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.
"""
with ops.colocate_with(self._handle):
with ops.op_scope([self._handle, value, lengths], name,
"TensorArraySplit"):
lengths = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(
handle=self._handle,
value=value,
lengths=lengths,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
return ta
def split(self, value, lengths, name=None):
"""Split the values of a `Tensor` into the TensorArray."""
with ops.op_scope(
[self._handle, value, lengths], name, "TensorArraySplit"):
lengths = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(
handle=self._handle, value=value, lengths=lengths, flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
return ta
def split(self, value, lengths, name=None):
"""Split the values of a `Tensor` into the TensorArray."""
with ops.op_scope([self._handle, value, lengths], name,
"TensorArraySplit"):
lengths = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(handle=self._handle,
value=value,
lengths=lengths,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
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.colocate_with(self._handle):
with ops.op_scope([self._handle, value, lengths], name,
"TensorArraySplit"):
lengths_64 = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(
handle=self._handle,
value=value,
lengths=lengths_64,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
ta._infer_shape = self._infer_shape
ta._elem_shape = self._elem_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[1].get_shape()
clengths = tensor_util.constant_value(flow_out.op.inputs[2])
elem_shape = tensor_shape.unknown_shape()
if val_shape.dims:
if clengths is not None and clengths.max() == clengths.min(
):
elem_shape = tensor_shape.TensorShape(
[clengths[0]] + val_shape.dims[1:])
if ta._elem_shape:
if not elem_shape == ta._elem_shape[0]:
raise ValueError(
"Inconsistent shapes: saw %s but expected %s "
"(and infer_shape=True)" %
(elem_shape, ta._elem_shape[0]))
else:
ta._elem_shape.append(elem_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.colocate_with(self._handle):
with ops.name_scope(name, "TensorArraySplit",
[self._handle, value, lengths]):
lengths_64 = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(
handle=self._handle, value=value, lengths=lengths_64,
flow_in=self._flow, name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
ta._infer_shape = self._infer_shape
ta._elem_shape = self._elem_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[1].get_shape()
clengths = tensor_util.constant_value(flow_out.op.inputs[2])
elem_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
if clengths is not None and clengths.max() == clengths.min():
elem_shape = tensor_shape.TensorShape(
[clengths[0]] + val_shape.dims[1:])
if ta._elem_shape:
if not elem_shape == ta._elem_shape[0]:
raise ValueError(
"Inconsistent shapes: saw %s but expected %s "
"(and infer_shape=True)" % (elem_shape, ta._elem_shape[0]))
else:
ta._elem_shape.append(elem_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.
"""
with ops.colocate_with(self._handle):
with ops.op_scope([self._handle, value, lengths], name, "TensorArraySplit"):
lengths = math_ops.to_int64(lengths)
flow_out = gen_data_flow_ops._tensor_array_split(
handle=self._handle, value=value, lengths=lengths, flow_in=self._flow, name=name
)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
return ta
