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Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorflow/python/ops/gen_sparse_ops.py
Ulad 36e4e4f8a5 feature: "ANN commit 2"
2023-06-19 00:49:18 +02:00

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"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
"""
import collections
from tensorflow.python import pywrap_tfe as pywrap_tfe
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 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 typing import TypeVar
def add_many_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, container="", shared_name="", name=None):
r"""Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`,
`sparse_values`, and `sparse_shape`, where
```sparse_indices.shape[1] == sparse_shape.shape[0] == R```
An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor`
having a first `sparse_indices` column taking values between `[0, N)`, where
the minibatch size `N == sparse_shape[0]`.
The input `SparseTensor` must have rank `R` greater than 1, and the first
dimension is treated as the minibatch dimension. Elements of the `SparseTensor`
must be sorted in increasing order of this first dimension. The stored
`SparseTensor` objects pointed to by each row of the output `sparse_handles`
will have rank `R-1`.
The `SparseTensor` values can then be read out as part of a minibatch by passing
the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure
the correct `SparseTensorsMap` is accessed, ensure that the same
`container` and `shared_name` are passed to that Op. If no `shared_name`
is provided here, instead use the *name* of the Operation created by calling
`AddManySparseToTensorsMap` as the `shared_name` passed to
`TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
Args:
sparse_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the minibatch `SparseTensor`.
`sparse_indices[:, 0]` must be ordered values in `[0, N)`.
sparse_values: A `Tensor`.
1-D. The `values` of the minibatch `SparseTensor`.
sparse_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the minibatch `SparseTensor`.
The minibatch size `N == sparse_shape[0]`.
container: An optional `string`. Defaults to `""`.
The container name for the `SparseTensorsMap` created by this op.
shared_name: An optional `string`. Defaults to `""`.
The shared name for the `SparseTensorsMap` created by this op.
If blank, the new Operation's unique name is used.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "AddManySparseToTensorsMap", name, sparse_indices,
sparse_values, sparse_shape, "container", container, "shared_name",
shared_name)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return add_many_sparse_to_tensors_map_eager_fallback(
sparse_indices, sparse_values, sparse_shape, container=container,
shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
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, _outputs = _op_def_library._apply_op_helper(
"AddManySparseToTensorsMap", sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape,
container=container,
shared_name=shared_name, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "container",
_op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"AddManySparseToTensorsMap", _inputs_flat, _attrs, _result)
_result, = _result
return _result
AddManySparseToTensorsMap = tf_export("raw_ops.AddManySparseToTensorsMap")(_ops.to_raw_op(add_many_sparse_to_tensors_map))
def add_many_sparse_to_tensors_map_eager_fallback(sparse_indices, sparse_values, sparse_shape, container, shared_name, name, ctx):
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")
_attr_T, (sparse_values,) = _execute.args_to_matching_eager([sparse_values], ctx, [])
sparse_indices = _ops.convert_to_tensor(sparse_indices, _dtypes.int64)
sparse_shape = _ops.convert_to_tensor(sparse_shape, _dtypes.int64)
_inputs_flat = [sparse_indices, sparse_values, sparse_shape]
_attrs = ("T", _attr_T, "container", container, "shared_name", shared_name)
_result = _execute.execute(b"AddManySparseToTensorsMap", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"AddManySparseToTensorsMap", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def add_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, container="", shared_name="", name=None):
r"""Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
A `SparseTensor` is represented by three tensors: `sparse_indices`,
`sparse_values`, and `sparse_shape`.
This operator takes the given `SparseTensor` and adds it to a container
object (a `SparseTensorsMap`). A unique key within this container is generated
in the form of an `int64`, and this is the value that is returned.
The `SparseTensor` can then be read out as part of a minibatch by passing
the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure
the correct `SparseTensorsMap` is accessed, ensure that the same
`container` and `shared_name` are passed to that Op. If no `shared_name`
is provided here, instead use the *name* of the Operation created by calling
`AddSparseToTensorsMap` as the `shared_name` passed to
`TakeManySparseFromTensorsMap`. Ensure the Operations are colocated.
Args:
sparse_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the `SparseTensor`.
sparse_values: A `Tensor`. 1-D. The `values` of the `SparseTensor`.
sparse_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the `SparseTensor`.
container: An optional `string`. Defaults to `""`.
The container name for the `SparseTensorsMap` created by this op.
shared_name: An optional `string`. Defaults to `""`.
The shared name for the `SparseTensorsMap` created by this op.
