def prepare_data(problem, hparams, params, config):
  """Construct input pipeline."""
  input_fn = problem.make_estimator_input_fn(
      tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
  dataset = input_fn(params, config)
  features, _ = dataset.make_one_shot_iterator().get_next()
  inputs, labels = features["targets"], features["inputs"]
  inputs = tf.to_float(inputs)
  input_shape = inputs.shape.as_list()
  inputs = tf.reshape(inputs, [hparams.batch_size] + input_shape[1:])
  labels = tf.reshape(labels, [hparams.batch_size])
  return inputs, labels, features
def prepare_data ( problem , hparams , params , config ) : input_fn = problem . make_estimator_input_fn ( tf . estimator . ModeKeys . EVAL , hparams , force_repeat = True ) dataset = input_fn ( params , config ) features , _ = dataset . make_one_shot_iterator ( ) . get_next ( ) inputs , labels = features [ "targets" ] , features [ "inputs" ] inputs = tf . to_float ( inputs ) input_shape = inputs . shape . as_list ( ) inputs = tf . reshape ( inputs , [ hparams . batch_size ] + input_shape [ 1 : ] ) labels = tf . reshape ( labels , [ hparams . batch_size ] ) return inputs , labels , features
Construct input pipeline.