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seanyen
seanyen / tensorflow python
Repository URL to install this package:
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Version:
1.14.0 ▾
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"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
"""
import collections as _collections
import six as _six
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.util.tf_export import kwarg_only as _kwarg_only
from tensorflow.tools.docs import doc_controls as _doc_controls
def assign(ref, value, validate_shape=True, use_locking=True, name=None):
r"""Update 'ref' by assigning 'value' to it.
This operation outputs "ref" after the assignment is done.
This makes it easier to chain operations that need to use the reset value.
Args:
ref: A mutable `Tensor`.
Should be from a `Variable` node. May be uninitialized.
value: A `Tensor`. Must have the same type as `ref`.
The value to be assigned to the variable.
validate_shape: An optional `bool`. Defaults to `True`.
If true, the operation will validate that the shape
of 'value' matches the shape of the Tensor being assigned to. If false,
'ref' will take on the shape of 'value'.
use_locking: An optional `bool`. Defaults to `True`.
If True, the assignment will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("assign op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if validate_shape is None:
validate_shape = True
validate_shape = _execute.make_bool(validate_shape, "validate_shape")
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"Assign", ref=ref, value=value, validate_shape=validate_shape,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "validate_shape",
_op.get_attr("validate_shape"), "use_locking",
_op.get_attr("use_locking"))
_execute.record_gradient(
"Assign", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def Assign(ref, value, validate_shape=True, use_locking=True, name=None):
return assign(ref=ref, value=value, validate_shape=validate_shape, use_locking=use_locking, name=name)
Assign.__doc__ = assign.__doc__
Assign = _doc_controls.do_not_generate_docs(_kwarg_only(Assign))
tf_export("raw_ops.Assign")(Assign)
def assign_eager_fallback(ref, value, validate_shape=True, use_locking=True, name=None, ctx=None):
raise RuntimeError("assign op does not support eager execution. Arg 'output_ref' is a ref.")
def assign_add(ref, value, use_locking=False, name=None):
r"""Update 'ref' by adding 'value' to it.
This operation outputs "ref" after the update is done.
This makes it easier to chain operations that need to use the reset value.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
value: A `Tensor`. Must have the same type as `ref`.
The value to be added to the variable.
use_locking: An optional `bool`. Defaults to `False`.
If True, the addition will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("assign_add op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"AssignAdd", ref=ref, value=value, use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "use_locking",
_op.get_attr("use_locking"))
_execute.record_gradient(
"AssignAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def AssignAdd(ref, value, use_locking=False, name=None):
return assign_add(ref=ref, value=value, use_locking=use_locking, name=name)
AssignAdd.__doc__ = assign_add.__doc__
AssignAdd = _doc_controls.do_not_generate_docs(_kwarg_only(AssignAdd))
tf_export("raw_ops.AssignAdd")(AssignAdd)
def assign_add_eager_fallback(ref, value, use_locking=False, name=None, ctx=None):
raise RuntimeError("assign_add op does not support eager execution. Arg 'output_ref' is a ref.")
def assign_sub(ref, value, use_locking=False, name=None):
r"""Update 'ref' by subtracting 'value' from it.
This operation outputs "ref" after the update is done.
This makes it easier to chain operations that need to use the reset value.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
value: A `Tensor`. Must have the same type as `ref`.
The value to be subtracted to the variable.
use_locking: An optional `bool`. Defaults to `False`.
If True, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("assign_sub op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"AssignSub", ref=ref, value=value, use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "use_locking",
_op.get_attr("use_locking"))
_execute.record_gradient(
"AssignSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def AssignSub(ref, value, use_locking=False, name=None):
return assign_sub(ref=ref, value=value, use_locking=use_locking, name=name)
AssignSub.__doc__ = assign_sub.__doc__
AssignSub = _doc_controls.do_not_generate_docs(_kwarg_only(AssignSub))
tf_export("raw_ops.AssignSub")(AssignSub)
def assign_sub_eager_fallback(ref, value, use_locking=False, name=None, ctx=None):
raise RuntimeError("assign_sub op does not support eager execution. Arg 'output_ref' is a ref.")
def count_up_to(ref, limit, name=None):
r"""Increments 'ref' until it reaches 'limit'.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `int32`, `int64`.