If blank, the new Operation's unique name is used.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "AddSparseToTensorsMap", name, sparse_indices, sparse_values,
sparse_shape, "container", container, "shared_name", shared_name)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return add_sparse_to_tensors_map_eager_fallback(
sparse_indices, sparse_values, sparse_shape, container=container,
shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
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, _outputs = _op_def_library._apply_op_helper(
"AddSparseToTensorsMap", sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape,
container=container, shared_name=shared_name,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "container",
_op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"AddSparseToTensorsMap", _inputs_flat, _attrs, _result)
_result, = _result
return _result
AddSparseToTensorsMap = tf_export("raw_ops.AddSparseToTensorsMap")(_ops.to_raw_op(add_sparse_to_tensors_map))
def add_sparse_to_tensors_map_eager_fallback(sparse_indices, sparse_values, sparse_shape, container, shared_name, name, ctx):
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")
_attr_T, (sparse_values,) = _execute.args_to_matching_eager([sparse_values], ctx, [])
sparse_indices = _ops.convert_to_tensor(sparse_indices, _dtypes.int64)
sparse_shape = _ops.convert_to_tensor(sparse_shape, _dtypes.int64)
_inputs_flat = [sparse_indices, sparse_values, sparse_shape]
_attrs = ("T", _attr_T, "container", container, "shared_name", shared_name)
_result = _execute.execute(b"AddSparseToTensorsMap", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"AddSparseToTensorsMap", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_DeserializeManySparseOutput = collections.namedtuple(
"DeserializeManySparse",
["sparse_indices", "sparse_values", "sparse_shape"])
def deserialize_many_sparse(serialized_sparse, dtype, name=None):
r"""Deserialize and concatenate `SparseTensors` from a serialized minibatch.
The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
`N` is the minibatch size and the rows correspond to packed outputs of
`SerializeSparse`. The ranks of the original `SparseTensor` objects
must all match. When the final `SparseTensor` is created, it has rank one
higher than the ranks of the incoming `SparseTensor` objects
(they have been concatenated along a new row dimension).
The output `SparseTensor` object's shape values for all dimensions but the
first are the max across the input `SparseTensor` objects' shape values
for the corresponding dimensions. Its first shape value is `N`, the minibatch
size.
The input `SparseTensor` objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run `SparseReorder` to restore index ordering.
For example, if the serialized input is a `[2 x 3]` matrix representing two
original `SparseTensor` objects:
index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
and
index = [ 2]
[10]
values = [4, 5]
shape = [30]
then the final deserialized `SparseTensor` will be:
index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
Args:
serialized_sparse: A `Tensor` of type `string`.
2-D, The `N` serialized `SparseTensor` objects.
Must have 3 columns.
dtype: A `tf.DType`.
The `dtype` of the serialized `SparseTensor` objects.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (sparse_indices, sparse_values, sparse_shape).
sparse_indices: A `Tensor` of type `int64`.
sparse_values: A `Tensor` of type `dtype`.
sparse_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "DeserializeManySparse", name, serialized_sparse, "dtype",
dtype)
_result = _DeserializeManySparseOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return deserialize_many_sparse_eager_fallback(
serialized_sparse, dtype=dtype, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"DeserializeManySparse", serialized_sparse=serialized_sparse,
dtype=dtype, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("dtype", _op._get_attr_type("dtype"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"DeserializeManySparse", _inputs_flat, _attrs, _result)
_result = _DeserializeManySparseOutput._make(_result)
return _result
DeserializeManySparse = tf_export("raw_ops.DeserializeManySparse")(_ops.to_raw_op(deserialize_many_sparse))
def deserialize_many_sparse_eager_fallback(serialized_sparse, dtype, name, ctx):
dtype = _execute.make_type(dtype, "dtype")
serialized_sparse = _ops.convert_to_tensor(serialized_sparse, _dtypes.string)
_inputs_flat = [serialized_sparse]
_attrs = ("dtype", dtype)
_result = _execute.execute(b"DeserializeManySparse", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"DeserializeManySparse", _inputs_flat, _attrs, _result)
_result = _DeserializeManySparseOutput._make(_result)
return _result
_DeserializeSparseOutput = collections.namedtuple(
"DeserializeSparse",
["sparse_indices", "sparse_values", "sparse_shape"])
def deserialize_sparse(serialized_sparse, dtype, name=None):
r"""Deserialize `SparseTensor` objects.
