Makers_AI_Tutorial_InProgress.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import MNIST dataset from Keras\n",
    "\n",
    "from keras.datasets import mnist\n",
    "(train_x, train_y), (test_x, test_y) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape is (60000, 28, 28)\n",
      "Input type is <class 'numpy.ndarray'>\n",
      "Labels:\n",
      "[5 0 4 ... 5 6 8]\n",
      "Labels shape is (60000,)\n",
      "Labels type is <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Data exploration\n",
    "\n",
    "print(\"Inputs shape is \" + str(train_x.shape))\n",
    "print(\"Input type is \" + str(type(train_x)))\n",
    "print(\"Labels:\")\n",
    "print(train_y)\n",
    "print(\"Labels shape is \" + str(train_y.shape))\n",
    "print(\"Labels type is \" + str(type(train_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Input shape: 60000 examples\n",
    "# Each image: 28 x 28\n",
    "\n",
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Matplotlib: Data visualization library\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1824a275f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the input samples\n",
    "\n",
    "sample_num = 0\n",
    "\n",
    "plt.imshow(train_x[sample_num], cmap=plt.get_cmap('gray'))\n",
    "print(train_y[sample_num])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flatten 28*28 images to a 784 vector for each image\n",
    "\n",
    "num_pixels = train_x.shape[1] * train_x.shape[2]\n",
    "train_x_flattened = train_x.reshape(train_x.shape[0], num_pixels).astype('float32')\n",
    "test_x_flattened = test_x.reshape(test_x.shape[0], num_pixels).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize pixel values to between 0-1\n",
    "train_x_flattened = train_x_flattened / 255.\n",
    "test_x_flattened = test_x_flattened / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "\n",
    "# Use Keras to categorize the outputs (\"one-hot\" vectors)\n",
    "train_y_categorical = keras.utils.to_categorical(train_y, num_classes=10)\n",
    "test_y_categorical = keras.utils.to_categorical(test_y, num_classes=10)\n",
    "\n",
    "# Let's see the result of categorizing the outputs\n",
    "print(test_y_categorical[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Activation\n",
    "from keras.models import Sequential\n",
    "\n",
    "# Initialize simple neural network model\n",
    "model = Sequential()\n",
    "\n",
    "# TODO: add layers to the model\n",
    "\n",
    "# Hidden layer 1: 500 neurons, 'relu' activation\n",
    "\n",
    "# Hidden layer 2: 250 neurons, 'relu' activation\n",
    "\n",
    "# Output layer: 10 neurons (one for each class), softmax activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model, get ready to train\n",
    "\n",
    "# TODO: compile the model\n",
    "    # Loss: categorical cross-entropy\n",
    "    # Optimizer: stochastic gradient descent (SGD)\n",
    "    # Additional metrics: Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-cc12bbe2a596>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Print model summary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "# Print model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant libraries\n",
    "from keras_tqdm import TQDMNotebookCallback #TQDM: progress bars\n",
    "from keras.callbacks import TensorBoard # Tensorboard: training plots\n",
    "\n",
    "# Clear any existing Tensorboard logs\n",
    "import shutil\n",
    "shutil.rmtree('./logs', ignore_errors=True)\n",
    "\n",
    "# Set up callback links to refer back to during training\n",
