def CellFn(theta, state0, inputs):
"""A cell fn is exectued inside of StackedRecurrent."""
del state0
fprop_inputs = []
for input_idx in range(len(state_shapes[i])):
name = 's{}'.format(input_idx)
if state_shapes[i][input_idx] is not None:
inputs[name].set_shape(state_shapes[i][input_idx])
fprop_inputs.append(inputs[name])
else:
fprop_inputs.append(None)
with py_utils.RemoveAssertContext(remove=True):
with CellFnFPropOpReplacementWrapper():
tf.logging.info('cell {} input {}'.format(
i, fprop_inputs))
mb_tensor = inputs[_MICRO_BATCH_STATE_NAME]
SetOverWriteGlobalStep(mb_tensor)
_, cell = self._cells[i]
outputs = cell.FProp(theta, *fprop_inputs)
state1 = py_utils.NestedMap()
state1[_MICRO_BATCH_STATE_NAME] = mb_tensor
outputs = _ToTuple(outputs)
assert len(outputs) == len(state_shapes[i + 1])
for output_idx in range(len(outputs)):
if outputs[output_idx] is not None:
name = 's{}'.format(output_idx)
state1[name] = outputs[output_idx]
return state1, py_utils.NestedMap()
def CellFn(theta, state0, inputs):
"""A cell fn is exectued inside of StackedRecurrent."""
del state0
def _FPropInputSetShape(name, t_shape):
if t_shape is None:
return None
inputs[name].set_shape(t_shape.ToTensorShape().as_list())
return inputs[name]
if p.nested_map_fprop:
# pylint: disable=protected-access
fprop_inputs = state_shapes[i]._RecursiveMap(
_FPropInputSetShape)
# pylint: enable=protected-access
else:
fprop_inputs = []
for input_idx, input_shape in enumerate(state_shapes[i]):
name = 's{}'.format(input_idx)
fprop_inputs.append(
_FPropInputSetShape(name, input_shape))
with py_utils.RemoveAssertContext(remove=True):
with CellFnFPropOpReplacementWrapper():
tf.logging.info('cell {} input {}'.format(
i, fprop_inputs))
mb_tensor = inputs[_MICRO_BATCH_STATE_NAME]
SetOverWriteGlobalStep(mb_tensor)
_, cell = self._cells[i]
fprop_inputs = _ToTuple(fprop_inputs)
outputs = cell.FProp(theta, *fprop_inputs)
if p.nested_map_fprop:
assert py_utils.IsCompatible(outputs, state_shapes[i + 1])
state1 = outputs.Filter(lambda x: x is not None)
else:
state1 = py_utils.NestedMap()
outputs = _ToTuple(outputs)
assert len(outputs) == len(state_shapes[i + 1])
for output_idx in range(len(outputs)):
if outputs[output_idx] is not None:
name = 's{}'.format(output_idx)
state1[name] = outputs[output_idx]
state1[_MICRO_BATCH_STATE_NAME] = mb_tensor
return state1, py_utils.NestedMap()
