Basic classification MNIST Fashion dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL \n",
    "import scipy\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data must be preprocessed before training the network. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATEAAAD8CAYAAAAfZJO2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHNZJREFUeJzt3X2QXOV15/HvmVdJo9EbEkII2QIsbGR7EewYZEg52AQbXK7IxMYFlcU4S1nsLqxDij9wtNky+wdbLq+BkDhhI4zWUAUmxECsEJV5kY0xtgEJgZFAwRIgIyGhV5CEpJFmus/+0Xecnpd7bs90z3Tf0e9Ddannnn7ufaZn5nDvc08/j7k7IiJ51VTvDoiIVENJTERyTUlMRHJNSUxEck1JTERyTUlMRHJNSUxEck1JTERyTUlMRHKtZSwP1mbtPoGOsTykyHGlm0Mc86NWzT4+9+kO37uvUNFrX3j56GPufkk1x6tWVUnMzC4B7gCage+7+7ej10+gg/PsomoOKSKB53x11fvYu6/A8499oKLXNs/ZNDOKm9k84F7gJKAILHf3O8zsZuDrwO7kpcvcfVXS5i+Ba4AC8A13fyw6xoiTmJk1A38HXAxsA9aY2Up3f3Wk+xSR+nOgSLFWu+sFbnT3dWbWCbxgZk8ksdvd/bvlLzazhcAVwEeBk4EnzewMd089NazmTOxcYLO7v5Ec/AFgCaAkJpJjjtOTnjOGty/3HcCO5PlBM9sIzA2aLAEecPejwJtmtplSrvl1WoNqBvbnAlvLvt42VOfMbKmZrTWztT0creJwIjJWihX+NxxmNh84G3gu2XS9mb1sZivMbHqyraK8Uq6aJDbU4OGgeX3cfbm7d7l7VyvtVRxORMaC4xS8sgcws+8kJXksHWqfZjYZeAi4wd0PAHcCpwOLKJ2p3dr30iG7FKjmcnIbMK/s61OA7VXsT0QaRDHOG+X2uHtX9AIza6WUwO5z94cB3H1nWfwu4NHky2HnlWrOxNYAC8zsVDNrozQYt7KK/YlIA3CggFf0yGJmBtwNbHT328q2zyl72WXAhuT5SuAKM2s3s1OBBcDz0TFGfCbm7r1mdj3wGKUSixXu/spI9ycijWMYZ2JZLgCuAtab2UvJtmXAlWa2iFLO3AJcC+Dur5jZg5RuEPYC10V3JqHKOrGkrmNVNfsQkcbiQE+Npq1392cYepwrNW+4+y3ALZUeY0wr9kWk8XmFl4qNQklMRPpzKOQnhymJiUh/pYr9/FASE5EBjMKQw1iNSUlMRPopDewriYlITpXqxJTERCTHijoTE5G80pmYiOSaYxRyNHO9kpiIDKLLSRHJLcc45s317kbFlMREpJ9SsasuJ0UkxzSwL43DMn4Zq5ytoPmEGWH83c+dkRqbcv+zVR0763uzltbUmPccq+7Y1cr6uURqNMNE+u6NgutMTERyrKgzMRHJq9LAfn5SQ356KiJjQgP7IpJ7BdWJiUheqWJfRHKvqLuTIpJXpQ+AK4lJg7Dm+OMj3tsbxpsWLQzjG6+dHLc/kh5rPXRu2LblSDxJcuvja8N4VbVgWTVoGe8rFieBavpmLcGfbfzjrIhj9OhjRyKSV+6o2FVE8sxU7Coi+eXoTExEck4D+yKSW45pUkQRya/Skm35SQ356amIjJHjaPFcM9sCHAQKQK+7d9WiU1I7YU0R2XViWz83LYz/6Sd/EcZ/ufu01Njv2k8K2/rEMEzLH30yjJ/x92+nxnq3vBXvPGPOrqz3LUvz9OnpwUIhbFs4cCA9WIOpxpzjr2L/0+6+pwb7EZEGcdyciYnI+ONux9WZmAOPm5kD/+Duy2vQJxGpo9LA/vHzsaML3H27mZ0IPGFm/+buT5e/wMyWAksBJjCpysOJyOjL1xz7VfXU3bcn/+4CHgEGfaLX3Ze7e5e7d7XSXs3hRGQMlAb2raJHFjObZ2Y/M7ONZvaKmf15sn2GmT1hZpuSf6cn283M/sbMNpvZy2Z2TtYxRpzEzKzDzDr7ngOfBTaMdH8i0jgKNFX0qEAvcKO7nwksBq4zs4XAN4HV7r4AWJ18DXApsCB5LAXuzDpANZeTs4FHrDRlSQtwv7v/pIr9iUgDqGXFvrvvAHYkzw+a2UZgLrAEuDB52T3AU8BNyfZ73d2BZ81smpnNSfYzpBEnMXd/AzhrpO1lbBS7u6tqf+zs98P4l6fGc3pNaOpJjf28KZ4v7O2fzgvjhf8Q9+13t3Wmxoovnh+2PWFDXKs15cXUvykA9nxqbhjf/R/TC7pmZyzHOf3J11Njtq82BQfDWChkppmV/xIsT7vBZ2bzgbOB54DZfYnJ3Xck4+pQSnBby5ptS7bVPomJyPjkDj3FipPYnkqK3M1sMvAQcIO7H7D0SSeHCoQlvEpiItJP6XKydncnzayVUgK7z90fTjbv7LtMNLM5wK5k+zag/BT8FGB7tP/83EcVkTFTSD4/mfXIYqVTrruBje5