cifar10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cifar10.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/vasiliyeskin/MachineLearningExperiences/blob/master/cifar10.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "ps6VA4r1C_wm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Распознавание объектов на изображениях из набора данных CIFAR-10\n",
        "\n",
        "Пример из курса [\"Программирование глубоких нейронных сетей на Python\"](https://www.asozykin.ru/courses/nnpython).\n",
        "\n",
        "Чтобы запускать и редактировать код, сохраните копию этого ноутбука себе (File->Save a copy in Drive...). Свою копию вы сможете изменять и запускать."
      ]
    },
    {
      "metadata": {
        "id": "T6hmBuIyjRUu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Flatten\n",
        "from keras.layers import Dropout\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.misc import toimage\n",
        "%matplotlib inline "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PfEKaHglj2VL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Размер мини-выборки\n",
        "batch_size = 32\n",
        "# Количество классов изображений\n",
        "nb_classes = 10\n",
        "# Количество эпох для обучения\n",
        "nb_epoch = 25\n",
        "# Размер изображений\n",
        "img_rows, img_cols = 32, 32\n",
        "# Количество каналов в изображении: RGB\n",
        "img_channels = 3\n",
        "# Названия классов из набора данных CIFAR-10\n",
        "classes=['самолет', 'автомобиль', 'птица', 'кот', 'олень', 'собака', 'лягушка', 'лошадь', 'корабль', 'грузовик']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Tt2d0pKXDR1c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Подготовка данных"
      ]
    },
    {
      "metadata": {
        "id": "kzQx_ZOZDNYV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Загружаем данные**"
      ]
    },
    {
      "metadata": {
        "id": "ARLcknFUj4Oc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1EJSbfDoDrnV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Просмотр примеров данных**"
      ]
    },
    {
      "metadata": {
        "id": "KCXgj92ckE-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "b2ea36c8-b217-49ed-8913-765c7640b602"
      },
      "cell_type": "code",
      "source": [
        "n = 9545\n",
        "plt.imshow(toimage(X_train[n]))\n",
        "plt.show()\n",
        "print(\"Номер класса:\", y_train[n])\n",
        "print(\"Тип объекта:\", classes[y_train[n][0]])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mu+vDNTzuxoLjHhiOohAbO+V+r25Iuiljnnvmead8+UK3qxTggFWt98DvA//+sycBGFgl\nRxWuOsR9fwC8vH6t2LZRqJfXyCTn2ChF0WuNzlRWd3RFSQGq6IqSAlTRFSUFqKIrSgpQRVeUFNAV\nqzuOUkixrJmRreSZpvvQ/8GCFRyguOEFse2o1csSsvcuDS34x77rHWI/hNx167ZuEbt40qpxzO8Z\nUVb2lIbKDsqegcqm7U55/7Lka46Z2pbsM3LoweE8duwQ+232eBS2lJPltwDmVeRApMFcMm9gth5a\nuQueTLtDBTkrbrnkfs/W75C9EPnJpLV782R4nSN75Nx7v3zuMbHtkV89IbYtH3R7jyr7JV90uRx6\nH8p1z43lQXd0RUkBquiKkgJU0RUlBaiiK0oKUEVXlBSgiq4oKaAt95ox5gjgHuDaqJrqzcDRQFwt\n8Bpr7X1S/3o26eqIZTnkvGsL+90F9vb3uKDWbtggti0zy5OybOjmy0+8KfarCR6N+fvL7p18r1zs\n8YChZCmhI997OAAbNsk541YulwsV7pznXqvxDaNin96e5NrPXxq6mBqrlspjvSnnLdv2ymanfFmv\nXCwx2LEtKauHt+b2cTlwqO4JiDpkqXv+wwfKZZz6liddke846r8A8NxGOYDmwcefFNu2jo2LbUOH\nHu6UF3LJ+zsTyTKyunhpp/baAHA98NCMpi9aa3/Q2bCKonSTdr66l4HT2U1pZEVR3r60U2SxBtSM\nSZzmutwY8zlgFLjcWisXkFYUZU4Jmp5ywdMxxlwNbI1+o58MbLPWPmWMuRJYYa29XOr7vH2x+S5z\nyF6ZsKIoIqLRoqOz7tba6b/X1wDf9j3/Ax85u+Xxltd+w+J3HAZAr+fHw9KC24DzoVXyh8baf59p\nSniL0z54bMvjS++6l389/0wAVq2Wz7pLxrjxkmxo8RnjBodaM8Wc9IVr+ek1fwHMwhi33t3PZ4yb\nmJEh