MNIST Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST Tensorflow 2\n",
    "This is an example of building a multi-layer network in Tensorflow 2 to solve the MNIST problem.\n",
    "The objective is to be low-level enough to understand the operation on a layer-by-layer,\n",
    "matrix operation level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Jun 18 10:36:22 EDT 2020\r\n"
     ]
    }
   ],
   "source": [
    "! date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runnning python version 3.5.2 (default, Apr 16 2020, 17:47:17) \n",
      "[GCC 5.4.0 20160609].\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sys;\n",
    "print( \"Runnning python version %s.\\n\" % sys.version );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tensorflow version 2.2.0.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import nvgpu;\n",
    "import tensorflow as tf;\n",
    "print( \"Running tensorflow version %s.\\n\" % (tf.__version__,) );\n",
    "\n",
    "# this prints out which CPU/GPU is being used\n",
    "tf.debugging.set_log_device_placement(True);\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time;\n",
    "import numpy as np;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pynvml as nv;\n",
    "nv.nvmlInit();\n",
    "GPU_NO = nv.nvmlDeviceGetCount();\n",
    "handles = [nv.nvmlDeviceGetHandleByIndex(i) for i in range(GPU_NO)];\n",
    "def gpu_utilization():\n",
    "    return [nv.nvmlDeviceGetUtilizationRates(handle).gpu for handle in handles];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the only place where I want to use Keras\n",
    "mnist = tf.keras.datasets.fashion_mnist;\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "# show the shapes of the data\n",
    "print( x_train.shape );\n",
    "print( y_train.shape );\n",
    "print( x_test.shape );\n",
    "print( y_test.shape );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "# some short-cut names\n",
    "\n",
    "EPS = np.finfo(np.double).eps\n",
    "\n",
    " \n",
    "\n",
    "sigmoid = tf.nn.sigmoid;\n",
    "softmax = tf.nn.softmax;\n",
    "\n",
    "vs_init = tf.initializers.GlorotUniform();\n",
    "zeros_init = tf.zeros;\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "class Layer: \n",
    "    \"\"\"\n",
    "    This respresents a layer in a neural network.\n",
    "    \n",
    "    It's attributes are:\n",
    "        name     - a name given to the Layer,\n",
    "        node_no  - the number of nodes in the Layer,\n",
    "        transfer - the transfer function used by the nodes in the Layer,\n",
    "        inputs   - a WeightLayer that provides inputs to this Layer,\n",
    "        outputs  - a WeightLayer that receives the outputs of this Layer,\n",
    "        biases   - a tf.Variable that holds the output bias values for \n",
    "                   this Layer.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__( self, PARAMETERS, name, node_no, transfer ):\n",
    "        \"\"\"\n",
    "        Constructor function that sets object attributes to match\n",
    "        arguments, sets the outputs attribute to None, and initalizes\n",
    "        the biases.\n",
    "        \"\"\"\n",
    "        self.params   = PARAMETERS;\n",
    "        self.name     = name;\n",
    "        self.node_no  = node_no;\n",
    "        self.transfer = transfer;\n",
    "        self.inputs   = None;\n",
    "        self.outputs  = None;\n",
    "        self.biases   = tf.Variable( \n",
    "                              self.params['BIAS_INIT']( [ self.node_no ], dtype=DTYPE ),             \n",
    "                              name='B_%s' % (self.name,),\n",
    "                              trainable=True ); \n",
    "\n",
    "    def __rshift__( self, other ):\n",
    "        \"\"\"\n",
    "        This overloads the right shift operator (>>) to allow users\n",
    "        to create a network by connecting two Layers together.\n",
    "        \"\"\"\n",
    "        return NN(self,other);\n",
    "    \n",
    "    @tf.function\n",
    "    def output( self, input ):\n",
    "        \"\"\"\n",
    "        Create decorated function to compute the activation values\n",
    "        of the neurons in this Layer.\n",
