이미지_분석(0317).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMT9+9S4X5G4Aoho/MdVZQO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/%EC%9D%B4%EB%AF%B8%EC%A7%80_%EB%B6%84%EC%84%9D(0317).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5sRJA2C0pYje",
        "outputId": "5eef3b56-95ba-47d7-fea9-310923f8e832"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# 아래 내용을 이용하여 훈련 및 검증 데이터를 얻으시오.\n",
        "from keras.datasets import mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "X_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_test에서 5개의 데이터를 이미지로 출력하시오.\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for i in range(5):\n",
        "  # plt.imshow(출력하고 싶은 이미지, 색상)\n",
        "  # plt.imshow(X_test[i], cmap=plt.blues)\n",
        "  # plt.imshow(X_test[i], cmap=plt.cm.rainbow)\n",
        "  plt.imshow(X_test[i], cmap=plt.cm.binary)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "_yERbezKqYLH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fbcfdedf-506e-48b0-b2fc-eb27d75f8eeb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANS0lEQVR4nO3db6hU953H8c8niYWggrpeRFJZm5oHSsJqGWXBUFzLlvx5YBpC0AfFDWEt+QNt0gcJbkh9kEBYtpY+WAq3G1O7NhFjG2KCbJKKJJSQkhvj5mrCbv5wpYrxXhHS1BC7pt99cI/lau6cuXfOmT/X7/sFw8yc75w5Xw73c8/M+c3MzxEhAJe/K3rdAIDuIOxAEoQdSIKwA0kQdiCJq7q5sYULF8bSpUu7uUkglZGREZ0+fdqT1SqF3fZNkn4q6UpJ/xERT5Q9funSpRoaGqqySQAlGo1G01rbL+NtXynp3yXdLGmFpE22V7T7fAA6q8p79jWSPoiIjyLiz5J2S9pQT1sA6lYl7NdI+sOE+8eLZRexvcX2kO2hsbGxCpsDUEXHz8ZHxGBENCKiMTAw0OnNAWiiSthPSFoy4f5Xi2UA+lCVsL8p6TrbX7P9FUkbJe2rpy0AdWt76C0iztu+X9JLGh962xERR2vrDECtKo2zR8R+Sftr6gVAB/FxWSAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5KoNIsruuPQoUOl9dtvv71pbWRkpOZu+sfLL79cWl++fHnT2pIlS+pup+9VCrvtEUmfSvpC0vmIaNTRFID61XFk/4eIOF3D8wDoIN6zA0lUDXtIetn2W7a3TPYA21tsD9keGhsbq7g5AO2qGvYbI+Ibkm6WdJ/tb176gIgYjIhGRDQGBgYqbg5AuyqFPSJOFNejkp6TtKaOpgDUr+2w255te+6F25K+LelIXY0BqFeVs/GLJD1n+8LzPB0R/1VLV7jISy+9VFo/d+5clzrpL/v27Sut79ixo2lt9+7ddbfT99oOe0R8JOnvauwFQAcx9AYkQdiBJAg7kARhB5Ig7EASfMW1D5w/f760vn///i51MrM0GuVfsty+fXvT2tmzZ0vXnT17dls99TOO7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBOPsfeDgwYOl9ddff720/tBDD9XZzoxx5syZ0vrRo0eb1j777LPSdRlnBzBjEXYgCcIOJEHYgSQIO5AEYQeSIOxAEoyzd8Hw8HBpfePGjaX1ZcuWlda3bt067Z4uB61+ShoX48gOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzt4Fjz/+eGm91Xerd+3aVVqfM2fOtHuaCVp9X/3VV18trRfTiaPQ8shue4ftUdtHJixbYPsV2+8X1/M72yaAqqbyMv4Xkm66ZNnDkg5ExHWSDhT3AfSxlmGPiNckXfp6aoOkncXtnZJuq7kvADVr9wTdoog4Wdz+WNKiZg+0vcX2kO2hsbGxNjcHoKrKZ+MjIiRFSX0wIhoR0RgYGKi6OQBtajfsp2wvlqTierS+lgB0Qrth3ydpc3F7s6Tn62kHQKe0HGe3/YykdZIW2j4u6UeSnpC0x/bdko5JurOTTfa7vXv3ltZbza/e6vvqq1evnnZPl4PHHnustN5qHH3dunVNa/