "  3.171212e-07,\n",
        "  3.171212e-07,\n",
        "  2.378409e-07,\n",
        "  2.378409e-07,\n",
        "  1.7838067e-07,\n",
        "  1.7838067e-07,\n",
        "  1.337855e-07,\n",
        "  1.337855e-07,\n",
        "  1.0033913e-07,\n",
        "  1.0033913e-07]\n",
        "\n",
        "top3_acc2 = [0.2039642333984375,\n",
        "  0.3274078369140625,\n",
        "  0.419036865234375,\n",
        "  0.4839324951171875,\n",
        "  0.5266265869140625,\n",
        "  0.5580596923828125,\n",
        "  0.58770751953125,\n",
        "  0.6025543212890625,\n",
        "  0.615478515625,\n",
        "  0.627197265625,\n",
        "  0.6404266357421875,\n",
        "  0.6468048095703125,\n",
        "  0.654083251953125,\n",
        "  0.659637451171875,\n",
        "  0.660675048828125,\n",
        "  0.667572021484375,\n",
        "  0.66827392578125,\n",
        "  0.6712188720703125,\n",
        "  0.673370361328125,\n",
        "  0.6812896728515625,\n",
        "  0.6812744140625,\n",
        "  0.6820526123046875,\n",
        "  0.681671142578125,\n",
        "  0.682464599609375,\n",
        "  0.6859893798828125,\n",
        "  0.686248779296875,\n",
        "  0.6841583251953125,\n",
        "  0.68798828125,\n",
        "  0.68609619140625,\n",
        "  0.6839599609375,\n",
        "  0.688507080078125,\n",
        "  0.683197021484375,\n",
        "  0.68731689453125,\n",
        "  0.6858367919921875,\n",
        "  0.6888580322265625,\n",
        "  0.6849517822265625,\n",
        "  0.6927032470703125,\n",
        "  0.6875762939453125,\n",
        "  0.6843414306640625,\n",
        "  0.6898040771484375,\n",
        "  0.6904296875,\n",
        "  0.6919097900390625,\n",
        "  0.690185546875,\n",
        "  0.687591552734375,\n",
        "  0.69171142578125,\n",
        "  0.6894683837890625,\n",
        "  0.69219970703125,\n",
        "  0.689666748046875,\n",
        "  0.6897430419921875,\n",
        "  0.68902587890625]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YjvcucZYqe-e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Графики"
      ]
    },
    {
      "metadata": {
        "id": "aJQODaisqeJw",
        "colab_type": "code",
        "outputId": "13a8485b-45c8-4975-f832-faf1577295a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(cat_acc, 'r--', label='cat_acc with aug')\n",
        "ax.plot(top3_acc, label='top3_acc with aug')\n",
        "ax.plot(cat_acc2, '--', label='cat_acc w/o aug')\n",
        "ax.plot(top3_acc2, label='top3_acc w/o aug')\n",
        "ax.legend()\n",
        "ax.set_title('Accuracies curves')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+9z+e0s1NpNtaKThzJa7i4skOZ8IkjCS7+/bSEm0jmokRy8T56MIb0B06bbF2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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f607bda2b50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pukUBHAyqlHP",
        "colab_type": "code",
        "outputId": "939ca8e2-51db-451a-b6b0-fd2d722bcf7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(loss, 'g', label='loss with aug')\n",
        "ax.plot(loss2, label='loss w/o aug')\n",
        "ax.legend()\n",
        "ax.set_title('Loss curve')\n",
        "ax.set_xlabel('Epochs')\n",
        "ax.set_ylabel('Value')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0,0.5,u'Value')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ufy3T357M3za+hNfrbfvNQoiI1FoC6A28CdwI5CilngAGaa2LgxJZkA3sHYfJ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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60906412d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2nmwXc70q1JR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Вывод:\n",
        "\n",
        "Качество при такой агументации ухудшилось, lr слишком маленький для выборки с аугументацией данных.\n",
        "Ни одна из агументаций не была полезной, но последняя агументация дала результат лучше, чем первая"
      ]
    },
    {
      "metadata": {
        "id": "ONnLBXjg7wOB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download data from Kaggle"
      ]
    },
    {
      "metadata": {
        "id": "OmX6oRj47wOD",
        "colab_type": "code",
        "outputId": "f6ce38f3-8229-40f7-b1b7-814631fd1805",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "cell_type": "code",
      "source": [
        "# we need to use python 3 version of Kaggle!\n",
        "# remove python 2 version with `pip uninstall kaggle`\n",
        "! pip3 install kaggle --upgrade"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/83/9b/ac57e15fbb239c6793c8d0b7dfd1a4c4a025eaa9f791b5388a7afb515aed/kaggle-1.5.0.tar.gz (53kB)\n",
            "\u001b[K    100% |████████████████████████████████| 61kB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.10.15)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Collecting python-slugify (from kaggle)\n",
            "  Downloading https://files.pythonhosted.org/packages/00/ad/c778a6df614b6217c30fe80045b365bfa08b5dd3cb02e8b37a6d25126781/python-slugify-1.2.6.tar.gz\n",