    "        \n",
    "        input is the input to the entire NN.\n",
    "        \n",
    "        This is the main operation of a Layer.  It adds the output\n",
    "        values to the net values, and applies a transfer function.\n",
    "        \"\"\"\n",
    "        return self.transfer( tf.nn.bias_add( \n",
    "                                    self.inputs.output(input), \n",
    "                                    self.biases ) );\n",
    "\n",
    "    # @tf.function\n",
    "    # This function cannot be decorated because it won't work with the tape\n",
    "    def update( self, loss, tape ):\n",
    "        gradient = tape.gradient( loss, self.biases );\n",
    "        self.biases.assign( self.biases - \\\n",
    "                            self.params['LEARNING_RATE']*gradient);\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "    \n",
    "class InputLayer(Layer):\n",
    "    \"\"\"\n",
    "    Special Class of Layer that is an input layer.\n",
    "    \n",
    "    It's transfer, inputs, init, and outputs attributes are all undefined.\n",
    "    \n",
    "    It overrides the __init__ and output functions.\n",
    "    It inherits __rshift__ from Layer. \n",
    "    \"\"\"\n",
    "    def __init__( self, PARAMETERS, name, node_no ):\n",
    "        \"\"\"\n",
    "        Constructor function that sets object attributes to match\n",
    "        arguments.\n",
    "        \"\"\"\n",
    "        self.params   = PARAMETERS;\n",
    "        self.name     = name;\n",
    "        self.node_no  = node_no;\n",
    "        \n",
    "    @tf.function\n",
    "    def output( self, inputs ):\n",
    "        \"\"\"\n",
    "        Create decorated function to compute the activation values\n",
    "        of the neurons in this InputLayer.\n",
    "        \n",
    "        It just returns the passed inputs value.\n",
    "        \"\"\"\n",
    "        return inputs;\n",
    "\n",
    "    # @tf.function\n",
    "    # This function cannot be decorated because it won't work with the tape\n",
    "    def update( self, loss, tape ):\n",
    "        pass;\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "class WeightLayer:\n",
    "    \"\"\"\n",
    "    This class represents a layer of weights between two Layers of \n",
    "    neurons.\n",
    "    \n",
    "    It has the following attributes:\n",
    "        src     - a Layer or InputLayer of neurons that passes activation\n",
    "                  values through the WeightLayer,\n",
    "        name    - a name given to the WeightLayer,\n",
    "        init    - initialization function to set weight values,\n",
    "        dest    - a Layer or InputLayer of neurons that receives a signal\n",
    "                  from the WeightLayer.\n",
    "        weights - a tf.Variable that holds the weight values for \n",
    "                this WeightLayer.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__( self, PARAMETERS, src, dest ):\n",
    "        \"\"\"\n",
    "        Constructor function that sets object attributes to match\n",
    "        arguments.\n",
    "        \n",
    "        It also sets the name based on the src.name and dest.name\n",
    "        and initializes weights.\n",
    "        \"\"\"\n",
    "        self.params  = PARAMETERS;\n",
    "        self.src     = src;\n",
    "        # make init a function that will randomize weights\n",
    "        self.name    = \"W_%s_%s\" % (src.name,dest.name);\n",
    "        \n",
    "        self.dest    = dest;\n",
    "        self.weights = tf.Variable( \n",
    "                           self.params['WEIGHT_INIT']( [ src.node_no, \n",
    "                                                         dest.node_no ], \n",
    "                                        dtype=DTYPE ), \n",
    "                           name=self.name,\n",
    "                              trainable=True  );\n",
    "        \n",
    "        self.src.outputs = self;\n",
    "        self.dest.inputs = self;\n",
    "        \n",
    "    @tf.function\n",
    "    def output( self, inputs ):\n",
    "        \"\"\"\n",
    "        Create decorated function to compute the net values\n",
    "        passed to the dest Layer.\n",
    "        \n",
    "        This is the main operation of a WeightLayer, it performs\n",
    "        a matrix multiplication of the src Layer activations \n",
    "        (pattern-wise) by the weights.\n",
    "        \"\"\"\n",
    "        return tf.matmul( self.src.output(inputs), self.weights );\n",
    "    \n",
    "    # @tf.function\n",
    "    # This function cannot be decorated because it won't work with the tape\n",
    "    def update( self, loss, tape ):\n",
    "        gradient = tape.gradient( loss, self.weights );\n",
    "        self.weights.assign(self.weights - \n",
    "                            self.params['LEARNING_RATE']*gradient);\n",
    "        \n",
    "\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "class NN:\n",
    "    \"\"\"\n",
    "    This class respresents a Neural Networks.\n",
    "    \n",
    "    The NNs attributes are:\n",
    "        layers       - an list of layers in the network from input to output,\n",
    "        input_layer  - a reference to layers[0] --- the input layer,\n",
    "        output_layer - a reference to layers[-1] --- the output layer,\n",
    "        loss_fn      - loss function.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__( self, l0, l1 ):\n",
    "        \"\"\"\n",
    "        Construct a network with an input layer, l0, and one more \n",
    "        layer, l1.\n",
    "        \"\"\"\n",
    "        # make init a function that will randomize weights\n",
    "        self.params       = l0.params;\n",
    "        self.layers       = [ l0, l1 ];\n",
    "        self.input_layer  = self.layers[0];\n",
    "        self.weights      = [ WeightLayer(self.params,l0,l1) ];\n",
    "        self.output_layer = self.layers[-1];\n",
    "        self.loss_fn      = None;\n",
    "        \n",
    "    def __rshift__( self, other ):\n",
    "        \"\"\"\n",
    "        Add a Layer to the network.\n",
    "        \"\"\"\n",
    "        if isinstance(other,Layer):\n",
    "            self.layers.append( other );\n",
    "            self.weights.append( WeightLayer(self.params,self.layers[-2],other) );\n",
    "            self.output_layer = self.layers[-1];\n",
    "        \n",
    "            return self;\n",
    "        \n",
    "        \n",
    "        if isinstance(other,tf.python.eager.def_function.Function):\n",
    "            # assume its a loss function\n",
    "            self.loss_fn = other;\n",
    "            \n",
    "            return self;\n",
    "            \n",
    "        else:\n",
    "            print( type(other) );\n",
    "\n",
    "    @tf.function\n",
    "    def output( self, inputs ):\n",
    "        \"\"\"\n",
    "        Create decorated function to compute the output values\n",
    "        generated by the NN for a given input.\n",
    "        \"\"\"\n",
    "        return self.output_layer.output( inputs );\n",
    "            \n",
    "    @tf.function\n",
    "    def loss( self, target, inputs ):\n",
    "        \"\"\"\n",
    "        Apply the loss function to comp ute the loss.\n",
    "        \"\"\"\n",
    "        return self.loss_fn( target, self.output( inputs ) );\n",
    "    \n",
    "    @tf.function\n",
    "    def train( self, target, inputs ):\n",
    "        \"\"\"\n",
    "        Propagate the inputs through the network and\n",
    "        update the weights and layers (biases) based on\n",
    "        the target values.\n",
    "        \"\"\"\n",
    "        # compute the loss and its gradient\n",
    "        with tf.GradientTape(persistent=True) as tape:\n",
    "            loss = self.loss(target, inputs);\n",
    "            \n",
    "        # udpate the weights based on the loss and gradient\n",
    "        for weight in self.weights:\n",
    "            weight.update( loss, tape );\n",
    "            \n",
    "        # udpate the layers (biases) based on the loss and gradient\n",
    "        for layer in self.layers:\n",
    "            layer.update( loss, tape );\n",
    "        del( tape );\n",
    "            \n",
    "\n",
    "            \n",
    "    #@tf.function\n",
