def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_c = initial_state[1]
input_h = array_ops.expand_dims(input_h, axis=0)
input_c = array_ops.expand_dims(input_c, axis=0)
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 2:self.units * 3],
self.kernel[:, self.units * 3:],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 2:self.units * 3],
self.recurrent_kernel[:, self.units * 3:],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:self.units * 6],
self.bias[self.units * 6:self.units * 7],
self.bias[self.units * 7:],
],
shape=self._vector_shape)
args = {
'input': inputs,
'input_h': input_h,
'input_c': input_c,
'params': params,
'is_training': True,
}
outputs, h, c, _, _ = gen_cudnn_rnn_ops.CudnnRNNV2(**args)
if self.stateful or self.return_state:
h = h[0]
c = c[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = array_ops.transpose(outputs, perm=(1, 0, 2))
else:
output = outputs[-1]
return output, [h, c]
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_c = initial_state[1]
input_h = array_ops.expand_dims(input_h, axis=0)
input_c = array_ops.expand_dims(input_c, axis=0)
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 2:self.units * 3],
self.kernel[:, self.units * 3:],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 2:self.units * 3],
self.recurrent_kernel[:, self.units * 3:],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:self.units * 6],
self.bias[self.units * 6:self.units * 7],
self.bias[self.units * 7:],
],
shape=self._vector_shape)
outputs, h, c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
inputs,
input_h=input_h,
input_c=input_c,
params=params,
is_training=True)
if self.stateful or self.return_state:
h = h[0]
c = c[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = array_ops.transpose(outputs, perm=(1, 0, 2))
else:
output = outputs[-1]
return output, [h, c]
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_h = array_ops.expand_dims(input_h, axis=0)
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, self.units:self.units * 2],
self.kernel[:, :self.units],
self.kernel[:, self.units * 2:],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units * 2:],
],
biases=[
self.bias[self.units:self.units * 2],
self.bias[:self.units],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 5:],
],
shape=self._vector_shape)
outputs, h, _, _ = gen_cudnn_rnn_ops.cudnn_rnn(
inputs,
input_h=input_h,
input_c=0,
params=params,
is_training=True,
rnn_mode='gru')
if self.stateful or self.return_state:
h = h[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = array_ops.transpose(outputs, perm=(1, 0, 2))
else:
output = outputs[-1]
return output, [h]
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_c = initial_state[1]
input_h = array_ops.expand_dims(input_h, axis=0)
input_c = array_ops.expand_dims(input_c, axis=0)
params = []
if build_info.is_rocm_build:
# ROCm MIOpen's weight sequence for LSTM is different from both canonical
# and Cudnn format
# MIOpen: [i, f, o, c] Cudnn/Canonical: [i, f, c, o]
# i is input gate weights.
# f is forget gate weights.
# o is output gate weights.
# c is cell gate weights.
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 3:],
self.kernel[:, self.units * 2:self.units * 3],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 3:],
self.recurrent_kernel[:, self.units * 2:self.units * 3],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:self.units * 6],
self.bias[self.units * 7:],
self.bias[self.units * 6:self.units * 7],
],
shape=self._vector_shape)
else:
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 2:self.units * 3],
self.kernel[:, self.units * 3:],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 2:self.units * 3],
self.recurrent_kernel[:, self.units * 3:],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:self.units * 6],
self.bias[self.units * 6:self.units * 7],
self.bias[self.units * 7:],
],
shape=self._vector_shape)
args = {
'input': inputs,
'input_h': input_h,
'input_c': input_c,
'params': params,
'is_training': True,
}
outputs, h, c, _, _ = gen_cudnn_rnn_ops.cudnn_rnnv2(**args)
if self.stateful or self.return_state:
h = h[0]
c = c[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = array_ops.transpose(outputs, perm=(1, 0, 2))
else:
output = outputs[-1]
return output, [h, c]
def _process_batch(self, inputs, initial_state):
if not self.time_major:
inputs = array_ops.transpose(inputs, perm=(1, 0, 2))
input_h = initial_state[0]
input_h = array_ops.expand_dims(input_h, axis=0)
params = []
if build_info.is_rocm_build:
# ROCm MIOpen's weight sequence for GRU is same with the canonical format
# but different from that of Cudnn
# MIOpen/Canonical: [z, r, h] Cudnn: [r, z, h]
# z is update gate weights.
# r is reset gate weights.
# h is output gate weights.
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, :self.units],
self.kernel[:, self.units:self.units * 2],
self.kernel[:, self.units * 2:],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, self.units * 2:],
],
biases=[
self.bias[:self.units],
self.bias[self.units:self.units * 2],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 5:],
],
shape=self._vector_shape)
else:
params = recurrent_v2._canonical_to_params( # pylint: disable=protected-access
weights=[
self.kernel[:, self.units:self.units * 2],
self.kernel[:, :self.units],
self.kernel[:, self.units * 2:],
self.recurrent_kernel[:, self.units:self.units * 2],
self.recurrent_kernel[:, :self.units],
self.recurrent_kernel[:, self.units * 2:],
],
biases=[
self.bias[self.units:self.units * 2],
self.bias[:self.units],
self.bias[self.units * 2:self.units * 3],
self.bias[self.units * 4:self.units * 5],
self.bias[self.units * 3:self.units * 4],
self.bias[self.units * 5:],
],
shape=self._vector_shape)
args = {
'input': inputs,
'input_h': input_h,
'input_c': 0,
'params': params,
'is_training': True,
'rnn_mode': 'gru',
}
outputs, h, _, _, _ = gen_cudnn_rnn_ops.cudnn_rnnv2(**args)
if self.stateful or self.return_state:
h = h[0]
if self.return_sequences:
if self.time_major:
output = outputs
else:
output = array_ops.transpose(outputs, perm=(1, 0, 2))
else:
output = outputs[-1]
return output, [h]
