text: #!/usr/bin/env python3
  import os.path
  import tensorflow as tf
  import helper
  import warnings
  import time
  from datetime import timedelta
  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()))
  
  L2_REG = 1e-4
  STDEV = 1e-2
  KEEP_PROB = 0.6
  LEARNING_RATE = 1e-3
  EPOCHS = 30
  BATCH_SIZE = 5
  IMAGE_SHAPE_KITI = (160, 576)
  NUM_CLASSES = 2
  
  DATA_DIR = './data'
  RUNS_DIR = './runs'
  
  
  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'
  
      graph = tf.get_default_graph()
      tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
  
      img_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 img_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
      """
      conv_layer_7_1x1 = tf.layers.conv2d(
          inputs=vgg_layer7_out,
          filters=num_classes,
          kernel_size=1,
          strides=1,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
      deconv_layer_1_output = tf.layers.conv2d_transpose(
          inputs=conv_layer_7_1x1,
          filters=num_classes,
          kernel_size=4,
          strides=2,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
      conv_layer_4_1x1 = tf.layers.conv2d(
          inputs=vgg_layer4_out,
          filters=num_classes,
          kernel_size=1,
          strides=1,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
      skip_connection_1 = tf.add(deconv_layer_1_output, conv_layer_4_1x1)
      deconv_layer_2 = tf.layers.conv2d_transpose(
          inputs=skip_connection_1,
          filters=num_classes,
          kernel_size=4,
          strides=2,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
      layer3_conv_1x1 = tf.layers.conv2d(
          inputs=vgg_layer3_out,
          filters=num_classes,
          kernel_size=1,
          strides=1,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
      skip_connection_2 = tf.add(deconv_layer_2, layer3_conv_1x1)
      output_conv_layer = tf.layers.conv2d_transpose(
          inputs=skip_connection_2,
          filters=num_classes,
          kernel_size=16,
          strides=8,
          padding='same',
          kernel_initializer=tf.random_normal_initializer(stddev=STDEV),
          kernel_regularizer=tf.contrib.layers.l2_regularizer(L2_REG)
      )
  
      return output_conv_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)
      """
      logits = tf.reshape(nn_last_layer, (-1, num_classes))
      labels = tf.reshape(correct_label, (-1, num_classes))
  
      cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
  
      # # l2_reg does not help here apparently  ...
      # regularization_loss = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) # Scalar
      # cross_entropy_loss = cross_entropy_loss + regularization_loss
  
      optimizer = tf.train.AdamOptimizer(learning_rate)
      # optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.99)
      # optimizer = tf.train.GradientDescentOptimizer(learning_rate)
      # optimizer = tf.train.RMSPropOptimizer(learning_rate, decay=0.9, momentum=0.0)
  
      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
      """
      losses = []
      for epoch in range(epochs):
          loss = None
          s_time = time.time()
          for image, labels in get_batches_fn(batch_size):
              _, loss = sess.run(
                  [train_op, cross_entropy_loss],
                  feed_dict={input_image: image,
                             correct_label: labels,
                             keep_prob: KEEP_PROB,
                             learning_rate: LEARNING_RATE}
              )
              losses.append(loss)
          print("[Epoch: {0}/{1} Loss: {2:4f} Time: {3}]".format(epoch + 1, epochs, loss,
                                                                 str(timedelta(seconds=(time.time() - s_time)))))
      helper.plot_loss(RUNS_DIR, losses, "loss_graph")
  
  
  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/
  
      print("Start training...")
  
      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
  
          input_image, keep_prob, layer3, layer4, layer7 = load_vgg(sess, vgg_path)
          output_layer = layers(layer3, layer4, layer7, NUM_CLASSES)
  
          correct_label = tf.placeholder(dtype=tf.float32, shape=(None, None, None, NUM_CLASSES), name='correct_label')
          learning_rate = tf.placeholder(dtype=tf.float32, name='learning_rate')
  
          logits, train_op, cross_entropy_loss = optimize(output_layer, correct_label, learning_rate, NUM_CLASSES)
  
          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_KITI, logits, keep_prob, input_image)
  
          # OPTIONAL: Apply the trained model to a video
  
  
  if __name__ == '__main__':
      run()
  