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)
      """
      # TODO: Implement function
  
      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()
      w1 = graph.get_tensor_by_name(vgg_input_tensor_name)
      keep = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
      layer3 = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
      layer4 = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
      layer7 = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)
  
      return w1, keep, 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
      l2_reg_val = 0.001
  
      # Conv Layer:
      ###########################################################################
      conv_1x1_7 = tf.layers.conv2d(vgg_layer7_out,
                          num_classes,
                          1,
                          strides=(1,1),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      conv_1x1_4 = tf.layers.conv2d(vgg_layer4_out,
                          num_classes,
                          1,
                          strides=(1,1),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      conv_1x1_3 = tf.layers.conv2d(vgg_layer3_out,
                          num_classes,
                          1,
                          strides=(1,1),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      # Upsample:
      upsample_7 = tf.layers.conv2d_transpose(conv_1x1_7,
                          num_classes,
                          4,
                          strides=(2,2),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      # Adding a Skip Layer and Upscale
      skip_layer_1 = tf.add(upsample_7, conv_1x1_4)
      up_skip_layer_1 = tf.layers.conv2d_transpose(skip_layer_1,
                          num_classes,
                          4,
                          strides=(2,2),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      # Adding a Skip Layer and Upscale
      skip_layer_2 = tf.add(up_skip_layer_1, conv_1x1_3)
      up_skip_layer_2 = tf.layers.conv2d_transpose(skip_layer_2,
                          num_classes,
                          16,
                          strides=(8,8),
                          padding='same',
                          kernel_initializer= tf.random_normal_initializer(stddev=0.01),
                          kernel_regularizer=tf.contrib.layers.l2_regularizer(l2_reg_val))
  
      return up_skip_layer_2
  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
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
      correct_label = tf.reshape(correct_label, (-1,num_classes))
  
      # Define loss function
      cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=correct_label,logits=logits))
  
      # Define Optimizer
      adam_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
  
      # Define train operation
      train_op = adam_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
      """
      # TODO: Implement function
  
      sess.run(tf.global_variables_initializer())
      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: 0.5,
                          learning_rate: 2e-5 })
              print("{};{:.3}".format((epoch + 1),loss))
  tests.test_train_nn(train_nn)
  
  
  def run():
      num_classes = 2
      image_shape = (160, 576)  # KITTI dataset uses 160x576 images
      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
  
          # TensorFlow Placeholders
          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)
  
          # TODO: Train NN using the train_nn function
          epochs = 50
          batch_size = 5
  
          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()
  