            "Requirement already satisfied, skipping upgrade: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Collecting Unidecode>=0.04.16 (from python-slugify->kaggle)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/59/ef/67085e30e8bbcdd76e2f0a4ad8151c13a2c5bce77c85f8cad6e1f16fb141/Unidecode-1.0.22-py2.py3-none-any.whl (235kB)\n",
            "\u001b[K    100% |████████████████████████████████| 235kB 5.2MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle, python-slugify\n",
            "  Running setup.py bdist_wheel for kaggle ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/8b/21/3b/a0076243c6ae12a6215b2da515fe06b539aee7217b406e510e\n",
            "  Running setup.py bdist_wheel for python-slugify ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/e3/65/da/2045deea3098ed7471eca0e2460cfbd3fdfe8c1d6fa6fcac92\n",
            "Successfully built kaggle python-slugify\n",
            "Installing collected packages: Unidecode, python-slugify, kaggle\n",
            "Successfully installed Unidecode-1.0.22 kaggle-1.5.0 python-slugify-1.2.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-06T15:29:19.895974Z",
          "start_time": "2018-11-06T15:26:21.334564Z"
        },
        "id": "uc4hwR5i7wOL",
        "colab_type": "code",
        "outputId": "2f845441-c04b-40e6-ebd2-875e46330f8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "# get <token> from https://www.kaggle.com/<user>/account, click \"Create New API Token\", open json file\n",
        "! KAGGLE_USERNAME=pabramov KAGGLE_KEY=_ kaggle competitions download -c quickdraw-doodle-recognition -f train_simplified.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train_simplified.zip to /content\n",
            "100% 7.37G/7.37G [00:53<00:00, 144MB/s]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:08:31.048432Z",
          "start_time": "2018-11-07T16:08:31.004128Z"
        },
        "id": "hvElYnQZ7wOV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# open zip file, will read everything from it\n",
        "zf = zipfile.ZipFile(\"train_simplified.zip\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G1n5dGSb7wOb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data generators from disk (no need to store in RAM)"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:00.917240Z",
          "start_time": "2018-11-07T16:09:00.906369Z"
        },
        "id": "onVpT7pO7wOe",
        "colab_type": "code",
        "outputId": "25207304-40da-4684-90d8-b00f5bf1b8fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "class_labels = map(lambda x: x.replace(\".csv\", \"\"), zf.namelist())\n",
        "class_labels[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['fence', 'yoga', 'horse', 'sandwich', 'cat']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:01.213496Z",
          "start_time": "2018-11-07T16:09:01.203670Z"
        },
        "id": "ovGifLAO7wOm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loop through file eternally\n",
        "def get_eternal_csv_generator(fn, debug=False):\n",
        "    while True:\n",
        "        with zf.open(fn) as f:\n",
        "            f.readline()  # skip header\n",
        "            for line in csv.reader(f, delimiter=',', quotechar='\"'):\n",
        "                yield line[1], line[5]\n",
        "            if debug:\n",
        "                print fn, \"is done, starting from the beginning...\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:01.651230Z",
          "start_time": "2018-11-07T16:09:01.642356Z"
        },
        "id": "dPnmnYS77wOu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def raw_batch_generator(batch_size, debug=False):\n",
        "    generators = np.array([get_eternal_csv_generator(fn, debug) for fn in zf.namelist()])\n",
        "    while True:\n",
        "        random_indices = np.random.randint(0, len(generators), size=batch_size)\n",
        "        yield [gen.next() for gen in generators[random_indices]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "c_VjFBAT7wO2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Images generator"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:04.267262Z",
          "start_time": "2018-11-07T16:09:04.251004Z"
        },
        "id": "6gPnfqkJ7wO5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# copy-paste from https://www.kaggle.com/jpmiller/image-based-cnn\n",
        "\n",
        "import ast\n",
        "from PIL import Image, ImageDraw\n",
        "\n",
        "IMG_SIZE = 64\n",
        "\n",
        "# faster conversion function\n",
        "def draw_it(strokes):\n",
        "    image = Image.new(\"P\", (256, 256), color=255)\n",
        "    image_draw = ImageDraw.Draw(image)\n",
        "    for stroke in ast.literal_eval(strokes):\n",
        "        for i in range(len(stroke[0])-1):\n",
        "            image_draw.line([stroke[0][i], \n",
        "                             stroke[1][i],\n",
        "                             stroke[0][i+1], \n",
        "                             stroke[1][i+1]],\n",
        "                            fill=0, width=5)\n",
        "    image = image.resize((IMG_SIZE, IMG_SIZE))\n",
