기본 이미지 분류.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "electronic-complex",
   "metadata": {},
   "source": [
    "**MNIST 임포트**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "continuous-seminar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "# tensorflow와 tf.keras를 임포트합니다\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# 헬퍼(helper) 라이브러리를 임포트합니다\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 주피터에서 matplotlib 사용하기 위해\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "rolled-russia",
   "metadata": {},
   "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 [==============================] - 7s 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 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "reverse-charlotte",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "# 변수를 만들어 따로 저장"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forward-intellectual",
   "metadata": {},
   "source": [
    "**탐색**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "intensive-lawrence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape # 60,000개의 이미지, 각 28X28픽셀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "sticky-texas",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels) # 60,000개의 레이블"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "protective-uniform",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels # 0~9 사이의 정수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "standing-spank",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape # 10,000개의 이미지, 각 28X28픽셀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "compound-surname",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels) # 10,000개의 레이블"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-exception",
   "metadata": {},
   "source": [
    "**전처리**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "insured-wrist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure() # matplotlib으로 그림그림\n",
    "plt.imshow(train_images[0]) # train_images[0]을 그릴 것\n",
    "plt.colorbar()\n",
    "plt.grid(False) # grid 없앰\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "global-gothic",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "# 값의 범위를 0~1사이로 조정하기 