text: from pathlib import Path
  import tensorflow as tf
  import helper
  import warnings
  from distutils.version import LooseVersion
  import project_tests as tests
  
  
  # Check TensorFlow Version
  if not LooseVersion(tf.__version__) >= LooseVersion('1.0'):
      raise AssertionError( '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'
      tf.saved_model.loader.load(sess, [vgg_tag], str(vgg_path))
      graph = tf.get_default_graph()
      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'
  
      image_input = graph.get_tensor_by_name(vgg_input_tensor_name)
      keep_prob = 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 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
      conv_1x1_layer_7 = tf.layers.conv2d(
          inputs=vgg_layer7_out,
          filters=num_classes,
          kernel_size=1,
          strides=(1, 1),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3),
          name="inception"
      )
  
      conv_1x1_layer_4 = tf.layers.conv2d(
          inputs=vgg_layer4_out,
          filters=num_classes,
          kernel_size=1,
          strides=(1, 1),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(0.001),
          name="conv_1x1_layer_4"
      )
  
      conv_1x1_layer_3 = tf.layers.conv2d(
          inputs=vgg_layer3_out,
          filters=num_classes,
          kernel_size=1,
          strides=(1, 1),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3),
          name="conv_1x1_layer_3"
      )
  
      output = tf.layers.conv2d_transpose(
          inputs=conv_1x1_layer_7,
          filters=num_classes,
          kernel_size=4,
          strides=(2, 2),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3),
          name="decoder_layer1"
      )  # upsampling up by 2
  
      output = tf.add(output, conv_1x1_layer_4)  # 1st skip layer
  
      output = tf.layers.conv2d_transpose(
          inputs=output,
          filters=num_classes,
          kernel_size=4,
          strides=(2, 2),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3),
          name="decoder_layer2"
      )  # upsampling up by 2
  
      output = tf.add(output, conv_1x1_layer_3)  # 2nd skip layer
  
      output = tf.layers.conv2d_transpose(
          inputs=output,
          filters=num_classes,
          kernel_size=16,
          strides=(8, 8),
          padding="same",
          kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3),
          name="decoder_layer3"
      )  # upsampling up by 8
  
      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)
      """
      # TODO: Implement function
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
  
      cross_entropy_loss = tf.nn.softmax_cross_entropy_with_logits(labels=correct_label,
                                                                      logits=logits)
      loss_operation = tf.reduce_mean(cross_entropy_loss)
      optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
      training_operation = optimizer.minimize(loss_operation)
  
      return logits, training_operation, 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())
      # sess.run(tf.local_variables_initializer())
  
      print("Training...")
      steps = 0
      for epoch in range(epochs):
          print("Epoch {}".format(epoch))
          for images, gt_images in get_batches_fn(batch_size):
              steps += 1
  
              feed = {input_image: images,
                      correct_label: gt_images,
                      keep_prob: 0.5}
  
              _, cross_entropy_loss2 = sess.run([train_op, cross_entropy_loss], feed_dict=feed)
  
              print('Epoch: {};Step: {};Loss: {}'.format(epoch, steps, cross_entropy_loss2))
  
  
  tests.test_train_nn(train_nn)
  
  
  def run():
      num_classes = 2
      image_shape = (160, 576)
      learning_rate = 0.0001
      epochs = 10
      batch_size = 10
      data_dir = Path('./data')
      runs_dir = Path('./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 = data_dir / 'vgg'
          # Create function to get batches
          get_batches_fn = helper.gen_batch_function(
              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
          correct_label = tf.placeholder(tf.float32, (None, image_shape[0], image_shape[1], 2))
          image_input, keep_prob, layer3, layer4, layer7 = load_vgg(sess, vgg_path)
          nn_last_layer = layers(layer3, layer4, layer7, 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, image_input,
                   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, image_input)
  
          # OPTIONAL: Apply the trained model to a video
  
  
  if __name__ == '__main__':
      run()
  