        "    return np.array(image, dtype=np.uint8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:07.131184Z",
          "start_time": "2018-11-07T16:09:07.120659Z"
        },
        "id": "tmb0VBsL7wPC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def images_and_labels_generator_with_aug(batch_size):\n",
        "  image_gen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    rescale=3.2,\n",
        "    zoom_range=-0.3,\n",
        "    vertical_flip=True,\n",
        "    horizontal_flip=True\n",
        "  )\n",
        "\n",
        "  for batch in raw_batch_generator(batch_size):\n",
        "    batch_images = []\n",
        "    batch_labels = []\n",
        "    for e in batch:\n",
        "      image = draw_it(e[0])\n",
        "      batch_images.append(image)\n",
        "      image = np.tile(np.expand_dims(image, axis=2), (1, 1, 3))\n",
        "      batch_labels.append(e[1])\n",
        "      batch_images.append(image_gen.random_transform(image)[:,:,0])\n",
        "      batch_labels.append(e[1])\n",
        "        \n",
        "      \n",
        "    batch_images = np.stack(batch_images, axis=0)\n",
        "    yield batch_images, batch_labels\n",
        "  \n",
        "\n",
        "\n",
        "def images_and_labels_generator(batch_size):\n",
        "  \n",
        "  for batch in raw_batch_generator(batch_size):\n",
        "    batch_images = []\n",
        "    batch_labels = []\n",
        "    for e in batch:\n",
        "      batch_images.append(draw_it(e[0]))\n",
        "      batch_labels.append(e[1])\n",
        "    batch_images = np.stack(batch_images, axis=0)\n",
        "    yield batch_images, batch_labels\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:31.235828Z",
          "start_time": "2018-11-07T16:09:31.078072Z"
        },
        "id": "Y-RvvF5U7wPH",
        "colab_type": "code",
        "outputId": "e5d689f0-4e72-4f9b-961a-4c296d46a306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "cell_type": "code",
      "source": [
        "b = images_and_labels_generator(32).next()\n",
        "plt.imshow(b[0][10, :, :])\n",
        "plt.title(b[1][10])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MVx0RVwGfAN6Rmc9PIkdEXFYdjCQzn6T/pJ7E9N3vArZHxLeBDwJ/xQQej8x8ttqd6VXT\nmf+E/q5l24/HWKdVb6voDwPvBYiIS4FjmXmypfs+m9anq46IVwKfAbZl5tzBp0lMm/0W4ONVpvOZ\n0PTdmXltZr4pMy8H7qJ/1H0S/y/XRcRN1fIF9N+NuLvtHIx5WvXW/kw1Iv6O/pPsv4GPZOZTLd3v\nZfT3BS8CTgHPAtfRf4up1nTVDeX4EP39ru8PXL2L/pO8zRxr6Q9PNwJr6Q9bH2cR03ePIdPNwA+B\nf207R0S8Avgy8CrgHPqPx3fazlFlWda06sP49+hSAfxknFQAiy4VwKJLBbDoUgEsulQAiy4VwKJL\nBfgf6FPHz42dkP8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f60748c50d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-06T22:00:38.205119Z",
          "start_time": "2018-11-06T22:00:38.199739Z"
        },
        "id": "0fXV7Ha-7wPO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Train simple network"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:09:43.935122Z",
          "start_time": "2018-11-07T16:09:43.339804Z"
        },
        "id": "5VtR-ApI7wPP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:10:14.627454Z",
          "start_time": "2018-11-07T16:10:14.614258Z"
        },
        "id": "SLpyMQ6W7wPX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# reset graph when you change architecture!\n",
        "def reset_tf_session():\n",
        "    curr_session = tf.get_default_session()\n",
        "    # close current session\n",
        "    if curr_session is not None:\n",
        "        curr_session.close()\n",
        "    # reset graph\n",
        "    K.clear_session()\n",
        "    # create new session\n",
        "    config = tf.ConfigProto()\n",
        "    config.gpu_options.allow_growth = True\n",
        "    s = tf.InteractiveSession(config=config)\n",
        "    K.set_session(s)\n",
        "    return s"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:10:20.833559Z",
          "start_time": "2018-11-07T16:10:20.820480Z"
        },
        "scrolled": false,
        "id": "Rbi0xWej7wPd",
        "colab_type": "code",
        "outputId": "4fcf0366-e707-4613-f137-5a8003cda8dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "cell_type": "code",
      "source": [
        "NUM_CLASSES = len(class_labels)\n",
        "class_to_idx = {c: idx for idx, c in enumerate(class_labels)}\n",
        "class_to_idx.items()[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('snowman', 134),\n",
              " ('eyeglasses', 254),\n",
              " ('ceiling fan', 18),\n",
              " ('camel', 90),\n",
              " ('stove', 119)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:11:58.574802Z",
          "start_time": "2018-11-07T16:11:58.565415Z"
        },
        "id": "BbDL8q5D7wPj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_iterator(batch_size):\n",
        "    for batch in images_and_labels_generator_with_aug(batch_size):\n",
        "        images = batch[0].astype('float32')\n",
        "        images = images/255 - 0.5\n",
        "        images = np.expand_dims(images, -1)\n",
        "        labels = keras.utils.to_categorical(map(class_to_idx.get, batch[1]), NUM_CLASSES)\n",