위해"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "generous-gossip",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAAD8CAYAAADJwUnTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcw0lEQVR4nO3de5Bc5Xnn8e8zN11GM7ogIYSQLWELG9kEwcoSF5eNjS+CSiGzdihkx4YNG7Ebk40d75ZZ4jUsW9kiTsDLrgnJ2ChAiksw2LFCtMZGGGPsgCUEQRcWEEIgCd0lJCFpNDPdz/7RLei5nOf0TPdM9xG/T1WXpvvp9/Q7Z2YenfOe57yvuTsiIlnSUOsOiIgMlhKXiGSOEpeIZI4Sl4hkjhKXiGSOEpeIZI4Sl4gMGzNbamY7zWxtQtzM7H+b2QYze97Mzi5nu0pcIjKc7gQWBvGLgNnFxxLg9nI2qsQlIsPG3Z8A9gZvWQTc7QVPARPMbFradpuq1cFytNgoH03rSH6kyLtKJ4fo8qNWyTY++4lW37M3V9Z7n3n+6Dqgs+SlDnfvGMTHTQc2lzzfUnxtW9SoosRlZguBW4FG4AfuflP0/tG0ssAurOQjRSTwtK+oeBt79ub47SPvKeu9jdNe7nT3eRV/6CANOXGZWSNwG/BpCllypZktc/f11eqciIw8B/LkR+rjtgIzSp6fUnwtVMkY13xgg7tvdPcu4H4K56sikmGO0+25sh5VsAz4SvHq4jnAfncPTxOhslPFgc5NF/R9k5ktoXC1gNGMreDjRGSkVOuIy8zuAy4AJpvZFuB6oBnA3f8GWA5cDGwADgP/rpztDvvgfHGgrgOg3SZpDh2ROuc4uSpNd+Xui1PiDnx1sNutJHEN6dxUROpfnvo+xqgkca0EZpvZLAoJ63Lgi1XplYjUjAO54zVxuXuPmV0DPEKhHGKpu6+rWs9EpGaO5yMu3H05hcE1ETlOONBd51O6j2jlvIjUP8eP31NFETlOOeTqO28pcYlIb4XK+fqmxCUifRg5KrpPe9gpcYlIL4XBeSUuEcmQQh2XEpeIZExeR1wikiU64hKRzHGMXJ3P6q7EJSL96FRRRDLFMbq8sdbdCClxiUgvhQJUnSqKSMZocF5qy1J+ASucBaDxhElhfN9nT0uMtd/7VEWfnfa9WVNzYsy7uyr77Eql/Vwiwzxzg7uRcx1xiUjG5HXEJSJZUhicr+/UUN+9E5ERp8F5EcmknOq4RCRLVDkvIpmU11VFEcmSwk3WSlxSQ9YY37rhPT1hvGHunDD+wtXj4vZHkmPNh+aHbZuOxBMIN/9sVRivqFYrrUYsZb9i8R9+JX2zpuDPNv5xlsUxunXLj4hkiTsqQBWRrDEVoIpItjg64hKRDNLgvIhkimOaSFBEsqWwPFl9p4b67p2I1IAWhJUaC2t+SK/j2vzZCWH8S+f+Koz/etepibHXRp0UtvUxYZimT50bxk/7662JsZ5Nr8cbT5nzKm2/pWmcODE5mMuFbXMHDiQHqzBVl3OcV86b2SbgIJADetx9XjU6JSK1Ve9HXNVIq59w97lKWiLHB3cj7w1lPcphZgvN7EUz22Bm1w4Qf4+Z/cLMnjWz583s4rRt6lRRRHopDM5X55YfM2sEbgM+DWwBVprZMndfX/K2bwEPuPvtZjYHWA7MjLZb6RGXAz8zs2fMbElCx5eY2SozW9XN0Qo/TkSGX2HO+XIeZZgPbHD3je7eBdwPLOrzHgfai1+PB95I22ilR1wfdfetZnYi8HMz+3/u/kSvHrl3AB0A7TZpeGf5F5GKFQbnyx7jmmxmpXe7dxT/5o+ZDmwueb4FWNBnGzdQOAD6Y6AV+FTah1aUuNx9a/HfnWb2YwrZ9Ym4lYjUu0FUzu+uwvj2YuBOd7/ZzM4F/t7MPuzuidODDPlU0cxazazt2NfAZ4C1Q92eiNSHY5Xz5TzKsBWYUfL8lOJrpa4CHgBw938BRgOTo41WcsQ1FfixFeYtagLudfefVrA9GQb5zs6K2ned9VYY/8L4eE6s0Q3dibFfNsTzbW19bEYYz/1O3LfXbmlLjOWfPS9se8LauJaq/dltYXz3x6aH8V3/JnnUZGrKcpMTH30lMWZ7q3O9rYqLZawEZpvZLAoJ63Lgi33e8zpwIXCnmZ1OIXHtijY65O/S3TcCZw61vYjUJ3fozlcncbl7j5ldAzwCNAJL3X2dmd0IrHL3ZcA3gO+b2dcpDLFd6R5XAKscQkR6KZwqVq9y3t2XUyhxKH3t2yVfrwfOH8w2lbhEpJ96r5xX4hKRXgZZDlETSlwi0kd1TxWHgxKXiPSjOedl+EVLaaVMz/LWZeeE8a/MeTyMv9I9JYyf0rI3MfZ7Jz8TtuX34/j3Xvx4GD+0cXxirKE13i/bz4mPOLYuir9v746nvZm4OvlPr+GKHWHbA13JUwXlVowK25ajcFVRy5OJSIZo6mYRySSdKopIpuiqoohkkq4qikimuBs9SlwikjU6VRSRTNEYl5QnqsMaZud887dh/BPj1ofxNNOD9bIOeUvY9s1caxi