AI_Class/028/CNN3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터셋 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 2s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 이미지 라벨 설명\n",
    "0 : T-shirt/top  \n",
    "1 : Trouser  \n",
    "2 : Pullover  \n",
    "3 : Dress  \n",
    "4 : Coat  \n",
    "5 : Sandal  \n",
    "6 : Shirt  \n",
    "7 : Sneaker  \n",
    "8 : Bag  \n",
    "9 : Ankel boot  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
      "    0   1   4   0   0   0   0   1   1   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
      "   54   0   0   0   1   3   4   0   0   3]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
      "  144 123  23   0   0   0   0  12  10   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
      "  107 156 161 109  64  23  77 130  72  15]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
      "  216 163 127 121 122 146 141  88 172  66]\n",
      " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
      "  223 223 215 213 164 127 123 196 229   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
      "  235 227 224 222 224 221 223 245 173   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
      "  180 212 210 211 213 223 220 243 202   0]\n",
      " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
      "  169 227 208 218 224 212 226 197 209  52]\n",
      " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
      "  198 221 215 213 222 220 245 119 167  56]\n",
      " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
      "  232 213 218 223 234 217 217 209  92   0]\n",
      " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
      "  222 221 216 223 229 215 218 255  77   0]\n",
      " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
      "  211 218 224 223 219 215 224 244 159   0]\n",
      " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
      "  224 234 176 188 250 248 233 238 215   0]\n",
      " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
      "  255 255 221 234 221 211 220 232 246   0]\n",
      " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
      "  188 154 191 210 204 209 222 228 225   0]\n",
      " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
      "  168 219 221 215 217 223 223 224 229  29]\n",
      " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
      "  239 223 218 212 209 222 220 221 230  67]\n",
      " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
      "  199 206 186 181 177 172 181 205 206 115]\n",
      " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
      "  195 191 198 192 176 156 167 177 210  92]\n",
      " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
      "  210 210 211 188 188 194 192 216 170   0]\n",
      " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
      "  182 182 181 176 166 168  99  58   0   0]\n",
      " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(train_images[0])\n",
    "print(train_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0번 클래스 의류\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPZElEQVR4nO3dX4xc5XnH8d9v/9jGNjQ2BtuAldDIlYpSFaIVTUVbUaFEQC9MVBGFi4ioqEZqkILERSlVFS5R1SSK1CqSKS5OlBBFSmh8gZogKy1N1SIW5ICp00KpAQcX4/DHYGN7/zy92AEtZud913Nm5oz9fD/SanbPO2fm8fH+9szMc855HRECcO4ba7sAAMNB2IEkCDuQBGEHkiDsQBITw3yyFV4Zq7RmmE95VvCKyeL4yYtWFMdXvjbTdSxOneqppqFYe15xePa88r5o4sjx8uMn7DSd0DGdipNeaqxR2G1fL+kbksYl/X1E3Fe6/yqt0e/4uiZPeU6auGRLcfy52y8rjm+9/5WuY7P/+2JPNQ3D/NRVxfFfXbGqOH7xzqeK43Hy5BnXdLZ7PPZ0Hev5ZbztcUl/J+kGSVdIusX2Fb0+HoDBavKe/WpJz0fECxFxStL3JG3rT1kA+q1J2C+V9PKinw92ln2A7e22p21PzyjfyypgVDQJ+1IfAnzoE5GI2BERUxExNamVDZ4OQBNNwn5Q0uJPli6T1P2TIgCtahL2JyRttX257RWSPi9pd3/KAtBvPbfeImLW9h2SfqyF1tvOiHi2b5WdQ8bXrSuOv/S5cuvtz7Y9Uhx/44+6H7vwzFuXFNc9NlN+a3Vsptzj37TmaHH81yZPdB379Lp/LK77F//6x8Vxz32yOL5hx78Xx7Np1GePiEcklX8TAYwEDpcFkiDsQBKEHUiCsANJEHYgCcIOJDHU89mzmnvjjeL4irfK510/dN8NxfHfvfOJrmNf3PxvxXV/f9WR4vi68dXF8WdPvVscPzDb/RiDu566ubjuJT8eL46fWlscxmnYswNJEHYgCcIOJEHYgSQIO5AEYQeSoPU2AuZXLHnl3/dNvDlfHP+Xf7i669jkn8wV1319rty/Wj/+TnF8/4mtxfEHf/GprmMbv12+lPRbl5dbb+e9Vt4u+CD27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBH32ETD5TvkU1+Mbyn+TL3hxtuvYE381VVx3z5bufXBJOrGhfAzABQfKve5NR7r3+Y9fVO6jz9d+O8ul4TTs2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCfrsI2BsttxnrzWUj28o96tLVh8p98nX/l+5tpnV5f3F25d1/xVz+VR7ubZZauP4gEZht31A0tuS5iTNRkT5CA4ArenHnv0PI6I80wCA1vGeHUiiadhD0k9sP2l7+1J3sL3d9rTt6RmdbPh0AHrV9GX8NRHxiu2LJT1q+xcR8djiO0TEDkk7JOkCr+cjFaAljfbsEfFK5/awpIcldb/MKYBW9Rx222tsn//e95I+I2lfvwoD0F9NXsZvlPSw7fce57sR8U99qSqZGCv30R3ldz9jhX71fKUFf+IjLX5GWzsfvfKmb36CE9rPRM9hj4gXJP12H2sBMEC03oAkCDuQBGEHkiDsQBKEHUiCU1xHwKm15RbS/Mry+uMnuveootJ6c2XW49r60aD7FZVdTW18blXvz50Re3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSII++wiIyv9CtZddGK/1qmunmdaeu8njj3WfaXpZj107fRcfxJ4dSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Kgzz4Cav3kiePlayqXzjmvnjNe6aPXplWuajAH0DizhfUVe3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSII++wionhNeUTqvu/F14Qe4O5iv/PaNnyw36d+9iCmbz0T1v9L2TtuHbe9btGy97UdtP9e5XTfYMgE0tZy/2w9Kuv60ZXdL2hMRWyXt6fwMYIRVwx4Rj0l6/bTF2yTt6ny/S9JN/S0LQL/1+o5sY0QckqTO7cXd7mh7u+1p29Mz4mBnoC0D/zQ+InZExFRETE2qMkMhgIHpNeyv2t4sSZ3bw/0rCcAg9Br23ZJu7Xx/q6Qf9accAINS7bPbfkjStZI22D4o6SuS7pP0fdu3SXpJ0s2DLPJsN7FpY3G81uuuXdu9dM74IPvky1Hq889PlP9hk4V55yVpdk15fGzNmu7PfexYcd1zUTXsEXFLl6Hr+lwLgAHicFkgCcIOJEHYgSQIO5AEYQeS4BTXIYjj7xbHq5dMbnA55qqmj910SueC2pTMK46Wnzxje62EPTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEGffQgiep9y+VzmynaZ48JGfcWeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSoM8+BJ5otpmr0y4P8E92m88dY+Xz1T1XeYCxwgEM87WVzz3s2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCfrsQ+A1q8t3qFy73ZXxKLSja73oWp98kOfahyt99Mr57sV/uKSx81Z1Hct4Tfnqnt32TtuHbe9btOxe27+0vbfzdeNgywTQ1HJexj8o6folln89Iq7sfD3S37IA9Fs17BHxmKTXh1ALgAFq8gHdHbaf7rzMX9ftTra32562PT2j2qRmAAal17B/U9LHJV0p6ZCkr3a7Y0TsiIipiJiaFFcQBNrSU9gj4tWImIuIeUn3S7q6v2UB6Leewm5786IfPytpX7f7AhgN1T677YckXStpg+2Dkr4i6VrbV2qhQ3xA0u2DK/EcUOknV+c4r4w3mmO99tgtqvXhazye9IL8XVTDHhG3LLH4gQHUAmCAOFwWSIKwA0kQdiAJwg4kQdiBJDjFdRgmRrgFVGvbNWzNldpntVNYY7z85NXTb1dMVu6QC3t2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCPvsw1C6ZXLncc5NLSTeeUrnJ6bMq99JrUzLXH7wyfmHXq6VJR37V7LnPQuzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJ+uxDECvL51VXp01u0o4e5GWoB8xzzaZsnl/NDESLsWcHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSTosw9BTFYucF6bsrl2ffQR7pWXjM02K3xspnaHRg9/zqluDttbbP/U9n7bz9r+cmf5etuP2n6uc1u4UgCAti3nb9+spLsi4jclfUrSl2xfIeluSXsiYqukPZ2fAYyoatgj4lBEPNX5/m1J+yVdKmmbpF2du+2SdNOAagTQB2f0rsb2xyRdJelxSRsj4pC08AdB0sVd1tlue9r29IxONiwXQK+WHXbbayX9QNKdEXF0uetFxI6ImIqIqUlxYgLQlmWF3fakFoL+nYj4YWfxq7Y3d8Y3Szo8mBIB9EO19Wbbkh6QtD8ivrZoaLekWyXd17n90UAqPAfUTnGtP0B52POFVc/i9lPtEtq11tvs+d1fSY7wJNoDs5w++zWSviDpGdt7O8vu0ULIv2/7NkkvSbp5IBUC6Itq2CPiZ+p+2Md1/S0HwKCcxS/yAJwJwg4kQdiBJAg7kARhB5LgFNchmFtZ6erW+smzlScoTdlcWbVNtWMAalNZj82U/3Vvbu3eZ7/wn8uPfS5izw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSdBnH4J3tqxqtH61H11oN5fOdZcGf5nqGOt+EIDnyw9em6q6dvzB6iOVRn0y7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAn67EMwcaLcT56vXFa+dv30+VKvvNKrrp0zXu3DV4wXzjkv1q36MQIza8v/uIkD9NkXY88OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0ksZ372LZK+JWmTpHlJOyLiG7bvlfSnkl7r3PWeiHhkUIWezc7fs784/sZvfKI4fvIjlX7yu2dc0vvq54yXm/y1YwCaOL6pXFytD79q74GuYxk78Ms5qGZW0l0R8ZTt8yU9afvRztjXI+JvBlcegH5ZzvzshyQd6nz/tu39ki4ddGEA+uuM3rPb/pikqyQ93ll0h+2nbe+0va7LOtttT9uentHJZtUC6Nmyw257raQfSLozIo5K+qakj0u6Ugt7/q8utV5E7IiIqYiYmlT3ubcADNaywm57UgtB/05E/FCSIuLViJiLiHlJ90u6enBlAmiqGnbblvSApP0R8bVFyzcvuttnJe3rf3kA+mU5n8ZfI+kLkp6xvbez7B5Jt9i+UgsXGz4g6fYB1HdOmDt6tDi+5W9/Xhx/c9tvFcff3dD9b/bMmuKq1ctUj81VenMVpcevnV57wYFyb2397v8sjte2ezbL+TT+Z1r6rGh66sBZhCPogCQIO5AEYQeSIOxAEoQdSIKwA0lwKelhcLlXPX/sWHH8gu/+R3m8MDaxeVNx3dmPXlwcP7mufIhz7RTX817u3uuOAweL69a2S/U01dJ2jwGemzui2LMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKOIfYbbb8m6cVFizZIOjK0As7MqNY2qnVJ1Narftb20Yi4aKmBoYb9Q09uT0fEVGsFFIxqbaNal0RtvRpWbbyMB5Ig7EASbYd9R8vPXzKqtY1qXRK19WootbX6nh3A8LS9ZwcwJIQdSKKVsNu+3vZ/2X7e9t1t1NCN7QO2n7G91/Z0y7XstH3Y9r5Fy9bbftT2c53bJefYa6m2e23/srPt9tq+saXattj+qe39tp+1/eXO8la3XaGuoWy3ob9ntz0u6b8lfVrSQUlPSLolIspX/B8S2wckTUVE6wdg2P4DSe9I+lZEfKKz7K8lvR4R93X+UK6LiD8fkdrulfRO29N4d2Yr2rx4mnFJN0n6olrcdoW6PqchbLc29uxXS3o+Il6IiFOSvidpWwt1jLyIeEzS66ct