51M/fIj/c9rJADT65cIDaz3GuHWCMe4QT8aamca4/7VlA3+9ODSc7qzImV3KHmPcwr1gjLvi\nxhu57rLLAFg/6o4lgM6NccdIxriDW+W3f/8GLjznTwDYXJEP///kB7IaduReM8Z8zxgT33UnAs92\nch1FUbpDO1b3o4FvAgcCVWPMeYRW+DuMMVPABHCJ7xqNRjKiKZYNuyLbIo5aIpRCmki6Y2L6J+Uc\nY5NbkrteLMscsr/Yr2fIvbvl++VIon5H+aeYoXnJtqGRUFYpyTvp8hH57dqy1R0dNlGR16qnXnTI\nwtx1k0V5HvMLcimk4Xce5JSvyPeKfcqjySjAFatXA7BxkzuvGkA1cEfKAaxY7r53ghHZJfrzF5KR\nj49Gsv/47W/Fflu3ybv2sqFFYlvN4UYD2Dk1JcpyvXK+RB/tGOOeINy1Z/K9jkZUFKXr6Mk4RUkB\nquiKkgJU0RUlBaiiK0oKUEVXlBTQleSQQ9VkNsFYdtSihWK/w/rd0xuuyYcoto7IbqGJYjLhYSxb\nepBcZmgK93iVzUn3VMyCguxOGm4kXWHzY5knPGn0Jfm4QnHUHX23oCCffFwylEx4eOB+oaxUlDNA\njk7JCRa31t1t403ZFTk4kBwrF8lW7C8nxJzyrPFm4cTnk088JfZ5bGPS/frgk+FhmK1V+ZDQsHCw\nC2DBfPn+flGItBwkWdZq05bQBTm4RL6/feiOrigpQBVdUVKAKrqipABVdEVJAaroipICVNEVJQV0\nxb22fzbpWollB/bKU5hXdEdeLVvpjkwCCHLu6CmAnv6kO2bZytB9s2CB7LYYKrtdXrma/DlZek2O\nutpebXWvrQK2P2sBKJdlN041kF1efUJ9uGZWjg7M9CTb+iLZgMP1FtNfka+5YMo9jypyHPV4MRm9\nlgnCemyeIDqef3NMbHv0dXfCo3Vjck25nfVkNNz2YiTzFNObNzRfbNs8Jsex78DtAuypJ9/ncizz\n1MTzoTu6oqQAVXRFSQGq6IqSAlTRFSUFqKIrSgroitV9v8FkjrFY1p+XgziGF7sttUsPGnLKASZr\nco60ZY4AmoOXhbLAYx0dW7feKd/81Mtin3pNtpAPzkuuR3lLGJQS5GTrdNAjfy7nA+Gt9JSvqjiW\nvtkILerby1WxX73i2R+q7nmMT7pz2gE8P570NMSyZzxZcV/YJucH3FxyW7TLgW9vS1rW65GsP5Bz\nAJam5CCrHVPyHEeWuzPSVh0elFhW3Slfz4fu6IqSAlTRFSUFqKIrSgpQRVeUFKCKrigpQBVdUVJA\nW+41Y8w3gOOj538deAy4DcgCm4CLrbViNEbOEbgSyw4+/GBx3EUjydI0ACNL5JJAy7fJrrfa5mTB\nwXoke3VHMrAiprhTyJE2IbvQCgW59E+9knQ1xbJGXXY3ZrPJXGIxDeEzu1KR5/imo/TPtu3ha902\nKe8BPlfZhpLbLffkdjkAZe1UazDJNcBdL4fvy9ik7LoqeV5bU4y7kXPo5XuSETSxrN+jKlMTcg69\npiOQKiZbcEfsNBz57t6SyWvvY7c7ujHmJOAIa+37gVOBfwC+CnzLWns88BJwaUejK4rSFdr56v4I\ncH709xgwQFiLbU0kuxf40F6fmaIoe412iizWgfi7yWXA/cAp076qjwJyKVJFUeacoCnkv56JMeZs\n4CrgI8CL1tolkXw1cKu19jip77qX1jYPWC3/FlcUZa8gWibaNcadAnwJONVaO26MmTDGFKy1RWA5\nsNHX/y/P+XjL47uefpTzjzoWgP/+3+QPgCWCMe6gFZ6MHs+/JrbVRluvd8Kdv+Thj74XgMF+OWuK\nZIwrjcnGoLzHGEe+ddk/8P1f8sg54TwanrPp2X7ZGJcXMrhUPGfWZxrjzlrzS9acFc5jLo1xr27d\nyIGLlgEwNikXyShVZEOdtH3VZF0gn2s1jhXL2yn0hhmIhqRYAqBYledY7Zcz0yxacYBTXsi13t8v\nPPNTDj3ypOiRPI8Xnvmx2NaOMW6Y0BB6hrU2jvx4EDg3+vtc4IHdXUdRlLmjnR39AmARcKcxJpZ9\nAviOMeZTwGvALb4LbNmSjECKZU89Ni72O+0Y926/oyZHmk2tk9vKryTzuO18/hUAmp5PXilfWC6Q\nE5rVkXfS4lTSEzkxFn5ryPXL3wSygbyTvjrm3jE3TMg56DYVW693FvDQ+vD9WD8pr8fr2+W8Za/v\ndJeG2uqJ5ss6Shq9UYrcjZ5flnXP7kzTPV5vrxyFlskkB8vmQtlESd61G5559Oblck3ZjNv1Vm8m\n751Y1nSU82qHdoxxNwA3OJo+3NGIiqJ0HT0ZpygpQBVdUVKAKrqipABVdEVJAaroipICupIc8s0d\nE6Ls5XqyLeZBofxMpSG7rgaasltouJiMDHtxWyjLT8run1qP28fT8JRIqmbkwxyVGQkKTweefCM8\nvDLsicyrC6WhAJ5e53avrR2X13f9ROsc/xG498XQBfmGJwFkqS77vMSDlk056WV9KnkgqbJLJq9x\nJiPPMdcjR41JlMrJA1qxrCGHw5FxJJWMGXZExMX0ZNz9XNFr8RGgRnMfRa8pivK7jyq6oqQAVXRF\nSQGq6IqSAlTRFSUFqKIrSgrointteyPpmohla6dk98mmHe5IqFpGnnbGE90zMsMd8xfAj8ZCl1Wz\np+LoEY2XEz4PM7LLqOxJ8lia8ZL/Grjn9bCmVvMNTzJEeanYstMduz8hT4NqkJz/68VQ1mjKHZue\ntsATUCZfT5ZlPGuc75Ej0aSJlMueKLRGcoEb9UjmiUfPZOQXncvJ/WoVt5u44Yimr1Wj57aXJyaB\n7uiKkgJU0RUlBaiiK0oKUEVXlBSgiq4oKaArVvcxR0BDLBsrytbuXiFYoO6wFu/Ck2Qs10yO9Xg0\nftVj0q4F0jXlz0lfHrF6M9nv6Z2htb3hyQsHcqBGA6G8T7BnFvJaZF3OeEr/BB7Tr5Q+PPCY43uy\nSet5Ty6U5XKeYBiHlXxXW909/6Yvz5xrjrHMkxbdZ1nPBPI9Uq9Jc0zSiHLutZuePTGPjnopivI7\nhSq6oqQAVXRFSQGq6IqSAlTRFSUFqKIrSgpot8jiN4Djo+d/nbB6z9HAtugp11hr75P6V7PJYWJZ\ntSa7jKYET0Lgcatk9jCnVlwQquZzGSG4qHyuDo9bxeXGqUQuPJ/rypc/DSHQJPDktXP2aYZuvqYn\nR1rgcVFlhSKRvuCUjKNPNhe6VmuCCwqgVpdfm+yG8r1ncpMPn3vN51asV92vzZUzrh4FwPiu52O3\nim6MOQk4wlr7fmPMQuBXwE+AL1prf9DRqIqidJV2dvRHgEejv8eAARBq9CqK8raknSKLdSDOvXsZ\ncD/hd8jLjTGfA0aBy621W/fZLBVFmRVBu0fqjDFnA1cBHwGOAbZZa58yxlwJrLDWXi71fe43v20e\nftg798Z8FUWREX/At2uMOwX4EnCqtXYceGha8xrg277+R7/n5JbHpckN9A0sB6Bals+6S3aTQKh9\nDbszxrUanyrNCfJBWDCh5jlj3pTOi3sMVl5j3IxfPs36BEE2nEfgfUs8NdylrC8+Y9yM9WjUd5DJ\nDoXdOjTGSdlW9sQYN1Ucpb+wBJDPgwPUhPPs4DPGee6PGfdAo1ElExdZcMQnxAwODItt8xcsEdvq\nwi/gmca4jeufZtnKo8IpeoxxG9b9WmzbrXvNGDMMXAOcYa3dHsm+Z4xZFT3lRODZ3V1HUZS5o50d\n/QJgEXCnMSaW3QTcYYyZAiaAS/yXkEvM+JKMuXJnAQSenxvNPXST1XbJ5H6Sy8vrCvP+Ikq+5syu\nHVkuN9Vsym3SgL61cm3MQTRGJpDzsfl2Z2nH8b0v1WrydcWyhs+FJraA/C22M/eUN/quR/6m1WzI\n0YN1obRVw9Endq91lJSP9oxxNwA3OJpu6WhERVG6jp6MU5QUoIquKClAFV1RUoAquqKkAFV0RUkB\nXUkO2awmD8XEMv/hF7f7wZfgr+EpneNyrTTj5/sOiIhz9LnkPC4oR1vGkUBzJg2P601yGwUZ+bM8\ncHzOZ+LkkB4Xmg+Xawj8iRxdc29EST4Dz8GjzhIletxTHi9wJiuvR9YRnRnjch2+dWl3P5dLzuem\nawfd0RUlBaiiK0oKUEVXlBSgiq4oKUAVXVFSgCq6oqSArrjXXDHFsawpxVGDxxOy9z+fXK6m2eCt\n8eVy87UTVeWNcZf6y26hpuM1N6Pn+5JDNjz17cQoNU88t3Pu0fP9DjSfy64TXK85lPX0dBbN510r\nIc+BKzlkLOvUzaY7uqKkAFV0RUkBquiKkgJU0RUlBaiiK0oKUEVXlBTQFfeaz13gRXTxyG4Vf+0y\nx/N97r3d4B9pz5JUNiKZ3ym35+41KZosnkny+fHa+sbyRcRJr1t+ZW6XXOBpawfpdcvXy2SS6hDL\n8j1yjUCfK9J3m9eF98YVlRe/j1Kf3aE7uqKkAFV0RUkBquiKkgJU0RUlBaiiK0oK2K3V3RjTD9wM\n7Af0AX8D/Bq4jTBiYhNwsbW2LF/Fk4zLi/QcX989tdJ2atWdDfJ6+GbTWeBNp+uxN9e40z6zmcee\n3zv5fDJwJZ8Pyy3lcnLZpbqvbJTHIl8X2lwzjK3tfi+KTDt3zpnA49baE4CPAn8PfBX4lrX2eOAl\n4NKORlcUpSu0U3vtjmkPVwKvE1ZQ/dNIdi/weXZTOllRlLmj7QMzxphfACuAM4AHp31VHwX23wdz\nUxRlL9G2oltrjzPG/Ffg/9J6zGm3GROefuY/OOKIw1pkjeaOdofepzSak3M9BeDtNI+JuZ4CAPXm\ntrmeAgBTxfG5ngIA42Mvz6p/O8a4o4FRa+16a+1TxpgcsNMYU7DWFoHlwEbfNY468n0tjxvNHWSC\noVlMe8+OdYpXaU6SCQZmMY+9Q7vzyHgzzEjX9q1V62d0ozlBJhgMx0I2Pu1tZ83Md6ze3EY2WBi1\n+ebvyzAj9ZP79PW1GuOmiuP0F4YB6C/47ldPFp+9YIwbH3uZ4ZFVgN8Yt3PHq2JbO+/YB4C/BDDG\n7AcMAg8C50bt5wIPtHEdRVHmiHa+uv8LcKMx5mdAAfg08DhwqzHmU8BrwC2+C2SC5CdXLPOV1ek8\noEGZNYFv7WdXHiiBc6jGjP/b7ujt57gVd5HLJdUhlnm/GXmm0fTkymvsgXst3sk7K0PVntW9CFzo\naPpwRyMqitJ19GScoqQAVXRFSQGq6IqSAlTRFSUFqKIrSgoIOjXXK4ryu4Pu6IqSAlTRFSUFqKIr\nSgpQRVeUFKCKrigpQBVdUVJxPJvlAAAC0UlEQVRAV0oyxRhjrgXeRxig81lr7WPdHD+aw4nAXcBz\nkegZa+1nujyHI4B7gGuttf9kjFnJHiXb3GfzuBk4GoizPlxjrb2vC/P4BnA84f34deAx5mY9Zs7j\nLLq4HnsnEaubru3oxpgTgEOste8HLgOu69bYDh621p4Y/eu2kg8A1wMPTRN3PdmmMA+AL05bm24o\n+UnAEdF9cSrwD8zNerjmAd1dj32WiLWbX91PBu4GsNY+D8w3xswmzczvKmXgdFqz8pwIrIn+vhf4\n0BzNYy54BDg/+nsMGGBu1sM1Dzl1zD7AWnuHtfYb0cPpiVhnvRbd/Oq+FHhi2uMtkWwukscdZoxZ\nAywAvmKt/XG3BrbW1oCaMWa6eKDbyTaFeQBcboz5XDSPy621W/fxPOpAnDDvMuB+4JQ5WA/XPOp0\neT1g3yRinUtj3G6TSu4jXgS+ApwNfIIwe04yc//cMVfrAuFvwSuttR8EngKu7tbAxpizCRXs8hlN\nXV2PGfOYk/Ww1h5HaB/Y40SsEt1U9I2EO3jMMkLjQlex1m6IviI1rbVrgc2ECS7nkgljTCH6e7fJ\nNvcV1tqHrLVPRQ/XAEd2Y1xjzCnAl4DTrLXjzNF6zJxHt9fDGHN0ZJglGndXItboKR2vRTcV/UfA\neQDGmHcDG621O7s4PtHYFxljPh/9vZTQwrmh2/OYwdsi2aYx5nvGmFXRwxOBZ7sw5jBwDXCGtXZ7\nJO76erjmMQfrsc8SsXY1es0Y87eEL6YBfNpa++uuDf7WHOYBtwMjQJ7wN/r9XRz/aOCbwIFAlfBD\n5iJCt0ofYbLNS6y11TmYx/XAlcAUMBHNY3Qfz+NPCL8SvzBN/AngO3R3PVzzuInwK3xX1iPauW8k\nNMQVCH9iPg7cyizXQsNUFSUF6Mk4RUkBquiKkgJU0RUlBaiiK0oKUEVXlBSgiq4oKUAVXVFSgCq6\noqSA/w9bGe8yMZaOawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26ed90d940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Номер класса: [8]\n",
            "Тип объекта: корабль\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2kyCZJECE5Vu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Нормализуем данные**"
      ]
    },
    {
      "metadata": {
        "id": "C33r-0yNkJUA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train.astype('float32')\n",
        "X_test = X_test.astype('float32')\n",
        "X_train /= 255\n",
        "X_test /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qj07Mr7CE9Zt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Преобразуем правильные ответы в формат one hot encoding**"
      ]
    },
    {
      "metadata": {