/fs4/h/FdpyVPa5O24OkPXo6nvXkrqBEDaOyJf6bn/MGzibHPT1oZtv3OQ2ckxhr8UNi2XEpcIpIpquMSkUxSHZeIZIo79FRpIsHhosQlIv3oVFFEMkVjXCKSSa7EJSJZo8F5SZcyZ9ZwevmtE8P4nvZxYXx7z4QwfkJj8hJibQ1HwrYzm3eH8V255DotgMbm5OXPujyeb+q/f+ifwnjn6c1hvNni5c3OG528yvzvrf9K2LaVjWG8Uu4a4xKRzDFyuqooIlmjMS4RyRTdqygi2eM1HXYtixKXiPSjq4oikimuwXkRySKdKkpdmzIquc4KYLR1h/EWi9cPfKN7YmLs5SMfCNu+dCCuMVs4dV0Y7w5qtRqDecIgvQ7r5OZ9YbzT4zqvaK+ePzWu03oujFZHvV9VTD0eNLOlZrbTzNaWvDbJzH5uZi8X/03+7RSRTHEvJK5yHrVSzonsncDCPq9dC6xw99nAiuJzETlO5N3KetRKauJy9yeAvuuoLwLuKn59F/C56nZLRGrJvbxHrQx1jGuqu28rfr0dmJr0RjNbAiwBGM3YIX6ciIwUx8jX+VXFinvn7g7JI53u3uHu89x9XjOjKv04ERkBXuajVoaauHaY2TSA4r87q9clEampKg/Om9lCM3vRzDaY2YDj4WZ2mZmtN7N1ZnZv2jaHmriWAVcUv74C+MkQtyMi9ahKh1xm1gjcBlwEzAEWm9mcPu+ZDfxX4Hx3/xDwtbTtpo5xmdl9wAXAZDPbAlwP3AQ8YGZXAa8Bl6V/C5IoZV1Fa4znjvKe5FqqxolxpcrHJ6wJ47ty7WH8zVw8bjmh8XBi7GDP6LDt3iPxtj84alsYX314ZmJsSktchxX1G2BT1+QwPnvU9jD+nR0XJsZmjO57Lay3ngs/lhjzp/8lbFuuKpY6zAc2uPtGADO7n8LFvdIFO/8QuM3d9xU+21PP4FITl7svTggl73kRySwH8vmyE9dkM1tV8rzD3TtKnk8HNpc83wIs6LON0wDM7NdAI3CDu/80+lBVzotIbw6Uf8S1293nVfiJTcBsCmd2pwBPmNkZ7v5mUoP6vuYpIjVRxTqurcCMkuenFF8rtQVY5u7d7v4q8BKFRJZIiUtE+qtePcRKYLaZzTKzFuByChf3Sv0jhaMtzGwyhVPH8IZNnSqKSB/Vuw/R3XvM7BrgEQrjV0vdfZ2Z3QiscvdlxdhnzGw9kAP+i7vvibarxCUi/VWxutTdlwPL+7z27ZKvHfjT4qMsSlz1IGWwwJriH1NUDrH5qtPDtp8cGy/D9ZvO6WF8StPBMB5NLTNt1P6wbdvUzjCeVooxqSl5yp6DuTFh27ENR8N42vd9dku8tNrXHz07Mdb24fBgg/bmYISnGgdKDl7+VcWaUOISkQEocYlI1mgGVBHJHCUuEcmUwRWg1oQSl4j0o8UyRCR7dFVRRLLGdMQlaay5JYznO+N6psjkNV1hfHcuXkZrQkM8vUtLyjJeXUEd13mTXg3b7kqptVp9ZFYYb2s8khib0hDXYc1ojmup1nTOCOPLD70/jF/1u48mxu7r+HTYtuWnv0mMmcc/r7LUenrTMihxiUgfpsF5EckgHXGJSObka92BmBKXiPSmOi4RySJdVRSR7KnzxKUZUEUkc7J1xBUs42VNcT2SNabk6IY4nu8M5mfKx7VMabw7rrWqxK1/+70wvrlnQhjf3h3H05bxygXTozx1ZHzYdnRDdxif0nQgjB/Ix3VgkYP5eOm0aJ4xSO/7N094OTH2o/2fCtuOBJ0qiki2OLrlR0QySEdcIpI1OlUUkexR4hKRzFHiEpEsMdepoohkka4qlq+S9QPTaqE8LqupqSOL5ofxzZ+L68S+dNZvE2Pbe9rCts8enhnGxwdzWgG0pqw/2OnJ9XVvdE0M26bVQkXrJgKcGNR55Tyu29vaHfctTVp925aeYM3HS+K5wibcPaQuDUq9H3GlVs6b2VIz22lma0teu8HMtprZc8XHxcPbTREZUV7mo0bKueXnTmDhAK9/193nFh/LB4iLSBb5O+NcaY9aSU1c7v4EsHcE+iIi9eI4OOJKco2ZPV88lUwcEDCzJWa2ysxWdROPh4hIfbB8eY9aGWriuh14HzAX2AbcnPRGd+9w93nuPq+ZUUP8OBGRdwwpcbn7DnfPuXse+D4QXxYTkWw5Hk8VzWxaydNLgbVJ7xWRjMnA4HxqHZeZ3QdcAEw2sy3A9cAFZjaXQs7dBFxdjc5EdVqVapp2UhjvnjU1jO89fWxi7PBJcbHe3ItfCONXTv27ML4r1x7Gmy15v23uPiFse9bYTWH8sf1zwvjupnFhPKoDO681eU4qgDfzyfsc4OSmfWH8mxu+kBibOjaulfrBe+ML5d0eD/C82B0Pi+zPJ8/n9Z/m/