Should be from a scalar `Variable` node.
limit: An `int`.
If incrementing ref would bring it above limit, instead generates an
'OutOfRange' error.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("count_up_to op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
limit = _execute.make_int(limit, "limit")
_, _, _op = _op_def_lib._apply_op_helper(
"CountUpTo", ref=ref, limit=limit, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("limit", _op.get_attr("limit"), "T", _op.get_attr("T"))
_execute.record_gradient(
"CountUpTo", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def CountUpTo(ref, limit, name=None):
return count_up_to(ref=ref, limit=limit, name=name)
CountUpTo.__doc__ = count_up_to.__doc__
CountUpTo = _doc_controls.do_not_generate_docs(_kwarg_only(CountUpTo))
tf_export("raw_ops.CountUpTo")(CountUpTo)
def count_up_to_eager_fallback(ref, limit, name=None, ctx=None):
raise RuntimeError("count_up_to op does not support eager execution. Arg 'ref' is a ref.")
def destroy_temporary_variable(ref, var_name, name=None):
r"""Destroys the temporary variable and returns its final value.
Sets output to the value of the Tensor pointed to by 'ref', then destroys
the temporary variable called 'var_name'.
All other uses of 'ref' *must* have executed before this op.
This is typically achieved by chaining the ref through each assign op, or by
using control dependencies.
Outputs the final value of the tensor pointed to by 'ref'.
Args:
ref: A mutable `Tensor`. A reference to the temporary variable tensor.
var_name: A `string`.
Name of the temporary variable, usually the name of the matching
'TemporaryVariable' op.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("destroy_temporary_variable op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
var_name = _execute.make_str(var_name, "var_name")
_, _, _op = _op_def_lib._apply_op_helper(
"DestroyTemporaryVariable", ref=ref, var_name=var_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "var_name", _op.get_attr("var_name"))
_execute.record_gradient(
"DestroyTemporaryVariable", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def DestroyTemporaryVariable(ref, var_name, name=None):
return destroy_temporary_variable(ref=ref, var_name=var_name, name=name)
DestroyTemporaryVariable.__doc__ = destroy_temporary_variable.__doc__
DestroyTemporaryVariable = _doc_controls.do_not_generate_docs(_kwarg_only(DestroyTemporaryVariable))
tf_export("raw_ops.DestroyTemporaryVariable")(DestroyTemporaryVariable)
def destroy_temporary_variable_eager_fallback(ref, var_name, name=None, ctx=None):
raise RuntimeError("destroy_temporary_variable op does not support eager execution. Arg 'ref' is a ref.")
def is_variable_initialized(ref, name=None):
r"""Checks whether a tensor has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
Args:
ref: A mutable `Tensor`.
Should be from a `Variable` node. May be uninitialized.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `bool`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("is_variable_initialized op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"IsVariableInitialized", ref=ref, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"))
_execute.record_gradient(
"IsVariableInitialized", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def IsVariableInitialized(ref, name=None):
return is_variable_initialized(ref=ref, name=name)
IsVariableInitialized.__doc__ = is_variable_initialized.__doc__
IsVariableInitialized = _doc_controls.do_not_generate_docs(_kwarg_only(IsVariableInitialized))
tf_export("raw_ops.IsVariableInitialized")(IsVariableInitialized)
def is_variable_initialized_eager_fallback(ref, name=None, ctx=None):
raise RuntimeError("is_variable_initialized op does not support eager execution. Arg 'ref' is a ref.")
def resource_count_up_to(resource, limit, T, name=None):
r"""Increments variable pointed to by 'resource' until it reaches 'limit'.
Args:
resource: A `Tensor` of type `resource`.
Should be from a scalar `Variable` node.
limit: An `int`.
If incrementing ref would bring it above limit, instead generates an
'OutOfRange' error.