3iZpV+f7XVr4ZRm6LrWNhIg4FBFPdb5/W9J704y3uu0KdQ1FG2G/VNLLi34+qNGa7z0k/cT2k7a3t13MEjZGxCFp4ZdHUvm6UsNXncZ7mE6bZnxktl0v05831UbYl7ow2Cj1/66JiE9KukHSlzovV7E8y5rGe1iWmGZ8JPQ6/XlTbYT9oKQti36+TNIrLdSxpIh4pXN7WNLDGr2pqF99bwbdzu3hlut53yhN473UNOMagW3X5vTnbYT9CUlbbV9ue4Wkz0va3UIdH2J7TeeDE9leI+kzGr2pqHdLurXz/a2SftRiLR8wKtN4d5tmXC1vu9anP4+IoX9JulELn8j/j6S/bKOGLnX9uqSfd76ebbs2SQ9p4WXdjBZeEd0m6UJJeyQ917ldP0K1fVvSM5Ke1kKwNrdU2+9p4a3h05L2dr5ubHvbFeoaynbjcFkgCY6gA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h+Q6oog0VyRfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = train_images[2,:].reshape(28,28)\n",
    "print(f'{int(train_labels[2])}번 클래스 의류')\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 이미지 형태 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_images.shape)\n",
    "print(train_labels.shape)\n",
    "print(test_images.shape)\n",
    "print(test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images, test_images = train_images / 255.0, test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 206s 3ms/sample - loss: 0.3819 - acc: 0.8622\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 209s 3ms/sample - loss: 0.2692 - acc: 0.8994\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 207s 3ms/sample - loss: 0.2309 - acc: 0.9152\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 206s 3ms/sample - loss: 0.2035 - acc: 0.9257\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 208s 3ms/sample - loss: 0.1808 - acc: 0.9324\n",
      "10000/10000 - 7s - loss: 0.2654 - acc: 0.9104\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.26536102355122565, 0.9104]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Reshape((28, 28, 1)))\n",
    "model.add(Conv2D(128, 5, activation='relu',padding=\"same\"))\n",
    "model.add(MaxPooling2D(2))\n",
    "model.add(Conv2D(128, 5, activation='relu',padding=\"same\"))\n",
    "model.add(MaxPooling2D(2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
    "\n",
    "model.fit(train_images, train_labels, epochs=5)\n",
    "model.evaluate(test_images, test_labels, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.51998095e-06 2.97410261e-05 7.83925934e-05 1.02068288e-05\n",
      " 9.29055677e-05 4.79578346e-01 4.33504829e-06 5.05344868e-01\n",
      " 1.22262549e-03 1.36310775e-02]\n",
      "예측 : 7번 클래스 의류\n",
      "정답 : 5번 클래스 의류\n"
     ]
    },
    {
     "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": [
    "count = 9999\n",
    "print(predictions[count])\n",
    "print('예측 : ', np.argmax(predictions[count]), '번 클래스 의류', sep=\"\")\n",
    "image = test_images[count,:].reshape(28,28)\n",
    "print('정답 : ', f'{int(test_labels[count])}번 클래스 의류', sep=\"\")\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}