CJs+2OmhPGqqPM6rtTE5e6LB3j5jmHoi4jUi6wnLhF5dzFqe8WwHJpzXkR6q/IYl5ktNLMXzWyDmV0bvO/zZuZmNi9tm0pcItJfla4qmlkjcBtwETAHWGxm/QZOzawN+BPg6XK6p8QlIv1VrxxiPrDB3Te6exdwP7BogPf9D+AvgM5yNqrEJSL9DOJUcfKxO2OKjyV9NjUd2FzyfEvxtXc+y+xsYIa7/3O5/aurwfmjF30kjJ/4ZxsTY3Pbt4Rt54x5Mox35uPlzaIpVtYfmZ4YAzicbwnjL3fFpRr7e+KygMZgJHVnVzytzc2vxkthrZj/N2H8W28MdP/9OxrGJP+3vCcXl1J8fly8/BjEP7Or3/NEYuzUlp1h24cPTQvjb6RMezO1eX8Yn9m8KzH2b9teCtvWWTnEbndPHZNKYmYNwC3AlYNpV1eJS0TqgFf1quJWYEbJ81OKrx3TBnwYeNwK66aeBCwzs0vcfVXSRpW4RKS/6tVxrQRmm9ksCgnrcuCLb3+M+35g8rHnZvY48J+jpAUa4xKRAVSrHMLde4BrgEeAF4AH3H2dmd1oZpcMtX864hKR/qpYOV+caHR5n9e+nfDeC8rZphKXiPRW45kfyqHEJSK9GPW/WIYSl4j0o8RVyuIlyBb8z5Vh8wvb1iXGDns8jUhanVZaXU5kfFO8FNXR7ng37+yOp61Jc9qo7YmxS9ufC9s+8b0FYfyjnX8cxl/5ZDwlz4ojydO37OqJv+/LX/1kGF/9+owwfs7MVxNjZ7RtTYxBeu1cW2Nc4B1NNQRwKJ/8+/pUZ1zfNiKUuEQkc5S4RCRTajy7aTmUuESkPyUuEcmaep9IUIlLRPrRqaKIZIsKUEUkk5S43tF9YitvfDl57dgbxv+fsP29e89JjM0YvTds+96W3WH8zDGvhfFIW0Nc0/OB9rim5+FDp4Txx9/8YBif1vxmYuxXh98Xtr3/hr8M41d+/Rth/Nzl/yGMH5iZfB9/T2v819F+5p4w/q2z4nnnWiyXGHszF9dpTRp1KIxPaIxr99JEdYdtDclLugE0fuD9iTHbFM87Vw5VzotIJlm+vjOXEpeI9KYxLhHJIp0qikj2KHGJSNboiEtEskeJS0Qypbqr/AyL1MRlZjOAu4GpFPJwh7vfamaTgH8AZgKbgMvcfV+0rYZuGLsjeY88fGBu2JdTxySvRbe7O14/8JG3zgjjp4wJu874xuTamvcH82EBPNc5IYz/dNeHwvjJY+L1BXd0j0+M7eluDdseDuaFArjju7eE8Zt3xOsyXjppdWLszJa4TuvNfLyWy/qU9SgP5kcnxjo9np9tf0qdV1vw+wDQ7fGfVqMn/x1MaIhrxA6ccUJiLLej8mORLNRxlbPKTw/wDXefA5wDfNXM5gDXAivcfTawovhcRI4H7uU9aiQ1cbn7NndfXfz6IIUlhqYDi4C7im+7C/jcMPVRREZYtZYnGy6DOq40s5nAWcDTwFR331YMbadwKikiWXc8FaCa2TjgIeBr7n6guFw2AO7uZgPnXzNbAiwBaGkd+rzuIjJy6n1wvqyVrM2smULSusfdf1R8eYeZTSvGpwE7B2rr7h3uPs/d5zWNigeKRaQ+WL68R62kJi4rHFrdAbzg7qWXmJYBVxS/vgL4SfW7JyIjzqn7wflyThXPB74MrDGz54qvXQfcBDxgZlcBrwGXpW2osStP2+ajifG8W2IM4LHdydO7TB19MGw7t21zGH/xcHxpfc2RkxNjq5veE7Yd09gdxse3xNPitDYl7zOAyc3J3/usUQMeCL8tmvoFYGVn/L39xymPh/HXe5KHB/7p0Glh2/WHk/c5wMSUZeHWHEhuf7inJWx7NBf/aXT2xOU140fFP9OPTEqeRulFpoVtd50ZTBX067Bp2eq9HCI1cbn7kxRKOwZyYXW7IyJ1IeuJS0TeXbJQgKrEJSK9uWsiQRHJoPrOW0pcItKfThVFJFsc0KmiiGROfeetEU5cbx2h4ZfPJoZ/+LPzw+b/bdEPE2O/TFnC6+Htcd3Nga54epcpY5OXq2oP6qgAJjXHS12NT6lHGm3x8mb7epLvSDjaEE/fkkusdCnYfjR5yhyAX+dnh/HufGNi7GgQg/T6t71dk8P4yWP2J8YO9iRPeQOw6eCkML57/7gw3jk2/tN6Mpe8bNzCk9aFbcfsTP6ZNcS/KmWr5qmimS0EbgUagR+4+0194n8K/HsKM9HsAv7A3cP1Asu65UdE3l0s72U9Urdj1gjcBlwEzAEWF6fFKvUsMM/dfwd4EPhO2naVuESkNx/EI918YIO7b3T3LuB+ClNivfNx7r9w92OnHU8B8QrJaIxLRPooFKCWfa442cxWlTzvcPeOkufTgdL77bYAC4LtXQX837QPVeISkf7Kn/lht