trainer.py 文件源码

def run(self):
    """Run training."""
    # Create logging directory if not exists.
    if not os.path.isdir(self._train_log_dir):
      os.makedirs(self._train_log_dir)

    # Load data and compute loss function
    self._initialize()

    # Visualize input images in Tensorboard.
    self._summary_ops.append(tf.image_summary("Image_Train", self._observations, max_images=5))

    # Initialize optimizer.
    optimizer = tf.train.AdadeltaOptimizer(self._config.learning_rate)
    train_op = slim.learning.create_train_op(self._loss, optimizer)

    # Use `slim.learning.train` to manage training.
    slim.learning.train(train_op=train_op,
                        logdir=self._train_log_dir,
                        graph=self._graph,
                        number_of_steps=self._config.train_steps,
                        summary_op=tf.merge_summary(self._summary_ops),
                        save_summaries_secs=self._config.save_summaries_secs,
                        save_interval_secs=self._config.save_interval_secs)