Question

我有一个训练循环，我想要注入一个函数，例如：

def example():
    input("Press the <ENTER> key to continue...")

这是我的模特

def main(unused_argv):
  assert FLAGS.input_file_pattern, "--input_file_pattern is required"
  assert FLAGS.train_dir, "--train_dir is required"

  model_config = configuration.ModelConfig()
  model_config.input_file_pattern = FLAGS.input_file_pattern
  model_config.inception_checkpoint_file = FLAGS.inception_checkpoint_file
  training_config = configuration.TrainingConfig()

  # Create training directory.
  train_dir = FLAGS.train_dir
  if not tf.gfile.IsDirectory(train_dir):
    tf.logging.info("Creating training directory: %s", train_dir)
    tf.gfile.MakeDirs(train_dir)

  # Build the TensorFlow graph.
  g = tf.Graph()
  with g.as_default():
    # Build the model.
    model = show_and_tell_model.ShowAndTellModel(
        model_config, mode="train", train_inception=FLAGS.train_inception)
    model.build()

    # Set up the learning rate.
    learning_rate_decay_fn = None
    if FLAGS.train_inception:
      learning_rate = tf.constant(training_config.train_inception_learning_rate)
    else:
      learning_rate = tf.constant(training_config.initial_learning_rate)
      if training_config.learning_rate_decay_factor > 0:
        num_batches_per_epoch = (training_config.num_examples_per_epoch /
                                 model_config.batch_size)
        decay_steps = int(num_batches_per_epoch *
                          training_config.num_epochs_per_decay)

        def _learning_rate_decay_fn(learning_rate, global_step):
          return tf.train.exponential_decay(
              learning_rate,
              global_step,
              decay_steps=decay_steps,
              decay_rate=training_config.learning_rate_decay_factor,
              staircase=True)

        learning_rate_decay_fn = _learning_rate_decay_fn

    # Set up the training ops.
    train_op = tf.contrib.layers.optimize_loss(
        loss=model.total_loss,
        global_step=model.global_step,
        learning_rate=learning_rate,
        optimizer=training_config.optimizer,
        clip_gradients=training_config.clip_gradients,
        learning_rate_decay_fn=learning_rate_decay_fn)

    # Set up the Saver for saving and restoring model checkpoints.
    saver = tf.train.Saver(max_to_keep=training_config.max_checkpoints_to_keep)

  # Run training.
  tf.contrib.slim.learning.train(
      train_op,
      train_dir,
      log_every_n_steps=FLAGS.log_every_n_steps,
      graph=g,
      global_step=model.global_step,
      number_of_steps=FLAGS.number_of_steps,
      init_fn=model.init_fn,
      saver=saver)


if __name__ == "__main__":
  tf.app.run()

我尝试添加一个名为tester的函数：

def tester(x):
  print('test')
  return

就在#run训练之前，我把

inp = tf.placeholder(tf.float32)
tf.py_func(tester, [inp], tf.float32)

将函数注入到tensorflow训练循环中？

0 个答案: