text: import tensorflow as tf
  import dataset
  import time
  from datetime import timedelta
  import math
  import random
  import numpy as np
  
  
  from numpy.random import seed
  seed(1)
  from tensorflow import set_random_seed
  set_random_seed(2)
  
  batch_size = 10
  
  classes = ['nike','puma','sony']
  num_classes = len(classes)
  
  validation_size = 0.2
  img_size = 300
  num_channels = 3
  train_path='/home/vijayaganesh/Kanjoos-HackGT/Kanjoos_Backend/data/training_data'
  
  # We shall load all the training and validation images and labels into memory using openCV and use that during training
  data = dataset.read_train_sets(train_path, img_size, classes, validation_size=validation_size)
  
  
  print("Complete reading input data. Will Now print a snippet of it")
  print("Number of files in Training-set:\t\t{}".format(len(data.train.labels)))
  print("Number of files in Validation-set:\t{}".format(len(data.valid.labels)))
  
  
  
  session = tf.Session()
  x = tf.placeholder(tf.float32, shape=[None, img_size,img_size,num_channels], name='x')
  
  ## labels
  y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
  y_true_cls = tf.argmax(y_true, dimension=1)
  
  
  
  ##Network graph params
  filter_size_conv1 = 3
  num_filters_conv1 = 32
  
  filter_size_conv2 = 3
  num_filters_conv2 = 32
  
  filter_size_conv3 = 3
  num_filters_conv3 = 64
  
  fc_layer_size = 128
  
  def create_weights(shape):
      return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
  
  def create_biases(size):
      return tf.Variable(tf.constant(0.05, shape=[size]))
  
  
  
  def create_convolutional_layer(input,
                 num_input_channels,
                 conv_filter_size,
                 num_filters):
  
      ## We shall define the weights that will be trained using create_weights function.
      weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])
      ## We create biases using the create_biases function. These are also trained.
      biases = create_biases(num_filters)
  
      ## Creating the convolutional layer
      layer = tf.nn.conv2d(input=input,
                       filter=weights,
                       strides=[1, 1, 1, 1],
                       padding='SAME')
  
      layer += biases
  
      ## We shall be using max-pooling.
      layer = tf.nn.max_pool(value=layer,
                              ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1],
                              padding='SAME')
      ## Output of pooling is fed to Relu which is the activation function for us.
      layer = tf.nn.relu(layer)
  
      return layer
  
  
  
  def create_flatten_layer(layer):
      #We know that the shape of the layer will be [batch_size img_size img_size num_channels]
      # But let's get it from the previous layer.
      layer_shape = layer.get_shape()
  
      ## Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it.
      num_features = layer_shape[1:4].num_elements()
  
      ## Now, we Flatten the layer so we shall have to reshape to num_features
      layer = tf.reshape(layer, [-1, num_features])
  
      return layer
  
  
  def create_fc_layer(input,
               num_inputs,
               num_outputs,
               use_relu=True):
  
      #Let's define trainable weights and biases.
      weights = create_weights(shape=[num_inputs, num_outputs])
      biases = create_biases(num_outputs)
  
      # Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow
      layer = tf.matmul(input, weights) + biases
      if use_relu:
          layer = tf.nn.relu(layer)
  
      return layer
  
  
  layer_conv1 = create_convolutional_layer(input=x,
                 num_input_channels=num_channels,
                 conv_filter_size=filter_size_conv1,
                 num_filters=num_filters_conv1)
  layer_conv2 = create_convolutional_layer(input=layer_conv1,
                 num_input_channels=num_filters_conv1,
                 conv_filter_size=filter_size_conv2,
                 num_filters=num_filters_conv2)
  
  layer_conv3= create_convolutional_layer(input=layer_conv2,
                 num_input_channels=num_filters_conv2,
                 conv_filter_size=filter_size_conv3,
                 num_filters=num_filters_conv3)
  
  layer_flat = create_flatten_layer(layer_conv3)
  
  layer_fc1 = create_fc_layer(input=layer_flat,
                       num_inputs=layer_flat.get_shape()[1:4].num_elements(),
                       num_outputs=fc_layer_size,
                       use_relu=True)
  
  layer_fc2 = create_fc_layer(input=layer_fc1,
                       num_inputs=fc_layer_size,
                       num_outputs=num_classes,
                       use_relu=False)
  
  y_pred = tf.nn.softmax(layer_fc2,name='y_pred')
  
  y_pred_cls = tf.argmax(y_pred, dimension=1)
  session.run(tf.global_variables_initializer())
  cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,
                                                      labels=y_true)
  cost = tf.reduce_mean(cross_entropy)
  optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)
  correct_prediction = tf.equal(y_pred_cls, y_true_cls)
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
  
  session.run(tf.global_variables_initializer())
  
  
  def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
      acc = session.run(accuracy, feed_dict=feed_dict_train)
      val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
      msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%},  Validation Loss: {3:.3f}"
      print(msg.format(epoch + 1, acc, val_acc, val_loss))
  
  total_iterations = 0
  
  saver = tf.train.Saver()
  def train(num_iteration):
      global total_iterations
  
      for i in range(total_iterations,
                     total_iterations + num_iteration):
  
          x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
          x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)
  
  
          feed_dict_tr = {x: x_batch,
                             y_true: y_true_batch}
          feed_dict_val = {x: x_valid_batch,
                                y_true: y_valid_batch}
  
          session.run(optimizer, feed_dict=feed_dict_tr)
  
          if i % int(data.train.num_examples/batch_size) == 0:
              val_loss = session.run(cost, feed_dict=feed_dict_val)
              epoch = int(i / int(data.train.num_examples/batch_size))
  
              show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)
              saver.save(session, 'Logo Classifier')
  
  
      total_iterations += num_iteration
  
  train(num_iteration=80)
  