Code/Day34-NumRecognizer(Hard way).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing Tensorflow\n",
    "\n",
    "Be sure to \"conda install tensorflow\" or \"conda install tensorflow-gpu\" first!\n",
    "\n",
    "## The world's simplest Tensorflow application\n",
    "\n",
    "Let's begin by writing a really simple program to illustrate Tensorflow's main concepts. We'll set up two Variables, named \"a\" and \"b\", which each contain a tensor which contains a single value - the number 1, and the number 2.\n",
    "\n",
    "We then create a graph \"f\" that adds these two tensors together. But \"f = a + b\" just creates the graph; it doesn't actually perform the addition yet.\n",
    "\n",
    "Next we need to initialize any global variables before we run the graph.\n",
    "\n",
    "And finally, we create a Tensorflow Session object, run our variable initializer, and execute the graph with eval(). \n",
    "\n",
    "This returns the sum of 1 + 2 in a rather complex, yet highly scalable manner :)\n",
    "\n",
    "## Important API note\n",
    "\n",
    "We are using the Tensorflow version 1 API in this example, just to illustrate what is going on at a low level inside of tensorflow. In Tensorflow 2, many of these functions have been replaced by their higher-level Keras API's, and this sort of low-level maniuplation isn't really possible in many cases any longer.\n",
    "\n",
    "Don't worry, we're covering Keras and the Tensorflow 2 API next. This exercise is just to give you an appreciation of what's going on under the hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\nInstructions for updating:\nnon-resource variables are not supported in the long term\n3\n"
    }
   ],
   "source": [
    "#Switch to Tensorflow V1 APIs - we'll cover V2 next, don't worry.\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "\n",
    "a = tf.Variable(1, name=\"a\")\n",
    "b = tf.Variable(2, name=\"b\")\n",
    "f = a + b\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "with tf.Session() as s:\n",
    "    init.run()\n",
    "    print( f.eval() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## And now for something more interesting: Handwriting recognition\n",
    "\n",
    "The standard example for machine learning these days is the MNIST data set, a collection of 70,000 handwriting samples of the numbers 0-9. Our challenge - to predict which number each handwritten image represents.\n",
    "\n",
    "Although we'll talk about neural networks that are specifically well suited for image recognition later, we actually don't need to go there for this relatively simple task. We can achieve decent results without a whole lot of code.\n",
    "\n",
    "Each image is 28x28 grayscale pixels, so we can treat each image as just a 1D array, or tensor, of 784 numbers. As long as we're consistent in how we flatten each image into an array, it'll still work. Yes, it would be even better if we could preserve the 2D structure of the data while training - but we'll get there later.\n",
    "\n",
    "Let's start by importing the data set, which conveniently is part of tensorflow itself. We will reshape the images into the 1D arrays of 784 pixels that we expect, and the label data into one-hot-encoded categorical format, which we'll talk about in a second:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(train_images_2d, train_labels_n),(test_images_2d, test_labels_n) = mnist.load_data()\n",
    "\n",
    "train_images = train_images_2d.reshape(train_images_2d.shape[0], 784)\n",
    "test_images = test_images_2d.reshape(test_images_2d.shape[0], 784)\n",
    "train_images = train_images.astype('float32')\n",
    "test_images = test_images.astype('float32')\n",
    "train_images /= 255\n",
    "test_images /= 255\n",
    "\n",
    "train_labels = tf.keras.utils.to_categorical(train_labels_n, 10)\n",
    "test_labels = tf.keras.utils.to_categorical(test_labels_n, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST provides 60,000 samples in a training data set, and 10,000 samples in a test data set.\n",
    "\n",
    "If you're new to the concept of train/test - it's important to evaluate the performance of our neural network using data it's never seen before. Otherwise it's kinda like giving students a math test for problems they already have the answers for. So, we use a completely different set of images to train our neural network from the images used for testing its accuracy.\n",
    "\n",
    "The training images are therefore a tensor of shape [60,000, 784] - 60,000 instances of 784 numbers that represent each image.\n",