        "        yield images, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:11:59.923327Z",
          "start_time": "2018-11-07T16:11:59.898171Z"
        },
        "scrolled": true,
        "id": "_lao-Ked7wPq",
        "colab_type": "code",
        "outputId": "fa6c1079-1304-4bad-b02e-adf16c5fcd45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "sample = train_iterator(32).next()\n",
        "print sample[0].shape\n",
        "print sample[1].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 64, 64, 1)\n",
            "(64, 340)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:12:13.491949Z",
          "start_time": "2018-11-07T16:12:13.485091Z"
        },
        "id": "MA3MF04E7wP1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import necessary building blocks\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Dense, Activation, Dropout, GlobalAveragePooling2D, \\\n",
        "    BatchNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:15:15.162847Z",
          "start_time": "2018-11-07T16:15:15.148961Z"
        },
        "id": "XfqW3lnf7wQG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def make_model():\n",
        "  \"\"\"\n",
        "  Define your model architecture here.\n",
        "  Returns `Sequential` model.\n",
        "  \"\"\"\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(Conv2D(16, (3, 3), padding='same', activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  \n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(MaxPooling2D())\n",
        "\n",
        "  \n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(512, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  #model.add(MaxPooling2D())\n",
        "\n",
        "  '''  \n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(1024, (3, 3), padding='same', activation='relu'))\n",
        "  model.add(BatchNormalization())\n",
        "  '''\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  model.add(Dense(64, activation='relu'))\n",
        "  model.add(Dense(NUM_CLASSES, activation=\"softmax\"))\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:15:16.796282Z",
          "start_time": "2018-11-07T16:15:16.726472Z"
        },
        "id": "WvdZUFxG7wQM",
        "colab_type": "code",
        "outputId": "3696c794-8161-4784-9527-fff834aaec97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1667
        }
      },
      "cell_type": "code",
      "source": [
        "# describe model\n",
        "s = reset_tf_session()  # clear default graph\n",
        "model = make_model()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 64, 64, 16)        160       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 64, 64, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 64, 64, 16)        2320      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 64, 64, 16)        64        \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 64, 64, 16)        2320      \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 64, 64, 16)        64        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 32, 32, 32)        4640      \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_7 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_8 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_10 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 4, 4, 256)         295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_13 (Batc (None, 4, 4, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 4, 4, 256)         590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_14 (Batc (None, 4, 4, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 4, 4, 256)         590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_15 (Batc (None, 4, 4, 256)         1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 2, 2, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 2, 2, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "batch_normalization_16 (Batc (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_17 (Batc (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "batch_normalization_18 (Batc (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 340)               22100     \n",
            "=================================================================\n",
            "Total params: 7,931,444\n",
            "Trainable params: 7,925,396\n",
            "Non-trainable params: 6,048\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:21:07.829654Z",
          "start_time": "2018-11-07T16:21:07.822122Z"
        },
        "id": "mCItlDci7wQY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# custom metrics we need\n",
        "from keras.metrics import categorical_accuracy, top_k_categorical_accuracy\n",
        "\n",
        "def top_3_accuracy(y_true, y_pred):\n",
        "    return top_k_categorical_accuracy(y_true, y_pred, k=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:16:27.284265Z",
          "start_time": "2018-11-07T16:16:27.184294Z"
        },
        "id": "AExY9tR67wQi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 256\n",