T: A `tf.DType` from: `tf.int32, tf.int64`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `T`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ResourceCountUpTo", name, _ctx._post_execution_callbacks, resource,
"limit", limit, "T", T)
return _result
except _core._FallbackException:
try:
return resource_count_up_to_eager_fallback(
resource, limit=limit, T=T, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
limit = _execute.make_int(limit, "limit")
T = _execute.make_type(T, "T")
_, _, _op = _op_def_lib._apply_op_helper(
"ResourceCountUpTo", resource=resource, limit=limit, T=T, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("limit", _op.get_attr("limit"), "T", _op.get_attr("T"))
_execute.record_gradient(
"ResourceCountUpTo", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ResourceCountUpTo(resource, limit, T, name=None):
return resource_count_up_to(resource=resource, limit=limit, T=T, name=name)
ResourceCountUpTo.__doc__ = resource_count_up_to.__doc__
ResourceCountUpTo = _doc_controls.do_not_generate_docs(_kwarg_only(ResourceCountUpTo))
tf_export("raw_ops.ResourceCountUpTo")(ResourceCountUpTo)
def resource_count_up_to_eager_fallback(resource, limit, T, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function resource_count_up_to
"""
_ctx = ctx if ctx else _context.context()
limit = _execute.make_int(limit, "limit")
T = _execute.make_type(T, "T")
resource = _ops.convert_to_tensor(resource, _dtypes.resource)
_inputs_flat = [resource]
_attrs = ("limit", limit, "T", T)
_result = _execute.execute(b"ResourceCountUpTo", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResourceCountUpTo", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resource_scatter_nd_add(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse addition to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that addition would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
print sess.run(add)
```
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A `Tensor` of type `resource`.
A resource handle. Must be from a VarHandleOp.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`. A Tensor. Must have the same type as ref. A tensor of
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ResourceScatterNdAdd", name, _ctx._post_execution_callbacks, ref,
indices, updates, "use_locking", use_locking)
return _result
except _core._FallbackException:
try:
return resource_scatter_nd_add_eager_fallback(
ref, indices, updates, use_locking=use_locking, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ResourceScatterNdAdd", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
return _op
_result = None
return _result
def ResourceScatterNdAdd(ref, indices, updates, use_locking=True, name=None):
return resource_scatter_nd_add(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ResourceScatterNdAdd.__doc__ = resource_scatter_nd_add.__doc__
ResourceScatterNdAdd = _doc_controls.do_not_generate_docs(_kwarg_only(ResourceScatterNdAdd))
tf_export("raw_ops.ResourceScatterNdAdd")(ResourceScatterNdAdd)
def resource_scatter_nd_add_eager_fallback(ref, indices, updates, use_locking=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function resource_scatter_nd_add
"""
_ctx = ctx if ctx else _context.context()
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_attr_T, (updates,) = _execute.args_to_matching_eager([updates], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
ref = _ops.convert_to_tensor(ref, _dtypes.resource)
_inputs_flat = [ref, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking",
use_locking)
_result = _execute.execute(b"ResourceScatterNdAdd", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def resource_scatter_nd_sub(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse subtraction to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
```
For example, say we want to subtract 4 scattered elements from a rank-1 tensor
with 8 elements. In Python, that subtraction would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True)
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
sub = tf.scatter_nd_sub(ref, indices, updates)
with tf.Session() as sess:
print sess.run(sub)
```
The resulting update to ref would look like this:
[1, -9, 3, -6, -4, 6, 7, -4]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A `Tensor` of type `resource`.
A resource handle. Must be from a VarHandleOp.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`. A Tensor. Must have the same type as ref. A tensor of
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ResourceScatterNdSub", name, _ctx._post_execution_callbacks, ref,
indices, updates, "use_locking", use_locking)
return _result
except _core._FallbackException:
try:
return resource_scatter_nd_sub_eager_fallback(
ref, indices, updates, use_locking=use_locking, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ResourceScatterNdSub", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
return _op
_result = None
return _result
def ResourceScatterNdSub(ref, indices, updates, use_locking=True, name=None):
return resource_scatter_nd_sub(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ResourceScatterNdSub.__doc__ = resource_scatter_nd_sub.__doc__
ResourceScatterNdSub = _doc_controls.do_not_generate_docs(_kwarg_only(ResourceScatterNdSub))
tf_export("raw_ops.ResourceScatterNdSub")(ResourceScatterNdSub)
def resource_scatter_nd_sub_eager_fallback(ref, indices, updates, use_locking=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function resource_scatter_nd_sub
"""
_ctx = ctx if ctx else _context.context()
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_attr_T, (updates,) = _execute.args_to_matching_eager([updates], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
ref = _ops.convert_to_tensor(ref, _dtypes.resource)
_inputs_flat = [ref, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking",
use_locking)
_result = _execute.execute(b"ResourceScatterNdSub", 0, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_result = None
return _result
def resource_scatter_nd_update(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse `updates` to individual values or slices within a given
variable according to `indices`.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
```
For example, say we want to update 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
print sess.run(update)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A `Tensor` of type `resource`.