7vPq8ZHmtnvA/OAj6e9V4lLRPoZxBFXmq3AjJLnpxRf6/15Zp8C/gz4uLvHswqgMS4R6au6Y1wrgdlmNsvMWoDLKUyJ9TYzOwv4W+ASd4+nMynSEZeI9FG9exXdvcfMrgEeoVAOsdTd15nZjcAqd18G/CUwDvhhcWbl1939kmi75iM4GVi7TfIFNvSZcPZ/6ZzE2Kl/9GLYdv6EV8P46gPxvFOvB3U93SnLaDU3xAMGY5u7wvjolHqmlsbkObUaUv5bzKfUcbU2xn1LmyusvSl5Xqq2xnjOqoYKp9hsDL733+6fWdG221K+7x6PfyfOHf9KYmzpq+eFbcdfvCEx9rSv4IDvjX+oKdrbpvv8s/6orPeu+NW3nqnWGNdg6IhLRHo7HhaEFZF3oRpOy1wOJS4R6a++85YSl4j0Z/n6PldU4hKR3pzBFKDWhBKXiPRieDULUIeFEpeI9KfE1UdDMAdTPl7jb/w9TyXG9twTf+yDn/9sGF9w3cow/rsz/zUx9sGWHWHb5pTj7tEp155bG+KynM7glyzt1ognj8wI47mULTy27/Qw/mb3mMTYjsPtYdvmoD6tHNE6nUd64nnK9h+J5+tqbIj/sDsfj+cKe3V98vxx45fHv4sjQolLRDJFY1wikkW6qigiGeM6VRSRjHGUuEQkg+r7TFGJS0T6Ux2XiGRP1hOXmc0A7gamUjj77XD3W83sBuAPKayDBnCduy9P/cSUWq3h0vrQ02F87UNx+7XMSozZR8I5zzhyUnItE8CoPfHcTgffG7dvfyV53caGo/FCe/l/fSGMp3urgrYHwmg8C1llWlLiUyr+hJcq3kLNuEOuvs8Vyzni6gG+4e6rzawNeMbMfl6Mfdfd/2r4uiciNZH1Iy533wZsK3590MxeoLDkkIgcr+o8cQ1qsQwzmwmcBRw777rGzJ43s6VmNjGhzRIzW2Vmq7pJXbxDRGrNgbyX96iRshOXmY0DHgK+5u4HgNuB9wFzKRyR3TxQO3fvcPd57j6vmVGV91hEhpmD58t71EhZVxXNrJlC0rrH3X8E4O47SuLfBx4elh6KyMhy6n5wPvWIywrrBd0BvODut5S8Pq3kbZcCa6vfPRGpCffyHjVSzhHX+cCXgTVm9lzxteuAxWY2l0J+3gRcPQz9ywRfuSaMxxOkpGv/zdDb1vf/m1K36nxwvpyrik/CgIvvpddsiUgG6SZrEckaBzStjYhkjo64RCRbjo9bfkTk3cTBa1ijVQ4lLhHpr4ZV8eVQ4hKR/jTGJSKZ4q6riiKSQTriEpFscTxXmwk/y6XEJSK9HZvWpo4pcYlIf3VeDjGoiQRF5PjngOe9rEc5zGyhmb1oZhvM7NoB4qPM7B+K8aeLE5aGlLhEpDev3kSCZtYI3AZcBMyhMKvMnD5vuwrY5+7vB74L/EXadpW4RKQfz+XKepRhPrDB3Te6exdwP7Coz3sWAXcVv34QuLA4D2CiER3jOsi+3Y/6g6+VvDQZ2D2SfRiEeu1bvfYL1Lehqmbf3lvpBg6y75FH/cHJZb59tJmtKnne4e4dJc+nA5tLnm8BFvTZxtvvcfceM9sPnECwT0Y0cbl7r+XqzGyVu88byT6Uq177Vq/9AvVtqOqtb+6+sNZ9SKNTRREZTluBGSXPTym+NuB7zKwJGA/siTaqxCUiw2klMNvMZplZC3A5sKzPe5YBVxS//gLwmHtcul/rOq6O9LfUTL32rV77BerbUNVz3ypSHLO6BngEaASWuvs6M7sRWOXuyygsxvP3ZrYB2EshuYUsJbGJiNQdnSqKSOYocYlI5tQkcaXdAlBLZrbJzNaY2XN96lNq0ZelZrbTzNaWvDbJzH5uZi8X/51YR327wcy2Fvfdc2Z2cY36NsPMfmFm681snZn9SfH1mu67oF91sd+yZMTHuIq3ALwEfJpCMdpKYLG7rx/RjiQws03APHevebGimX0MeAu4290/XHztO8Bed7+pmPQnuvs366RvNwBvuftfjXR/+vRtGjDN3VebWRvwDPA54EpquO+Cfl1GHey3LKnFEVc5twAI4O5PULjKUqr09oi7KPzij7iEvtUFd9/m7quLXx8EXqBQnV3TfRf0SwapFolroFsA6umH58DPzOwZM1tS684MYKq7byt+vR2YWsvODOAaM3u+eCpZk9PYUsWZBs4CnqaO9l2ffkGd7bd6p8H5/j7q7mdTuJv9q8VTorpULNKrp3qW24H3AXOBbcDNteyMmY0DHgK+5u4HSmO13HcD9Kuu9lsW1CJxlXMLQM24+9bivzuBH1M4ta0nO4pjJcfGTHbWuD9vc/cd7p7zwqJ836eG+87Mmikkh3vc/UfFl2u+7wbqVz3tt6yoReIq5xaAmjCz1uKgKWbWCnwGWBu3GnGlt0dcAfykhn3p5VhSKLqUGu274pQodwAvuPstJaGa7rukftXLfsuSmlTOFy/3/i/euQXgz0e8EwMws1MpHGVB4Xaoe2vZNzO7D7iAwrQnO4DrgX8EHgDeA7wGXObuIz5IntC3Cyic7jiwCbi6ZExpJPv2UeBXwBrg2Gx311EYT6rZvgv6tZg62G9Zolt+RCRzNDgvIpmjxCUimaPEJSKZo8QlIpmjxCUimaPEJSKZo8QlIpnz/wG6tXQ4tsEc5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0]) \n",