+W1loJXB18vxq4Mdl27+a3KVcDOyPxsNAZ2IiMkBfiUWNXABcBaw3s5eSbcuAbwMPmtk1wFvA5UlsFfB5YDNwGPizrAMoiYnIALW7nHT3Zxh6nAvgoiFe78B1wzmGkpiIDKI59mVsRcuLZUwp8/5XFofxry58Koy/3jMrjJ/Sti81dvnJL4Rt+U9x/Huv/WEYP/TG1NRYU0f8vryzOD4TeXtJ/H17TzxVz/R16X96TVfvDNseOJY+vVFhdfWfiindnTx+PjspIuOMpqcWkdzT5aSI5FaN706OOiUxERnkeJoUUUTGGXejV0lMRPJMl5MiklsaE5Phi+q8Rtnim54P45+e/GpV+58bTEBwyNvCtu8VOsL4txb+axjffUb6VDxZi8N+f1M8Vc/7QQ0aQHNv/DNd/J9fTI19acaasO13Hvp4aqzJD4VtK6UkJiK5pToxEck91YmJSG65Q2/lkyLWnZKYiAyiy0kRyS2NiYlI7rmSmIjkmQb2ZXgy5vwaTZvePzGM750yOYy/0zstjJ/QnL6sWmfTkbDt/NY9YXx3Ib0ODKC5NX1JuGMez5f1vz76L2G8+8zWMN5q8ZJv509IX/vi8le/Grbt4I0wXi13jYmJSK4ZBd2dFJE805iYiOSWPjspIvnmdR2mHTYlMREZRHcnRSS3XAP7IpJ34+py0sxWAF8Adrn7x5JtM4B/BOYDW4CvuPu7o9dNGS2z2tPruAAmWE8Yb7N4fcXtPdNTY5uOfDhs+9sDcQ3bJbNfCeM9QS1YczDPGWTXeZ3cGv+6d3tcRxa9qxfMjuvAXgqjtZGnu5OVnDP+ALhkwLZvAqvdfQGwOvlaRMYB91ISq+TRCDKTmLs/DQxcxnkJcE/y/B7gizXul4jUUdGtokcjGOmY2Gx33wHg7jvMLD7vF5FcGVdjYtUys6XAUoAJTBrtw4lIlRyjmKO7kyPt6U4zmwOQ/Lsr7YXuvtzdu9y9q5X2ER5ORMaSV/hoBCNNYiuBq5PnVwM/rk13RKTuxtvAvpn9EPg18GEz22Zm1wDfBi42s03AxcnXIjJe5OhULHNMzN2vTAldVOO+HL8y1p205njuK+9Nr9Vqnp5epwXwh9PWh/HdhSlh/L1CPM45rflwauxg74Sw7b4j8b4/0r4jjK87PD81NqstrvOK+g2w5djMML6g/Z0w/p2d6X8+8yYMLAbor/eiT6XG/Llfh20rVauzrJQ605uBrwO7k5ctc/dVSewvgWuAAvANd38s6xiq2BeRfhwoFmt2qfgD4HvAvQO23+7u3y3fYGYLgSuAjwInA0+a2RnuHlYe5+cWhIiMDQfcKntk7WroOtM0S4AH3P2ou78JbAbOzWqkJCYig7hX9qjC9Wb2spmtMLO+MY+5wNay12xLtoWUxERksMoH9mea2dqyx9IK9n4ncDqwCNgB3JpsH+rULjNVakxMRAYYVvnEHnfvGs7e3X3n749kdhfwaPLlNmBe2UtPAdJXVEnoTExEBhvFEou+QvnEZcCG5PlK4AozazezU4EFwPNZ+9OZWCPIGFywlvjHFJVYbL3mzLDtZybFS5P9qjsekpjVcjCMR9PhzGnfH7btnN0dxrPKO2a0pE8zdLAwMWw7qeloGM/6vs9pi5eb+4snz0mNdX5sb9h2Smtw7lGLm4oOXqO7k0md6YWULju3Ad8CLjSzRaUjsQW4FsDdXzGzB4FXgV7guqw7k6AkJiJDqk0SS6kzvTt4/S3ALcM5hpKYiAzWINX4lVASE5HBlMREJLf6il1zQklMRAbRpIgikm+1++zkqFMSE5FBTGdiMhzW2hbGi91xvVRk5vpjYXxPIV5abFpTPCVNW8bSZseCOrHzZ7wZtt2dUcu17sipYbyz+UhqbFZTXOc1rzWu1VrfPS+Mrzr0oTB+zReeTI39cPnFYdu2n/wqNWYe/7wq0kBzhVVCSUxEBqhshopGoSQmIoPpTExEcq1Y7w5UTklMRPpTnZiI5J3uTopIvuUoiWk+MRHJtXydiQVLm1lLXO9kzRn5uimOF7uD+aWKmVMehbwnruWqxh3/8L0wvrV3Whh/pyeOZy1tVgimdHn2yNSw7YSmnjA+q+VAGD9QjOvMIgeL8XJy0TxpkN33m07YlBp7eP8fhW3Hgi4nRSS/HH3sSERyTmdiIpJnupwUkXxTEhORXFMSE5G8MtflpIjk3Xi6O2lmK4AvALvc/WPJtpuBrwO7k5ctc/dV1XammvUVs2qtPC7bqasjS84N41u/GNeh/enZ6euLvtPbGbZ98fD8MD41mJMLoCNjfcZuT6/f235setg2q9YqWlcS4MSgjqzgcV3g2z1x37Jk1c9t6w3WxPzjeK6zafeOqEvDkqczsUoq9n8AXDLE9tvdfVHyqDqBiUgDGcUVwGst80zM3Z82s/mj3xURaQg5GxOr5rOT15vZy2a2wsyqO/cWkcaSozOxkSaxO4HTgUXADuDWtBea2VIzW2tma3uIx09EpDFYsbJHIxhREnP3ne5ecPcicBeQOjLt7svdvcvdu1ppH2k/RUSGNKIkZmZzyr68DNhQm+6ISEPI0eVkJSUWPwQuBGaa2TbgW8CFZraI0rexBbh2FPsoImMpZwP7ldydvHKIzXePQl/COrBqtcw5KYz3nDo7jO87c1Jq7PBJcWHgos9vDONfm/3/wvjuwpQw3mrp79vWnhPCtmdP2hLGf7p/YRjf0zI5jEd1Zud3pM+pBfBeMf09Bzi55d0wftPmL6fGZk+Ka7G+/8G4aqjH4wGh13rioZP9xfT5yL6x8Gdh20eYFcZrYjwlMRE5DimJiUheGY1z57ESSmIi0l/OxsS0UIiIDFaju5NJMfwuM9tQtm2GmT1hZpuSf6cn283M/sbMNieF9OdU0lUlMREZrHYlFj9g8GevvwmsdvcFwOrka4BLgQXJYymlovpMSmIiMkjfnGJZjyzu/jSwb8DmJcA9yfN7gC+Wbb/XS54Fpg2oSR1SQ42JHb30E2H8xP/xRmps0ZRtYduFE58J493FeMm3aFqYV4/MDdseLraF8U3H4vKP/b1xqUFzMAq761g8Fc+tb8bLg60+9/+G8b/aPtQEJ/+uaWL6b/reQlye8aXJ8ZJsEP/Mrv3A06mx09p2hW0fPRT/7WzPmKpnduv+MD6/dXdq7E86fxu2HQclFrPdfQeAu+8wsxOT7XOBrWWv25Zs2xHtrKGSmIg0AB/W3cmZZra27Ovl7r58hEcequAyM50qiYnIYJWfie1x965h7n2nmc1JzsLmAH2nxduAeWWvOwXYnrUzjYmJyCC1GhNLsRK4Onl+NfDjsu1fTe5SLgb29112RnQmJiKD1WhMLOWz198GHjSza4C3gMuTl68CPg9sBg4Df1bJMZTERKS/Gs5QkfLZa4CLhnitA9cN9xhKYiLSj5Gvin0lMREZREksjcXLsp33v9eEzS/qfCU1dtjjqU+y6sCy6n4iU1vi5bmO9sRv866eeKqdLGe0v5Mau2zKS2Hbp793Xhj/g+7/HsZf/0w8jdDqI+lTzuzujb/vK978TBhf99a8ML54/pupsY93vh22zarN62zuDuPR9EgAh4rpv6/Pdsf1c2NCSUxEck1JTERyK2ezWCiJichgSmIikmeaFFFEck2XkyKSXw20HFsllMREZDAlsaH1nNjB9qtSFwvn5ql/G7a/f9/i1Ni8CQPnXevvg217wvhZE38XxiOdTXHN0IenxDVDjx46JYw/9d5Hwvic1vdSY784fHrY9oGb/08Y/9pf3BjGP7nqv4TxA/PT5xjo7Yj/UqactTeM/9XZ/xrG26yQGnuvENeBzWg/FManNce1gVmiusbOpvRl7gCaP/yh1JhtiefNq4Qq9kUk96yYnyymJCYi/WlMTETyTpeTIpJvSmIikmc6ExORfFMSE5HcGt5qR3WXmcTMbB5wL3ASUKS0JNMdZjYD+EdgPrAF+Iq7vxvtq6kHJu1Mf3cePbAo7MtpE9PX6tvTE6+v+Nj7Hw/jp0wMu87U5vTanQ8F83kBvNQ9LYz/ZPdHw/jJE+P1F3f2TE2N7e3pCNseDua1Arj79tvC+K0743UrL5uxLjV2VltcB/ZeMV7H5tWM9ToPFiekxro9nl9uf0YdWWfw+wDQ4/GfVrOn/x1Ma4pr0A58/ITUWGFn9ecleasTq2S1o17gRnc/E1gMXGdmC0lfilxE8s69skcDyExi7r7D3dclzw8CGymtypu2FLmI5NwoL9lWU8M69zSz+cDZwHOkL0UuInk2XotdzWwy8BBwg7sfMBtqxfEh2y0FlgK0dYx8HnsRGTt5GtivaAVwM2ullMDuc/eHk807kyXIGbAUeT/uvtzdu9y9q6U9HmQWkcZgxcoejSAziVnplOtuYKO7l9+qSluKXETyzMnVwH4ll5MXAFcB682sb/2vZaQvRZ6q+ViRzq1HU+NFjy9Rf7onfUqa2RMOhm0XdW4N468djm/Xrz9ycmpsXcsHwrYTm3vC+NS2eCqfjpb09wxgZmv6935q+5AnyL8XTVcDsKY7/t7+66ynwvhbvelDCP9y6Iyw7auH099zgOkZS+WtP5De/nBvW9j2aCH+0+jujUt2prbHP9NPzEif+uk15oRtd58VTG/0y7BpxRpl0L4SmUnM3Z+hVDoylEFLkYvIODCekpiIHF/yVuyqJCYi/blrUkQRybn85DAlMREZTJeTIpJfDuhyUkRyLT85bIyT2PtHaPr5i6nhf3r8grD5/1zyT6mxn2csa/boO3Fdz4Fj8ZQ0syalL+E1JajTApjRGi//NTWj3mmCxUu+vdub/kmIo03xlDOF1OqZkneOpk/zA/DL4oIw3lNsTo0dDWKQXV+379jMMH7yxP2psYO96dP0AGw5OCOM79k/OYx3T4r/tJ4ppC+ld8lJr4RtJ+5K/5k1xb8qFdPlpIjkWi3vTprZFuAgUAB63b1rJPMRpqnos5MichzxYTwq92l3X+TuXcnXNZuPUElMRPopFbt6RY8q1Gw+QiUxERmsWOEDZprZ2rLH0iH25sDjZvZCWbzffITAiOcj1JiYiAwyjLOsPWWXiGkucPftycSpT5jZv1XXu/50JiYi/dV4TMzdtyf/7gIeAc6lwvkIK6EkJiIDlD47Wckji5l1mFln33Pgs8AGajgfYUNdTp5206/D+N+//OX0tv/ttbDtpSdtCOPrDsTzZr0V1A39JphrDKC1KZ4Cc1LrsTA+IaNeqq05fU6wpoz/XRYz6sQ6muO+Zc11NqM9vUauszmec6upyqlDm4Pv/fn988O2syfFtX8fmrInjPd6fH7wyamvp8ZWvHl+