text: #!/usr/bin/env python3
  import os.path
  import tensorflow as tf
  import helper
  import warnings
  from distutils.version import LooseVersion
  import project_tests as tests
  
  
  # Check TensorFlow Version
  assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
  print('TensorFlow Version: {}'.format(tf.__version__))
  
  # Check for a GPU
  if not tf.test.gpu_device_name():
      warnings.warn('No GPU found. Please use a GPU to train your neural network.')
  else:
      print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
  
  
  def load_vgg(sess, vgg_path):
      """
      Load Pretrained VGG Model into TensorFlow.
      :param sess: TensorFlow Session
      :param vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb"
      :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
      """
      #   Use tf.saved_model.loader.load to load the model and weights
      vgg_tag = 'vgg16'
      vgg_input_tensor_name = 'image_input:0'
      vgg_keep_prob_tensor_name = 'keep_prob:0'
      vgg_layer3_out_tensor_name = 'layer3_out:0'
      vgg_layer4_out_tensor_name = 'layer4_out:0'
      vgg_layer7_out_tensor_name = 'layer7_out:0'
      
      tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
      graph = tf.get_default_graph()
      image_input = graph.get_tensor_by_name(vgg_input_tensor_name)
      keep_prob = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
      layer3_out = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
      layer4_out = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
      layer7_out = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)
      
      return image_input, keep_prob, layer3_out, layer4_out, layer7_out
  
  tests.test_load_vgg(load_vgg, tf)
  
  
  def layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes):
      """
      Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.
      :param vgg_layer3_out: TF Tensor for VGG Layer 3 output
      :param vgg_layer4_out: TF Tensor for VGG Layer 4 output
      :param vgg_layer7_out: TF Tensor for VGG Layer 7 output
      :param num_classes: Number of classes to classify
      :return: The Tensor for the last layer of output
      """
      # TODO: Implement function
      conv_1x1_7 = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, padding='same',
                                    kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                    kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      output_7 = tf.layers.conv2d_transpose(conv_1x1_7, num_classes, 4, 2, padding='same', 
                                            kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                            kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      
      # add 1st skip connection by making sure that the shape are same
      conv_1x1_4 = tf.layers.conv2d(vgg_layer4_out, num_classes, 1, padding='same',
                                    kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                    kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      skip_con1 = tf.add(output_7, conv_1x1_4)
      output_4 = tf.layers.conv2d_transpose(skip_con1, num_classes, 4, 2, padding='same', 
                                            kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                            kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      
      # add 2nd skip connection
      conv_1x1_3 = tf.layers.conv2d(vgg_layer3_out, num_classes, 1, padding='same', 
                                    kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                    kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      skip_con2 = tf.add(output_4, conv_1x1_3)
      output = tf.layers.conv2d_transpose(skip_con2, num_classes, 16, 8, padding='same',
                                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
          
      return output
  
  tests.test_layers(layers)
  
  
  def optimize(nn_last_layer, correct_label, learning_rate, num_classes):
      """
      Build the TensorFLow loss and optimizer operations.
      :param nn_last_layer: TF Tensor of the last layer in the neural network
      :param correct_label: TF Placeholder for the correct label image
      :param learning_rate: TF Placeholder for the learning rate
      :param num_classes: Number of classes to classify
      :return: Tuple of (logits, train_op, cross_entropy_loss)
      """
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
      correct_label = tf.reshape(correct_label, (-1,num_classes))
      # when adding l2-regularization, realize that merely passing a regularizer in the arguments of the tf.layers is not enough. 
      # You also have to manually add all those regularization loss terms to your loss function, otherwise they are not doing 
      # anything. I didn’t know this at first and thought I’m doing l2-regularization when I actually wasn’t. 
      cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=correct_label))
      reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
      reg_constant = 0.01  # Choose an appropriate one.
      cost = cross_entropy_loss + reg_constant * sum(reg_losses)
      optimizer = tf.train.AdamOptimizer(learning_rate)
      train_op = optimizer.minimize(cost)
  #     train_op = optimizer.minimize(cross_entropy_loss)
      
      return logits, train_op, cost
  #     return logits, train_op, cross_entropy_loss
  
  tests.test_optimize(optimize)
  
  
  def train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image,
               correct_label, keep_prob, learning_rate):
      """
      Train neural network and print out the loss during training.
      :param sess: TF Session
      :param epochs: Number of epochs
      :param batch_size: Batch size
      :param get_batches_fn: Function to get batches of training data.  Call using get_batches_fn(batch_size)
      :param train_op: TF Operation to train the neural network
      :param cross_entropy_loss: TF Tensor for the amount of loss
      :param input_image: TF Placeholder for input images
      :param correct_label: TF Placeholder for label images
      :param keep_prob: TF Placeholder for dropout keep probability
      :param learning_rate: TF Placeholder for learning rate
      """
      for epoch in range(epochs):
          epoch_cost = 0. 
          for image, label in get_batches_fn(batch_size):
              feed_dict = {
                  input_image: image, 
                  correct_label: label,       
                  keep_prob: 0.5,
                  learning_rate: 0.001}
              _ , minibatch_cost = sess.run([train_op, cross_entropy_loss], feed_dict=feed_dict)
              
  #             epoch_cost += minibatch_cost / num_batch
  #             if epoch % 10 == 0:                
  #             print ("Epoch Cost after epoch %i: %f" % (epoch, epoch_cost))
              print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
              print()
      
      
  tests.test_train_nn(train_nn)
  
  
  def run():
      num_classes = 2
      image_shape = (160, 576)
      data_dir = './data'
      runs_dir = './runs'
      
      
      tests.test_for_kitti_dataset(data_dir)
  
      # Download pretrained vgg model
      helper.maybe_download_pretrained_vgg(data_dir)
  
      # OPTIONAL: Train and Inference on the cityscapes dataset instead of the Kitti dataset.
      # You'll need a GPU with at least 10 teraFLOPS to train on.
      #  https://www.cityscapes-dataset.com/
      tf.reset_default_graph() # to be able to rerun the model without overwriting tf variables
      with tf.Session() as sess:
          # Path to vgg model
          vgg_path = os.path.join(data_dir, 'vgg')
          # Create function to get batches
          get_batches_fn = helper.gen_batch_function(os.path.join(data_dir, 'data_road/training'), image_shape)
  
          # OPTIONAL: Augment Images for better results
          #  https://datascience.stackexchange.com/questions/5224/how-to-prepare-augment-images-for-neural-network
  
          # Build NN using load_vgg, layers, and optimize function
          correct_label = tf.placeholder(tf.int32, [None, None, None, num_classes], name='correct_label')
          learning_rate = tf.placeholder(tf.float32, name='learning_rate')
          
          input_image, keep_prob, layer3_out, layer4_out, layer7_out = load_vgg(sess, vgg_path)
          layer_output = layers(layer3_out, layer4_out, layer7_out, num_classes)
          logits, train_op, cross_entropy_loss = optimize(layer_output, correct_label, learning_rate, num_classes)
              
          # Train NN using the train_nn function
          epochs = 20 
          batch_size = 64
          
          sess.run(tf.global_variables_initializer())
          train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image,
               correct_label, keep_prob, learning_rate)
  
          # Save inference data using helper.save_inference_samples
          helper.save_inference_samples(runs_dir, data_dir, sess, image_shape, logits, keep_prob, input_image)
  
          # OPTIONAL: Apply the trained model to a video
  
  
  
  if __name__ == '__main__':
      run()
  