    "tensorboard = TensorBoard()\n",
    "callbacks_list = [TQDMNotebookCallback(), tensorboard]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
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       "HBox(children=(IntProgress(value=0, description='Epoch 0', max=54000), HTML(value='')))"
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
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       "<keras.callbacks.History at 0x113650b38>"
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     },
     "execution_count": 19,
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   "source": [
    "# Fit the model to the training data\n",
    "# 10 epochs, 32 batch size\n",
    "\n",
    "# Validation split: reserve some of our training data to use as \"validation\" data\n",
    "# Won't train on validation data -- check validation loss to look for overfitting\n",
    "\n",
    "# Pass in our callbacks to track training progress\n",
    "\n",
    "# TODO: train the model\n",
    "    # Number of epochs: 5\n",
    "    # Batch size: 32\n",
    "    # Validation split: 0.1\n",
    "    # Verbose setting: use TQDM progress bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 64us/step\n",
      "[0.07437518193386496, 0.9771]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate trained model on test data\n",
    "\n",
    "# Returns final test loss and test accuracy\n",
    "loss_and_metrics = model.evaluate(test_x_flattened, test_y_categorical, batch_size=128)\n",
    "\n",
    "print(loss_and_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7.3829615e-06 1.5711279e-07 2.7452162e-04 1.3182210e-03 1.6091647e-08\n",
      "  3.2331081e-07 1.2449125e-10 9.9836475e-01 2.7114975e-06 3.1952830e-05]]\n",
      "7\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADO5JREFUeJzt3V2IXfW5x/Hf76QpiOlFYjUMNpqeogerSKKjCMYS9VhyYiEWg9SLkkLJ9CJKCyVU7EVzWaQv1JvAlIbGkmMrpNUoYmNjMQ1qcSJqEmNiElIzMW9lhCaCtNGnF7Nsp3H2f+/st7XH5/uBYfZez3p52Mxv1lp77bX/jggByOe/6m4AQD0IP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpD7Vz43Z5uOEQI9FhFuZr6M9v+1ltvfZPmD7gU7WBaC/3O5n+23PkrRf0h2SxiW9LOneiHijsAx7fqDH+rHnv1HSgYg4FBF/l/RrSSs6WB+APuok/JdKOjLl+Xg17T/YHrE9Znusg20B6LKev+EXEaOSRiUO+4FB0sme/6ikBVOef66aBmAG6CT8L0u6wvbnbX9a0tckbelOWwB6re3D/og4a/s+Sb+XNEvShojY07XOAPRU25f62toY5/xAz/XlQz4AZi7CDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkmp7iG5Jsn1Y0mlJH0g6GxHD3WgKQO91FP7KrRHx1y6sB0AfcdgPJNVp+EPSVts7bY90oyEA/dHpYf+SiDhq+xJJz9p+MyK2T52h+qfAPwZgwDgiurMie52kMxHxo8I83dkYgIYiwq3M1/Zhv+0LbX/mo8eSvixpd7vrA9BfnRz2z5f0O9sfref/I+KZrnQFoOe6dtjf0sY47Ad6rueH/QBmNsIPJEX4gaQIP5AU4QeSIvxAUt24qy+FlStXNqytXr26uOw777xTrL///vvF+qZNm4r148ePN6wdOHCguCzyYs8PJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0lxS2+LDh061LC2cOHC/jUyjdOnTzes7dmzp4+dDJbx8fGGtYceeqi47NjYWLfb6Rtu6QVQRPiBpAg/kBThB5Ii/EBShB9IivADSXE/f4tK9+xfe+21xWX37t1brF911VXF+nXXXVesL126tGHtpptuKi575MiRYn3BggXFeifOnj1brJ86dapYHxoaanvbb7/9drE+k6/zt4o9P5AU4QeSIvxAUoQfSIrwA0kRfiApwg8k1fR+ftsbJH1F0smIuKaaNk/SbyQtlHRY0j0R8W7Tjc3g+/kH2dy5cxvWFi1aVFx2586dxfoNN9zQVk+taDZewf79+4v1Zp+fmDdvXsPamjVrisuuX7++WB9k3byf/5eSlp0z7QFJ2yLiCknbqucAZpCm4Y+I7ZImzpm8QtLG6vFGSXd1uS8APdbuOf/8iDhWPT4uaX6X+gHQJx1/tj8ionQub3tE0kin2wHQXe3u+U/YHpKk6vfJRjNGxGhEDEfEcJvbAtAD7YZ/i6RV1eNVkp7oTjsA+qVp+G0/KulFSf9je9z2NyX9UNIdtt+S9L/VcwAzCN/bj4F19913F+uPPfZYsb579+6GtVtvvbW47MTEuRe4Zg6+tx9AEeEHkiL8QFKEH0iK8ANJEX4gKS71oTaXXHJJsb5r166Oll+5cmXD2ubNm4vLzmRc6gNQRPiBpAg/kBThB5Ii/EBShB9IivADSTFEN2rT7OuzL7744mL93XfL3xa/b9++8+4pE/b8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU9/Ojp26++eaGteeee6647OzZs4v1pUuXFuvbt28v1j+puJ8fQBHhB5Ii/EBShB9IivADSRF+ICnCDyTV9H5+2xskfUXSyYi4ppq2TtJqSaeq2R6MiKd71SRmruXLlzesNbuOv23btmL9xRdfbKsnTGplz/9LScummf7TiFhU/RB8YIZpGv6I2C5pog+9AOijTs7577P9uu0Ntud2rSMAfdFu+NdL+oKkRZKOSfpxoxltj9gesz3W5rYA9EBb4Y+IExHxQUR8KOnnkm4szDsaEcMRMdxukwC6r63w2x6a8vSrknZ3px0A/dLKpb5HJS2V9Fnb45J+IGmp7UWSQtJhSd/qYY8AeoD7+dGRCy64oFjfsWNHw9rVV19dXPa2224r1l944YViPSvu5wdQRPiBpAg/kBThB5Ii/EBShB9IiiG60ZG1a9cW64sXL25Ye+aZZ4rLcimvt9jzA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBS3NKLojvvvLNYf/zxx4v19957r2Ft2bLpvhT631566aViHdPjll4ARYQfSIrwA0kRfiApwg8kRfiBpAg/kBT38yd30UUXFesPP/xwsT5r1qxi/emnGw/gzHX8erHnB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkmt7Pb3uBpEckzZcUkkYj4me250n6jaSFkg5Luici3m2yLu7n77Nm1+GbXWu//vrri/WDBw8W66V79psti/Z0837+s5K+GxFflHSTpDW2vyjpAUnbIuIKSduq5wBmiKbhj4hjEfFK9fi0pL2SLpW0QtLGaraNku7qVZMAuu+8zvltL5S0WNKfJc2PiGNV6bgmTwsAzBAtf7bf9hxJmyV9JyL+Zv/7tCIiotH5vO0RSSOdNgqgu1ra89uercngb4qI31aTT9gequpDkk5Ot2xEjEbEcEQMd6NhAN3RNPye3MX/QtLeiPjJlNIWSauqx6skPdH99gD0SiuX+pZI+pOkXZI+rCY/qMnz/sckXSbpL5q81DfRZF1c6uuzK6+8slh/8803O1r/ihUrivUnn3yyo/Xj/LV6qa/pOX9E7JDUaGW3n09TAAYHn/ADkiL8QFKEH0iK8ANJEX4gKcIPJMVXd38CXH755Q1rW7du7Wjda9euLdafeuqpjtaP+rDnB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkuM7/CTAy0vhb0i677LKO1v38888X682+DwKDiz0/kBThB5Ii/EBShB9IivADSRF+ICnCDyTFdf4ZYMmSJcX6/fff36dO8EnCnh9IivADSRF+ICnCDyRF+IGkCD+QFOEHkmp6nd/2AkmPSJovKSSNRsTPbK+TtFrSqWrWByPi6V41mtktt9xSrM+ZM6ftdR88eLBYP3PmTNvrxmBr5UM+ZyV9NyJesf0ZSTttP1vVfhoRP+pdewB6pWn4I+KYpGPV49O290q6tNeNAeit8zrnt71Q0mJJf64m3Wf7ddsbbM9tsMyI7THbYx11CqCrWg6/7TmSNkv6TkT8TdJ6SV+QtEiTRwY/nm65iBiNiOGIGO5CvwC6pKXw256tyeBviojfSlJEnIiIDyLiQ0k/l3Rj79oE0G1Nw2/bkn4haW9E/GTK9KEps31V0u7utwegV1p5t/9mSV+XtMv2q9W0ByXda3uRJi//HZb0rZ50iI689tprxfrtt99erE9MTHSzHQyQVt7t3yHJ05S4pg/MYHzCD0iK8ANJEX4gKcIPJEX4gaQIP5CU+znEsm3GcwZ6LCKmuzT/Mez5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiCpfg/R/VdJf5ny/LPVtEE0qL0Nal8SvbWrm71d3uqMff2Qz8c2bo8N6nf7DWpvg9qXRG/tqqs3DvuBpAg/kFTd4R+tefslg9rboPYl0Vu7aumt1nN+APWpe88PoCa1hN/2Mtv7bB+w/UAdPTRi+7DtXbZfrXuIsWoYtJO2d0+ZNs/2s7bfqn5PO0xaTb2ts320eu1etb28pt4W2P6j7Tds77H97Wp6ra9doa9aXre+H/bbniVpv6Q7JI1LelnSvRHxRl8bacD2YUnDEVH7NWHbX5J0RtIjEXFNNe0hSRMR8cPqH+fciPjegPS2TtKZukdurgaUGZo6srSkuyR9QzW+doW+7lENr1sde/4bJR2IiEMR8XdJv5a0ooY+Bl5EbJd07qgZKyRtrB5v1OQfT9816G0gRMSxiHilenxa0kcjS9f62hX6qkUd4b9U0pEpz8c1WEN+h6SttnfaHqm7mWnMr4ZNl6TjkubX2cw0mo7c3E/njCw9MK9dOyNedxtv+H3ckoi4TtL/SVpTHd4OpJg8ZxukyzUtjdzcL9OMLP0vdb527Y543W11hP+opAVTnn+umjYQIuJo9fukpN9p8EYfPvHRIKnV75M19/MvgzRy83QjS2sAXrtBGvG6jvC/LOkK25+3/WlJX5O0pYY+Psb2hdUbMbJ9oaQva/BGH94iaVX1eJWkJ2rs5T8MysjNjUaWVs2v3cCNeB0Rff+RtFyT7/gflPT9Onpo0Nd/S3qt+tlTd2+SHtXkYeA/NPneyDclXSRpm6S3JP1B0rwB6u1XknZJel2TQRuqqbclmjykf13Sq9XP8rpfu0JftbxufMIPSIo3/ICkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x182eea6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make sure our predictions make sense\n",
    "import numpy as np\n",
    "\n",
    "sample_num = 0 #which test sample to look at\n",
    "\n",
    "# Predicted class\n",
    "test_sample = np.expand_dims(test_x_flattened[sample_num], axis=0) # create a one-sample \"batch\" to feed into model\n",
    "predicted_scores = model.predict(test_sample) # outputted probabilities vector\n",
    "print(predicted_scores)\n",
    "\n",
    "predicted_class = np.argmax(predicted_scores) # pick the class with the highest probability -> final prediction\n",
    "print(predicted_class)\n",
    "\n",
    "# Actual input\n",
    "plt.imshow(test_x[sample_num], cmap=plt.get_cmap('gray'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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