        "STEPS_PER_EPOCH = 128\n",
        "EPOCHS = 50\n",
        "\n",
        "s = reset_tf_session()  # clear default graph\n",
        "model = make_model()  # define our model\n",
        "\n",
        "# prepare model for fitting (loss, optimizer, etc)\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer=keras.optimizers.adam(clipnorm=5.),  # gradient clipping just in case\n",
        "    metrics=[categorical_accuracy, top_3_accuracy]  # report top 3 accuracy, correlates with MAP@3!\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:20:03.725667Z",
          "start_time": "2018-11-07T16:20:03.715510Z"
        },
        "id": "aEojNmTV7wQm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for saving the model after every epoch\n",
        "from keras.models import save_model\n",
        "\n",
        "class ModelSaveCallback(keras.callbacks.Callback):\n",
        "    def __init__(self, file_name):\n",
        "        super(ModelSaveCallback, self).__init__()\n",
        "        self.file_name = file_name\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        model_filename = self.file_name.format(epoch)\n",
        "        save_model(self.model, model_filename)\n",
        "        print(\"Model saved in {}\".format(model_filename))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3zbDVf8mRcFo",
        "colab_type": "code",
        "outputId": "bad5886d-117d-4ca8-89c2-f9816ab33092",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "GOOGLE_DRIVE_ROOT = GOOGLE_DRIVE_MOUNT + \"/\" + filter(lambda x: x[0] != '.', os.listdir(GOOGLE_DRIVE_MOUNT))[0]\n",
        "print GOOGLE_DRIVE_ROOT\n",
        "\n",
        "# will save checkpoints to Google Drive\n",
        "CHECKPOINT_TEMPLATE = GOOGLE_DRIVE_ROOT + \"/model_{}\"\n",
        "print CHECKPOINT_TEMPLATE"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive\n",
            "/content/gdrive/My Drive/model_{}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:22:04.352396Z",
          "start_time": "2018-11-07T16:22:03.726954Z"
        },
        "id": "3NK6h0hz7wQx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "last_finished_epoch = 0\n",
        "\n",
        "# you can continue from snapshot!!!\n",
        "#from keras.models import load_model\n",
        "#s = reset_tf_session()\n",
        "#last_finished_epoch = 49\n",
        "#model = load_model(\"model_{}\".format(last_finished_epoch), \n",
        "#                   custom_objects={\"top_3_accuracy\": top_3_accuracy})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nG0mGXk1Py_r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "\n",
        "def step_decay_schedule(initial_lr=1e-3, decay_factor=0.75, step_size=10):\n",
        "    '''\n",
        "    Wrapper function to create a LearningRateScheduler with step decay schedule.\n",
        "    '''\n",
        "    def schedule(epoch):\n",
        "        return initial_lr * (decay_factor ** np.floor(epoch/step_size))\n",
        "    \n",
        "    return LearningRateScheduler(schedule)\n",
        "\n",
        "lr_sched = step_decay_schedule(initial_lr=1e-4, decay_factor=0.75, step_size=2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:22:12.823711Z",
          "start_time": "2018-11-07T16:22:08.154274Z"
        },
        "id": "v4ky7LPD7wQ0",
        "colab_type": "code",
        "outputId": "11c26a70-c72a-4dbb-a6ec-eb739908b690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2657
        }
      },
      "cell_type": "code",
      "source": [
        "# fit the model with our eternal generator!\n",
        "graph_result = model.fit_generator(\n",
        "    train_iterator(BATCH_SIZE), \n",
        "    steps_per_epoch=STEPS_PER_EPOCH,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks=[ModelSaveCallback(\"model_{}\"),\n",
        "              ReduceLROnPlateau(model='top3_accuracy', factor=0.2,verbose=1,\n",
        "              patience=1, min_lr=0.0001),\n",
        "              lr_sched],\n",
        "    verbose=1,\n",
        "    initial_epoch=last_finished_epoch\n",
        ")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Exception tensorflow.python.framework.errors_impl.CancelledError: CancelledError() in <bound method _Callable.__del__ of <tensorflow.python.client.session._Callable object at 0x7f60906bd290>> ignored\n",
            "Exception tensorflow.python.framework.errors_impl.CancelledError: CancelledError() in <bound method _Callable.__del__ of <tensorflow.python.client.session._Callable object at 0x7f607de35250>> ignored\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "128/128 [==============================] - 86s 668ms/step - loss: 5.7523 - categorical_accuracy: 0.0087 - top_3_accuracy: 0.0234\n",
            "Model saved in model_0\n",
            "Epoch 2/50\n",
            "128/128 [==============================] - 80s 621ms/step - loss: 5.4325 - categorical_accuracy: 0.0277 - top_3_accuracy: 0.0661\n",
            "Model saved in model_1\n",
            "Epoch 3/50\n",
            "128/128 [==============================] - 80s 623ms/step - loss: 5.0528 - categorical_accuracy: 0.0630 - top_3_accuracy: 0.1322\n",