PmzWunpRmtZdgjYlOT0rdq7gVAB/FxWSAJwg4kQdiBJAg7kARhB5LgK641ePbZZ0vrZ8+eLa3fc889dbYzY4yMjJTWn3766dL6VVeV//k+8sgjTWuzZs0qXfdyxJEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JgnH2KPvnkk6a1N954o9Jz33vvvZXWn6kGBwdL661+xmzFihWl9fXr10+7p8sZR3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJx9ik6d+5c09rx48dL1920qdkP9Ob24YcfVlr/+uuvr6mTHDiyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLNP0dy5c5vWVq5cWbru8PBwaf3MmTOl9QULFpTW+9no6GjTWqvf229l7dq1ldbPpuWR3fYO26O2j0xYts32CduHi8stnW0TQFVTeRn/C0k3TbL8JxGxsrjsr7ctAHVrGfaIeE1S+etMAH2vygm6+22/U7zMn9/sQba32B6yPdTqN8UAdE67Yf+ZpK9LWinppKQfN3tgRAxGRCMiGgMDA21uDkBVbYU9Ik5FxBcR8RdJP5e0pt62ANStrbDbXjzh7nckHWn2WAD9oeU4u+1nJK2TtND2cUk/krTO9kpJIWlE0vc62GNfuPrqq5vWli1bVrru3r17S+u33npraf3BBx8srXfSkSPl/8dbfSf92LFjTWu22+rpgiuu4DNh09Ey7BEx2S8vPNmBXgB0EP8agSQIO5AEYQeSIOxAEoQdSIKvuNZg27ZtpfWIKK2/+OKLpfWNGzdOt6XatPrUY6vhs9OnT9fZzkXuuuuujj335YgjO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTh7DZYvX15a37NnT2n97bffLq1Xndq4ijvuuKPS+ps3b25a27VrV6XnLvvaMb6MIzuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME4ex9YtWpVpXo/u/baazv23K2mwr7hhhs6tu2ZiCM7kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBODs6quw381v9nn4rjKNPT8sju+0ltg/aftf2UdvfL5YvsP2K7feL6/mdbxdAu6byMv68pB9GxApJfy/pPtsrJD0s6UBEXCfpQHEfQJ9qGfaIOBkRh4rbn0p6T9I1kjZI2lk8bKek2zrVJIDqpnWCzvZSSask/V7Soog4WZQ+lrSoyTpbbA/ZHhobG6vQKoAqphx223Mk/VrSDyLijxNrMX6mZdKzLRExGBGNiGi0miQQQOdMKey2Z2k86L+KiN8Ui0/ZXlzUF0sa7UyLAOowlbPxlvSkpPciYvuE0j5JF34neLOk5+tvDzOd7Y5dMD1TGWdfK+m7koZtHy6WbZX0hKQ9tu+WdEzSnZ1pEUAdWoY9In4nqdm/0W/V2w6ATuHjskAShB1IgrADSRB2IAnCDiTBV1zRUZ9//nnb6zIlc704sgNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoyzo6OeeuqpprV58+aVrvvoo4/W3U5qHNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2dFRq1evblp74IEHStddv3593e2kxpEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JoOc5ue4mkX0paJCkkDUbET21vk/TPksaKh26NiP2dahQz0wsvvNDrFlCYyodqzkv6YUQcsj1X0lu2XylqP4mIf+tcewDqMpX52U9KOlnc/tT2e5Ku6XRjAOo1rffstpdKWiXp98Wi+22/Y3uH7flN1tlie8j20NjY2GQPAdAFUw677TmSfi3pBxHxR0k/k/R1SSs1fuT/8WTrRcRgRDQiojEwMFBDywDaMaWw256l8aD/KiJ+I0kRcSoivoiIv0j6uaQ1nWsTQFUtw27bkp6U9F5EbJ+wfPGEh31H0pH62wNQl6mcjV8r6buShm0fLpZtlbTJ9kqND8eNSPpeRzoEUIupnI3/nSRPUmJMHZhB+AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCUdE9zZmj0k6NmHRQkmnu9bA9PRrb/3al0Rv7aqzt7+NiEl//62rYf/Sxu2hiGj0rIES/dpbv/Yl0Vu7utUbL+OBJAg7kESvwz7Y4+2X6dfe+rUvid7a1ZXeevqeHUD39PrIDqBLCDuQRE/Cbvsm2/9j+wPbD/eih2Zsj9getn3Y9lCPe9lhe9T2kQnLFth+xfb7xfWkc+z1qLdttk8U++6w7Vt61NsS2wdtv2v7qO3vF8t7uu9K+urKfuv6e3bbV0r6X0n/KOm4pDclbYqId7vaSBO2RyQ1IqLnH8Cw/U1Jf5L0y4i4vlj2r5LORMQTxT/K+RHxUJ/0tk3Sn3o9jXcxW9HiidOMS7pN0j+ph/uupK871YX91osj+xpJH0TERxHxZ0m7JW3oQR99LyJek3TmksUbJO0sbu/U+B9L1zXprS9ExMmIOFTc/lTShWnGe7rvSvrqil6E/RpJf5hw/7j6a773kPSy7bdsb+l1M5NYFBEni9sfS1rUy2Ym0XIa7266ZJrxvtl37Ux/XhUn6L7sxoj4hqSbJd1XvFztSzH+Hqyfxk6nNI13t0wyzfhf9XLftTv9eVW9CPsJSUsm3P9qsawvRMSJ4npU0nPqv6moT12YQbe4Hu1xP3/VT9N4TzbNuPpg3/Vy+vNehP1NSdfZ/prtr0jaKGlfD/r4EtuzixMnsj1b0rfVf1NR75O0ubi9WdLzPezlIv0yjXezacbV433X8+nPI6LrF0m3aPyM/IeS/qUXPTTp61pJ/11cjva6N0nPaPxl3f9p/NzG3ZL+RtIBSe9L+q2kBX3U239KGpb0jsaDtbhHvd2o8Zfo70g6XFxu6fW+K+mrK/uNj8sCSXCCDkiCsANJEHYgCcIOJEHYgSQIO5AEYQeS+H+NzP5+6CgKOAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 생성\n",
        "# input layer 28*28 형식의 이미지, 활성함수 relu, 출력 크기 512\n",
        "# output layer 활성함수 softmax, 출력크기 10\n",
        "from keras import models \n",
        "from keras import layers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(512, activation='relu', input_shape=(28*28,)))\n",
        "model.add(layers.Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "GGBcr3BzsM3q"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics='accuracy' )"
      ],
      "metadata": {
        "id": "qYrDO2bqu3oR"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 이미지 축소\n",
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Sl9Mr-3vSI7",
        "outputId": "9dc5b5cd-3736-4753-d909-f7f880d31fed"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_reshape = X_train.reshape(60000, 28*28)\n",
        "X_train_reshape.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BxAd-i7vjsF",
        "outputId": "b6082c1e-5578-4590-b357-ac77d410cf62"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
              "       0.5803922 , 0.8980392 , 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.98039216, 0.7137255 , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.09411765, 0.44705883, 0.8666667 , 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.7882353 , 0.30588236, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.09019608, 0.25882354, 0.8352941 , 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.7764706 , 0.31764707,\n",
              "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.07058824, 0.67058825, 0.85882354,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.7647059 ,\n",
              "       0.3137255 , 0.03529412, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.21568628, 0.6745098 ,\n",
              "       0.8862745 , 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.95686275, 0.52156866, 0.04313726, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.53333336, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.83137256, 0.5294118 , 0.5176471 , 0.0627451 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# label 정보 one_hot 인코딩\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "y_train_onthot = to_categorical(y_train)\n",
