python/7_Deep_Learning/2_MNIST_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (60000,)\n",
      "(10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154ea4bfb38>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0I3dv+QtvFXqbob6Hrn9dufncc+wW9/aPkv5L0geSzmabV6nv+XVh912ir/kq4H7jHX5AULzDDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8Pt/ALPExulGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_image = x_train[0]\n",
    "plt.imshow(single_image, cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154ea5594e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAADnVJREFUeJzt3X+M1PWdx/HX+2yJiRSDsniroNtrNs0ZEwEn5JTLsSfYUEPExlQgodnGWojWH40Yz/BPiWJCiLVHommkJylrCqWxKATNXY3ReE20OJC12OOOGrNXOHBZQrNIMBD0fX/sl2aLO58ZZr4z39l9Px8JmZnv+/ud79uvvPjOzGfm+zF3F4B4/qboBgAUg/ADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwjqS63c2bRp07yrq6uVuwRCGRgY0PHjx62WdRsKv5ktkrRR0iWS/s3d16fW7+rqUrlcbmSXABJKpVLN69b9st/MLpH0nKRvSrpe0nIzu77e5wPQWo28558r6UN3/8jdz0r6paQl+bQFoNkaCf81kg6Nenw4W/ZXzGylmZXNrDw0NNTA7gDkqZHwj/Whwhd+H+zum9y95O6ljo6OBnYHIE+NhP+wpJmjHs+QdKSxdgC0SiPhf09St5l91cwmSVomaVc+bQFotrqH+tz9nJk9IOk/NDLUt9nd/5BbZwCaqqFxfnd/TdJrOfUCoIX4ei8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQbV0im5MPHv37k3Wn3322Yq1LVu2JLft7e1N1h988MFkfc6cOcl6dJz5gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCohsb5zWxA0ieSPpN0zt1LeTSF9tHf35+sL1y4MFk/efJkxZqZJbft6+tL1nfu3JmsnzhxIlmPLo8v+fyzux/P4XkAtBAv+4GgGg2/S/qNme01s5V5NASgNRp92T/P3Y+Y2XRJr5vZf7v726NXyP5RWClJ1157bYO7A5CXhs787n4kuz0m6WVJc8dYZ5O7l9y91NHR0cjuAOSo7vCb2WVm9pXz9yV9Q9IHeTUGoLkaedl/laSXs+GaL0na6u7/nktXAJqu7vC7+0eSbsyxFxRgz549yfpdd92VrA8PDyfrqbH8KVOmJLedNGlSsn78eHqE+Z133qlYu+mmmxra90TAUB8QFOEHgiL8QFCEHwiK8ANBEX4gKC7dPQGcPn26Ym3fvn3JbVesWJGsHzlypK6eatHd3Z2sP/bYY8n60qVLk/V58+ZVrK1bty657Zo1a5L1iYAzPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ExTj/BLBq1aqKta1bt7awk4tTbXrvU6dOJevz589P1t96662Ktf379ye3jYAzPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ExTj/OFBtPHz37t0Va+7e0L57enqS9cWLFyfrjz76aMXa1Vdfndx29uzZyfrUqVOT9TfffLNirdHjMhFw5geCIvxAUIQfCIrwA0ERfiAowg8ERfiBoKqO85vZZkmLJR1z9xuyZVdI2i6pS9KApLvd/c/Na3Ni6+/vT9YXLlyYrJ88ebJiLTVFtiTdfvvtyfq2bduS9dRv5iXpqaeeqli79957k9t2dHQk6zfemJ4hPvXf/uqrrya3rTbfwZw5c5L18aCWM//PJS26YNnjkt5w925Jb2SPAYwjVcPv7m9LOnHB4iWStmT3t0i6M+e+ADRZve/5r3L3o5KU3U7PryUArdD0D/zMbKWZlc2sPDQ01OzdAahRveEfNLNOScpuj1Va0d03uXvJ3UvVPsAB0Dr1hn+XpN7sfq+knfm0A6BVqobfzLZJekfS183ssJl9T9J6SbeZ2R8l3ZY9BjCOVB3nd/flFUoLcu5lwjp48GCyvmHDhmR9eHg4WU+9ners7Exu29vbm6xPnjw5Wa/2e/5q9aKcPn06WX/66aeT9XaeD6FWfMMPCIrwA0ERfiAowg8ERfiBoAg/EBSX7s7BmTNnkvXU5aul6j8vnTJlSrLe19dXsVYqlZLbfvrpp8l6VIcOHSq6habjzA8ERfiBoAg/EBThB4Ii/EBQhB8IivADQTHOn4Nql3muNo5fzc6d6WulzJ8/v6HnR0yc+YGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMb5c/DII48k6+6erPf09CTrjOPXp9pxb9a24wVnfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8Iquo4v5ltlrRY0jF3vyFbtlbS9yUNZautcffXmtVkO9i9e3fFWn9/f3JbM0vW77jjjrp6QlrquFf7fzJr1qy822k7tZz5fy5p0RjLf+Lus7I/Ezr4wERUNfzu/rakEy3oBUALNfKe/wEz+72ZbTazqbl1BKAl6g3/TyV9TdIsSUcl/bjSima20szKZlYeGhqqtBqAFqsr/O4+6O6fufvnkn4maW5i3U3uXnL3UkdHR719AshZXeE3s85RD78l6YN82gHQKrUM9W2T1CNpmpkdlvQjST1mNkuSSxqQtKqJPQJogqrhd/flYyx+oQm9tLXUPPZnz55Nbjt9+vRkfenSpXX1NNGdOXMmWV+7dm3dz71gwYJkff369XU/93jBN/yAoAg/EBThB4Ii/EBQhB8IivADQXHp7ha49NJLk/XOzs5kfaKqNpS3bt26ZH3Dhg3J+syZMyvWVq9endx28uTJyfpEwJkfCIrwA0ERfiAowg8ERfiBoAg/EBThB4JinL8FIl+aO3VZ82rj9Nu3b0/WlyxZkqzv2LEjWY+OMz8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBMU4f43cva6aJL3yyivJ+saNG+vqqR0888wzyfqTTz5ZsTY8PJzcdsWKFcl6X19fso40zvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EFTVcX4zmympT9LfSvpc0iZ332hmV0jaLqlL0oCku939z81rtVhmVldNkj7++ONk/aGHHkrW77nnnmT9yiuvrFh79913k9u++OKLyfr777+frB86dChZv+666yrWFi1alNz2/vvvT9bRmFrO/OckrXb3v5f0D5J+YGbXS3pc0hvu3i3pjewxgHGiavjd/ai778vufyLpgKRrJC2RtCVbbYukO5vVJID8XdR7fjPrkjRb0u8kXeXuR6WRfyAkTc+7OQDNU3P4zWyypF9L+qG7n7yI7VaaWdnMykNDQ/X0CKAJagq/mX1ZI8H/hbufvyrioJl1ZvVOScfG2tbdN7l7yd1LHR0defQMIAdVw28jH2W/IOmAu4/+CdcuSb3Z/V5JO/NvD0Cz1PKT3nmSviNpv5mdvw7zGknrJf3KzL4n6U+Svt2cFse/c+fOJevPPfdcsv7SSy8l65dffnnF2sGDB5PbNuqWW25J1m+99daKtSeeeCLvdnARqobf3X8rqdJA9oJ82wHQKnzDDwiK8ANBEX4gKMIPBEX4gaAIPxAUl+6u0c0331yxNnfu3OS2e/bsaWjf1X4SPDg4WPdzT5s2LVlftmxZsj6eLzseHWd+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiKcf4azZgxo2Jtx44dFWuS9PzzzyfrqWmsG/Xwww8n6/fdd1+y3t3dnWc7aCOc+YGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKHP3lu2sVCp5uVxu2f6AaEqlksrlcnrO+AxnfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8Iqmr4zWymmb1pZgfM7A9m9nC2fK2Z/Z+Z9Wd/bm9+uwDyUsvFPM5JWu3u+8zsK5L2mtnrWe0n7v5089oD0CxVw+/uRyUdze5/YmYHJF3T7MYANNdFvec3sy5JsyX9Llv0gJn93sw2m9nUCtusNLOymZWHhoYaahZAfmoOv5lNlvRrST9095OSfirpa5JmaeSVwY/H2s7dN7l7yd1LHR0dObQMIA81hd/MvqyR4P/C3XdIkrsPuvtn7v65pJ9JSs9WCaCt1PJpv0l6QdIBd39m1PLOUat9S9IH+bcHoFlq+bR/nqTvSNpvZv3ZsjWSlpvZLEkuaUDSqqZ0CKApavm0/7eSxvp98Gv5twOgVfiGHxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKiWTtFtZkOS/nfUommSjresgYvTrr21a18SvdUrz96uc/earpfX0vB/YedmZXcvFdZAQrv21q59SfRWr6J642U/EBThB4IqOvybCt5/Srv21q59SfRWr0J6K/Q9P4DiFH3mB1CQQsJvZovM7H/M7EMze7yIHioxswEz25/NPFwuuJfNZnbMzD4YtewKM3vdzP6Y3Y45TVpBvbXFzM2JmaULPXbtNuN1y1/2m9klkg5Kuk3SYUnvSVru7v/V0kYqMLMBSSV3L3xM2Mz+SdIpSX3ufkO2bIOkE+6+PvuHc6q7/0ub9LZW0qmiZ27OJpTpHD2ztKQ7JX1XBR67RF93q4DjVsSZf66kD939I3c/K+mXkpYU0Efbc/e3JZ24YPESSVuy+1s08pen5Sr01hbc/ai778vufyLp/MzShR67RF+FKCL810g6NOrxYbXXlN8u6TdmttfMVhbdzBiuyqZNPz99+vSC+7lQ1ZmbW+mCmaXb5tjVM+N13ooI/1iz/7TTkMM8d58j6ZuSfpC9vEVtapq5uVXGmFm6LdQ743Xeigj/YUkzRz2eIelIAX2Myd2PZLfHJL2s9pt9ePD8JKnZ7bGC+/mLdpq5eayZpdUGx66dZrwuIvzvSeo2s6+a2SRJyyTtKqCPLzCzy7IPYmRml0n6htpv9uFdknqz+72SdhbYy19pl5mbK80srYKPXbvNeF3Il3yyoYx/lXSJpM3u/lTLmxiDmf2dRs720sgkpluL7M3Mtknq0civvgYl/UjSK5J+JelaSX+S9G13b/kHbxV669HIS9e/zNx8/j12i3v7R0n/KWm/pM+zxWs08v66sGOX6Gu5CjhufMMPCIpv+AFBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCOr/AcanH/Dq1TtRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "single_image = x_train[1]\n",