            "Model saved in model_2\n",
            "Epoch 4/50\n",
            "128/128 [==============================] - 81s 633ms/step - loss: 4.7616 - categorical_accuracy: 0.0987 - top_3_accuracy: 0.1892\n",
            "Model saved in model_3\n",
            "Epoch 5/50\n",
            "128/128 [==============================] - 82s 639ms/step - loss: 4.5227 - categorical_accuracy: 0.1247 - top_3_accuracy: 0.2332\n",
            "Model saved in model_4\n",
            "Epoch 6/50\n",
            "128/128 [==============================] - 83s 645ms/step - loss: 4.3472 - categorical_accuracy: 0.1497 - top_3_accuracy: 0.2710\n",
            "Model saved in model_5\n",
            "Epoch 7/50\n",
            "128/128 [==============================] - 82s 641ms/step - loss: 4.1747 - categorical_accuracy: 0.1770 - top_3_accuracy: 0.3070\n",
            "Model saved in model_6\n",
            "Epoch 8/50\n",
            "128/128 [==============================] - 82s 638ms/step - loss: 4.0565 - categorical_accuracy: 0.1911 - top_3_accuracy: 0.3276\n",
            "Model saved in model_7\n",
            "Epoch 9/50\n",
            "128/128 [==============================] - 81s 632ms/step - loss: 3.9502 - categorical_accuracy: 0.2089 - top_3_accuracy: 0.3510\n",
            "Model saved in model_8\n",
            "Epoch 10/50\n",
            "128/128 [==============================] - 82s 644ms/step - loss: 3.8842 - categorical_accuracy: 0.2177 - top_3_accuracy: 0.3639\n",
            "Model saved in model_9\n",
            "Epoch 11/50\n",
            "128/128 [==============================] - 82s 643ms/step - loss: 3.8076 - categorical_accuracy: 0.2317 - top_3_accuracy: 0.3813\n",
            "Model saved in model_10\n",
            "Epoch 12/50\n",
            "128/128 [==============================] - 81s 630ms/step - loss: 3.7380 - categorical_accuracy: 0.2413 - top_3_accuracy: 0.3939\n",
            "Model saved in model_11\n",
            "Epoch 13/50\n",
            "128/128 [==============================] - 82s 638ms/step - loss: 3.7023 - categorical_accuracy: 0.2475 - top_3_accuracy: 0.4019\n",
            "Model saved in model_12\n",
            "Epoch 14/50\n",
            "128/128 [==============================] - 81s 634ms/step - loss: 3.6433 - categorical_accuracy: 0.2588 - top_3_accuracy: 0.4155\n",
            "Model saved in model_13\n",
            "Epoch 15/50\n",
            "128/128 [==============================] - 81s 634ms/step - loss: 3.6269 - categorical_accuracy: 0.2598 - top_3_accuracy: 0.4176\n",
            "Model saved in model_14\n",
            "Epoch 16/50\n",
            "128/128 [==============================] - 81s 634ms/step - loss: 3.6079 - categorical_accuracy: 0.2632 - top_3_accuracy: 0.4220\n",
            "Model saved in model_15\n",
            "Epoch 17/50\n",
            "128/128 [==============================] - 82s 638ms/step - loss: 3.5800 - categorical_accuracy: 0.2672 - top_3_accuracy: 0.4277\n",
            "Model saved in model_16\n",
            "Epoch 18/50\n",
            "128/128 [==============================] - 81s 633ms/step - loss: 3.5590 - categorical_accuracy: 0.2714 - top_3_accuracy: 0.4312\n",
            "Model saved in model_17\n",
            "Epoch 19/50\n",
            "128/128 [==============================] - 81s 636ms/step - loss: 3.5353 - categorical_accuracy: 0.2758 - top_3_accuracy: 0.4402\n",
            "Model saved in model_18\n",
            "Epoch 20/50\n",
            "128/128 [==============================] - 81s 633ms/step - loss: 3.5202 - categorical_accuracy: 0.2765 - top_3_accuracy: 0.4414\n",
            "Model saved in model_19\n",
            "Epoch 21/50\n",
            "128/128 [==============================] - 82s 642ms/step - loss: 3.4939 - categorical_accuracy: 0.2818 - top_3_accuracy: 0.4432\n",
            "Model saved in model_20\n",
            "Epoch 22/50\n",
            "128/128 [==============================] - 82s 639ms/step - loss: 3.5006 - categorical_accuracy: 0.2822 - top_3_accuracy: 0.4459\n",
            "Model saved in model_21\n",
            "Epoch 23/50\n",
            "128/128 [==============================] - 80s 629ms/step - loss: 3.5026 - categorical_accuracy: 0.2807 - top_3_accuracy: 0.4425\n",
            "Model saved in model_22\n",
            "Epoch 24/50\n",
            "128/128 [==============================] - 83s 646ms/step - loss: 3.4876 - categorical_accuracy: 0.2846 - top_3_accuracy: 0.4476\n",
            "Model saved in model_23\n",
            "Epoch 25/50\n",
            "128/128 [==============================] - 88s 689ms/step - loss: 3.4821 - categorical_accuracy: 0.2826 - top_3_accuracy: 0.4471\n",
            "Model saved in model_24\n",
            "Epoch 26/50\n",
            "128/128 [==============================] - 83s 645ms/step - loss: 3.4789 - categorical_accuracy: 0.2836 - top_3_accuracy: 0.4464\n",
            "Model saved in model_25\n",
            "Epoch 27/50\n",
            "128/128 [==============================] - 81s 633ms/step - loss: 3.4470 - categorical_accuracy: 0.2905 - top_3_accuracy: 0.4566\n",