    "\n",
    "The label data is encoded as \"one_hot\" when we loaded it above. Think of one_hot as a binary representation of the label data - that is, which number each handwriting sample was intended to represent. Mathematically one_hot represents a dimension for every possible label value. Every dimension is set to the value 0, except for the \"correct\" one which is set to 1. For example, the label vector representing the number 1 would be [0, 1, 0, 0, 0, 0, 0, 0, 0, 0] (remember we start counting at 0.) It's just a format that's optimized for how the labels are applied during training.\n",
    "\n",
    "So the training label data is a tensor of shape [60,000, 10] - 60,000 test images each associated with 10 binary values that indicate whether or not the image represents a given number from 0-9.\n",
    "\n",
    "Let's define a little function to let us visualize what the input data looks like, and pick some random training image to see what it is we're up against:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
    },
    {
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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def display_sample(num):\n",
    "    #Print the one-hot array of this sample's label \n",
    "    print(train_labels[num])  \n",
    "    #Print the label converted back to a number\n",
    "    label = train_labels[num].argmax(axis=0)\n",
    "    #Reshape the 784 values to a 28x28 image\n",
    "    image = train_images[num].reshape([28,28])\n",
    "    plt.title('Sample: %d  Label: %d' % (num, label))\n",
    "    plt.imshow(image, cmap=plt.get_cmap('gray_r'))\n",
    "    plt.show()\n",
    "    \n",
    "display_sample(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, you can see the \"one_hot\" representation of the number this image represents in the array that was printed out from the training label for image# 100, as well as what this particular sample looks like. You can tell that some of the training data would even be challenging for a human to classify!\n",
    "\n",
    "Go ahead and try different input images to get a feel of the data we're given. Any value between 0 and 55,000 will work.\n",
    "\n",
    "As a reminder, we're flattening each image to a 1D array of 784 (28 x 28) numerical values. Each one of those values will be an input node into our deep neural network. Let's visualize how the data is being fed into it just to drive that point home:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "images = train_images[0].reshape([1,784])\n",
    "for i in range(1, 500):\n",
    "    images = np.concatenate((images, train_images[i].reshape([1,784])))\n",
    "plt.imshow(images, cmap=plt.get_cmap('gray_r'),aspect = 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is showing the first 500 training samples, one on each row. Imagine each pixel on each row getting fed into the bottom layer of a neural network 768 neurons (or \"units\") wide as we train our neural network.\n",
    "\n",
    "So let's start setting up that artificial neural network. We'll start by creating \"placeholders\" for the input images and for the \"correct\" labels for each. Think of these as parameters - we build up our neural network model without knowledge of the actual data that will be fed into it; we just need to construct it in such a way that our data will fit in.\n",
    "\n",
    "So our \"input_images\" placeholder will be set up to hold an array of values that consist of 784 floats (28x28), and our \"target_labels\" placeholder will be set up to hold an array of values that consist of 10 floats (one-hot format for 10 digits.)\n",
    "\n",
    "While training, we'll assign input_images to the training images and target_labels to the training lables. While testing, we'll use the test images and test labels instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_images = tf.placeholder(tf.float32, shape=[None, 784])\n",
    "target_labels = tf.placeholder(tf.float32, shape=[None, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's set up our deep neural network. We'll need an input layer with one node per input pixel per image, or 784 nodes. That will feed into a hidden layer of some arbitrary size - let's pick 512. That hidden layer will output 10 values, corresonding to scores for each classification to be fed into softmax.\n",
    "\n",
    "We'll need to reserve variables to keep track of the all the weights and biases for both layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_nodes = 512\n",
    "\n",