2Hb23/4qNbbF45rEitVuwsPZwCPJdPYtwP3u/hMzW8Mw5yNM01BJTEQaQA0Xz3X3N4Czhti+lxrNR6gkJiKDNcjU05VQEhORwfKTw5TERGQwKzbIUkYVUBITkf6cvkLWXFASE5F+jKo/UjSmlMREZDAlsUBTMIdUMV4Dcep9z6bG9t4XH/ZHX/pcGD9v2Zow/oX5v0mNfaRtZ9i2NePcfELG/eyOpriWqzv4hcuqZn7myLwwXsjYw0/fPTOMv9czMTW28/CUsG1rUP9WiWgd0yO98Txr+4/E8401N8V/5N1PxXOdvflq+vx3U1fFv4tjQklMRHJLY2Iikne6OykiOea6nBSRHHOUxEQk5/JzNakkJiKDqU5MRPJtPCUxM5sH3AucROkkc7m732FmNwNfB3YnL13m7qsyj5hRCzZaOh56LoxveChuv4FTU2P2iT8O2x45Kb1WCqB9bzwn18EPxu2nvJ4+h1TT0XghwuJvNobxbO9X0fZAGI1nUatOW0Z8VtVH+G3Ve6gbdyjk53qykjOxXuBGd1+XzND4gpk9kcRud/fvjl73RKQuxtOZWLISSd+qJAfNbCMwd7Q7JiJ1lKMkNqw59s1sPnA20Hdtdr2ZvWxmK8xsekqbpX3LOfUQXzaJSANwoOiVPRpAxUnMzCYDDwE3uPsB4E7gdGARpTO1W4dq5+7L3b3L3btaaa9Bl0VkdDl4sbJHA6jo7qSZtVJKYPe5+8MA7r6zLH4X8Oio9FBExpaTq4H9zDMxKy1Tcjew0d1vK9s+p+xll1FahklExgP3yh4NoJIzsQuAq4D1ZvZSsm0ZcKWZLaKUt7cA145KD3PA16wP4/GkLtmmpK/QlSk//z+VhtIgCaoSldydfAaGXJwwuyZMRHKocc6yKqGKfRHpzwFNxSMiuaYzMRHJr/H3sSMROZ44eIPUgFVCSUxEBmuQavxKKImJyGAaExOR3HLX3UkRyTmdiYlIfjleqM/kpSOhJCYi/fVNxZMTw5pPTESOEzWcisfMLjGz18xss5l9s9Zd1ZmYiPTjgNfoTMzMmoG/Ay4GtgFrzGylu79akwOgMzERGchrOiniucBmd3/D3Y8BDwBLatldnYmJyCA1HNifC2wt+3obcF6tdg5jnMQO8u6eJ/1HvyvbNBPYM5Z9GIZG7Vuj9gvUt5GqZd8+WO0ODvLuY0/6j2ZW+PIJZra27Ovl7r687OuhpvGq6V2DMU1i7t5vOT8zW+vuXWPZh0o1at8atV+gvo1Uo/XN3S+p4e62AfPKvj4F2F7D/WtMTERG1RpggZmdamZtwBXAyloeQGNiIjJq3L3XzK4HHgOagRXu/kotj1HvJLY8+yV106h9a9R+gfo2Uo3ct6q5+ypGcTp78xx9RkpEZCCNiYlIrtUliY32xxCqYWZbzGy9mb004NZxPfqywsx2mdmGsm0zzOwJM9uU/Du9gfp2s5m9nbx3L5nZ5+vUt3lm9jMz22hmr5jZnyfb6/reBf1qiPctr8b8cjL5GMJvKfsYAnBlLT+GUA0z2wJ0uXvda4rM7FPA+8C97v6xZNt3gH3u/u3kfwDT3f2mBunbzcD77v7dse7PgL7NAea4+zoz6wReAL4IfI06vndBv75CA7xveVWPM7FR/xjCeOHuTwP7BmxeAtyTPL+H0h/BmEvpW0Nw9x3uvi55fhDYSKlyvK7vXdAvqUI9kthQH0NopB+kA4+b2QtmtrTenRnCbHffAaU/CuDEOvdnoOvN7OXkcrMul7rlzGw+cDbwHA303g3oFzTY+5Yn9Uhio/4xhCpd4O7nAJcC1yWXTVKZO4HTgUXADuDWenbGzCYDDwE3uPuBeval3BD9aqj3LW/qkcRG/WMI1XD37cm/u4BHKF3+NpKdydhK3xjLrjr35/fcfae7F7y03tdd1PG9M7NWSoniPnd/ONlc9/duqH410vuWR/VIYqP+MYSRMrOOZMAVM+sAPgtsiFuNuZXA1cnzq4Ef17Ev/fQliMRl1Om9MzMD7gY2uvttZaG6vndp/WqU9y2v6lLsmtxC/mv+/WMIt4x5J4ZgZqdROvuC0qcZ7q9n38zsh8CFlGY52Al8C/hn4EHgA8BbwOXuPuYD7Cl9u5DSJZEDW4Br+8agxrhvfwD8AlgP9E16tYzS+FPd3rugX1fSAO9bXqliX0RyTRX7IpJrSmIikmtKYiKSa0piIpJrSmIikmtKYiKSa0piIpJrSmIikmv/H8L9mI+KyBmYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()#0-250 color range axis is added\n",
    "plt.gca().grid(False) #remove the grid lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We scale these values to a range of 0 to 1 before feeding to the neural network model. For this, cast the datatype of the image components from an integer to a float, and divide by 255. Here's the function to preprocess the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()#0-250 color range axis is added\n",
