def split(self, value, lengths, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
value = ops.convert_to_tensor(value, name="value")
with self._maybe_colocate_with(value):
lengths_64 = math_ops.to_int64(lengths)
if self._infer_shape and context.in_graph_mode():
clengths = tensor_util.constant_value(lengths_64)
if value.shape.dims is not None:
if clengths is not None and clengths.max() == clengths.min():
self._merge_element_shape(
tensor_shape.TensorShape([clengths[0]]).concatenate(
value.shape[1:]))
flow_out = gen_data_flow_ops._tensor_array_split_v3(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
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 split(self, value, lengths, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
value = ops.convert_to_tensor(value, name="value")
with self._maybe_colocate_with(value):
lengths_64 = math_ops.to_int64(lengths)
if self._infer_shape and context.in_graph_mode():
clengths = tensor_util.constant_value(lengths_64)
if value.shape.dims is not None:
if clengths is not None and clengths.max(
) == clengths.min():
self._merge_element_shape(
tensor_shape.TensorShape([
clengths[0]
]).concatenate(value.shape[1:]))
flow_out = gen_data_flow_ops._tensor_array_split_v3(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
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 split(self, value, lengths, name=None):
"""Split the values of a `Tensor` into the TensorArray.
Args:
value: (N+1)-D. Tensor of type `dtype`. The Tensor to split.
lengths: 1-D. int32 vector with the lengths to use when splitting
`value` along its first dimension.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the split occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
value = ops.convert_to_tensor(value, name="value")
with self._maybe_colocate_with(value):
lengths_64 = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split_v3(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
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
if ta._infer_shape and context.in_graph_mode():
val_shape = flow_out.op.inputs[1].get_shape()
clengths = tensor_util.constant_value(flow_out.op.inputs[2])
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
if clengths is not None and clengths.max() == clengths.min(
):
element_shape = tensor_shape.TensorShape(
[clengths[0]] + val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta
def split(self, value, lengths, name=None):
"""Split the values of a `Tensor` into the TensorArray.
Args:
value: (N+1)-D. Tensor of type `dtype`. The Tensor to split.
lengths: 1-D. int32 vector with the lengths to use when splitting
`value` along its first dimension.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the split occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
value = ops.convert_to_tensor(value, name="value")
with self._maybe_colocate_with(value):
lengths_64 = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split_v3(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
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
if ta._infer_shape and context.in_graph_mode():
val_shape = flow_out.op.inputs[1].get_shape()
clengths = tensor_util.constant_value(flow_out.op.inputs[2])
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
if clengths is not None and clengths.max() == clengths.min():
element_shape = tensor_shape.TensorShape([clengths[0]] +
val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta