def scatter(self, indices, value, name=None):
"""Scatter the values of a `Tensor` in specific indices of a `TensorArray`.
Args:
indices: A `1-D` `Tensor` taking values in `[0, max_value)`. If
the `TensorArray` is not dynamic, `max_value=size()`.
value: (N+1)-D. Tensor of type `dtype`. The Tensor to unpack.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the scatter occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArrayScatter",
[self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and context.in_graph_mode():
self._merge_element_shape(value.shape[1:])
with self._maybe_colocate_with(value):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle,
indices=indices,
value=value,
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 scatter(self, indices, value, name=None):
"""Scatter the values of a `Tensor` in specific indices of a `TensorArray`.
Args:
indices: A `1-D` `Tensor` taking values in `[0, max_value)`. If
the `TensorArray` is not dynamic, `max_value=size()`.
value: (N+1)-D. Tensor of type `dtype`. The Tensor to unpack.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the scatter occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArrayScatter", [self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
_maybe_set_device(self._handle.op, value)
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle, indices=indices, value=value, flow_in=self._flow, name=name
)
ta = TensorArray(dtype=self._dtype, handle=self._handle, flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[2].get_shape()
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
element_shape = tensor_shape.TensorShape(val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta
def scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and context.in_graph_mode():
self._merge_element_shape(value.shape[1:])
with self._maybe_colocate_with(value):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle,
indices=indices,
value=value,
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 scatter(self, indices, value, name=None):
"""See TensorArray."""
with ops.name_scope(name, "TensorArrayScatter",
[self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
if self._infer_shape and context.in_graph_mode():
self._merge_element_shape(value.shape[1:])
with self._maybe_colocate_with(value):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle,
indices=indices,
value=value,
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 scatter(self, indices, value, name=None):
"""Scatter the values of a `Tensor` in specific indices of a `TensorArray`.
Args:
indices: A `1-D` `Tensor` taking values in `[0, max_value)`. If
the `TensorArray` is not dynamic, `max_value=size()`.
value: (N+1)-D. Tensor of type `dtype`. The Tensor to unpack.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the scatter occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if the shape inference fails.
"""
with ops.name_scope(name, "TensorArrayScatter",
[self._handle, value, indices]):
value = ops.convert_to_tensor(value, name="value")
_maybe_set_device(self._handle.op, value)
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_scatter_v3(
handle=self._handle,
indices=indices,
value=value,
flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype,
handle=self._handle,
flow=flow_out)
ta._infer_shape = self._infer_shape
ta._element_shape = self._element_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[2].get_shape()
element_shape = tensor_shape.unknown_shape()
if val_shape.dims is not None:
element_shape = tensor_shape.TensorShape(
val_shape.dims[1:])
ta._merge_element_shape(element_shape)
return ta