        "id": "VHzeTThQFEs1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
        "Y_test = np_utils.to_categorical(y_test, nb_classes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mnT8h77PFLXu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Создаем нейронную сеть"
      ]
    },
    {
      "metadata": {
        "id": "U0MLXRCqkPGC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Создаем последовательную модель\n",
        "model = Sequential()\n",
        "# Первый сверточный слой\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "                        input_shape=(32, 32, 3), activation='relu'))\n",
        "# Второй сверточный слой\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\n",
        "# Первый слой подвыборки\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# Слой регуляризации Dropout\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "# Третий сверточный слой\n",
        "model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
        "# Четвертый сверточный слой\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "# Второй слой подвыборки\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# Слой регуляризации Dropout\n",
        "model.add(Dropout(0.25))\n",
        "# Слой преобразования данных из 2D представления в плоское\n",
        "model.add(Flatten())\n",
        "# Полносвязный слой для классификации\n",
        "model.add(Dense(512, activation='relu'))\n",
        "# Слой регуляризации Dropout\n",
        "model.add(Dropout(0.5))\n",
        "# Выходной полносвязный слой\n",
        "model.add(Dense(nb_classes, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hBCmSlqqFgvd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Печатаем информацию о сети**"
      ]
    },
    {
      "metadata": {
        "id": "QwTq8WR1Frx9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "outputId": "f1c816f1-4817-488f-ed94-0e9ab3e1456e"
      },
      "cell_type": "code",
      "source": [
        "print(model.summary())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               1606144   \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,676,842\n",
            "Trainable params: 1,676,842\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iZ8E1aF0FS1s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Компилируем модель**"
      ]
    },
    {
      "metadata": {
        "id": "VyVlJMdGkrv-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Aq23si1YFYhF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Обучаем нейронную сеть"
      ]
    },
    {
      "metadata": {
        "id": "kyH5Ec9RkznF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "outputId": "65b5c837-52aa-4459-99eb-67f0db83df66"
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, Y_train,\n",
        "              batch_size=batch_size,\n",
        "              epochs=nb_epoch,\n",
        "              validation_split=0.1,\n",
        "              shuffle=True,\n",
        "              verbose=2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 45000 samples, validate on 5000 samples\n",
            "Epoch 1/25\n",
            " - 25s - loss: 1.5541 - acc: 0.4328 - val_loss: 1.1850 - val_acc: 0.5846\n",
            "Epoch 2/25\n",
            " - 24s - loss: 1.1328 - acc: 0.5988 - val_loss: 0.9571 - val_acc: 0.6638\n",
            "Epoch 3/25\n",
            " - 24s - loss: 0.9748 - acc: 0.6567 - val_loss: 0.8255 - val_acc: 0.7058\n",
            "Epoch 4/25\n",
            " - 25s - loss: 0.8732 - acc: 0.6924 - val_loss: 0.7750 - val_acc: 0.7388\n",
            "Epoch 5/25\n",