            "Model saved in model_26\n",
            "Epoch 28/50\n",
            "128/128 [==============================] - 80s 625ms/step - loss: 3.4477 - categorical_accuracy: 0.2921 - top_3_accuracy: 0.4556\n",
            "Model saved in model_27\n",
            "Epoch 29/50\n",
            "128/128 [==============================] - 82s 640ms/step - loss: 3.4554 - categorical_accuracy: 0.2921 - top_3_accuracy: 0.4541\n",
            "Model saved in model_28\n",
            "Epoch 30/50\n",
            "128/128 [==============================] - 82s 644ms/step - loss: 3.4434 - categorical_accuracy: 0.2923 - top_3_accuracy: 0.4565\n",
            "Model saved in model_29\n",
            "Epoch 31/50\n",
            "128/128 [==============================] - 79s 618ms/step - loss: 3.4309 - categorical_accuracy: 0.2914 - top_3_accuracy: 0.4570\n",
            "Model saved in model_30\n",
            "Epoch 32/50\n",
            "128/128 [==============================] - 85s 662ms/step - loss: 3.4438 - categorical_accuracy: 0.2908 - top_3_accuracy: 0.4550\n",
            "Model saved in model_31\n",
            "Epoch 33/50\n",
            "128/128 [==============================] - 86s 675ms/step - loss: 3.4409 - categorical_accuracy: 0.2906 - top_3_accuracy: 0.4540\n",
            "Model saved in model_32\n",
            "Epoch 34/50\n",
            "128/128 [==============================] - 86s 670ms/step - loss: 3.4320 - categorical_accuracy: 0.2918 - top_3_accuracy: 0.4596\n",
            "Model saved in model_33\n",
            "Epoch 35/50\n",
            "128/128 [==============================] - 84s 656ms/step - loss: 3.4433 - categorical_accuracy: 0.2912 - top_3_accuracy: 0.4551\n",
            "Model saved in model_34\n",
            "Epoch 36/50\n",
            "128/128 [==============================] - 82s 643ms/step - loss: 3.4251 - categorical_accuracy: 0.2941 - top_3_accuracy: 0.4582\n",
            "Model saved in model_35\n",
            "Epoch 37/50\n",
            "128/128 [==============================] - 81s 636ms/step - loss: 3.4522 - categorical_accuracy: 0.2889 - top_3_accuracy: 0.4505\n",
            "Model saved in model_36\n",
            "Epoch 38/50\n",
            "128/128 [==============================] - 81s 629ms/step - loss: 3.4239 - categorical_accuracy: 0.2923 - top_3_accuracy: 0.4580\n",
            "Model saved in model_37\n",
            "Epoch 39/50\n",
            "128/128 [==============================] - 80s 622ms/step - loss: 3.4304 - categorical_accuracy: 0.2919 - top_3_accuracy: 0.4553\n",
            "Model saved in model_38\n",
            "Epoch 40/50\n",
            "128/128 [==============================] - 81s 633ms/step - loss: 3.4387 - categorical_accuracy: 0.2942 - top_3_accuracy: 0.4581\n",
            "Model saved in model_39\n",
            "Epoch 41/50\n",
            "128/128 [==============================] - 80s 629ms/step - loss: 3.4317 - categorical_accuracy: 0.2957 - top_3_accuracy: 0.4592\n",
            "Model saved in model_40\n",
            "Epoch 42/50\n",
            "128/128 [==============================] - 80s 627ms/step - loss: 3.4286 - categorical_accuracy: 0.2916 - top_3_accuracy: 0.4562\n",
            "Model saved in model_41\n",
            "Epoch 43/50\n",
            "128/128 [==============================] - 87s 678ms/step - loss: 3.4224 - categorical_accuracy: 0.2950 - top_3_accuracy: 0.4593\n",
            "Model saved in model_42\n",
            "Epoch 44/50\n",
            "128/128 [==============================] - 84s 653ms/step - loss: 3.4313 - categorical_accuracy: 0.2939 - top_3_accuracy: 0.4596\n",
            "Model saved in model_43\n",
            "Epoch 45/50\n",
            "128/128 [==============================] - 80s 629ms/step - loss: 3.4359 - categorical_accuracy: 0.2912 - top_3_accuracy: 0.4541\n",
            "Model saved in model_44\n",
            "Epoch 46/50\n",
            "128/128 [==============================] - 80s 623ms/step - loss: 3.4302 - categorical_accuracy: 0.2921 - top_3_accuracy: 0.4575\n",
            "Model saved in model_45\n",
            "Epoch 47/50\n",
            "128/128 [==============================] - 81s 637ms/step - loss: 3.4246 - categorical_accuracy: 0.2940 - top_3_accuracy: 0.4589\n",
            "Model saved in model_46\n",
            "Epoch 48/50\n",
            "128/128 [==============================] - 86s 670ms/step - loss: 3.4309 - categorical_accuracy: 0.2911 - top_3_accuracy: 0.4575\n",
            "Model saved in model_47\n",
            "Epoch 49/50\n",
            "128/128 [==============================] - 82s 638ms/step - loss: 3.4271 - categorical_accuracy: 0.2923 - top_3_accuracy: 0.4580\n",
            "Model saved in model_48\n",
            "Epoch 50/50\n",
            "128/128 [==============================] - 82s 643ms/step - loss: 3.4164 - categorical_accuracy: 0.2939 - top_3_accuracy: 0.4615\n",