The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
the last dimension stores serialized `SparseTensor` objects and the other N
dimensions (N >= 0) correspond to a batch. The ranks of the original
`SparseTensor` objects must all match. When the final `SparseTensor` is
created, its rank is the rank of the incoming `SparseTensor` objects plus N;
the sparse tensors have been concatenated along new dimensions, one for each
batch.
The output `SparseTensor` object's shape values for the original dimensions
are the max across the input `SparseTensor` objects' shape values for the
corresponding dimensions. The new dimensions match the size of the batch.
The input `SparseTensor` objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run `SparseReorder` to restore index ordering.
For example, if the serialized input is a `[2 x 3]` matrix representing two
original `SparseTensor` objects:
index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
and
index = [ 2]
[10]
values = [4, 5]
shape = [30]
then the final deserialized `SparseTensor` will be:
index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
Args:
serialized_sparse: A `Tensor`. Must be one of the following types: `string`, `variant`.
The serialized `SparseTensor` objects. The last dimension
must have 3 columns.
dtype: A `tf.DType`.
The `dtype` of the serialized `SparseTensor` objects.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (sparse_indices, sparse_values, sparse_shape).
sparse_indices: A `Tensor` of type `int64`.
sparse_values: A `Tensor` of type `dtype`.
sparse_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "DeserializeSparse", name, serialized_sparse, "dtype", dtype)
_result = _DeserializeSparseOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return deserialize_sparse_eager_fallback(
serialized_sparse, dtype=dtype, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"DeserializeSparse", serialized_sparse=serialized_sparse, dtype=dtype,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("dtype", _op._get_attr_type("dtype"), "Tserialized",
_op._get_attr_type("Tserialized"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"DeserializeSparse", _inputs_flat, _attrs, _result)
_result = _DeserializeSparseOutput._make(_result)
return _result
DeserializeSparse = tf_export("raw_ops.DeserializeSparse")(_ops.to_raw_op(deserialize_sparse))
def deserialize_sparse_eager_fallback(serialized_sparse, dtype, name, ctx):
dtype = _execute.make_type(dtype, "dtype")
_attr_Tserialized, (serialized_sparse,) = _execute.args_to_matching_eager([serialized_sparse], ctx, [_dtypes.string, _dtypes.variant, ], _dtypes.string)
_inputs_flat = [serialized_sparse]
_attrs = ("dtype", dtype, "Tserialized", _attr_Tserialized)
_result = _execute.execute(b"DeserializeSparse", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"DeserializeSparse", _inputs_flat, _attrs, _result)
_result = _DeserializeSparseOutput._make(_result)
return _result
def serialize_many_sparse(sparse_indices, sparse_values, sparse_shape, out_type=_dtypes.string, name=None):
r"""Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
The `SparseTensor` must have rank `R` greater than 1, and the first dimension
is treated as the minibatch dimension. Elements of the `SparseTensor`
must be sorted in increasing order of this first dimension. The serialized
`SparseTensor` objects going into each row of `serialized_sparse` will have
rank `R-1`.
The minibatch size `N` is extracted from `sparse_shape[0]`.
Args:
sparse_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the minibatch `SparseTensor`.
sparse_values: A `Tensor`.
1-D. The `values` of the minibatch `SparseTensor`.
sparse_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the minibatch `SparseTensor`.
out_type: An optional `tf.DType` from: `tf.string, tf.variant`. Defaults to `tf.string`.
The `dtype` to use for serialization; the supported types are `string`
(default) and `variant`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SerializeManySparse", name, sparse_indices, sparse_values,
sparse_shape, "out_type", out_type)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return serialize_many_sparse_eager_fallback(
sparse_indices, sparse_values, sparse_shape, out_type=out_type,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.string
out_type = _execute.make_type(out_type, "out_type")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SerializeManySparse", sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape, out_type=out_type,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "out_type",
_op._get_attr_type("out_type"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SerializeManySparse", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SerializeManySparse = tf_export("raw_ops.SerializeManySparse")(_ops.to_raw_op(serialize_many_sparse))
def serialize_many_sparse_eager_fallback(sparse_indices, sparse_values, sparse_shape, out_type, name, ctx):
if out_type is None:
out_type = _dtypes.string
out_type = _execute.make_type(out_type, "out_type")
_attr_T, (sparse_values,) = _execute.args_to_matching_eager([sparse_values], ctx, [])
sparse_indices = _ops.convert_to_tensor(sparse_indices, _dtypes.int64)
sparse_shape = _ops.convert_to_tensor(sparse_shape, _dtypes.int64)
_inputs_flat = [sparse_indices, sparse_values, sparse_shape]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"SerializeManySparse", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SerializeManySparse", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def serialize_sparse(sparse_indices, sparse_values, sparse_shape, out_type=_dtypes.string, name=None):
r"""Serialize a `SparseTensor` into a `[3]` `Tensor` object.