    "plt.imshow(single_image, cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(y_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################\n",
    "## y_train이 가지고 있는 값들은 '숫자에 대한 라벨'입니다.       ##\n",
    "## 이 값을 그대로 사용하면 CNN은 이를 라벨로 구분할 수 없습니다. ##\n",
    "## 따라서 One-Hot Encoding 형태로 변환하는 작업이 필요합니다.    ##\n",
    "##################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(y_test[0]) :  <class 'numpy.uint8'> 7 \n",
      "type(y_cat_test[0]) :  <class 'numpy.ndarray'> [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "y_cat_test = to_categorical(y_test, 10)\n",
    "y_cat_train = to_categorical(y_train, 10)\n",
    "print(\n",
    "    'type(y_test[0]) : ', type(y_test[0]), y_test[0],\n",
    "    '\\ntype(y_cat_test[0]) : ', type(y_cat_test[0]), y_cat_test[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train / x_train.max()\n",
    "x_test = x_test / x_test.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x154ea5b4e48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[0], cmap='gray_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.reshape(60000, 28, 28, 1)\n",
    "x_test = x_test.reshape(10000, 28, 28, 1)\n",
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################\n",
    "## Build and Train Models ##\n",
    "############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
    "    # Dense\n",
    "    # Conv2D     - Convoluitonal 2-D Layer\n",
    "    # MaxPool2D  - Max Pooling 2-D Layer\n",
    "    # Flatten    - Convert 2-D to 1-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# CONVOLUTIONAL LAYER\n",
    "\n",
    "model.add(Conv2D(\n",
    "    filters=32,\n",
    "    kernel_size=(4, 4),\n",
    "        # 위 두 값은 어느 정도 조절할 수 있는데,\n",
    "        # 간단한 이미지라면 filter를 줄이고\n",
    "        # 복잡한 이미지라면 filter를 높일 수 있습니다.\n",
    "    input_shape=(28, 28, 1),\n",
    "        # input_shape는 고정값입니다.\n",
    "    activation='relu', \n",
    "        # reul는 Rectified Linear Unit 함수입니다.\n",
    "))\n",
    "\n",
    "# POOLING LAYER\n",
    "\n",
    "model.add(MaxPool2D(\n",
    "    pool_size=(2, 2)\n",
    "        # 원하는 겂은 넣으면 됩니다.\n",
    "        # 사실 요즘은 책 및 논문에서 말한 값들을 넣는 추세기도 하더라고요.\n",
    "))\n",
    "\n",
    "# CONVERT 2-D to 1-D\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "# DENSE LAYER\n",
    "\n",
    "model.add(Dense(\n",
    "    128,\n",
    "        # 가장 많이 쓰이는 값을 넣었으며, 가변적으로 변경하세요\n",
    "    activation='relu'\n",