            " - 25s - loss: 0.8031 - acc: 0.7184 - val_loss: 0.7865 - val_acc: 0.7320\n",
            "Epoch 6/25\n",
            " - 25s - loss: 0.7567 - acc: 0.7342 - val_loss: 0.6900 - val_acc: 0.7646\n",
            "Epoch 7/25\n",
            " - 25s - loss: 0.7159 - acc: 0.7476 - val_loss: 0.6885 - val_acc: 0.7690\n",
            "Epoch 8/25\n",
            " - 25s - loss: 0.6785 - acc: 0.7629 - val_loss: 0.6633 - val_acc: 0.7774\n",
            "Epoch 9/25\n",
            " - 24s - loss: 0.6422 - acc: 0.7742 - val_loss: 0.6624 - val_acc: 0.7698\n",
            "Epoch 10/25\n",
            " - 25s - loss: 0.6196 - acc: 0.7804 - val_loss: 0.6660 - val_acc: 0.7812\n",
            "Epoch 11/25\n",
            " - 25s - loss: 0.5932 - acc: 0.7907 - val_loss: 0.6390 - val_acc: 0.7878\n",
            "Epoch 12/25\n",
            " - 25s - loss: 0.5731 - acc: 0.7965 - val_loss: 0.6246 - val_acc: 0.7914\n",
            "Epoch 13/25\n",
            " - 25s - loss: 0.5482 - acc: 0.8044 - val_loss: 0.6577 - val_acc: 0.7880\n",
            "Epoch 14/25\n",
            " - 24s - loss: 0.5441 - acc: 0.8076 - val_loss: 0.6814 - val_acc: 0.7764\n",
            "Epoch 15/25\n",
            " - 24s - loss: 0.5250 - acc: 0.8124 - val_loss: 0.6212 - val_acc: 0.7884\n",
            "Epoch 16/25\n",
            " - 24s - loss: 0.5066 - acc: 0.8208 - val_loss: 0.6346 - val_acc: 0.7900\n",
            "Epoch 17/25\n",
            " - 25s - loss: 0.4982 - acc: 0.8244 - val_loss: 0.6554 - val_acc: 0.7916\n",
            "Epoch 18/25\n",
            " - 25s - loss: 0.4929 - acc: 0.8246 - val_loss: 0.6377 - val_acc: 0.7910\n",
            "Epoch 19/25\n",
            " - 25s - loss: 0.4676 - acc: 0.8335 - val_loss: 0.6811 - val_acc: 0.7842\n",
            "Epoch 20/25\n",
            " - 25s - loss: 0.4673 - acc: 0.8362 - val_loss: 0.6438 - val_acc: 0.7968\n",
            "Epoch 21/25\n",
            " - 24s - loss: 0.4549 - acc: 0.8400 - val_loss: 0.6441 - val_acc: 0.7982\n",
            "Epoch 22/25\n",
            " - 25s - loss: 0.4453 - acc: 0.8447 - val_loss: 0.6343 - val_acc: 0.8018\n",
            "Epoch 23/25\n",
            " - 25s - loss: 0.4362 - acc: 0.8466 - val_loss: 0.6510 - val_acc: 0.7958\n",
            "Epoch 24/25\n",
            " - 24s - loss: 0.4202 - acc: 0.8526 - val_loss: 0.6654 - val_acc: 0.7948\n",
            "Epoch 25/25\n",
            " - 25s - loss: 0.4201 - acc: 0.8518 - val_loss: 0.7407 - val_acc: 0.7794\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bHssPqMqF9sv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Оцениваем качетсво обучения сети"
      ]
    },
    {
      "metadata": {
        "id": "5y4AHwlNou5y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1edc87b9-5a11-49f4-fbdc-9bbd8433141f"
      },
      "cell_type": "code",
      "source": [
        "# Оцениваем качество обучения модели на тестовых данных\n",
        "scores = model.evaluate(X_test, Y_test, verbose=0)\n",
        "print(\"Точность работы на тестовых данных: %.2f%%\" % (scores[1]*100))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Точность работы на тестовых данных: 77.18%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Z_xxp2UrNJvz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "c9eeb820-46c6-4937-a0d1-d1ef5abaafab"
      },
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "acc_values = history_dict['acc']\n",
        "val_acc_values = history_dict['val_acc']\n",
        "epochs = range(1, len(acc_values) + 1)\n",
