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)
      """
      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'
  
      # Load VGG model
      tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
      graph = tf.get_default_graph()
  
      # Extract layers by name
      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
      """
      # 1x1 convolution of VGG layer 7
      layer7_conv_1x1 = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, padding='same',
                                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                         kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      # Upsampling by 2
      layer7_upsample = tf.layers.conv2d_transpose(layer7_conv_1x1, num_classes, 4, 2, padding='same',
                                                   kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      # 1x1 convolution of VGG layer 4
      layer4_conv_1x1 = tf.layers.conv2d(vgg_layer4_out, num_classes, 1, padding='same',
                                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                         kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      # Skip connections
      layer4_skip = tf.add(layer7_upsample, layer4_conv_1x1)
      # Upsampling by 2
      layer4_upsample = tf.layers.conv2d_transpose(layer4_skip, num_classes, 4, 2, padding='same',
                                                   kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      # 1x1 convolution of VGG layer 3
      layer3_conv_1x1 = tf.layers.conv2d(vgg_layer3_out, num_classes, 1, padding='same',
                                         kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                         kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      # Skip connections
      layer3_skip = tf.add(layer4_upsample, layer3_conv_1x1)
      # Upsampling by 8
      layer3_upsample = tf.layers.conv2d_transpose(layer3_skip, num_classes, 16, 8, padding='same',
                                                   kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
                                                   kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))
      return layer3_upsample
  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)
      """
      # Reshape to get a 2D tensor where each row represents a pixel and each column a class
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
      correct_label = tf.reshape(correct_label, (-1, num_classes))
      # Use standard cross entropy loss
      cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=correct_label))
      # Define optimizer
      optimizer = tf.train.AdamOptimizer(learning_rate)
      # Apply optimizer to the loss function
      train_op = optimizer.minimize(cross_entropy_loss)
  
      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
      """
      # Initialize all variables in the model
      sess.run(tf.global_variables_initializer())
  
      print('Starting training... for {} epochs.'.format(epochs))
      # Do training
      for epoch in range(epochs):
          print('Epoch: {}'.format(epoch + 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: 0.8,
                  learning_rate: 0.00001
              })
          print('Epoch: {}, loss: {}'.format(epoch + 1, loss))
      print('Training finished.')
  
  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)
  
      #epochs = 50
      batch_size = 8
  
      # 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/
  
      for epochs in [ 6, 12, 24, 48 ]:
          tf.reset_default_graph()
  
          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
  
              # Create TensorFlow placeholders
              correct_label = tf.placeholder(tf.int32, shape=[None, None, None, num_classes], name='correct_label')
              learning_rate = tf.placeholder(tf.float32, name='learning_rate')
  
              # Get layers from VGG
              input_image, keep_prob, layer3_out, layer4_out, layer7_out = load_vgg(sess, vgg_path)
  
              # Create layers for a fully convolutional network
              nn_last_layer = layers(layer3_out, layer4_out, layer7_out, num_classes)
  
              # Get TensorFlow loss and optimizer operations
              logits, train_op, cross_entropy_loss = optimize(nn_last_layer, correct_label, learning_rate, num_classes)
  
              # 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)
  
              # 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()
  