A resource handle. Must be from a VarHandleOp.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`.
A Tensor. Must have the same type as ref. A tensor of updated
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._thread_local_data.device_name,
"ResourceScatterNdUpdate", name, _ctx._post_execution_callbacks, ref,
indices, updates, "use_locking", use_locking)
return _result
except _core._FallbackException:
try:
return resource_scatter_nd_update_eager_fallback(
ref, indices, updates, use_locking=use_locking, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ResourceScatterNdUpdate", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
return _op
_result = None
return _result
def ResourceScatterNdUpdate(ref, indices, updates, use_locking=True, name=None):
return resource_scatter_nd_update(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ResourceScatterNdUpdate.__doc__ = resource_scatter_nd_update.__doc__
ResourceScatterNdUpdate = _doc_controls.do_not_generate_docs(_kwarg_only(ResourceScatterNdUpdate))
tf_export("raw_ops.ResourceScatterNdUpdate")(ResourceScatterNdUpdate)
def resource_scatter_nd_update_eager_fallback(ref, indices, updates, use_locking=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function resource_scatter_nd_update
"""
_ctx = ctx if ctx else _context.context()
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_attr_T, (updates,) = _execute.args_to_matching_eager([updates], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
ref = _ops.convert_to_tensor(ref, _dtypes.resource)
_inputs_flat = [ref, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "use_locking",
use_locking)
_result = _execute.execute(b"ResourceScatterNdUpdate", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def scatter_add(ref, indices, updates, use_locking=False, name=None):
r"""Adds sparse updates to a variable reference.
This operation computes
# Scalar indices
ref[indices, ...] += updates[...]
# Vector indices (for each i)
ref[indices[i], ...] += updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions add.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to add to `ref`.
use_locking: An optional `bool`. Defaults to `False`.
If True, the addition will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_add op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterAdd", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterAdd(ref, indices, updates, use_locking=False, name=None):
return scatter_add(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterAdd.__doc__ = scatter_add.__doc__
ScatterAdd = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterAdd))
tf_export("raw_ops.ScatterAdd")(ScatterAdd)
def scatter_add_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_add op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_div(ref, indices, updates, use_locking=False, name=None):
r"""Divides a variable reference by sparse updates.
This operation computes
```python
# Scalar indices
ref[indices, ...] /= updates[...]
# Vector indices (for each i)
ref[indices[i], ...] /= updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
```
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions divide.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of values that `ref` is divided by.
use_locking: An optional `bool`. Defaults to `False`.
If True, the operation will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_div op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterDiv", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterDiv", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterDiv(ref, indices, updates, use_locking=False, name=None):
return scatter_div(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterDiv.__doc__ = scatter_div.__doc__
ScatterDiv = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterDiv))
tf_export("raw_ops.ScatterDiv")(ScatterDiv)
def scatter_div_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_div op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_max(ref, indices, updates, use_locking=False, name=None):
r"""Reduces sparse updates into a variable reference using the `max` operation.
This operation computes
# Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions combine.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`, `int64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to reduce into `ref`.
use_locking: An optional `bool`. Defaults to `False`.
If True, the update will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_max op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterMax", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterMax", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterMax(ref, indices, updates, use_locking=False, name=None):
return scatter_max(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterMax.__doc__ = scatter_max.__doc__
ScatterMax = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterMax))
tf_export("raw_ops.ScatterMax")(ScatterMax)
def scatter_max_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_max op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_min(ref, indices, updates, use_locking=False, name=None):
r"""Reduces sparse updates into a variable reference using the `min` operation.
This operation computes
# Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions combine.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterAdd.png" alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`, `int64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to reduce into `ref`.
use_locking: An optional `bool`. Defaults to `False`.
