MNISTFIC.ipynb

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   "outputs": [],
   "source": [
    "## Importing modules\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import random\n",
    "\n",
    "from keras.datasets import mnist        # importing MNIST dataset included in keras\n",
    "from keras.models import Sequential     # change should be made?\n",
    "\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "X_train shape:  (60000, 28, 28)\ny_train shape:  (60000,)\nX_test shape:  (10000, 28, 28)\ny_test shape:  (10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "## Loading training data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "print(\"X_train shape: \", X_train.shape)\n",
    "print(\"y_train shape: \", y_train.shape)\n",
    "print(\"X_test shape: \", X_test.shape)\n",
    "print(\"y_test shape: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "## Checking dataset\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (9, 9)\n",
    "\n",
    "for i in range(9):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    number = random.randint(0, len(X_train))\n",
    "    plt.imshow(X_train[number], cmap='gray', interpolation='none')\n",
    "    plt.title(\"Class {}\".format(y_train[number]))\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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     ]
    }
   ],
   "source": [
    "## Examine a single digit closely\n",
    "\n",
    "def matprint(mat, fmt=\"g\"):\n",
    "    col_maxes = [max([len((\"{:\"+fmt+\"}\").format(x)) for x in col]) for col in mat.T]\n",
    "    for x in mat:\n",
    "        for i, y in enumerate(x):\n",
    "            print((\"{:\"+str(col_maxes[i])+fmt+\"}\").format(y), end=\"  \")\n",
    "        print(\"\")\n",
    "\n",
    "# CopyPaste :)\n",
    "matprint(X_train[number])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training matrix shape: (60000, 784)\nTesting matrix shape: (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "## Formatting the input data layer\n",
    "\n",
    "X_train = X_train.reshape(60000, 784)       # reshaping 60,000 28 x 28 matrices into 60,000 784-length vectors\n",
    "X_test = X_test.reshape(10000, 784)         # reshaping 10,000 28 x 28 matrices into 10,000 784-length vectors\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "X_train /= 255                              # normalizing each value for each pixel for the entire vector for each input!\n",
    "X_test /= 255\n",
    "\n",
    "print(\"Training matrix shape:\", X_train.shape)\n",
    "print(\"Testing matrix shape:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_classes = 10                             # number of uniquw digits\n",
    "\n",
    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Building a 3-layer fully connected network (FCN)\n",
    "\n",
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## First hidden layer\n",
    "\n",
    "model.add(Dense(512, input_shape=(784,)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Second hidden layer\n",
    "\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_4 (Dense)              (None, 512)               401920    \n_________________________________________________________________\nactivation_4 (Activation)    (None, 512)               0         \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 512)               0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 512)               262656    \n_________________________________________________________________\nactivation_5 (Activation)    (None, 512)               0         \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 512)               0         \n_________________________________________________________________\ndense_6 (Dense)              (None, 10)                5130      \n_________________________________________________________________\nactivation_6 (Activation)    (None, 10)                0         \n=================================================================\nTotal params: 669,706\nTrainable params: 669,706\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## Final output layer\n",
    "\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compiling the model\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 4s 66us/step - loss: 0.2608 - accuracy: 0.9219\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.1015 - accuracy: 0.9682\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 65us/step - loss: 0.0720 - accuracy: 0.9773\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.0545 - accuracy: 0.9822\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.0455 - accuracy: 0.9851\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 65us/step - loss: 0.0372 - accuracy: 0.9875\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.0344 - accuracy: 0.9882\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 65us/step - loss: 0.0300 - accuracy: 0.9900\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 65us/step - loss: 0.0241 - accuracy: 0.9920\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 4s 65us/step - loss: 0.0259 - accuracy: 0.9912\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1bd8044af98>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "## Training the model\n",
    "\n",
    "model.fit(X_train, Y_train, batch_size=150, epochs=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000/10000 [==============================] - 0s 21us/step\n",
      "Test score: 0.06287250198272741\n",
      "Test accuracy: 0.9833999872207642\n"
     ]
    }
   ],
   "source": [
    "## Evaluating model's accuracy on test data\n",
    "\n",
    "score = model.evaluate(X_test, Y_test)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_11\">\r\n     <g id=\"line2d_17\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"238.689706\" xlink:href=\"#mc8bfdb5548\" y=\"145.757411\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_18\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(218.964706 149.556629)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_12\">\r\n     <g id=\"line2d_18\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"238.689706\" xlink:href=\"#mc8bfdb5548\" y=\"175.864554\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_19\">\r\n      <!