    "    def train_epochs( self, y_train, x_train, y_test, x_test ):\n",
    "        \"\"\"\n",
    "        Perform multiple epochs of training based on global parameter\n",
    "        values.\n",
    "        \"\"\"\n",
    "        # create numpy arrays to store data\n",
    "        logs = {\n",
    "            \"losses\"        : np.zeros( (self.params['MAX_EPOCHS']+1,self.params['MINI_BATCH']), \\\n",
    "                                        dtype=np.float32 ),\n",
    "            \"corrects\"      : np.zeros( (self.params['MAX_EPOCHS']+1,self.params['MINI_BATCH']), \\\n",
    "                                        dtype=np.int32 ),\n",
    "            \"test_losses\"   : np.zeros( (self.params['MAX_EPOCHS']+1,), \\\n",
    "                                        dtype=np.float32 ),\n",
    "            \"test_corrects\" : np.zeros( (self.params['MAX_EPOCHS']+1,), \\\n",
    "                                        dtype=np.int32 ),\n",
    "            \"utilization\"   : np.zeros( (self.params['MAX_EPOCHS']+1,GPU_NO), \\\n",
    "                                        dtype=np.float32 ),\n",
    "        };\n",
    "\n",
    "        MB_SIZE = self.params['TRAINING_SIZE']//self.params['MINI_BATCH'];\n",
    "       \n",
    "        before = time.time();\n",
    "        for epoch in range(0,self.params['MAX_EPOCHS']+1):\n",
    "            logs['test_losses'][epoch] = nn.loss( one_hot(y_test), preprocess(x_test) );\n",
    "            test_conf = confusion(y_test,x_test);\n",
    "            logs['test_corrects'][epoch] = tf.linalg.trace( test_conf );\n",
    "\n",
    "\n",
    "            x,y = double_shuffle( x_train, y_train );\n",
    "            for mini_batch in range(0,self.params['MINI_BATCH']):\n",
    "\n",
    "                mbx = x[(MB_SIZE*mini_batch):(MB_SIZE*(mini_batch+1))];\n",
    "                mby = y[MB_SIZE*mini_batch:MB_SIZE*(mini_batch+1)];\n",
    "                logs['losses'][epoch][mini_batch] = nn.loss( one_hot(mby), \n",
    "                                                     preprocess(mbx) );\n",
    "        \n",
    "                conf = confusion( mby, mbx );\n",
    "                            \n",
    "                logs['corrects'][epoch][mini_batch] = tf.linalg.trace( conf );\n",
    "\n",
    "                self.train( one_hot(mby), preprocess(mbx) );\n",
    "   \n",
    "            logs['utilization'][epoch] = gpu_utilization();\n",
    "            sys.stdout.write( \"%d,\" % (epoch,) ); \n",
    "        after = time.time();\n",
    "        sys.stdout.write( \"\\n\" );\n",
    "        logs['test_conf'] = test_conf;\n",
    "        logs['total_time'] = after-before;\n",
    "        \n",
    "        return logs;\n",
    "\n",
    "##########################################################################\n",
    "# helper functions\n",
    "\n",
    "@tf.function\n",
    "def preprocess(input):\n",
    "    \"\"\"\n",
    "    This decorated function converts the 28x28 pixel images\n",
    "    into 784 dimensional feature vectors.  It also converts\n",
    "    the uint8 into DTYPE.\n",
    "    \"\"\"\n",
    "    return tf.reshape( tf.cast(input,DTYPE),\n",
    "                ( input.shape[0], input.shape[1]*input.shape[2])\n",
    "              )/256.0;\n",
    "\n",
    "@tf.function\n",
    "def one_hot( y_train ):\n",
    "    return tf.one_hot( y_train, 10 );\n",
    "\n",
    "@tf.function\n",
    "def loss( y_target, y_pred ):\n",
    "    return -tf.reduce_mean( y_target * tf.math.log(y_pred + EPS) + \\\n",
    "                            (1 - y_target) * tf.math.log(1 - y_pred + EPS) );\n",
    "\n",
    "@tf.function\n",
    "def un_hot( inputs ):\n",
    "    return tf.math.argmax(inputs,1);\n",
    "\n",
    "#@tf.function\n",
    "def confusion(targets,inputs):\n",
    "    return tf.math.confusion_matrix( \n",
    "        targets, \n",
    "        un_hot( nn.output( preprocess( inputs ) ) ) );\n",
    "\n",
    "# do not decorate this one\n",
    "#@tf.function\n",
    "def double_shuffle(inputs,targets):\n",
    "    if inputs.shape[0]!=targets.shape[0]:\n",
    "        raise Exception(\"Dimenionality missmatch!\");\n",