If True, the update will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_min op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterMin", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterMin", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterMin(ref, indices, updates, use_locking=False, name=None):
return scatter_min(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterMin.__doc__ = scatter_min.__doc__
ScatterMin = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterMin))
tf_export("raw_ops.ScatterMin")(ScatterMin)
def scatter_min_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_min op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_mul(ref, indices, updates, use_locking=False, name=None):
r"""Multiplies sparse updates into a variable reference.
This operation computes
```python
# Scalar indices
ref[indices, ...] *= updates[...]
# Vector indices (for each i)
ref[indices[i], ...] *= updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
```
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions multiply.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to multiply to `ref`.
use_locking: An optional `bool`. Defaults to `False`.
If True, the operation will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_mul op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterMul", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterMul", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterMul(ref, indices, updates, use_locking=False, name=None):
return scatter_mul(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterMul.__doc__ = scatter_mul.__doc__
ScatterMul = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterMul))
tf_export("raw_ops.ScatterMul")(ScatterMul)
def scatter_mul_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_mul op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_nd_add(ref, indices, updates, use_locking=False, name=None):
r"""Applies sparse addition to individual values or slices in a Variable.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
```
For example, say we want to add 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that addition would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
print sess.run(add)
```
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
A mutable Tensor. Should be from a Variable node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`. Must have the same type as `ref`.
A Tensor. Must have the same type as ref. A tensor of updated values
to add to ref.
use_locking: An optional `bool`. Defaults to `False`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_nd_add op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterNdAdd", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterNdAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterNdAdd(ref, indices, updates, use_locking=False, name=None):
return scatter_nd_add(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterNdAdd.__doc__ = scatter_nd_add.__doc__
ScatterNdAdd = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterNdAdd))
tf_export("raw_ops.ScatterNdAdd")(ScatterNdAdd)
def scatter_nd_add_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_nd_add op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_nd_sub(ref, indices, updates, use_locking=False, name=None):
r"""Applies sparse subtraction to individual values or slices in a Variable.
within a given variable according to `indices`.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
```
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
```
For example, say we want to subtract 4 scattered elements from a rank-1 tensor
with 8 elements. In Python, that subtraction would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
sub = tf.scatter_nd_sub(ref, indices, updates)
with tf.Session() as sess:
print sess.run(sub)
```
The resulting update to ref would look like this:
[1, -9, 3, -6, -4, 6, 7, -4]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
A mutable Tensor. Should be from a Variable node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`. Must have the same type as `ref`.
A Tensor. Must have the same type as ref. A tensor of updated values
to subtract from ref.
use_locking: An optional `bool`. Defaults to `False`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_nd_sub op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterNdSub", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterNdSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterNdSub(ref, indices, updates, use_locking=False, name=None):
return scatter_nd_sub(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterNdSub.__doc__ = scatter_nd_sub.__doc__
ScatterNdSub = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterNdSub))
tf_export("raw_ops.ScatterNdSub")(ScatterNdSub)
def scatter_nd_sub_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_nd_sub op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_nd_update(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse `updates` to individual values or slices within a given
variable according to `indices`.
`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `ref`.
It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th
dimension of `ref`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
$$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$
For example, say we want to update 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
print sess.run(update)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See `tf.scatter_nd` for more details about how to make updates to
slices.
See also `tf.scatter_update` and `tf.batch_scatter_update`.
Args:
ref: A mutable `Tensor`. A mutable Tensor. Should be from a Variable node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
updates: A `Tensor`. Must have the same type as `ref`.
A Tensor. Must have the same type as ref. A tensor of updated
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_nd_update op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterNdUpdate", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterNdUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterNdUpdate(ref, indices, updates, use_locking=True, name=None):
return scatter_nd_update(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterNdUpdate.__doc__ = scatter_nd_update.__doc__
ScatterNdUpdate = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterNdUpdate))
tf_export("raw_ops.ScatterNdUpdate")(ScatterNdUpdate)
def scatter_nd_update_eager_fallback(ref, indices, updates, use_locking=True, name=None, ctx=None):
raise RuntimeError("scatter_nd_update op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_sub(ref, indices, updates, use_locking=False, name=None):
r"""Subtracts sparse updates to a variable reference.
```python
# Scalar indices
ref[indices, ...] -= updates[...]
# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
```
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their (negated) contributions add.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterSub.png" alt>
</div>
Args:
ref: A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to subtract from `ref`.
use_locking: An optional `bool`. Defaults to `False`.
If True, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_sub op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = False
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterSub", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterSub(ref, indices, updates, use_locking=False, name=None):
return scatter_sub(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterSub.__doc__ = scatter_sub.__doc__
ScatterSub = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterSub))
tf_export("raw_ops.ScatterSub")(ScatterSub)
def scatter_sub_eager_fallback(ref, indices, updates, use_locking=False, name=None, ctx=None):
raise RuntimeError("scatter_sub op does not support eager execution. Arg 'output_ref' is a ref.")
def scatter_update(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse updates to a variable reference.
This operation computes
```python
# Scalar indices
ref[indices, ...] = updates[...]
# Vector indices (for each i)
ref[indices[i], ...] = updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]
```
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
If values in `ref` is to be updated more than once, because there are
duplicate entries in `indices`, the order at which the updates happen
for each value is undefined.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterUpdate.png" alt>
</div>
See also `tf.batch_scatter_update` and `tf.scatter_nd_update`.
Args:
ref: A mutable `Tensor`. Should be from a `Variable` node.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to store in `ref`.
use_locking: An optional `bool`. Defaults to `True`.
If True, the assignment will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("scatter_update op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if use_locking is None:
use_locking = True
use_locking = _execute.make_bool(use_locking, "use_locking")
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterUpdate", ref=ref, indices=indices, updates=updates,
use_locking=use_locking, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"),
"use_locking", _op.get_attr("use_locking"))
_execute.record_gradient(
"ScatterUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ScatterUpdate(ref, indices, updates, use_locking=True, name=None):
return scatter_update(ref=ref, indices=indices, updates=updates, use_locking=use_locking, name=name)
ScatterUpdate.__doc__ = scatter_update.__doc__
ScatterUpdate = _doc_controls.do_not_generate_docs(_kwarg_only(ScatterUpdate))
tf_export("raw_ops.ScatterUpdate")(ScatterUpdate)
def scatter_update_eager_fallback(ref, indices, updates, use_locking=True, name=None, ctx=None):
raise RuntimeError("scatter_update op does not support eager execution. Arg 'output_ref' is a ref.")
def temporary_variable(shape, dtype, var_name="", name=None):
r"""Returns a tensor that may be mutated, but only persists within a single step.
This is an experimental op for internal use only and it is possible to use this
op in unsafe ways. DO NOT USE unless you fully understand the risks.
It is the caller's responsibility to ensure that 'ref' is eventually passed to a
matching 'DestroyTemporaryVariable' op after all other uses have completed.
Outputs a ref to the tensor state so it may be read or modified.
E.g.
var = state_ops._temporary_variable([1, 2], types.float_)
var_name = var.op.name
var = state_ops.assign(var, [[4.0, 5.0]])
var = state_ops.assign_add(var, [[6.0, 7.0]])
final = state_ops._destroy_temporary_variable(var, var_name=var_name)
Args:
shape: A `tf.TensorShape` or list of `ints`.
The shape of the variable tensor.
dtype: A `tf.DType`. The type of elements in the variable tensor.
var_name: An optional `string`. Defaults to `""`.
Overrides the name used for the temporary variable resource. Default
value is the name of the 'TemporaryVariable' op (which is guaranteed unique).
name: A name for the operation (optional).
Returns:
A mutable `Tensor` of type `dtype`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("temporary_variable op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
shape = _execute.make_shape(shape, "shape")
dtype = _execute.make_type(dtype, "dtype")
if var_name is None:
var_name = ""
var_name = _execute.make_str(var_name, "var_name")
_, _, _op = _op_def_lib._apply_op_helper(
"TemporaryVariable", shape=shape, dtype=dtype, var_name=var_name,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("shape", _op.get_attr("shape"), "dtype", _op.get_attr("dtype"),
"var_name", _op.get_attr("var_name"))
_execute.record_gradient(
"TemporaryVariable", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def TemporaryVariable(shape, dtype, var_name="", name=None):
return temporary_variable(shape=shape, dtype=dtype, var_name=var_name, name=name)
TemporaryVariable.__doc__ = temporary_variable.__doc__
TemporaryVariable = _doc_controls.do_not_generate_docs(_kwarg_only(TemporaryVariable))
tf_export("raw_ops.TemporaryVariable")(TemporaryVariable)
def temporary_variable_eager_fallback(shape, dtype, var_name="", name=None, ctx=None):
raise RuntimeError("temporary_variable op does not support eager execution. Arg 'ref' is a ref.")
def variable(shape, dtype, container="", shared_name="", name=None):
r"""Use VariableV2 instead.