-- 25 -->\r\n      <g transform=\"translate(218.964706 179.663772)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"patch_8\">\r\n    <path d=\"M 238.689706 190.918125 \r\nL 238.689706 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_9\">\r\n    <path d=\"M 407.289706 190.918125 \r\nL 407.289706 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_10\">\r\n    <path d=\"M 238.689706 190.918125 \r\nL 407.289706 190.918125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_11\">\r\n    <path d=\"M 238.689706 22.318125 \r\nL 407.289706 22.318125 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g 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x=\"698.197266\" xlink:href=\"#DejaVuSans-108\"/>\r\n     <use x=\"725.980469\" xlink:href=\"#DejaVuSans-97\"/>\r\n     <use x=\"787.259766\" xlink:href=\"#DejaVuSans-115\"/>\r\n     <use x=\"839.359375\" xlink:href=\"#DejaVuSans-115\"/>\r\n     <use x=\"891.458984\" xlink:href=\"#DejaVuSans-32\"/>\r\n     <use x=\"923.246094\" xlink:href=\"#DejaVuSans-50\"/>\r\n    </g>\r\n   </g>\r\n  </g>\r\n  <g id=\"axes_3\">\r\n   <g id=\"patch_12\">\r\n    <path d=\"M 450.454412 190.918125 \r\nL 619.054412 190.918125 \r\nL 619.054412 22.318125 \r\nL 450.454412 22.318125 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g clip-path=\"url(#pe07434f713)\">\r\n    <image height=\"28\" id=\"imageb015d0357f\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" transform=\"matrix(6.035714 0 0 6.035714 450.454412 21.918125)\" width=\"28\" xlink:href=\"data:image/png;base64,\r\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\"/>\r\n   </g>\r\n   <g id=\"matplotlib.axis_5\">\r\n    <g id=\"xtick_7\">\r\n     <g id=\"line2d_19\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"453.465126\" xlink:href=\"#m93e0fdfbc0\" y=\"190.918125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_21\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(450.283876 205.516562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_8\">\r\n     <g id=\"line2d_20\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"513.679412\" xlink:href=\"#m93e0fdfbc0\" y=\"190.918125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_22\">\r\n      <!-- 10 -->\r\n      <g transform=\"translate(507.316912 205.516562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_21\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"573.893697\" xlink:href=\"#m93e0fdfbc0\" y=\"190.918125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_23\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(567.531197 205.516562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" 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<g id=\"line2d_24\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"450.454412\" xlink:href=\"#mc8bfdb5548\" y=\"85.543125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_26\">\r\n      <!-- 10 -->\r\n      <g transform=\"translate(430.729412 89.342344)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_16\">\r\n     <g id=\"line2d_25\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"450.454412\" xlink:href=\"#mc8bfdb5548\" y=\"115.650268\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_27\">\r\n      <!-- 15 -->\r\n      <g transform=\"translate(430.729412 119.449487)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-49\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-53\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_17\">\r\n     <g 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y=\"615.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_63\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(144.001786 630.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_14\">\r\n    <g id=\"ytick_37\">\r\n     <g id=\"line2d_58\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#mc8bfdb5548\" y=\"450.128839\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_64\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(13.5625 453.928058)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_38\">\r\n     <g id=\"line2d_59\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#mc8bfdb5548\" y=\"480.235982\"/>\r\n      </g>\r\n     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style=\"stroke:#000000;stroke-width:0.8;\" x=\"150.364286\" xlink:href=\"#m57f3a6aa2f\" y=\"403.318125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_33\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(144.001786 417.916562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_8\">\r\n    <g id=\"ytick_19\">\r\n     <g id=\"line2d_31\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m46958e5a56\" y=\"237.728839\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_34\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(13.5625 241.528058)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_20\">\r\n     <g id=\"line2d_32\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" 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style=\"stroke:#000000;stroke-width:0.8;\" x=\"362.128992\" xlink:href=\"#m57f3a6aa2f\" y=\"403.318125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_43\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(355.766492 417.916562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_10\">\r\n    <g id=\"ytick_25\">\r\n     <g id=\"line2d_40\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"238.689706\" xlink:href=\"#m46958e5a56\" y=\"237.728839\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_44\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(225.327206 241.528058)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_26\">\r\n     <g id=\"line2d_41\">\r\n      <g>\r\n       <use 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x=\"150.364286\" xlink:href=\"#m57f3a6aa2f\" y=\"615.718125\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_63\">\r\n      <!-- 20 -->\r\n      <g transform=\"translate(144.001786 630.316562)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-50\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"matplotlib.axis_14\">\r\n    <g id=\"ytick_37\">\r\n     <g id=\"line2d_58\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m46958e5a56\" y=\"450.128839\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_64\">\r\n      <!-- 0 -->\r\n      <g transform=\"translate(13.5625 453.928058)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-48\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_38\">\r\n     <g id=\"line2d_59\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"26.925\" xlink:href=\"#m46958e5a56\" 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "## Inspecting the output\n",
    "\n",
    "predicted_classes = model.predict_classes(X_test)\n",
    "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
    "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]\n",
    "\n",
    "plt.figure()\n",
    "for i, correct in enumerate(correct_indices[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.imshow(X_test[correct].reshape(28, 28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure()\n",
    "for i, incorrect in enumerate(incorrect_indices[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.imshow(X_test[incorrect].reshape(28, 28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))\n",
    "\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}