    "    shuffle = tf.random.shuffle( tf.range(inputs.shape[0]) );\n",
    "    \n",
    "    shuffled_inputs = tf.gather(inputs,shuffle);\n",
    "    shuffled_targets = tf.gather(targets,shuffle);\n",
    "\n",
    "    return shuffled_inputs.numpy(), shuffled_targets.numpy();\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing op Range in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op RandomShuffle in device /job:localhost/replica:0/task:0/device:CPU:0\n",
      "Executing op GatherV2 in device /job:localhost/replica:0/task:0/device:CPU:0\n",
      "unshuffled\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shuffled\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Test the double_shuffle function.\n",
    "\n",
    "Shuffle the data, then print out 10 numbers from the unshuffled data and the shuffled data\n",
    "to show that the labels match the images and the suffled numbers are shuffled.\n",
    "\"\"\"\n",
    "x2,y2 = double_shuffle(x_train,y_train);\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show10(x_train,y_train):\n",
    "    fig, axs = plt.subplots(1,10,figsize=(15,16));\n",
    "\n",
    "    for idx in range(10):\n",
    "        axs[idx].imshow( x_train[idx], cmap='Greys', interpolation='nearest');\n",
    "        axs[idx].set( title=\"\\\"\"+str(y_train[idx])+\"\\\"\" );\n",
    "    fig.tight_layout(pad=2.0);\n",
    "    plt.show();\n",
    "\n",
    "print( \"unshuffled\" );\n",
    "show10(x_train,y_train);\n",
    "\n",
    "print( \"shuffled\" );\n",
    "show10(x2,y2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def show_results( params, logs ):\n",
    "    MB_SIZE = params['TRAINING_SIZE']//params['MINI_BATCH'];\n",
    "    \n",
    "    fig, axs = plt.subplots(7,figsize=(15,45));\n",
    "\n",
    "    epochs = range(0,params['MAX_EPOCHS']+1);\n",
    "    batches = range(0,(params['MAX_EPOCHS']+1)*params['MINI_BATCH']);\n",
    "\n",
    "    losses_per_batch = np.reshape( logs[\"losses\"], [(params['MAX_EPOCHS']+1)*params['MINI_BATCH']] );\n",
    "\n",
    "    l, = axs[0].plot( batches, losses_per_batch );\n",
    "    axs[0].set(xlabel=r'batch', ylabel=r'loss',\n",
    "           title='Loss', ylim=(0,0.45)  );\n",
    "    axs[0].grid();\n",
    "\n",
    "    l, = axs[1].plot( epochs, np.mean( logs[\"losses\"], 1 ) );\n",
    "    axs[1].set(xlabel=r'epoch', ylabel=r'loss',\n",
    "           title='Average Loss', ylim=(0,0.45)  );\n",
    "    axs[1].grid();\n",
    "\n",
    "    corrects_per_batch = np.reshape( logs[\"corrects\"], [(params['MAX_EPOCHS']+1)*params['MINI_BATCH']] );\n",
    "\n",
    "    l, = axs[2].plot( batches, corrects_per_batch );\n",
    "    axs[2].set(xlabel=r'batch', ylabel=r'correct',\n",
    "           title='Correct', ylim=(0,MB_SIZE) );\n",
    "    axs[2].grid();\n",
    "\n",
    "    l, = axs[3].plot( epochs, np.sum( logs[\"corrects\"], 1 ) );\n",
    "    axs[3].set(xlabel=r'epoch', ylabel=r'correct',\n",
    "           title='Total Correct', ylim=(0,params['TRAINING_SIZE']) );\n",
    "    axs[3].grid();\n",
    "\n",
    "    l, = axs[4].plot( epochs, logs[\"test_losses\"] );\n",
    "    axs[4].set(xlabel=r'epochs', ylabel=r'loss',\n",
    "           title='Test Loss', ylim=(0,0.45));\n",
    "    axs[4].grid();\n",
    "\n",
    "    l, = axs[5].plot( epochs, logs[\"test_corrects\"] );\n",
    "    axs[5].set(xlabel=r'epoch', ylabel=r'correct',\n",
    "           title='Test Correct', ylim=(0,params['TEST_SIZE']) );\n",
    "    axs[5].grid();\n",
    "\n",
    "    l = {};\n",
    "    for gpu in range( GPU_NO ):\n",
    "        l[gpu], = axs[6].plot( epochs, logs[\"utilization\"][:,gpu] );\n",
    "    axs[6].set(xlabel=r'epoch', ylabel=r'utlization',\n",
    "           title='GPU Utilization', ylim=(0,100) );\n",
    "    axs[6].legend( tuple([l[gpu] for gpu in range(GPU_NO)]),\n",
    "            tuple(range(GPU_NO)) );\n",
    "    axs[6].grid();\n",
    "\n",
    "    fig.tight_layout(pad=2.0);\n",
    "    plt.show();\n",
    "\n",
    "    print( \"Confusion matrix of test data:\" );\n",
    "    print( logs[\"test_conf\"] );\n",
    "    print();\n",
    "    print( \"Total time: %s\" % (logs[\"total_time\"],) );\n",
