text: 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)
      """
      # TODO: Implement function
      #   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);
  
      image_input = tf.get_default_graph().get_tensor_by_name(vgg_input_tensor_name);
      keep_prob = tf.get_default_graph().get_tensor_by_name(vgg_keep_prob_tensor_name);
      layer3 = tf.get_default_graph().get_tensor_by_name(vgg_layer3_out_tensor_name);
      layer4 = tf.get_default_graph().get_tensor_by_name(vgg_layer4_out_tensor_name);
      layer7 = tf.get_default_graph().get_tensor_by_name(vgg_layer7_out_tensor_name);
  
      return image_input, keep_prob, layer3, layer4, layer7
  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
      
      #1x1 convolution of layer 7
      layer7_conv = 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))
  
      #upsample
      layer_up1 = tf.layers.conv2d_transpose(layer7_conv, num_classes, 4,
                                                  strides= (2,2),
                                                  padding= 'same',
                                                  kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                                  kernel_regularizer= tf.contrib.layers.l2_regularizer(1e-3))
      
      #1x1 convolution of layer 4
      layer4_conv = 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 layer
      skip_layer1 = tf.add(layer_up1, layer4_conv)
  
  
      #upsample
      layer_up2 = tf.layers.conv2d_transpose(skip_layer1, num_classes, 4,
                                          strides= (2, 2),
                                          padding= 'same',
                                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                          kernel_regularizer = tf.contrib.layers.l2_regularizer(1e-3))
  
      #1x1 convolution of layer 3
      layer3_conv = 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 layer
      skip_layer2 = tf.add(layer_up2, layer3_conv)
  
      #upsample
      last_layer = tf.layers.conv2d_transpose(skip_layer2, num_classes, 16,
                                                  strides= (8,8),
                                                  padding= 'same',
                                                  kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                                                  kernel_regularizer= tf.contrib.layers.l2_regularizer(1e-3))
  
  
  
      return last_layer
  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)
      """
      # TODO: Implement function
  
      #creating logits
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
      correct_label = tf.reshape(correct_label, (-1, num_classes))
  
      #creating loss function
      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 = 0.05
      loss = cross_entropy_loss + reg_constant * sum(reg_losses)
  
      #initializing an optimizer
      optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)
  
      #applying the optimizer to the loss function
      train_op = optimizer.minimize(loss)
  
      return logits, train_op, 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
      """
      # TODO: Implement function
      sess.run(tf.global_variables_initializer())
  
      print("training............");
      for i in range(epochs):
          print("EPOCH {}".format(i+1));
          for image, label in get_batches_fn(batch_size):
                  _, loss = sess.run([train_op, cross_entropy_loss],
                                          feed_dict={input_image: image, correct_label: label,
                                          keep_prob: 0.5, learning_rate: 0.0009})
                  print("Loss: = {}".format(loss))
          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/
  
      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
  
          # TODO: Build NN using load_vgg, layers, and optimize function
  
          epochs = 25;
          batch_size = 5;
  
          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, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)
  
          nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)
  
          logits, train_op, cross_entropy_loss = optimize(nn_last_layer, correct_label, learning_rate, num_classes)
  
          # TODO: Train NN using the train_nn function
  
          train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss,
                  input_image, correct_label, keep_prob, learning_rate)
  
          # TODO: 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()
  