    "input_weights = tf.Variable(tf.truncated_normal([784, hidden_nodes]))\n",
    "input_biases = tf.Variable(tf.zeros([hidden_nodes]))\n",
    "\n",
    "hidden_weights = tf.Variable(tf.truncated_normal([hidden_nodes, 10]))\n",
    "hidden_biases = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's set up the neural network itself. We'll define the input layer and associate it with our placeholder for input data. All this layer does is multiply these inputs by our input_weight tensor which will be learned over time.\n",
    "\n",
    "Then we'll feed that into our hidden layer, which applies the ReLU activation function to the weighted inputs with our learned biases added in as well.\n",
    "\n",
    "Finally our output layer, called digit_weights, multiplies in the learned weights of the hidden layer and adds in the hidden layer's bias term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer = tf.matmul(input_images, input_weights)\n",
    "hidden_layer = tf.nn.relu(input_layer + input_biases)\n",
    "digit_weights = tf.matmul(hidden_layer, hidden_weights) + hidden_biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure you noodle on the above block. This sets up a deep neural network like the one we talked about in our slides.\n",
    "\n",
    "output layer\n",
    "\n",
    "hidden layer\n",
    "\n",
    "input layer\n",
    "\n",
    "Next we will define our loss function for use in measuring our progress in gradient descent: cross-entropy, which applies a logarithmic scale to penalize incorrect classifications much more than ones that are close. Remember digit_weights is the output of our final layer, and we're comparing that against the target labels used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=digit_weights, labels=target_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will set up our gradient descent optimizer, initializing it with an aggressive learning rate (0.5) and our loss function defined above.\n",
    "\n",
    "That learning rate is an example of a hyperparameter that may be worth experimenting with and tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll want to train our neural network and measure its accuracy. First let's define some methods for measuring the accuracy of our trained model. \n",
    "\n",
    "correct_prediction will look at the output of our neural network (in digit_weights) and choose the label with the highest value, and see if that agrees with the target label given. During testing, digit_weights will be our prediction based on the test data we give the network, and target_labels is a placeholder that we will assign to our test labels. Ultimately this gives us a 1 for every correct classification, and a 0 for every incorrect classification.\n",
    "\n",
    "\"accuracy\" then takes the average of all the classifications to produce an overall score for our model's accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(digit_weights,1), tf.argmax(target_labels,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train this thing and see how it works! \n",
    "\n",
    "We'll set up a Tensorflow session, and initialize our variables. Next we will train our network in 2000 steps (or \"epochs\") with batches of 100 samples from our training data. At each step, we assign the input_images placeholder to the current batch of training images, and the target_labels placeholder to the current batch of training labels.\n",
    "\n",
    "Once training is complete, we'll measure the accuracy of our model using the accuracy graph we defined above. While measuring accuracy, we assign the input_images placeholder to our test images, and the target_labels placeholder to our test labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "Training epoch 1\nAccuracy: 0.2425\nTraining epoch 101\nAccuracy: 0.8658\nTraining epoch 201\nAccuracy: 0.8884\nTraining epoch 301\nAccuracy: 0.8944\nTraining epoch 401\nAccuracy: 0.8971\nTraining epoch 501\nAccuracy: 0.9031\nTraining epoch 601\nAccuracy: 0.9108\nTraining epoch 701\nAccuracy: 0.9114\nTraining epoch 801\nAccuracy: 0.9132\nTraining epoch 901\nAccuracy: 0.9131\nTraining epoch 1001\nAccuracy: 0.9142\nTraining epoch 1101\nAccuracy: 0.9135\nTraining epoch 1201\nAccuracy: 0.9206\nTraining epoch 1301\nAccuracy: 0.9203\nTraining epoch 1401\nAccuracy: 0.9252\nTraining epoch 1501\nAccuracy: 0.9262\nTraining epoch 1601\nAccuracy: 0.9256\nTraining epoch 1701\nAccuracy: 0.9258\nTraining epoch 1801\nAccuracy: 0.9307\nTraining epoch 1901\nAccuracy: 0.9278\n"
    }
   ],
   "source": [