Args:
shape: A `tf.TensorShape` or list of `ints`.
dtype: A `tf.DType`.
container: An optional `string`. Defaults to `""`.
shared_name: An optional `string`. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A mutable `Tensor` of type `dtype`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("variable op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
shape = _execute.make_shape(shape, "shape")
dtype = _execute.make_type(dtype, "dtype")
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_, _, _op = _op_def_lib._apply_op_helper(
"Variable", shape=shape, dtype=dtype, container=container,
shared_name=shared_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("shape", _op.get_attr("shape"), "dtype", _op.get_attr("dtype"),
"container", _op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_execute.record_gradient(
"Variable", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def Variable(shape, dtype, container="", shared_name="", name=None):
return variable(shape=shape, dtype=dtype, container=container, shared_name=shared_name, name=name)
Variable.__doc__ = variable.__doc__
Variable = _doc_controls.do_not_generate_docs(_kwarg_only(Variable))
tf_export("raw_ops.Variable")(Variable)
def variable_eager_fallback(shape, dtype, container="", shared_name="", name=None, ctx=None):
raise RuntimeError("variable op does not support eager execution. Arg 'ref' is a ref.")
def variable_v2(shape, dtype, container="", shared_name="", name=None):
r"""Holds state in the form of a tensor that persists across steps.
Outputs a ref to the tensor state so it may be read or modified.
TODO(zhifengc/mrry): Adds a pointer to a more detail document
about sharing states in tensorflow.
Args:
shape: A `tf.TensorShape` or list of `ints`.
The shape of the variable tensor.
dtype: A `tf.DType`. The type of elements in the variable tensor.
container: An optional `string`. Defaults to `""`.
If non-empty, this variable is placed in the given container.
Otherwise, a default container is used.
shared_name: An optional `string`. Defaults to `""`.
If non-empty, this variable is named in the given bucket
with this shared_name. Otherwise, the node name is used instead.
name: A name for the operation (optional).
Returns:
A mutable `Tensor` of type `dtype`.
"""
_ctx = _context._context or _context.context()
if _ctx is not None and _ctx._thread_local_data.is_eager:
raise RuntimeError("variable_v2 op does not support eager execution. Arg 'ref' is a ref.")
# Add nodes to the TensorFlow graph.
shape = _execute.make_shape(shape, "shape")
dtype = _execute.make_type(dtype, "dtype")
if container is None:
container = ""
container = _execute.make_str(container, "container")
if shared_name is None:
shared_name = ""
shared_name = _execute.make_str(shared_name, "shared_name")
_, _, _op = _op_def_lib._apply_op_helper(
"VariableV2", shape=shape, dtype=dtype, container=container,
shared_name=shared_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("shape", _op.get_attr("shape"), "dtype", _op.get_attr("dtype"),
"container", _op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_execute.record_gradient(
"VariableV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def VariableV2(shape, dtype, container="", shared_name="", name=None):
return variable_v2(shape=shape, dtype=dtype, container=container, shared_name=shared_name, name=name)
VariableV2.__doc__ = variable_v2.__doc__
VariableV2 = _doc_controls.do_not_generate_docs(_kwarg_only(VariableV2))
tf_export("raw_ops.VariableV2")(VariableV2)
def variable_v2_eager_fallback(shape, dtype, container="", shared_name="", name=None, ctx=None):
raise RuntimeError("variable_v2 op does not support eager execution. Arg 'ref' is a ref.")
def _InitOpDefLibrary(op_list_proto_bytes):
op_list = _op_def_pb2.OpList()
op_list.ParseFromString(op_list_proto_bytes)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
op_def_lib.add_op_list(op_list)
return op_def_lib
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