        "y_train_onthot[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSTF0k2AwsTW",
        "outputId": "041f9701-d65c-486f-ee1a-818905674f50"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 학습\n",
        "model.fit(X_train_reshape, y_train_onthot, epochs=5, batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOunF3Su1iTt",
        "outputId": "617046a4-e565-4369-ee0b-e8019623fff0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 6s 11ms/step - loss: 4.5526 - accuracy: 0.9016\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 6s 13ms/step - loss: 0.6764 - accuracy: 0.9543\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 6s 13ms/step - loss: 0.4561 - accuracy: 0.9658\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.3711 - accuracy: 0.9735\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 5s 11ms/step - loss: 0.3355 - accuracy: 0.9773\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc9b8e2ef90>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 평가 데이터 변환\n",
        "X_test.shape\n",
        "\n",
        "X_test_reshape = X_test.reshape(10000, 28*28)\n",
        "X_test_reshape_fit = X_test_reshape.astype('float32')/255\n",
        "y_test_onthot = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "Erzze2JL2lVI"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 평가\n",
        "model.evaluate(X_test_reshape_fit, y_test_onthot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiLpFuUe3E3Q",
        "outputId": "fa05163f-8df8-4059-c478-6de1634c9684"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 1.8316 - accuracy: 0.9398\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8316460847854614, 0.9398000240325928]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 예측\n",
        "pred = model.predict(X_test_reshape_fit)\n",
        "s = pd.Series(pred[0])\n",
        "s.sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7CJmkyGx3L_z",
        "outputId": "dda6d345-f752-4885-f337-0de64ddc891d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7    0.186123\n",
              "3    0.118028\n",
              "2    0.114750\n",
              "9    0.103643\n",
              "5    0.092560\n",
              "8    0.088636\n",
              "0    0.080128\n",
              "6    0.076544\n",
              "1    0.075184\n",
              "4    0.064405\n",
              "dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.argmax(pred[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYzioEgn5e7m",