    "sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) # Avoid GPU errors on Windows\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "def next_batch(num, data, labels):\n",
    "    idx = np.arange(0 , len(data))\n",
    "    np.random.shuffle(idx)\n",
    "    idx = idx[:num]\n",
    "    data_shuffle = [data[ i] for i in idx]\n",
    "    labels_shuffle = [labels[ i] for i in idx]\n",
    "\n",
    "    return np.asarray(data_shuffle), np.asarray(labels_shuffle)\n",
    "\n",
    "for x in range(2000):\n",
    "    (x_batch, y_batch) = next_batch(100, train_images, train_labels)\n",
    "    sess.run(optimizer, feed_dict={input_images: x_batch, target_labels: y_batch})\n",
    "    if (x % 100 == 0):\n",
    "        print(\"Training epoch \" + str(x+1))\n",
    "        print(\"Accuracy: \" + str(sess.run(accuracy, feed_dict={input_images: test_images, target_labels: test_labels})))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should have about 92% accuracy. Not bad! But hey, we're just starting.\n",
    "\n",
    "Let's take a look at some of the misclassified images and see just how good or bad our model is, compared to what your own brain can do. We'll go through the first 200 test images and look at the ones that are misclassified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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"
    },
    {
     "data": {
      "image/png": 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nQ8fr6c5SqcTAwMCo15Ouep49Ih4EKl2M+vRGGjOz9vE76MwS4bCbJcJhN0uEw26WCIfdLBEOu1ki/FXSNm5Ve0em37G5P+/ZzRLhsJslwmE3S4TDbpYIh90sEQ67WSIcdrNEOOxmiXDYzRLhsJslwmE3S4TDbpYIh90sEQ67WSIcdrNEOOxmiXDYzRLhsJslwmE3S4TDbpYIh90sEQ67WSIcdrNEVA27pBmS7pf0hKTHJH08n75E0vOS1ue3c1rfrpnVq5aLRLwCfCoi1kk6HPixpLV57UsR8cXWtWdmzVI17BGxFdia398l6QngmFY3ZmbNNabX7JL6gTnAD/NJl0t6RNJySVMqLLNI0oCkgaGhoYaaNbP61Rx2SYcBK4FPRMRO4KvA8cBssj3/0tGWi4hlEVGKiJKvvWXWOTWFXdJEsqDfGhF3AkTEtojYFxG/BW4G5rWuTTNrVC1H4wXcAjwREdeVTZ9eNtt8YEPz2zOzZqnlaPzbgYuARyWtz6ddCSyQNBsIYCPw4ZZ0aGZNUcvR+AcBjVK6q/ntmFmr+B10Zolw2M0S4bCbJcJhN0uEw26WCIfdLBEOu1kiHHazRDjsZolw2M0S4bCbJcJhN0uEw26WCIfdLBGKiPatTBoCni2bNBXY3rYGxqZbe+vWvsC91auZvR0bEaN+/1tbw/6qlUsDEVHqWAMFurW3bu0L3Fu92tWbn8abJcJhN0tEp8O+rMPrL9KtvXVrX+De6tWW3jr6mt3M2qfTe3YzaxOH3SwRHQm7pLMkPSnpKUlXdKKHSiRtlPRofhnqgQ73slzSoKQNZdN6Ja2V9LP856jX2OtQb11xGe+Cy4x3dNt1+vLnbX/NLmkC8FPgDGAz8CNgQUQ83tZGKpC0EShFRMffgCHpVGA38I2IeEs+7RpgR0RcnT9QTomIv+qS3pYAuzt9Ge/8akXTyy8zDlwAXEwHt11BX++jDdutE3v2ecBTEfFMRPwG+A5wfgf66HoR8QCwY8Tk84EV+f0VZP9Z2q5Cb10hIrZGxLr8/i5g+DLjHd12BX21RSfCfgzwXNnvm+mu670HcI+kH0ta1OlmRjEtIrZC9p8HOKrD/YxU9TLe7TTiMuNds+3qufx5ozoR9tEuJdVN5//eHhFzgbOBj+ZPV602NV3Gu11Gucx4V6j38ueN6kTYNwMzyn5/HbClA32MKiK25D8HgVV036Wotw1fQTf/Odjhfv5fN13Ge7TLjNMF266Tlz/vRNh/BMyS9HpJrwHeD6zpQB+vIqknP3CCpB7gTLrvUtRrgIX5/YXA6g72sp9uuYx3pcuM0+Ft1/HLn0dE22/AOWRH5J8GPtOJHir0dRzwk/z2WKd7A24je1q3l+wZ0SXAkcB9wM/yn71d1Ns3gUeBR8iCNb1Dvb2D7KXhI8D6/HZOp7ddQV9t2W5+u6xZIvwOOrNEOOxmiXDYzRLhsJslwmE3S4TDbpYIh90sEf8HHfC3PimEuG8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for x in range(200):\n",
    "    # Load a single test image and its label\n",
    "    x_train = test_images[x,:].reshape(1,784)\n",
    "    y_train = test_labels[x,:]\n",
    "    # Convert the one-hot label to an integer\n",
    "    label = y_train.argmax()\n",
    "    # Get the classification from our neural network's digit_weights final layer, and convert it to an integer\n",
    "    prediction = sess.run(digit_weights, feed_dict={input_images: x_train}).argmax()\n",
    "    # If the prediction does not match the correct label, display it\n",
    "    if (prediction != label) :\n",
    "        plt.title('Prediction: %d Label: %d' % (prediction, label))\n",
    "        plt.imshow(x_train.reshape([28,28]), cmap=plt.get_cmap('gray_r'))\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be honest, I'd be a little unsure about some of those myself!\n",
    "\n",
    "## Exercise\n",
    "\n",
    "See if you can improve upon the accuracy. Try using more hidden neurons (nodes). Try using fewer! Try a different learning rate. Try adding another hidden layer. Try different batch sizes. What's the best accuracy you can get from this multi-layer perceptron?"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "** Using hidden_layer = 300 and using GradientDescentOptimizer to 0.1 will increase accuracy to 93.3 and 93.6 % respectively **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.5 64-bit",
   "language": "python",
   "name": "python37564bitedcdccb018dd4367a14589b436323787"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5-final"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}