Image-Classifier-Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 [==============================] - 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": [
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-Shirt/Top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneakers', 'Bag', 'Ankle Boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape #60000 images, each image is 28 X 28 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images /255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2806 - accuracy: 0.8956\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2690 - accuracy: 0.9006\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2585 - accuracy: 0.9038\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2488 - accuracy: 0.9079: 0s -\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2399 - accuracy: 0.9106\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2314 - accuracy: 0.9137\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2250 - accuracy: 0.9163\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2162 - accuracy: 0.9188\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2112 - accuracy: 0.9205\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2038 - accuracy: 0.9242\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fdf9dabe668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3491 - accuracy: 0.8833\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels) # model performs better on training than testing data, caused by overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.2680534e-07, 3.2581989e-11, 2.7048834e-12, 3.7582661e-09,\n",
       "       2.9549963e-11, 6.8229463e-05, 5.0529421e-09, 1.6452378e-02,\n",
       "       1.3752308e-08, 9.8347843e-01], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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([])\n",
    "    plt.yticks([])\n",
    "    \n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "    \n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        colour = 'blue'\n",
    "    else:\n",
    "        colour = 'red'\n",
    "    \n",
    "    plt.xlabel(\"{}{:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                       100*np.max(predictions_array),\n",
    "                                       class_names[true_label]), color=colour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions[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('green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAC6CAYAAACQs5exAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAN/ElEQVR4nO3df5Bd5V3H8fc3G/I7kdBAgkISQSAEJoNjis2M/NKmoG1lmHZQB4ZC7RRU7DiKLVXHgAOUcTSjYMFip7SD1KKdwgBiW6uD0BoohB8JLb/SCjSNCdlCCEmWkB+Pf9wT3N3zXNibTfKF5P2a2cnd7/2ec59zFz777H3OuTdKKUiS9r0x2QOQpAOVASxJSQxgSUpiAEtSEgNYkpIYwJKUZGz2AKRsM2bMKHPnzs0ehvZTy5cv7y+lHFq7zwDWAW/u3Lk8/PDD2cPQfioinu92ny9BSFISA1iSkhjAkpTEAJakJD0twrlarL3pueeeo7+/P7LHIe0rPQWwq8XamxYuXJg9BGmf8iUISUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1ISA1iSkhjAkpTEAJakJAawJCUxgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrCk3TNrFkSM7GvWrOzRvi0ZwJJ2z7p1e6f3AGIAS1ISA1iSkhjAkpTEAJakJAawJCUxgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJxmYP4O3shhtuqNafeOKJEfeOVCmlWo+IUe1X0tuXM2BJSmIAS1ISA1iSkhjAkpTEAJakJKM+C2JgYKBanzhx4qj2MW7cuN0e0y59fX0j7r377rtbtTVr1lR7DzvssFbtggsuaNWuvvrq6vZHHnlkq9bL2Q47duwYcW8vz4GkfcsZsCQlMYAlKYkBLElJDGBJSjLqRbja4hPApZde2qqddtpp1d5eFuz2ltqlxCeffHK1t7ZAeMQRR7Rqt912W3X72iLeOeecU+2dOnVqq9ZtYa22ONftEufR8hJpafScAUtSEgNYkpIYwJKUxACWpCQGsCQl6eksiJ07d7J58+YhtdWrV1d777zzzlZty5Yt1d4TTzyxVTvkkEOqvZMmTaqOq+aFF15o1W6++eZq76xZs1q1GTNmVHvvuuuuVu3ss89u1TZs2FDd/p577mnVnnrqqWrvUUcd1aotXry42jtnzpxqfbRqZ1d0e87HjGn/TvdyaKnOGbAkJTGAJSmJASxJSQxgSUrS0yLcwMBA9ROBa4Yv1gHceuut1d4FCxa0at3eD7hWX7VqVbV35cqVrdrrr79e7T3llFNatUceeaTae+aZZ7ZqtcXBbsdw1llntWovvvhitfeZZ55p1ZYtW1btPf7441u1E044odq7cOHCVu3QQw+t9tYW0VxYk0bPGbAkJTGAJSmJASxJSQxgSUrS0yLcjh07Wld3vfTSS/Udj23v+pVXXqn23n777a3a9OnTq73btm1r1WrvmQuwaNGiVu3YY4+t9tau4KpdoQfQ39/fqtWu8ut2NV/tOast4gHMnj17RDWAjRs3tmr3339/tfehhx4a8RgOPvjgVq3bVXe19zqeN29etXf8+PHVunSgcAYsSUkMYElKYgBLUhIDWJKSGMCSlKSnsyDGjBnD5MmTh9Rql8oCXHTRRa3a3Llzq721swJee+21am9tRX7ChAnV3to+VqxYUe2tmTJlSrVeO1ugdonz2rVrq9vXLlGeNm1atbe239rZDlB//+JuZ2LUdHvOa5dJr1mzptpbe26uuuqqau/5558/5Ptu758s7a+cAUtSEgNYkpIYwJKUxACWpCQ9LcJt2LCh9WGbhx9+eLW3tkjTbfGo9sGT3S633b59+4geC2Dr1q2tWu0DJrvptihUu6T6oIMOatVql+VCb4twNd0uGZ45c2ar1u14a4t73RYza/VuP8vazyIiqr1Lly4d8v26deuqfdL+yhmwJCUxgCUpiQEsSUkMYElKYgBLUpKezoLYunVr6xOIjz766Gpv7c3Mu32i8urVq1u1Xi513blzZ7W3pltv7ayAbp+gXFvVr725+Pr166vb13onTpxY7a2dXdFN7Y3iux3vq6++2qp1O+uj1tvtMu3a5czPPvtstXf443V7vqX9lTNgSUpiAEtSEgNYkpIYwJKUpOf3Ax6+CPbAAw9Ue3u51LXWW/uUYahfslt7H1yATZs2tWq9XIrc19dXrdc+8blWq33SMtQvRe6mtgjXbQGs9r693Z7H2qXE3d4PuPZJ1LXjhfql4t32e+WVVw75fsmSJdU+aX/lDFiSkhjAkpTEAJakJAawJCUxgCUpSU9nQcyePZvrr7++VaupfRpv7VJZqJ8F0e1Mgdqqfu1TlQGmTp3aqtVW6aF+xkK3lf7a5b0DAwOtWrc3Iq8dW7fLcHsZVy+9tZ9P7ROnoX72SrdPWz7uuONatcWLF1d7h7vuuutG1CftL5wBS1ISA1iSkhjAkpTEAJakJD0twvX19TF9+vQhtWuuuWaPDkiSDhTOgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQGsCQlMYAlKYkBLElJDGBJSmIAS1ISA1iSkhjAkpSkp4+ll6Qss/5qFus2rxtR78zJM1l72dq9PKLRcwYs6R1hpOHba28mA1iSkhjAkpTEAJakJAawJCUxgCUpiQEsSUkMYElKYgBLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSmJASxJSQxgSUpiAEtSEgNYkpIYwJKUxACWpCQ9fSjn8uXL+yPi+b01GB3w5mQPQNqXegrgUsqhe2sgknSg8SUISUrS0wxYkt5pLr/88hH3XnvttXtxJG25M+CIPyXie0SsIOIxIn5xD+33XiIWjrgn4reIWNmM4+tEzGjqlzT1x4j4NhHzm/pxRCwn4nEiFjW1sUR8i4hJb/KYf0PEqc3tDxDxaLOP7xNx8aiPu/14m/bw/sYRcR8R/uKW9oAopSQ9ciwClgKnU8rWJvTGUcqaPbDve4HLKOXht+yBx4A1wHxK6SfiL4EtlHIFEdMoZWPT/+vA71LKWUQsBf4NeA64llI+RMTvAxsp5UtdHu8Q4B5KeQ8RBwHPAydTymoixgNzKeXpUR/70MfcRClTdmO7sZSyvct9S4BVlHLrKEf3thER6+n8PKS9YU639bPMmczhQD+lbAWglP437on4c+CDwETgv4GLKaU0ofkgcAZwMPDblHI/EROBm4H5wJPNdrv2dSPw7qb2VUpZMmwc0XxNJuInwDRgVTOmjYP6JgO7fltta/Y3CdhGxMHNeM98k+P9MPD15vZUOs/9T5rH2Qo83Yz3i8BGYCEwC/gkpXy1ue+PgXOB8cDtbxxLxB3AkcAE4G8p5aahRxgzgLuAq4DvAn8PzG7u/QNK+Q4RVwA/DcwF+om4ms5zOo7OX0ofopRngTuAzwD7TQC7uKw0pZScL5hS4LECzxS4ocBpg+47ZNDtWwp8sLl9b4G/bm7/WoFvNbf/sMAXmtsLCmwvsHDIvqCv2X7BoH3t6vlwgY0F/rfAfQX6Bj3+7xX4QYEfFTimqc1utl/WPN7SIeOvH++X3jiOzvefL/BigX8qcF6BMU39iwX+pcCYAvMLrGrq7ytwU4Fo7ru7wKnDjnFigScKvKv5flOBmQUeLLC4qX25wC8NOo4nm9tXFFheYGLz/fUFzmtujxtU7yuwPu2/G7/82o++8l4DLmUT8AvAx4H1wG1EXNjcewYRDxKxEvhl4IRBW36t+Xc5ndkawKnAPzb7XQGsGNR/LhGPAI82+5k/ZBydlwN+B/h5OjPAFcCnB43zs5RyNPAp4M+a2guUcjqlLAK2NNs9RcQtRNxGxLGVIz68Oc5d+/0Y8Ct0ZqSXAV8Y1HsHpeyklO8DM5va+5qvR4FHgHnAMc19nyDiceABOjPhXfWDgP+gM4v+96b2XuDviHgMuBOYRsTU5r47KWWgub0M+BMiPgXMeaNeyg7g9UHbSNpNuYspnf+Z7wXubcL2I0R8BbgBWEgpP2r+NJ4waKutzb87GDr+9ovZET9LJ9zeTSkvN3/eTxjWdVIzlh802/wzUFs2/QpwY6V+NZ1g/gSdP8ufA5YA5w3rG2g9dikrgZVE3AL8D3Bhc8/WQV0x6N/PUMrnhh3j6XRCdRGlbGleptn1ONvp/KI6E/ivpjam6R0Yth+AzYPG9mUiHgTeD3yDiI9Ryn82944HXkPSqOTNgDtnEhwzqHISnYWQXeHRT8QUOq+dvpX72BV4EScCC5r6NDqh8goRM4FfrWz7Y2A+EbteB1xM53Vkho3v/cCzw47hNODHdF4bnQTspPOLoXYmxJPAzzXbTWmCc5eTeOtFoG8AH22eE4j4GSIOA34KeLkJ33nAewZtU4CPAvOI2PVL5ZvApYOO4aTqo0UcBfyQUq6jM1Ne0NTfBaynlG1vMV5JbyFzBjwFuL5ZwNpOZ+Hr45SygYh/AFbSmU0+NIJ93QjcTMQKOmc1fBeAUh4n4lHge8APge+0tixlDRFXAvcRsY1OEF7Y3HspEe+ls+j2MvCRN7aLCDoz33Obyk10ZsBj6bykMdy/AhcDn6czm/0kEZ+jMzPePOgx60r5JhHHA8ua2eom4Hw6C3uXNMf+NJ2XIQZvt4OI3wTuImIjnZn6Z5v+sXR+eV1SecTfAM5vnpO1wF809TOAe950rJJGJO80tANRxLeBD1DKhuyh7LaIrwGfZk+fMicdgLwUed/6I/7/9K93nohxdBYIDV9pD3AGLElJnAFLUhIDWJKSGMCSlMQAlqQkBrAkJTGAJSnJ/wEQKc3ohuwk3gAAAABJRU5ErkJggg==\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": 27,
   "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 = 15\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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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))\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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "img = np.expand_dims(img, 0)\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.2680892e-07 3.2582052e-11 2.7048730e-12 3.7582661e-09 2.9549904e-11\n",
      "  6.8229463e-05 5.0529612e-09 1.6452361e-02 1.3752308e-08 9.8347843e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}