        "plt.plot(epochs, acc_values, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc_values, 'b', label='Validation acc')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JE7sNplH1XrIkwAMPpPPWW85h6ZBDwlxxRRmnnx4iOzuLTZuSH2gzMoiNCUS47LLaU7uj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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26cc8e5898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9Ax2lw6hRDlm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Сохраняем обученную нейронную сеть"
      ]
    },
    {
      "metadata": {
        "id": "uQhkelwPQ_PE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model_json = model.to_json()\n",
        "json_file = open(\"cifar10_model.json\", \"w\")\n",
        "json_file.write(model_json)\n",
        "json_file.close()\n",
        "model.save_weights(\"cifar10_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bn4CEGiURBz7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "311f791e-3fb0-48fb-ca8f-4be4cf6caf90"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cifar10_model.h5  cifar10_model.json  datalab  horse.png\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rV8R1WhKVpZK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "udkAZlWbVi7C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files.download(\"cifar10_model.json\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q1oY_XT2VvKp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files.download(\"cifar10_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CDf9Gw19GIGO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Применяем сеть для распознавания объектов на изображениях"
      ]
    },
    {
      "metadata": {
        "id": "wFujwhUEGWOG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Просмотр изображения из набора данных для тестирования**"
      ]
    },
    {
      "metadata": {
        "id": "bxaxjSOAGOPX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "0fe17944-fbb9-4a8e-f82c-1b203273a43f"
      },
      "cell_type": "code",
      "source": [
        "index=11\n",
        "plt.imshow(toimage(X_test[index]))\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LkKIoE0qkGBCIvzPGmPOBrwNnAEcBndba540xVwKzrbVLpL4jAz3FVKN7O6ui\nKHsM0cAzXmPcmcBVwFnW2l7gsZ2a7wduCeq/5pmHK44POuMfeOWRXwHVGeOeWSEbONJtsjHukSf+\nWnH8wIP/wblnXwDA3PnzxX6SMe72234u9invXXcRH5P1ZWv/Vtqb2gHIF+RiAPGobIiRjHH1LXIN\n9wX77Vdx/ODD93D2WeV959Ua49y/6mYEFNY44pgjKo7/7bZb+fwnPw3A7x5+ROzXGVBzPS9khGkM\n2Ot++BGVcRK/feT3nHPGmd75Agxur630GxPLbNuyTWzbb/5cp3ysMe7SJZ/jlzfdCAQb4z7wT58W\n23bpXjPGtADXAedYa7tKsnuMMWXNWAy8vKvzKIoycYxnRf8w0AbcbYwpy34G/NoYMwQMAB8POsFw\nLifK+ob9ZZLKdPcIbrQAU+CrL8mrzVpHtFlZlojLZYYmtTQ75TnH+yojO8kgGvG37pBF5DcX9DPL\ncUoAmprkFWxw0J+friybL6w2AG2b5NV5y0Z3RJntkfPrvfT6qorjf7vtVu74xd0ARGMB8xGwTkWE\nCRkOcPP99ennRFk+Lz+hTZrcIraddvrJYtthh7sjLdvnvkuUBaQADGQ8xrgfAT9yNN1e3SUVRak1\nujNOUUKAKrqihABVdEUJAaroihICVNEVJQTUJDlk36jf1VGW9Y3ILqPhvHtzQDotuzOmNbo32QAc\nuWC2KNu8+Q2xn33Z7RrK5+XNLUFksn4XT1kWtFExGndvigF5e0tjo7xhZt26taJseGTQ11ameZJc\nXqmzw70Tuj4lJ1dsn97ukx16mOd66uuT3XJb3toqtg2NSG7bAJecQx2KRe8ezAdEvc2ePUNs++Sn\nZM9zKu3+PHMOV16udP2c7B0MRFd0RQkBquiKEgJU0RUlBKiiK0oIUEVXlBCgiq4oIaAm7rWCI5ar\nLIsEhONE4+62hnS92Kdutr/OW5kpjpjoo44+FIDtnZ2+tjJbt7nbeub5k16WGR6So/IyjkSDRx00\nF4CBQTkZ4nBW9r1lC0L4WlF2AR5w4EGirKu3X+y36a0tYltGiOg77eQTxT5z9/G7Pd9z+mIAugM+\nl0EhXwHAsmX+SDSAdW9sFPsU8Y89V/RkqbQc3XjoIQeIbfmMfB/0DbtdmPGoP2JvtN+LKgysvxeA\nruiKEgJU0RUlBKiiK0oIUEVXlBCgiq4oIUAVXVFCQE3ca3URv1uoLMvF5DSK0ZTbpdEYbxT7RApy\nlFQu50+U+K6ZrQDMaHMngAQ4cF+/+wcgMyq7TvI52a3lil674H3HATA0LLuMhgbk6/X0+BM9Aryw\nWo7w6u9PO2RelFRmVB5/b3d0+IsEAAAGeUlEQVSv2BYtuF2Af3t2udjnlRUvVhx/8zvX8OC9DwFQ\n3+AfY5nGJvk+SCXct3YyLrsoo0V/DGBd1JuHhY6EjWWmTZYTcL61bpXYVl/v7lef9t/DmYGSmzFa\nncrqiq4oIUAVXVFCgCq6ooQAVXRFCQGq6IoSAnZpwjPG1AM/B9qBFPBt4AXgTrziSG8Bl1lrxWxW\nafzW4rIskZDLGpEQyvEUZEtsISfn9so6Ai6mNHgWzmxSLv2Ty7unKZuqrkx8JOr3NMye6eVNy2Xk\n+ciOyu9taMAdINFQLwcArbD+AI8pka7SteTgmumNcu46KYimOChb6v0hPpDp9XLP5ftlr8xwp9yW\nrHOvYYcvkAORmhv8VvyTDl8IQHt7m9gvnpW9IYmcPP/piNurlHJY1lNR776oS+y9oJZzgeeste8G\nPgR8H/gWcLO19mRgNXB5VVdXFKUmjKf22q93OpwDvIlXQbVco/UB4MvsonSyoigTx7i978aYZ4DZ\nwDnAozs9qm8D5Hy3iqJMOJGgUrxjMcYcDtwBzLDWTi3JFgB3WGtPkPoN93UV081yqV1FUfYIotFi\nPMa4RcA2a+1Ga+3zxpg40G+MSVtrh4FZgLsodolXHv+/FcdHfeATPHfvj4HgGuMiwjZL2D1j3Ls/\n+kWW3vF9ry0r98vl3WPMZl1mpF0z1hh3zmeu5re3XO1daw8b49a+uU3sM9YYd//Tf+a8E48HYGu3\nbIwbCsiCIxnjIgELylhD0fMb3+DwOfMAiDkMl2Xiid03xqUS7qIg4DfG3fP0Mi488Rgg2Bg3e7a/\nAEWZaW2t8vVa3Ft40/WVxuZzP/lVHrjtfwJQl5Az3ZzxT18Q28ZjjDsF+BKAMaYdaAQeBS4stV8I\nPDyO8yiKMkGM5zf6rcBPjDFPAWngs8BzwB3GmE8B64Hbg07gilspy+J18jcs0iIQsKIHPR/Eo/7v\ntXTS+4YM+qaXVvRMNiDfXUBur4IjeKIhVfoWrwt4WknLgSYtze7VoW2avKIccuA8n+yjF5wCQH+f\nnDNuZFR+kskJZapyAU9auYy/z6XnekE+LldkmXhcnuNYzN1Wl5RXxJjjHnj/aYsASAWUlKqrk92N\n9Q1ywEtKCGpxnW9K6fNNJgNcmwGMx+o+DFziaHpvVVdUFKXm6M44RQkBquiKEgJU0RUlBKiiK0oI\nUEVXlBCwWzvjFEX5/xNd0RUlBKiiK0oIUEVXlBCgiq4oIUAVXVFCgCq6ooSAmpRkKmOMuQE4Di8u\n7fPW2mdref3SGBYDvwFeKYlestZ+rsZjOBi4D7jBWnuTMWYOu5Fscy+O4+fAIqBU/4frrLUP1mAc\n1wIn492P3wOeZWLmY+w4zqOG87EnErFK1GxFN8a8G1horT0euAL4Ya2u7WCptXZx6a/WSt4A3Ag8\ntpO45sk2hXEAfG2nuamFkp8KHFy6L84CfsDEzIdrHFDb+dhriVhr+eh+GnAvgLV2JTDZGCNXNvzP\nyyjwfiqz8iwG7i/9/wBw+gSNYyJ4Eri49H8P0MDEzIdrHNXl864Sa+2vrbXXlg53TsT6tueilo/u\n04GdS2p2lGR9NRxDmQONMfcDU4BrrLV/qNWFrbU5IGeM2VncUOtkm8I4AJYYY75YGscSa+32vTyO\nPFDOg3UF8BBw5gTMh2sceWo8H7B3ErFOpDFOTh2yd1kFXAOcD3wML3uOnHak9kzUvID3W/BKa+17\ngOeBq2t1YWPM+XgKtmRMU03nY8w4JmQ+SolWzwN+QeX7r3ouaqnom/FW8DIz8YwLNcVau6n0iFS0\n1q4BtuAluJxIBowx5YyAu0y2ubew1j5mrX2+dHg/cEgtrmuMORO4CniftbaXCZqPseOo9XwYYxaV\nDLOUrrsjEWvpJVXPRS0V/RHgIgBjzJHAZmutnJhsL2GMudQY8+XS/9PxLJybaj2OMbwjkm0aY+4x\nxswvHS4GXq7BNVuA64BzrLVdJXHN58M1jgmYj72WiLWm0WvGmH/FezMF4LPW2hdqdvG/j6EJuAuY\nBCTxfqM/VMPrLwKuB+YCWbwvmUvx3CopvGSbH7fWytkU9944bgSuBIaAgdI45HzRe2Yc/wXvkfj1\nncQfA35MbefDNY6f4T3C12Q+Siv3T/AMcWm8n5jP4dVSeFtzoWGqihICdGecooQAVXRFCQGq6IoS\nAlTRFSUEqKIrSghQRVeUEKCKrighQBVdUULA/wOLoU6bq+M+kQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26cc8579e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ByO-DUMJGc32",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Преобразование тестового изображения**"
      ]
    },
    {
      "metadata": {
        "id": "kvZbyn6xGhUe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = X_test[index]\n",
        "x = np.expand_dims(x, axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hxcp2FfPGog1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Запуск распознавания**"
      ]
    },
    {
      "metadata": {
        "id": "Ap72-9_kNKU2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "prediction = model.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2Gldpmn2NQ6-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Печатаем результаты распознавания**"
      ]
    },
    {
      "metadata": {
        "id": "P7opRiSZNUku",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4d8d97e0-6641-4a75-b935-b304e703738a"
      },
      "cell_type": "code",
      "source": [
        "print(prediction)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[8.1685665e-12 3.7978654e-07 2.3843751e-17 1.1129013e-17 2.6797560e-21\n",
            "  1.3213343e-19 3.1908867e-19 1.5573232e-18 1.0378241e-11 9.9999964e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LQTiyEM3NfRG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Преобразуем результаты из формата one hot encoding**"