    "    print( \"Epochs/s: %s\" % (params['MAX_EPOCHS']/logs[\"total_time\"],) );\n",
    "    print( \"Updates/s: %s\" % (params['MAX_EPOCHS']*params['MINI_BATCH']/logs[\"total_time\"],) );\n",
    "    \n",
    "    print( \"Training Examples/Update: %s\" % (MB_SIZE,) );\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network 1:  Run-time 150s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op VarIsInitializedOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op LogicalNot in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Assert in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op RandomUniform in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Add in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_one_hot_57 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_preprocess_70 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_loss_179 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_195 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_un_hot_203 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op LessEqual in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op All in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Max in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Maximum in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op AddV2 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Pack in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Pack in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Shape in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op StridedSlice in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op SparseTensorDenseAdd in device /job:localhost/replica:0/task:0/device:CPU:0\n",
      "Executing op MatrixDiagPartV3 in device /job:localhost/replica:0/task:0/device:CPU:0\n",
      "Executing op Sum in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_one_hot_263 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_preprocess_274 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_loss_374 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_390 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_un_hot_397 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_train_2385 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,\n"
     ]
    }
   ],
   "source": [
    "# Mini-batch learning\n",
    "##########################################################################\n",
    "# This updates the weights after presentation a mini-batch training\n",
    "# data.  The size of the minibatch is 1/1000 of the entire training data\n",
    "# in this case 60 examples.\n",
    "# It requires few epochs, but runs inefficiently on the GPU.\n",
    "##########################################################################\n",
    "##########################################################################\n",
    "# Parameters\n",
    "\n",
    "DTYPE = tf.float32;\n",
    "\n",
    "PARAMETERS1 = {\n",
    "    \n",
    "\n",
    "    'WEIGHT_INIT' : vs_init,\n",
    "    'BIAS_INIT' : zeros_init,\n",
    "    \n",
    "    'LEARNING_RATE' : 0.2,\n",
    "\n",
    "\n",
    "    'MAX_EPOCHS' : 25,     # maximum number of epochs to run\n",
    "    'TRAINING_SIZE' : x_train.shape[0], # number of training examples\n",
    "    'TEST_SIZE' : x_test.shape[0], # number of testing examples\n",
    "\n",
    "\n",
    "    # use this for mini-batch learning\n",
    "    'MINI_BATCH' : 1000,   # number of mini_batches per epoch\n",
    "\n",
    "    # use this for iteration learning\n",
    "    #'MINI_BATCH' : x_train.shape[0],\n",
    "\n",
    "    # use this for epoch learning\n",
    "    #'MINI_BATCH' : 1,\n",
    "\n",
    "\n",
    "};\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "    \n",
    "# build the network\n",
    "    \n",
    "input_l  = InputLayer( PARAMETERS1, \"input\", 28*28 );\n",
    "hidden_l = Layer( PARAMETERS1, \"hidden\", 28*28, sigmoid );\n",
    "output_l = Layer( PARAMETERS1, \"output\", 10, softmax );\n",
    "\n",
    "nn = input_l >> hidden_l >> output_l >> loss;\n",
    "\n",
    "# train it\n",
    "\n",
    "logs1 = nn.train_epochs( y_train, x_train, y_test, x_test );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x3240 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix of test data:\n",
