def __call__(self, input_data, input_h, input_c, params, is_training=True):
"""Runs the forward step for the RNN model.
Args:
input_data: the input sequence to the RNN model. A Tensor of shape [?,
batch_size, input_size].
input_h: the initial hidden state for h. A Tensor of shape [num_layers,
batch_size, num_units].
input_c: the initial hidden state for c. This is only relevant for LSTM.
A Tensor of the same shape as input_h.
params: the parameter buffer created for this model.
is_training: whether this operation will be used in training or inference.
Returns:
output: the output sequuence.
output_h: the final state for h.
output_c: the final state for c. This is only relevant for LSTM.
"""
if self._rnn_mode != CUDNN_LSTM:
# For model that doesn't take input_c, replace with a dummy tensor.
input_c = array_ops.constant([], dtype=self._dtype)
output, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
input=input_data,
input_h=input_h,
input_c=input_c,
params=params,
rnn_mode=self._rnn_mode,
input_mode=self._input_mode,
direction=self._direction,
dropout=self._dropout,
seed=self._seed,
seed2=self._seed2,
is_training=is_training)
return (output, output_h, output_c)
def __call__(self, input_data, input_h, input_c, params, is_training=True):
"""Run the forward step for the RNN model.
Args:
input_data: the input sequence to the RNN model.
input_h: the initial hidden state for h.
input_c: the initial hidden state for c. This is only relevant for LSTM.
params: the parameter buffer created for this model.
is_training: whether this operation will be used in training or inference.
Returns:
output: the output sequuence.
output_h: the final state for h.
output_c: the final state for c. This is only relevant for LSTM.
"""
if self._rnn_mode != "lstm":
# For model that doesn't take input_c, replace with a dummy tensor.
input_c = array_ops.constant([], dtype=dtypes.float32)
output, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
input=input_data,
input_h=input_h,
input_c=input_c,
params=params,
rnn_mode=self._rnn_mode,
input_mode=self._input_mode,
direction=self._direction,
dropout=self._dropout,
seed=self._seed,
seed2=self._seed2,
is_training=is_training,
)
return (output, output_h, output_c)
def __call__(self, input_data, input_h, input_c, params, is_training=True):
"""Run the forward step for the RNN model.
Args:
input_data: the input sequence to the RNN model.
input_h: the initial hidden state for h.
input_c: the initial hidden state for c. This is only relevant for LSTM.
params: the parameter buffer created for this model.
is_training: whether this operation will be used in training or inference.
Returns:
output: the output sequuence.
output_h: the final state for h.
output_c: the final state for c. This is only relevant for LSTM.
"""
if self._rnn_mode != "lstm":
# For model that doesn't take input_c, replace with a dummy tensor.
input_c = array_ops.constant([], dtype=dtypes.float32)
output, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
input=input_data,
input_h=input_h,
input_c=input_c,
params=params,
rnn_mode=self._rnn_mode,
input_mode=self._input_mode,
direction=self._direction,
dropout=self._dropout,
seed=self._seed,
seed2=self._seed2,
is_training=is_training)
return (output, output_h, output_c)
def _cudnn_rnn(inputs,
input_h,
input_c,
params,
is_training,
rnn_mode,
input_mode=CUDNN_INPUT_LINEAR_MODE,
direction=CUDNN_RNN_UNIDIRECTION,
dropout=0.,
seed=0,
name=None):
"""Cudnn RNN.
Args:
inputs: the input sequence to the RNN model. A Tensor of shape [?,
batch_size, input_size].
input_h: the initial hidden state for h. A Tensor of shape [num_layers,
batch_size, num_units].
input_c: the initial hidden state for c. This is only relevant for LSTM.
A Tensor of the same shape as input_h.
params: the parameter buffer created for this model.
is_training: whether this operation will be used in training or inference
rnn_mode: one of ('lstm', 'gru', 'rnn_relu', 'rnn_tanh').
input_mode: indicate whether there is a linear projection between the
input and the actual computation before the first layer. It could be
'linear_input', 'skip_input' or 'auto_select'.
'linear_input' (default) always applies a linear projection of input
onto RNN hidden state. (standard RNN behavior).
'skip_input' is only allowed when input_size == num_units;
'auto_select' implies 'skip_input' when input_size == num_units;
otherwise, it implies 'linear_input'.
direction: the direction model that the model operates. Could be either
'unidirectional' or 'bidirectional'
dropout: whether to enable dropout. With it is 0, dropout is disabled.
seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
for behavior.
name: name of the operation.
Returns:
outputs, output_h, output_c
"""
_check_rnn_mode(rnn_mode)
check_direction(direction)
check_input_mode(input_mode)
seed, seed2 = random_seed.get_seed(seed)
outputs, output_h, output_c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
input=inputs,
input_h=input_h,
input_c=input_c,
params=params,
is_training=is_training,
rnn_mode=rnn_mode,
input_mode=input_mode,
direction=direction,
dropout=dropout,
seed=seed,
seed2=seed2,
name=name)
return (outputs, output_h, output_c)