      ]
    },
    {
      "metadata": {
        "id": "XVC_dTGqNohG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9e9e4374-4390-44e5-96e1-4b52b169f62b"
      },
      "cell_type": "code",
      "source": [
        "prediction = np.argmax(prediction)\n",
        "print(classes[prediction])"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "грузовик\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zJy8tKjuNyD2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Печатаем правильный ответ**"
      ]
    },
    {
      "metadata": {
        "id": "0zoGlCZWN2Id",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9a2afe1b-d444-4be4-b689-f8c5b8a3b338"
      },
      "cell_type": "code",
      "source": [
        "print(classes[y_test[index][0]])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "грузовик\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1QnZ1pfIA_jo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Распознаем дополнительное изображение"
      ]
    },
    {
      "metadata": {
        "id": "UjfiJbX7BEtA",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "e05563c4-18f2-49c8-dfc9-14b347598c72"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-006d463f-2b41-4cfe-ba2e-18e1374226f5\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-006d463f-2b41-4cfe-ba2e-18e1374226f5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving plane.jpg to plane.jpg\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "metadata": {
        "id": "eRy8lcOMXqi5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Проверяем загрузку файлов"
      ]
    },
    {
      "metadata": {
        "id": "Hig0d3VOBplC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "579d8783-429b-428d-9b97-32f4f3ad805d"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cifar10_model.h5  cifar10_model.json  datalab  horse.png  plane.jpg\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ep-7TkvbXunR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Смотрим загруженную картинку**"
      ]
    },
    {
      "metadata": {
        "id": "DpEJyRjDXUuh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "3b09eb1f-8b21-4bc1-aa10-e46097324c8f"
      },
      "cell_type": "code",
      "source": [
        "img_path = 'plane.jpg'\n",
        "img = image.load_img(img_path, target_size=(32, 32))\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ZBwhDkBDJ8QBaOiEOAANnRAHoKET4gDxbMmUCocRAlnSEGoKEGtAWp6KkbZxksZr2TcD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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26cc83f5c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "KTwbT6byXyrC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Преобразуем картинку в массив для распознавания**"
      ]
    },
    {
      "metadata": {
        "id": "QTdXEhQ3X13R",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = image.img_to_array(img)\n",
        "x /= 255\n",
        "x = np.expand_dims(x, axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oeeqy0cjX7j6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Запускаем распознавание**"
      ]
    },
    {
      "metadata": {
        "id": "cVIxQljlX-LZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a7c881d2-af14-493c-a40c-ae404087a86d"
      },
      "cell_type": "code",
      "source": [
        "prediction = model.predict(x)\n",
        "prediction = np.argmax(prediction)\n",
        "print(classes[prediction])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "самолет\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}