    "))\n",
    "\n",
    "\n",
    "model.add(Dense(\n",
    "    10,\n",
    "    activation='softmax'\n",
    "))\n",
    "\n",
    "model.compile(\n",
    "    loss='categorical_crossentropy',\n",
    "    optimizer='rmsprop',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 25, 25, 32)        544       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               589952    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 591,786\n",
      "Trainable params: 591,786\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "60000/60000 [==============================] - 17s 287us/step - loss: 0.1435 - acc: 0.9567\n",
      "Epoch 2/2\n",
      "60000/60000 [==============================] - 17s 289us/step - loss: 0.0490 - acc: 0.9854\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x154e8b4bac8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    x_train, \n",
    "    y_cat_train,\n",
    "    epochs=2\n",
    "        # epochs는 전체 학습용 데이터를 이용해서 한 번 도는 것을 의미\n",
    "        # 여기서 2를 넣었으니 2번 돕니다.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'acc']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 71us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04045661964484025, 0.9859]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_cat_test)\n",
    "    # 만약 *_train 에서는 정확도가 높았지만, test에서는 낮았다면\n",
    "    # overfitting된 것이므로 dropout을 통해서 조정하면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################################################\n",
    "## 모델 평가 완료 후, 처음 보는 이미지의 클래스 예측 ##\n",
    "######################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, ..., 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict_classes(x_test) # 예측\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat_test # 정답"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.99      0.99       980\n",
      "          1       0.99      1.00      0.99      1135\n",
      "          2       0.98      0.99      0.98      1032\n",
      "          3       0.98      0.99      0.99      1010\n",
      "          4       0.98      0.99      0.99       982\n",
      "          5       0.99      0.97      0.98       892\n",
      "          6       0.99      0.99      0.99       958\n",
      "          7       0.98      0.99      0.98      1028\n",
      "          8       0.99      0.98      0.98       974\n",
      "          9       0.99      0.97      0.98      1009\n",
      "\n",
      "avg / total       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = classification_report(\n",
    "    y_test,\n",
    "    predictions\n",
    ")\n",
    "print(result)\n",
    "    # 매우 높은 정확도로 숫자를 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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