    "plt.gca().grid(False) #remove the grid lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the first 25 images from the training set and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid('off')\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup the layers\n",
    "The basic building block of a neural network is the layer. Layers extract representations from the data fed into them. And, hopefully, these representations are more meaningful for the problem at hand.\n",
    "\n",
    "Most of deep learning consists of chaining together simple layers. Most layers, like tf.keras.layers.Dense, have parameters that are learned during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(64, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first layer in this network, tf.keras.layers.Flatten, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels. Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
    "\n",
    "After the pixels are flattened, the network consists of a sequence of two tf.keras.layers.Dense layers. These are densely-connected, or fully-connected, neural layers. The first Dense layer has 128 nodes (or neurons). The second (and last) layer is a 10-node softmax layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 digit classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before the model is ready for training, it needs a few more settings. These are added during the model's compile step:\n",
    "\n",
    "Loss function —This measures how accurate the model is during training. We want to minimize this function to \"steer\" the model in the right direction.\n",
    "\n",
    "Optimizer —This is how the model is updated based on the data it sees and its loss function.\n",
    "\n",
    "Metrics —Used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.train.AdamOptimizer(), \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model\n",
    "Training the neural network model requires the following steps:\n",
    "\n",
    "Feed the training data to the model—in this example, the train_images and train_labels arrays.\n",
    "The model learns to associate images and labels.\n",
    "We ask the model to make predictions about a test set—in this example, the test_images array. We verify that the predictions match the labels from the test_labels array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.5226 - acc: 0.8178\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 3s 42us/step - loss: 0.3925 - acc: 0.8619\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3558 - acc: 0.8704\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 3s 43us/step - loss: 0.3347 - acc: 0.8789\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 3s 44us/step - loss: 0.3176 - acc: 0.8842\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x1c0e8dc50b8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate accuracy\n",