      "tf.Tensor(\n",
      "[[840   0  16  44   8   5  72   0  15   0]\n",
      " [  5 954   6  26   6   0   1   0   2   0]\n",
      " [ 21   3 757  11 160   2  37   0   9   0]\n",
      " [ 29  12  23 863  44   1  23   0   5   0]\n",
      " [  0   0 105  30 815   2  40   0   8   0]\n",
      " [  0   0   0   1   0 938   0  38   4  19]\n",
      " [159   1 149  38 141   1 482   0  29   0]\n",
      " [  0   0   0   0   0  45   0 924   0  31]\n",
      " [  3   1  12   8   5   3  12   5 951   0]\n",
      " [  0   0   0   0   0  25   0  47   1 927]], shape=(10, 10), dtype=int32)\n",
      "\n",
      "Total time: 138.5973527431488\n",
      "Epochs/s: 0.18037862560283144\n",
      "Updates/s: 180.37862560283142\n",
      "Training Examples/Update: 60\n"
     ]
    }
   ],
   "source": [
    "show_results( PARAMETERS1, logs1 );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network 2:  Run-time 480s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing op __inference_loss_1355933 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_1355949 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_one_hot_1356010 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_preprocess_1356021 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_loss_1356121 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_1356137 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_un_hot_1356144 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_train_1358188 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      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     ]
    }
   ],
   "source": [
    "# Epoch-wise learning\n",
    "##########################################################################\n",
    "# This updates the weights after a complete presentation of all training\n",
    "# data.  It requires many epochs, but runs efficiently using the GPU\n",
    "# to do lots in parallel.\n",
    "##########################################################################\n",
    "# Parameters\n",
    "\n",
    "DTYPE = tf.float32;\n",
    "\n",
    "\n",
    "\n",
    "PARAMETERS2 = {\n",
    "    \n",
    "\n",
    "    'WEIGHT_INIT' : vs_init,\n",
    "    'BIAS_INIT' : zeros_init,\n",
    "    \n",
    "    'LEARNING_RATE' : 1.0,# made the learning rate higher than previous example\n",
    "\n",
    "\n",
    "    'MAX_EPOCHS' : 1500,     # maximum number of epochs to run\n",
    "    'TRAINING_SIZE' : x_train.shape[0], # number of training examples\n",
    "    'TEST_SIZE' : x_test.shape[0], # number of testing examples\n",
    "\n",
    "\n",
    "    # use this for mini-batch learning\n",
    "    #'MINI_BATCH' : 1000,   # number of mini_batches per epoch\n",
    "\n",
    "    # use this for iteration learning\n",
    "    #'MINI_BATCH' : x_train.shape[0],\n",
    "\n",
    "    # use this for epoch learning\n",
    "    'MINI_BATCH' : 1,\n",
    "\n",
    "\n",
    "};\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "    \n",
    "# build the network\n",
    "    \n",
    "input_l  = InputLayer( PARAMETERS2, \"input\", 28*28 );\n",
    "hidden_l = Layer( PARAMETERS2, \"hidden\", 28*28, sigmoid );\n",
    "output_l = Layer( PARAMETERS2, \"output\", 10, softmax );\n",
    "\n",
    "nn = input_l >> hidden_l >> output_l >> loss;\n",
    "\n",
    "# train it\n",
    "\n",
    "logs2 = nn.train_epochs( y_train, x_train, y_test, x_test );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing op DestroyResourceOp in device /job:localhost/replica:0/task:0/device:GPU:0\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x3240 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix of test data:\n",
      "tf.Tensor(\n",
      "[[844   4  22  52   6   4  49   0  19   0]\n",
      " [  5 943  12  32   6   0   0   0   2   0]\n",
      " [ 26   3 762  11 145   2  36   0  15   0]\n",
      " [ 40  11  18 861  38   1  26   0   5   0]\n",
      " [  3   3 127  39 770   2  50   0   6   0]\n",
      " [  0   0   0   2   0 896   0  61   5  36]\n",
      " [199   2 167  50 146   4 398   0  34   0]\n",
      " [  0   0   0   0   0  45   0 898   0  57]\n",
