def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._flow, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_scatter(
tensor=value, indices=indices, element_shape=-1)
return build_ta_with_new_flow(self, flow_out)
def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._flow, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
element_shape = self._element_shape[0] if self._element_shape else None
flow_out = list_ops.tensor_list_scatter(
tensor=value, indices=indices, input_handle=self._flow)
return build_ta_with_new_flow(self, flow_out)
def testScatterGrad(self):
with backprop.GradientTape() as tape:
c0 = constant_op.constant([1.0, 2.0])
tape.watch(c0)
l = list_ops.tensor_list_scatter(
c0, [1, 0], ops.convert_to_tensor([], dtype=dtypes.int32))
t0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
t1 = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t0), 2.0)
self.assertAllEqual(self.evaluate(t1), 1.0)
loss = t0 * t0 + t1 * t1
dt = tape.gradient(loss, c0)
self.assertAllEqual(self.evaluate(dt), [2., 4.])
def testScatterGrad(self):
with backprop.GradientTape() as tape:
c0 = constant_op.constant([1.0, 2.0])
tape.watch(c0)
l = list_ops.tensor_list_scatter(
c0, [1, 0], ops.convert_to_tensor([], dtype=dtypes.int32))
t0 = list_ops.tensor_list_get_item(l, 0, element_dtype=dtypes.float32)
t1 = list_ops.tensor_list_get_item(l, 1, element_dtype=dtypes.float32)
self.assertAllEqual(self.evaluate(t0), 2.0)
self.assertAllEqual(self.evaluate(t1), 1.0)
loss = t0 * t0 + t1 * t1
dt = tape.gradient(loss, c0)
self.assertAllEqual(self.evaluate(dt), [2., 4.])
def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._flow, value, indices]):
# TODO(b/129870929): Fix after all callers provide proper init dtype.
value = ops.convert_to_tensor(
value, preferred_dtype=self._dtype, name="value")
_check_dtypes(value, self._dtype)
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
element_shape = self._element_shape[0] if self._element_shape else None
flow_out = list_ops.tensor_list_scatter(
tensor=value, indices=indices, input_handle=self._flow)
return build_ta_with_new_flow(self, flow_out)
def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._flow, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_scatter(
tensor=value, indices=indices, element_shape=-1)
ta = TensorArray(
dtype=self._dtype,
handle=self.handle,
flow=flow_out,
colocate_with_first_write_call=self._colocate_with_first_write_call)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
ta._colocate_with = self._colocate_with
return ta
def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._flow, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and not context.executing_eagerly():
self._merge_element_shape(value.shape[1:])
flow_out = list_ops.tensor_list_scatter(
tensor=value, indices=indices, element_shape=-1)
ta = TensorArray(
dtype=self._dtype,
handle=self.handle,
flow=flow_out,
colocate_with_first_write_call=self._colocate_with_first_write_call)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
ta._colocate_with = self._colocate_with
return ta
