def check(num_units, input_size,
rnn_mode="lstm", num_layers=1, direction="unidirectional", input_mode="linear_input",
T=tf.float32, S=tf.int32):
common_kwargs = dict(
rnn_mode=rnn_mode, num_units=num_units, input_size=input_size,
num_layers=num_layers, direction=direction, input_mode=input_mode)
cu_size = cudnn_rnn_params_size(T=T, S=S, **common_kwargs)[0]
my_size = RecLayer._get_cudnn_param_size(**common_kwargs)
assert_equal(cu_size.eval(), my_size)
def params_size(self):
"""Calculate the size of the opaque parameter buffer needed for this model.
Returns:
The calculated parameter buffer size.
"""
return gen_cudnn_rnn_ops.cudnn_rnn_params_size(
num_layers=self._num_layers,
num_units=self._num_units,
input_size=self._input_size,
T=dtypes.float32,
S=dtypes.int32,
rnn_mode=self._rnn_mode,
input_mode=self._input_mode,
direction=self._direction)[0]
def params_size(self):
"""Calculates the size of the opaque parameter buffer needed for this model.
Returns:
The calculated parameter buffer size.
"""
return gen_cudnn_rnn_ops.cudnn_rnn_params_size(
num_layers=self._num_layers,
num_units=self._num_units,
input_size=self._input_size,
T=dtypes.float32,
S=dtypes.int32,
rnn_mode=self._rnn_mode,
input_mode=self._input_mode,
direction=self._direction)[0]