      " [  2   1  17  11   3   9  10   6 941   0]\n",
      " [  0   0   0   0   0  25   0  44   1 930]], shape=(10, 10), dtype=int32)\n",
      "\n",
      "Total time: 435.6038091182709\n",
      "Epochs/s: 3.4434960590363772\n",
      "Updates/s: 3.4434960590363772\n",
      "Training Examples/Update: 60000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "show_results( PARAMETERS2, logs2 );\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network 2:  Run-time 700s - not much output, be patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing op __inference_loss_1524813 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_1524829 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_one_hot_1524890 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_preprocess_1524901 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_loss_1525001 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_output_1525017 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_un_hot_1525024 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "Executing op __inference_train_1526988 in device /job:localhost/replica:0/task:0/device:GPU:0\n",
      "0,1,\n"
     ]
    }
   ],
   "source": [
    "# Element-wise learning\n",
    "##########################################################################\n",
    "# This updates the weights after each single example.\n",
    "# It only trains for one epoch.\n",
    "# Anticipated run time:  40s\n",
    "##########################################################################\n",
    "# Parameters\n",
    "\n",
    "DTYPE = tf.float32;\n",
    "\n",
    "  \n",
    "PARAMETERS3 = {\n",
    "    \n",
    "\n",
    "    'WEIGHT_INIT' : vs_init,\n",
    "    'BIAS_INIT' : zeros_init,\n",
    "    \n",
    "    'LEARNING_RATE' : 0.1,# made the learning rate very small\n",
    "\n",
    "\n",
    "    'MAX_EPOCHS' : 1,     # maximum number of epochs to run\n",
    "    'TRAINING_SIZE' : x_train.shape[0], # number of training examples\n",
    "    'TEST_SIZE' : x_test.shape[0], # number of testing examples\n",
    "\n",
    "\n",
    "    # use this for mini-batch learning\n",
    "    #'MINI_BATCH' : 1000,   # number of mini_batches per epoch\n",
    "\n",
    "    # use this for iteration learning\n",
    "    'MINI_BATCH' : x_train.shape[0],\n",
    "\n",
    "    # use this for epoch learning\n",
    "    #'MINI_BATCH' : 1,\n",
    "\n",
    "\n",
    "};\n",
    "\n",
    "\n",
    "\n",
    "##########################################################################\n",
    "##########################################################################\n",
    "\n",
    "\n",
    "    \n",
    "# build the network\n",
    "    \n",
    "input_l  = InputLayer( PARAMETERS3, \"input\", 28*28 );\n",
    "hidden_l = Layer( PARAMETERS3, \"hidden\", 28*28, sigmoid );\n",
    "output_l = Layer( PARAMETERS3, \"output\", 10, softmax );\n",
    "\n",
    "nn = input_l >> hidden_l >> output_l >> loss;\n",
    "\n",
    "# train it\n",
    "\n",
    "logs3 = nn.train_epochs( y_train, x_train, y_test, x_test );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x3240 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix of test data:\n",
      "tf.Tensor(\n",
      "[[732   0  10  35   0   0 211   0  12   0]\n",
      " [  6 945   8  28   3   0   6   2   2   0]\n",
      " [ 14   0 785   7  37   1 146   0  10   0]\n",
      " [ 46   8  14 847  12   0  67   0   6   0]\n",
      " [  6   1 214  46 470   0 254   0   9   0]\n",
      " [  1   0   0   2   0 889   0  68  11  29]\n",
      " [119   1 106  18  32   0 699   0  25   0]\n",
      " [  0   0   0   0   0  36   0 916   3  45]\n",
      " [  2   1   1   4   2   2  30   4 954   0]\n",
      " [  0   0   0   0   0  21   1  41   1 936]], shape=(10, 10), dtype=int32)\n",
      "\n",
      "Total time: 697.9054267406464\n",
      "Epochs/s: 0.001432858897043105\n",
      "Updates/s: 85.9715338225863\n",
      "Training Examples/Update: 1\n"
     ]
    }
   ],
   "source": [
    "show_results( PARAMETERS3, logs3 );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Jun 18 10:57:48 EDT 2020\r\n"
     ]
    }
   ],
   "source": [
    "! date"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}