        "outputId": "b6514207-4ee4-47bc-c44b-6147c7a7176b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "yoxgL7qR5jk-",
        "outputId": "da43ef6d-9aca-4d1c-f401-a078b8c4697b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc9b8d34490>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChfkBj2SYMVm",
        "outputId": "61bd9ae6-d812-4e43-e45d-0bace12692df"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tXO9Z--ShPf",
        "outputId": "aa18db14-b90c-4160-b37c-0fd351d7c740"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               401920    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                5130      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 저장\n",
        "from keras.models import load_model\n",
        "path = '/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data'\n",
        "model.save(path+'/mnist__v1.h5')"
      ],
      "metadata": {
        "id": "dN3MnG_YRR1B"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = load_model(path+'/mnist__v1.h5')\n",
        "model2.summary()"
      ],
      "metadata": {
        "id": "SOiJwug8SHJF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e7d83b9-096d-473d-b89b-b5b96dc5bff3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               401920    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                5130      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 407,050\n",
            "Trainable params: 407,050\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "\n",
        "img_path = '/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data/3.png'\n",
        "img=cv2.imread(img_path)"
      ],
      "metadata": {
        "id": "eFqm28vfSmwQ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "sKiXSW0VSzPB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6711dbc0-fe79-49ae-a334-8bc9621873e9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATyUlEQVR4nO3dbYxc5XUH8P9/ZvbN9q5f18Y1VmwDVQqVAnRjUKGEKC01tIpBqlD4EBEJ1VEFEpFSqYh+CB9R1CRKqzaSKShOS0mjJAgkSBvHQqJUDWVNjbFBwUDWwWaxx5jYXmPv7sycfphLtIG95yzzsjOe5/+TVjt7n7lzzzwzZ+/unPs8D80MItL7Cp0OQEQWh5JdJBFKdpFEKNlFEqFkF0lEaTEPtmbNGtu0aVNbHrv87nttedyWYFPNbdbk0aPdnWIP2/zEvYev1mruviuWj7jtfaVFTZ0Fm5iYwIkTJ+Z96k1FTHIbgG8DKAL4ZzN70Lv/pk2bMD4+3swhc/3Trn/37xC8s8L3nfemLfh7B81gcPQC/fIo2fgfaNGxw46JnlwtP/ZCodjUsem9KPBDO3P2vLvvbbd8zm2/aO2o294pY2NjuW0Nv0tIFgH8I4CbAVwO4A6Slzf6eCLSXs38z74VwOtm9qaZzQD4PoDtrQlLRFqtmWTfAOCtOT8fybb9FpI7SI6THC+Xy00cTkSa0fZP481sp5mNmdnY6Gh3/p8jkoJmkv0ogI1zfr442yYiXaiZZH8BwGUkN5PsB/AFAE+2JiwRabWGS29mViF5D4D/RL309oiZHWxZZB9XUForBHWcoLrlikYOhvXkYH8z/wEqrOa2lcLSmN/Mon8+KET95jx5M//gxeBcZEHpjU4tPCo5WoevfmiHpursZvY0gKdbFIuItJEulxVJhJJdJBFKdpFEKNlFEqFkF0mEkl0kEd05KLcRwfhkBMNA43Jxft21WAxq+EFszihQAIAV/Nj7nZpwif4w0kqp4rbXgjp7NC58yOm3aIhrNeqYqM7uXL9gwfUHvVdl15ldJBlKdpFEKNlFEqFkF0mEkl0kEUp2kUT0Tuktmj22yXmL3b2D8pMFv1OD6hZY8ktUh09O57btnfSn2J486c+yOjXrBxf166W/M5Tb9oebh919Nw8PuO3ValB6c161YjC8Ni7GXnh0ZhdJhJJdJBFKdpFEKNlFEqFkF0mEkl0kEUp2kUT0TJ29GK5kGg5i9fd25pqOph0eLDVX4//h/x5z2//7zbO5bWf9EazuMFAAQNWv8UfV6teO5tfxf/5aftwA8KXr17rt164fdNunq/n9zmgF2R4c5Kozu0gilOwiiVCyiyRCyS6SCCW7SCKU7CKJULKLJKJn6uwRhnX4xvePVkWeCYrRDz076bbvffXXbvuSofxx332F/OWcAaBW8evsfQN+PdrCfs3vnFPv+sf+h93vuO0j2y922z+50hkPH11f0IOaSnaSEwDOAKgCqJjZWCuCEpHWa8WZ/bNmdqIFjyMibaT/2UUS0WyyG4CfktxLcsd8dyC5g+Q4yfFyudzk4USkUc0m+/VmdjWAmwHcTfKGD9/BzHaa2ZiZjY2OjjZ5OBFpVFPJbmZHs+/HATwOYGsrghKR1ms42UkuJTn8wW0ANwE40KrARKS1mvk0fh2Ax7N5w0sA/s3M/qMlUTUgmr/cgrpqtL9XTu4P5nX/yV6/WHHwDX9c98hI/tzrAFCZzR+0XgjKyWtXLHXbB4PloidO+wPmzVnOenDA7/Nz77vN2P2L02775mvyx8MXg8n6m11noBs1nOxm9iaAT7UwFhFpI5XeRBKhZBdJhJJdJBFKdpFEKNlFEtEzQ1zbXSrpc0pI7wXlp2cP+ENU2eeX7maCYaiDS/py2/7qMxe5+25Zu8RtL5h/Ptjz0im3/YcH8peMrprfb/3Ba/q6M001AJw4P5vbtjycKrr3hsDqzC6SCCW7SCKU7CKJULKLJELJLpIIJbtIIpTsIonomTp7pFgMpkQO9vfq7K9PTrn7npjxp3PuL/ovw/mqPxf17X+wMrdt65YV7r5ngnmu6TxvAPjza1a57eMT+X3zyym/X0rB+Nzz7/t1+nPT+e0rg2HJsN4b4qozu0gilOwiiVCyiyRCyS6SCCW7SCKU7CKJULKLJKJn6uw18+vF0Qq9DKZM9pYePnLMnwp6OqgHF5b4wa0edpYeBrBlbf500Gcrfr/M+qVuFIKOGwjG4q8e6c9tOzR1zt23FNT4LaqFO2Pxw6nDqfHsInKBUrKLJELJLpIIJbtIIpTsIolQsoskQskukoieqbNbML950VtzGUAhmEd8tppfd/30pcvdfT91af54cwBYGdTRl/b7sZX68ttnatFS1W4zSsHSxtFS2KfPO+0Fv0Zfg3+NwNIh/+07vCS/xo+af4GBBe+XC1H4jEg+QvI4yQNztq0iuZvkoey7/24WkY5byK+v7wLY9qFt9wHYY2aXAdiT/SwiXSxMdjN7FsDJD23eDmBXdnsXgFtbHJeItFij/5isM7PJ7PY7ANbl3ZHkDpLjJMfL5XKDhxORZjX9KYTVP6HJ/RTGzHaa2ZiZjY2OjjZ7OBFpUKPJfozkegDIvh9vXUgi0g6NJvuTAO7Mbt8J4InWhCMi7RLW2Uk+BuBGAGtIHgHwNQAPAvgBybsAHAZwezuDXIhiUA+O6slRvbjm1PE3bljm7lss+Aev1Joci+/9zg7qxUWnFA0AQ8GY8hcm/DHpvzyVv4b6QF/+uvIAcG7abcYlG4bc9tVD+dcvVM75a7v33qzxC0h2M7sjp+lzLY5FRNqo9y4TEpF5KdlFEqFkF0mEkl0kEUp2kUT0zBDXWjBkMSrNRdMS0ynGVILpmCvBMNNwSuRgQelSKf+5FfyqHl4p+6WzfYdPu+0/P+TvXy3ml9cKQT107XK/NPf5K/zlovucx38/mHq8F+nMLpIIJbtIIpTsIolQsoskQskukgglu0gilOwiieiZOnshmJbYHQaKcCQoGAxT9URDVPv6omsA/P3d0Mzvl6eef9dt3zfpjzNd0u8//oAzzXXFuT4AAG77zGq3/Yp1+UtVA8Bp5wKI6P3Qi9J7xiKJUrKLJELJLpIIJbtIIpTsIolQsoskQskukoieqbM3O/cvg7HVXntYBw9+pUb7R8wptBeD5zVc8seMLxnyx32XgusbvLH8s9MVd9//OnTWbf/d9f5U0lv68+fJngovrPCbL0Q6s4skQskukgglu0gilOwiiVCyiyRCyS6SCCW7SCJ6ps4e1ZOb5S3p3EyNvt3tUa8sHfB/35eCOeurVb8OX3TmjR8q+o99aMJfVvnva2W3/a//KH88/Ar/8oBoqv4LUnhmJ/kIyeMkD8zZ9gDJoyT3ZV+3tDdMEWnWQv6M/y6AbfNs/5aZXZl9Pd3asESk1cJkN7NnAZxchFhEpI2a+YDuHpL7sz/zV+bdieQOkuMkx8tl/38sEWmfRpP9OwAuAXAlgEkA38i7o5ntNLMxMxsbHR1t8HAi0qyGkt3MjplZ1cxqAB4CsLW1YYlIqzWU7CTXz/nxNgAH8u4rIt0hrLOTfAzAjQDWkDwC4GsAbiR5JerVyAkAX25jjAsUVJSDZq+ODgS17DbX+CO1an7shZIf263XrHXb/3ja75fDb/u18Ef3/zq37ZwTNwAs6fOL4W/9yl8b/n/emslt+/ym/LHuAIAOv6btECa7md0xz+aH2xCLiLSRLpcVSYSSXSQRSnaRRCjZRRKhZBdJRM8McS2ESyr77QZ/qGbBmQ+6Zv5jF4PY6tcmNXbs+v75JaxomuqVK/yppEeD2D+5cZnbPuP0+7++5C8XPRsMny2Y3y8Hj+VPRf2nQemtF8+CvficRGQeSnaRRCjZRRKhZBdJhJJdJBFKdpFEKNlFEtEzdfZmZ/4tBEsPewXrsMIfDJeM6vDFqM7O/NhqQaG9WvFr2VYM+qXgL7v86S1LctueOpg//BUATgXXHwwEHf/emfy24GmDPbhms87sIolQsoskQskukgglu0gilOwiiVCyiyRCyS6SiJ6psyMYU14LxqtHSz7TqcMzqJPPBiXb8xW/Fj5zPn9KZABYPpgf2wD9Onk0Y3LU7k1jDQCFvvzzSX/Jj63gP23Uglq494pXnWsT6npvzWad2UUSoWQXSYSSXSQRSnaRRCjZRRKhZBdJhJJdJBG9U2cv+jXXQs3/vcZgzPiA01PPvXLK3fex/e+57TX6x56annbb7/rsuty2bZtXufuem4mWqg7GlDt1dAA4fCZ/vPvUrD8W3oJ54WeqVbd92HnN+sN1BHpPeGYnuZHkMyRfIXmQ5L3Z9lUkd5M8lH1f2f5wRaRRC/kzvgLgq2Z2OYBrAdxN8nIA9wHYY2aXAdiT/SwiXSpMdjObNLMXs9tnALwKYAOA7QB2ZXfbBeDWdgUpIs37WB/QkdwE4CoAzwNYZ2aTWdM7AOb9x5HkDpLjJMfL5XIToYpIMxac7CSXAfgRgK+Y2em5bVZfWXDezzTMbKeZjZnZ2OjoaFPBikjjFpTsJPtQT/RHzezH2eZjJNdn7esBHG9PiCLSCmHpjfV5kB8G8KqZfXNO05MA7gTwYPb9ibZEuEAMpkyOpmuOfusVnSmVB+h34+n3/BLT4JC/fHCx4g8Ffea1/NLftZ9Y7u47MuA/djEoC74fTMn8k0P5sZ0JhscODPjHLgSv+ZY1g7ltQ+ES371nIXX26wB8EcDLJPdl2+5HPcl/QPIuAIcB3N6eEEWkFcJkN7PnkL8OwudaG46ItIsulxVJhJJdJBFKdpFEKNlFEqFkF0lEzwxxLURTQQftFrRXnFL5prX59VwAGAnqxdOFoN7c59fCDx+ezW37+s8mc9sA4KotI257seb3y8FfnXXbX5vMj23pkP+8Zmt+Eb9vyO/XsY35r0stGD6b5BBXEekNSnaRRCjZRRKhZBdJhJJdJBFKdpFEKNlFEtEzdfZILRzvHk1bnF/zHV3d5+57w2a/lv2UMx4dAJYGY869pY/fnPCnoT54yK/DW8l/i0RTSQ+V8tsrs/5rcq7iP/ZfXOP369UXLcttO3XWvz4gGKZ/QdKZXSQRSnaRRCjZRRKhZBdJhJJdJBFKdpFEKNlFEpFMnb0Q1NHjB8gf110L5iDfdrW/wO0b5aAWfvq82z44OJDbViz5FeMVffn7AoD1+/1Wrfm18vOz+csqzwRj5W+6Ir9ODgDbrwgWDjbn8cMB6703ol1ndpFEKNlFEqFkF0mEkl0kEUp2kUQo2UUSoWQXScRC1mffCOB7ANahXnzcaWbfJvkAgL8EUM7uer+ZPd2uQCNRHT2aNx7RvPNe3bXq77tyhb/++r1/ts5tf/x5f7z7/719JrftbFDLLhTy6+AA8P50/rzvAFAK+n3s4hW5bdf9Xn4bAFyz2Z+Pv1T0x/k7UxCA0fulB8+DC7mopgLgq2b2IslhAHtJ7s7avmVmf9e+8ESkVRayPvskgMns9hmSrwLY0O7ARKS1PtbfKiQ3AbgKwPPZpntI7if5CMl5r10kuYPkOMnxcrk8311EZBEsONlJLgPwIwBfMbPTAL4D4BIAV6J+5v/GfPuZ2U4zGzOzsdHR0RaELCKNWFCyk+xDPdEfNbMfA4CZHTOzqpnVADwEYGv7whSRZoXJzvrH2A8DeNXMvjln+/o5d7sNwIHWhycirbKQT+OvA/BFAC+T3Jdtux/AHSSvRL0cNwHgy22JsEXCJZuDqaYLxcZLMbOz/jDTZSN+iemum5a47aem1uS2vV0+5+47VfFjGxz0p8leNey3b1g1lP/Y/f7b77xfFUQtGF7rldei0hvYe0NcF/Jp/HMA5suUjtXUReTj670rB0RkXkp2kUQo2UUSoWQXSYSSXSQRSnaRRPTMVNJN19HDIbJOe82vVRf7/G62YIjsTMGPfWQ4fwjtyLB/bBb8YaKFaGhw0K9VZ/9zQZ3cvKmgATCYwrswb8U42zc4zzE49oVIZ3aRRCjZRRKhZBdJhJJdJBFKdpFEKNlFEqFkF0kEo/pzSw9GlgEcnrNpDYATixbAx9OtsXVrXIBia1QrY/uEmc07/9uiJvtHDk6Om9lYxwJwdGts3RoXoNgatVix6c94kUQo2UUS0elk39nh43u6NbZujQtQbI1alNg6+j+7iCyeTp/ZRWSRKNlFEtGRZCe5jeQvSL5O8r5OxJCH5ATJl0nuIzne4VgeIXmc5IE521aR3E3yUPZ93jX2OhTbAySPZn23j+QtHYptI8lnSL5C8iDJe7PtHe07J65F6bdF/5+dZBHAawD+BMARAC8AuMPMXlnUQHKQnAAwZmYdvwCD5A0ApgB8z8x+P9v2dQAnzezB7BflSjP7my6J7QEAU51exjtbrWj93GXGAdwK4EvoYN85cd2ORei3TpzZtwJ43czeNLMZAN8HsL0DcXQ9M3sWwMkPbd4OYFd2exfqb5ZFlxNbVzCzSTN7Mbt9BsAHy4x3tO+cuBZFJ5J9A4C35vx8BN213rsB+CnJvSR3dDqYeawzs8ns9jsA1nUymHmEy3gvpg8tM941fdfI8ufN0gd0H3W9mV0N4GYAd2d/rnYlq/8P1k210wUt471Y5llm/Dc62XeNLn/erE4k+1EAG+f8fHG2rSuY2dHs+3EAj6P7lqI+9sEKutn34x2O5ze6aRnv+ZYZRxf0XSeXP+9Esr8A4DKSm0n2A/gCgCc7EMdHkFyafXACkksB3ITuW4r6SQB3ZrfvBPBEB2P5Ld2yjHfeMuPocN91fPlzM1v0LwC3oP6J/BsA/rYTMeTEtQXAS9nXwU7HBuAx1P+sm0X9s427AKwGsAfAIQA/A7Cqi2L7FwAvA9iPemKt71Bs16P+J/p+APuyr1s63XdOXIvSb7pcViQR+oBOJBFKdpFEKNlFEqFkF0mEkl0kEUp2kUQo2UUS8f9t1aKjiQoy1wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img.shape"
      ],
      "metadata": {
        "id": "0YNCsZuiTDl1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8463d6da-39c1-418f-b0ed-4836c33870d3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 기본 이미지는 3개의 채널을 사용함\n",
        "# 채널을 1개로 줄이게 되면 3차원 데이터가 2차원 데이터로 변경됨\n",
        "\n",
        "img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "img_gray.shape"
      ],
      "metadata": {
        "id": "WOnzp1nXTZJV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcc86208-c964-47a4-a451-d092d8347f09"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(img_gray, cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Lzg8kPWTUq8S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "356a0752-2536-4b41-f561-83dd1c3d36da"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_gray_reshape=img_gray.reshape(1, 28*28)\n",
        "\n",
        "img_gray_reshape.shape"
      ],
      "metadata": {
        "id": "zCbowzV3THCl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "401c5799-4d32-4163-c74f-187c91ab36fe"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model2.predict(img_gray_reshape)\n",
        "# np.argmax(pred)\n",
        "s=pd.Series(pred[0])\n",
        "s.sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "-JA-_latUKeT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d40bff0-c608-41eb-ad5f-12b6230048a6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    1.000000e+00\n",
              "0    2.315779e-12\n",
              "3    5.188937e-14\n",
              "8    2.214755e-18\n",
              "4    5.263358e-25\n",
              "9    3.287875e-25\n",
              "7    8.485474e-29\n",
              "5    2.394980e-30\n",
              "6    1.447363e-30\n",
              "1    0.000000e+00\n",
              "dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}