(is_training,
                hparams,
                placeholders=None,
                direct_inputs=None,
                use_placeholders=True)
return graph
Builds the model graph.
Builds the model graph.
Builds the model graph .
def build_graph(is_training,
                hparams,
                placeholders=None,
                direct_inputs=None,
                use_placeholders=True):
  """Builds the model graph."""
  if placeholders is None and use_placeholders:
    placeholders = get_placeholders(hparams)
  initializer = tf.random_uniform_initializer(-hparams.init_scale,
                                              hparams.init_scale)
  with tf.variable_scope('model', reuse=None, initializer=initializer):
    graph = CoconetGraph(
        is_training=is_training,
        hparams=hparams,
        placeholders=placeholders,
        direct_inputs=direct_inputs,
        use_placeholders=use_placeholders)
  return graph
def build_graph ( is_training , hparams , placeholders = None , direct_inputs = None , use_placeholders = True ) : if placeholders is None and use_placeholders : placeholders = get_placeholders ( hparams ) initializer = tf . random_uniform_initializer ( - hparams . init_scale , hparams . init_scale ) with tf . variable_scope ( 'model' , reuse = None , initializer = initializer ) : graph = CoconetGraph ( is_training = is_training , hparams = hparams , placeholders = placeholders , direct_inputs = direct_inputs , use_placeholders = use_placeholders ) return graph