            "Model saved in model_49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2_etZvEYuIau",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "bimbo = graph_result.__dict__"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CXd_wANb7wQ9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Score test set"
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T12:18:38.307707Z",
          "start_time": "2018-11-07T12:18:34.929466Z"
        },
        "id": "5m_VlZz87wRB",
        "colab_type": "code",
        "outputId": "05f8367a-6c5a-4c0d-ea1f-67b318089d81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "# download test set\n",
        "! KAGGLE_USERNAME=pabramov KAGGLE_KEY=_ kaggle competitions download -c quickdraw-doodle-recognition -f test_simplified.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test_simplified.csv: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:25:30.169624Z",
          "start_time": "2018-11-07T16:25:30.148875Z"
        },
        "id": "PRLv3CLm7wRG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def test_csv_iterator(batch_size):\n",
        "    with open(\"test_simplified.csv\", \"r\") as f:\n",
        "        batch_keys = []\n",
        "        batch_images = []\n",
        "        f.readline()  # skip header\n",
        "        for line in csv.reader(f, delimiter=',', quotechar='\"'):\n",
        "            batch_keys.append(line[0])\n",
        "            batch_images.append(draw_it(line[2]))\n",
        "            if len(batch_images) == batch_size:\n",
        "                batch_images = np.stack(batch_images, axis=0)\n",
        "                batch_images = np.expand_dims(batch_images, -1)\n",
        "                batch_images = batch_images.astype('float32')\n",
        "                batch_images = batch_images/255 - 0.5\n",
        "                ### YOUR CODE HERE: normalize batch_images the same way as during training!\n",
        "                yield batch_keys, batch_images\n",
        "                batch_keys = []\n",
        "                batch_images = []\n",
        "        if batch_images:  # last batch\n",
        "            batch_images = np.stack(batch_images, axis=0)\n",
        "            batch_images = np.expand_dims(batch_images, -1)\n",
        "            batch_images = batch_images.astype('float32')\n",
        "            batch_images = batch_images/255 - 0.5\n",
        "            ### YOUR CODE HERE: normalize batch_images the same way as during training!\n",
        "            yield batch_keys, batch_images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:25:32.406415Z",
          "start_time": "2018-11-07T16:25:32.203842Z"
        },
        "id": "42MAwyPO7wRJ",
        "colab_type": "code",
        "outputId": "05ad606a-cfa5-450c-8b39-e9cdcf581dd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "! wc -l test_simplified.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "112200 test_simplified.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:26:30.259083Z",
          "start_time": "2018-11-07T16:25:32.579734Z"
        },
        "scrolled": true,
        "id": "mh593QQO7wRV",
        "colab_type": "code",
        "outputId": "e3b9012b-2fd2-494e-cc23-b07728877a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "with open(\"submission.csv\", \"w\", buffering=1*1024*1024) as f:\n",
        "    f.write(\"key_id,word\\n\")\n",
        "    for batch_keys, batch_images in tqdm.tqdm(test_csv_iterator(BATCH_SIZE), total=np.ceil(112200./BATCH_SIZE)):\n",
        "        probas = model.predict_proba(batch_images, BATCH_SIZE)\n",
        "        top_3_classes = np.argsort(probas, axis=1)[:, [-1, -2, -3]]\n",
        "        labels = map(lambda x: \" \".join(\"_\".join(class_labels[idx].split()) for idx in x), top_3_classes)\n",
        "        for key, labels in zip(batch_keys, labels):\n",
        "            f.write(key + \",\" + labels + \"\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 220/220.0 [04:25<00:00,  1.07s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T16:27:51.293010Z",
          "start_time": "2018-11-07T16:27:51.111607Z"
        },
        "id": "hdujIQD07wRe",
        "colab_type": "code",
        "outputId": "d80683e8-1c56-44ed-b2b0-104d56f81be5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "! wc -l submission.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "112200 submission.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "end_time": "2018-11-07T13:18:12.630549Z",
          "start_time": "2018-11-07T13:17:59.689315Z"
        },
        "id": "WwkaGLLm7wRi",
        "colab_type": "code",
        "outputId": "78ca3e11-bc9a-4061-f827-eae177d9f865",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        }
      },
      "cell_type": "code",
      "source": [
        "# submit to kaggle\n",
        "! KAGGLE_USERNAME=pabramov KAGGLE_KEY=_ kaggle competitions submit quickdraw-doodle-recognition -f submission.csv -m \"Try Harder #1\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100% 4.41M/4.41M [00:00<00:00, 12.8MB/s]\n",
            "Successfully submitted to Quick, Draw! Doodle Recognition Challenge"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}