    "plt.colorbar()\n",
    "plt.grid(False) \n",
    "plt.show()\n",
    "\n",
    "# 다시 그려보니 0~1사이의 값 가짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "advised-legislation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10)) # 그림크기 10x10\n",
    "for i in range(25): # 총 25개 그림\n",
    "    plt.subplot(5,5,i+1) # 세로5개, 가로5개 총 25개\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    # cmap=plt.cm.binary : 흑백으루\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "czech-headset",
   "metadata": {},
   "source": [
    "**층설정**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "failing-washer",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    # 1번째 레이어, Flatten에서 28x28픽셀의 이미지를 1차원 배열로 변환\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    # 2번째 레이어, Dense에서 128개의 노드\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "    # 3번째 레이어, Dense에서 10개의 노드\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "neither-legislature",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "contemporary-heaven",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 1s 573us/step - loss: 0.5036 - accuracy: 0.8224\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 1s 598us/step - loss: 0.3824 - accuracy: 0.8629\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 1s 585us/step - loss: 0.3426 - accuracy: 0.8763\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 1s 577us/step - loss: 0.3169 - accuracy: 0.8841\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 1s 564us/step - loss: 0.2982 - accuracy: 0.8909\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1862a881408>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "national-germany",
   "metadata": {},
   "source": [
    "**정확도 평가**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "powerful-jungle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.3449 - accuracy: 0.8760\n",
      "\n",
      "테스트 정확도: 0.8759999871253967\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\n테스트 정확도:', test_acc)\n",
    "\n",
    "# test셋의 정확도 < train셋의 정확도 : 오버피팅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moral-adobe",
