def create():
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
def create():
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
def create():
"""Create the TensorArray op."""
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
identical_element_shapes=infer_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
def create():
"""Create the TensorArray op."""
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
identical_element_shapes=infer_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
def create():
"""Create the TensorArray op."""
ta_kwargs = {}
if _ENABLE_IDENTICAL_ELEMENT_SHAPES:
ta_kwargs["identical_element_shapes"] = infer_shape
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope,
**ta_kwargs)
def create():
"""Create the TensorArray op."""
ta_kwargs = {}
if _ENABLE_IDENTICAL_ELEMENT_SHAPES:
ta_kwargs["identical_element_shapes"] = infer_shape
return gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope,
**ta_kwargs)
def __init__(self,
dtype,
size=None,
dynamic_size=None,
clear_after_read=None,
tensor_array_name=None,
handle=None,
flow=None,
infer_shape=True,
element_shape=None,
name=None):
"""Construct a new TensorArray or wrap an existing TensorArray handle.
A note about the parameter `name`:
The name of the `TensorArray` (even if passed in) is uniquified: each time
a new `TensorArray` is created at runtime it is assigned its own name for
the duration of the run. This avoids name collisions if a `TensorArray`
is created within a `while_loop`.
Args:
dtype: (required) data type of the TensorArray.
size: (optional) int32 scalar `Tensor`: the size of the TensorArray.
Required if handle is not provided.
dynamic_size: (optional) Python bool: If true, writes to the TensorArray
can grow the TensorArray past its initial size. Default: False.
clear_after_read: Boolean (optional, default: True). If True, clear
TensorArray values after reading them. This disables read-many
semantics, but allows early release of memory.
tensor_array_name: (optional) Python string: the name of the TensorArray.
This is used when creating the TensorArray handle. If this value is
set, handle should be None.
handle: (optional) A `Tensor` handle to an existing TensorArray. If this
is set, tensor_array_name should be None.
flow: (optional) A float `Tensor` scalar coming from an existing
`TensorArray.flow`.
infer_shape: (optional, default: True) If True, shape inference
is enabled. In this case, all elements must have the same shape.
element_shape: (optional, default: None) A `TensorShape` object specifying
the shape constraints of each of the elements of the TensorArray.
Need not be fully defined.
name: A name for the operation (optional).
Raises:
ValueError: if both handle and tensor_array_name are provided.
TypeError: if handle is provided but is not a Tensor.
"""
if handle is not None and tensor_array_name:
raise ValueError(
"Cannot construct with both handle and tensor_array_name")
if handle is not None and not isinstance(handle, ops.Tensor):
raise TypeError("Handle must be a Tensor")
if handle is None and size is None:
raise ValueError("Size must be provided if handle is not provided")
if handle is not None and size is not None:
raise ValueError("Cannot provide both a handle and size "
"at the same time")
if handle is not None and element_shape is not None:
raise ValueError("Cannot provide both a handle and element_shape "
"at the same time")
if handle is not None and dynamic_size is not None:
raise ValueError("Cannot provide both a handle and dynamic_size "
"at the same time")
if handle is not None and clear_after_read is not None:
raise ValueError("Cannot provide both a handle and clear_after_read "
"at the same time")
if clear_after_read is None:
clear_after_read = True
dynamic_size = dynamic_size or False
self._dtype = dtype
# Record the current static shape for the array elements. The element
# shape is defined either by `element_shape` or the shape of the tensor
# of the first write. If `infer_shape` is true, all writes checks for
# shape equality.
if element_shape is None:
self._infer_shape = infer_shape
self._element_shape = []
else:
self._infer_shape = True
self._element_shape = [tensor_shape.TensorShape(element_shape)]
with ops.name_scope(name, "TensorArray", [handle, size, flow]) as scope:
if handle is not None:
self._handle = handle
if flow is None:
raise ValueError("flow must not be None if handle is not None.")
self._flow = flow
else:
# Construct the TensorArray with an empty device. The first
# write into the TensorArray from a Tensor with a set device
# will retroactively set the device value of this op.
with ops.device(None), ops.colocate_with(None, ignore_existing=True):
self._handle, self._flow = gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
def __init__(self,
dtype,
size=None,
dynamic_size=None,
clear_after_read=None,
tensor_array_name=None,
handle=None,
flow=None,
infer_shape=True,
element_shape=None,
name=None):
"""Construct a new TensorArray or wrap an existing TensorArray handle.
A note about the parameter `name`:
The name of the `TensorArray` (even if passed in) is uniquified: each time
a new `TensorArray` is created at runtime it is assigned its own name for
the duration of the run. This avoids name collisions if a `TensorArray`
is created within a `while_loop`.
Args:
dtype: (required) data type of the TensorArray.
size: (optional) int32 scalar `Tensor`: the size of the TensorArray.
Required if handle is not provided.
dynamic_size: (optional) Python bool: If true, writes to the TensorArray
can grow the TensorArray past its initial size. Default: False.
clear_after_read: Boolean (optional, default: True). If True, clear
TensorArray values after reading them. This disables read-many
semantics, but allows early release of memory.
tensor_array_name: (optional) Python string: the name of the TensorArray.
This is used when creating the TensorArray handle. If this value is
set, handle should be None.
handle: (optional) A `Tensor` handle to an existing TensorArray. If this
is set, tensor_array_name should be None.
flow: (optional) A float `Tensor` scalar coming from an existing
`TensorArray.flow`.
infer_shape: (optional, default: True) If True, shape inference
is enabled. In this case, all elements must have the same shape.
element_shape: (optional, default: None) A `TensorShape` object specifying
the shape constraints of each of the elements of the TensorArray.
Need not be fully defined.
name: A name for the operation (optional).
Raises:
ValueError: if both handle and tensor_array_name are provided.
TypeError: if handle is provided but is not a Tensor.
"""
if handle is not None and tensor_array_name:
raise ValueError(
"Cannot construct with both handle and tensor_array_name")
if handle is not None and not isinstance(handle, ops.Tensor):
raise TypeError("Handle must be a Tensor")
if handle is None and size is None:
raise ValueError("Size must be provided if handle is not provided")
if handle is not None and size is not None:
raise ValueError("Cannot provide both a handle and size "
"at the same time")
if handle is not None and element_shape is not None:
raise ValueError("Cannot provide both a handle and element_shape "
"at the same time")
if handle is not None and dynamic_size is not None:
raise ValueError("Cannot provide both a handle and dynamic_size "
"at the same time")
if handle is not None and clear_after_read is not None:
raise ValueError(
"Cannot provide both a handle and clear_after_read "
"at the same time")
if clear_after_read is None:
clear_after_read = True
dynamic_size = dynamic_size or False
self._dtype = dtype
# Record the current static shape for the array elements. The element
# shape is defined either by `element_shape` or the shape of the tensor
# of the first write. If `infer_shape` is true, all writes checks for
# shape equality.
if element_shape is None:
self._infer_shape = infer_shape
self._element_shape = []
else:
self._infer_shape = True
self._element_shape = [tensor_shape.TensorShape(element_shape)]
with ops.name_scope(name, "TensorArray",
[handle, size, flow]) as scope:
if handle is not None:
self._handle = handle
if flow is None:
raise ValueError(
"flow must not be None if handle is not None.")
self._flow = flow
else:
# Construct the TensorArray with an empty device. The first
# write into the TensorArray from a Tensor with a set device
# will retroactively set the device value of this op.
with ops.device(None), ops.colocate_with(None,
ignore_existing=True):
self._handle, self._flow = gen_data_flow_ops._tensor_array_v3(
dtype=dtype,
size=size,
element_shape=element_shape,
dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name,
name=scope)
