def grad(self, source, flow=None, name=None):
# tensor_array_grad requires a flow input when forward
# TensorArrays are dynamically sized. This forces the creation
# of the grad TensorArray only once the final forward array's size
# is fixed.
if flow is None:
flow = self.flow
with ops.name_scope(name, "TensorArrayGrad", [self._handle]):
with ops.colocate_with(self._handle):
g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3(
handle=self._handle, source=source, flow_in=flow, name=name
)
with ops.control_dependencies([g_handle]):
flow = array_ops.identity(flow, name="gradient_flow")
g = TensorArray(dtype=self._dtype, handle=g_handle, flow=flow, infer_shape=self._infer_shape)
g._element_shape = self._element_shape
return g
def grad(self, source, flow=None, name=None):
# tensor_array_grad requires a flow input when forward
# TensorArrays are dynamically sized. This forces the creation
# of the grad TensorArray only once the final forward array's size
# is fixed.
if flow is None:
flow = self.flow
with ops.name_scope(name, "TensorArrayGrad", [self._handle]):
with ops.colocate_with(self._handle):
g_handle, unused_flow = gen_data_flow_ops._tensor_array_grad_v3(
handle=self._handle, source=source, flow_in=flow, name=name)
with ops.control_dependencies([g_handle]):
flow = array_ops.identity(flow, name="gradient_flow")
g = TensorArray(
dtype=self._dtype,
handle=g_handle,
flow=flow,
infer_shape=self._infer_shape,
colocate_with_first_write_call=False)
g._element_shape = self._element_shape
return g