   "metadata": {},
   "source": [
    "**예측**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "superb-accent",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "after-simulation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.38999233e-06, 1.11739529e-09, 9.67277401e-07, 1.87960813e-06,\n",
       "       5.14854310e-06, 1.45542575e-02, 8.42992722e-06, 1.55732613e-02,\n",
       "       2.85267131e-04, 9.69563365e-01], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "moderate-daniel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "assisted-marshall",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "japanese-quebec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([]) # 눈금(tick) 표시\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "    # predictions에서 가장 큰 값을 predicted_label로 가져옴\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue' # test_label과 일치 -> 블루\n",
    "  else:\n",
    "    color = 'red' # 불일치 -> 레드\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "descending-holly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "suffering-survey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "signal-reply",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 처음 X 개의 테스트 이미지와 예측 레이블, 진짜 레이블을 출력합니다\n",
    "# 올바른 예측은 파랑색으로 잘못된 예측은 빨강색으로 나타냅니다\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows)) # 행\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "boxed-nothing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# 테스트 세트에서 이미지 하나를 선택합니다\n",
    "img = test_images[0] # 첫번째걸루\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "banner-mixture",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# 이미지 하나만 사용할 때도 배치에 추가합니다\n",
    "img = (np.expand_dims(img,0)) # 차원추가 (28, 28) -> (1, 28, 28)\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "negative-tissue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7.38999961e-06 1.11739529e-09 9.67276492e-07 1.87960813e-06\n",
      "  5.14854310e-06 1.45542445e-02 8.42992722e-06 1.55732613e-02\n",
      "  2.85267131e-04 9.69563365e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "psychological-invasion",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_value_array(0, predictions_single, test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "presidential-watts",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}