    "\n",
    "Next, compare how the model performs on the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 27us/step\n",
      "Test accuracy: 0.8705\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions\n",
    "\n",
    "With the model trained, we can use it to make predictions about some images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.3963092e-05, 7.2306905e-10, 3.4183734e-07, 1.4224707e-06,\n",
       "       1.1694515e-07, 1.2310008e-02, 1.6743108e-05, 5.3395733e-02,\n",
       "       9.2256843e-05, 9.3415946e-01], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A prediction is an array of 10 numbers. These describe the \"confidence\" of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the model is most confident that this image is an ankle boot, or class_names[9]. And we can check the test label to see this is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot several images with their predictions. Correct prediction labels are green and incorrect prediction labels are red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first 25 test images, their predicted label, and the true label\n",
    "# Color correct predictions in green, incorrect predictions in red\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid('off')\n",
    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
    "    predicted_label = np.argmax(predictions[i])\n",
    "    true_label = test_labels[i]\n",
    "    if predicted_label == true_label:\n",
    "      color = 'green'\n",
    "    else:\n",
    "      color = 'red'\n",
    "    plt.xlabel(\"{} ({})\".format(class_names[predicted_label], \n",
    "                                  class_names[true_label]),\n",
    "                                  color=color)\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the trained model to make a prediction about a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tf.keras models are optimized to make predictions on a batch, or collection, of examples at once. So even though we're using a single image, we need to add it to a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.3963092e-05 7.2307038e-10 3.4183799e-07 1.4224707e-06 1.1694527e-07\n",
      "  1.2310013e-02 1.6743108e-05 5.3395722e-02 9.2256931e-05 9.3415946e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(img)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model.predict returns a list of lists,even though there is only one image. one for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prediction = predictions[0]\n",
    "\n",
    "np.argmax(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 3)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### START CODE HERE ###\n",
    "img_path = 'sneakers.jpg'\n",
    "### END CODE HERE ###\n",
    "img = keras.preprocessing.image.load_img(img_path, target_size=(64, 64))\n",
    "plt.imshow(img)\n",
    "\n",
    "x =  keras.preprocessing.image.img_to_array(img)\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "img = (np.expand_dims(x,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Image' object has no attribute 'ndim'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-66-c713ebc398cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\_impl\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[0;32m   1325\u001b[0m                        \u001b[1;34m'you should specify the `steps` '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1326\u001b[0m                        'argument.')\n\u001b[1;32m-> 1327\u001b[1;33m     \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\_impl\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, batch_size)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m         \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m         exception_prefix='input')\n\u001b[0m\u001b[0;32m    766\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\_impl\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m   \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstandardize_single_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\_impl\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'DataFrame'\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m   \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstandardize_single_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\_impl\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_single_array\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     86\u001b[0m   \u001b[1;32melif\u001b[0m \u001b[0mtensor_util\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m   \u001b[1;32melif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     89\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m   \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Image' object has no attribute 'ndim'"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(img)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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