Args:
sparse_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the `SparseTensor`.
sparse_values: A `Tensor`. 1-D. The `values` of the `SparseTensor`.
sparse_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the `SparseTensor`.
out_type: An optional `tf.DType` from: `tf.string, tf.variant`. Defaults to `tf.string`.
The `dtype` to use for serialization; the supported types are `string`
(default) and `variant`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SerializeSparse", name, sparse_indices, sparse_values,
sparse_shape, "out_type", out_type)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return serialize_sparse_eager_fallback(
sparse_indices, sparse_values, sparse_shape, out_type=out_type,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.string
out_type = _execute.make_type(out_type, "out_type")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SerializeSparse", sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape, out_type=out_type,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "out_type",
_op._get_attr_type("out_type"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SerializeSparse", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SerializeSparse = tf_export("raw_ops.SerializeSparse")(_ops.to_raw_op(serialize_sparse))
def serialize_sparse_eager_fallback(sparse_indices, sparse_values, sparse_shape, out_type, name, ctx):
if out_type is None:
out_type = _dtypes.string
out_type = _execute.make_type(out_type, "out_type")
_attr_T, (sparse_values,) = _execute.args_to_matching_eager([sparse_values], ctx, [])
sparse_indices = _ops.convert_to_tensor(sparse_indices, _dtypes.int64)
sparse_shape = _ops.convert_to_tensor(sparse_shape, _dtypes.int64)
_inputs_flat = [sparse_indices, sparse_values, sparse_shape]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"SerializeSparse", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SerializeSparse", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SparseAddOutput = collections.namedtuple(
"SparseAdd",
["sum_indices", "sum_values", "sum_shape"])
def sparse_add(a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, name=None):
r"""Adds two `SparseTensor` objects to produce another `SparseTensor`.
The input `SparseTensor` objects' indices are assumed ordered in standard
lexicographic order. If this is not the case, before this step run
`SparseReorder` to restore index ordering.
By default, if two values sum to zero at some index, the output `SparseTensor`
would still include that particular location in its index, storing a zero in the
corresponding value slot. To override this, callers can specify `thresh`,
indicating that if the sum has a magnitude strictly smaller than `thresh`, its
corresponding value and index would then not be included. In particular,
`thresh == 0` (default) means everything is kept and actual thresholding happens
only for a positive value.
In the following shapes, `nnz` is the count after taking `thresh` into account.
Args:
a_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
a_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector.
a_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
b_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
b_values: A `Tensor`. Must have the same type as `a_values`.
1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector.
b_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
thresh: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
0-D. The magnitude threshold that determines if an output value/index
pair takes space.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (sum_indices, sum_values, sum_shape).
sum_indices: A `Tensor` of type `int64`.
sum_values: A `Tensor`. Has the same type as `a_values`.
sum_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseAdd", name, a_indices, a_values, a_shape, b_indices,
b_values, b_shape, thresh)
_result = _SparseAddOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_add_eager_fallback(
a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseAdd", a_indices=a_indices, a_values=a_values, a_shape=a_shape,
b_indices=b_indices, b_values=b_values, b_shape=b_shape,
thresh=thresh, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "Treal",
_op._get_attr_type("Treal"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseAdd", _inputs_flat, _attrs, _result)
_result = _SparseAddOutput._make(_result)
return _result
SparseAdd = tf_export("raw_ops.SparseAdd")(_ops.to_raw_op(sparse_add))
def sparse_add_eager_fallback(a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([a_values, b_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(a_values, b_values) = _inputs_T
_attr_Treal, (thresh,) = _execute.args_to_matching_eager([thresh], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
a_indices = _ops.convert_to_tensor(a_indices, _dtypes.int64)
a_shape = _ops.convert_to_tensor(a_shape, _dtypes.int64)
b_indices = _ops.convert_to_tensor(b_indices, _dtypes.int64)
b_shape = _ops.convert_to_tensor(b_shape, _dtypes.int64)
_inputs_flat = [a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh]
_attrs = ("T", _attr_T, "Treal", _attr_Treal)
_result = _execute.execute(b"SparseAdd", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseAdd", _inputs_flat, _attrs, _result)
_result = _SparseAddOutput._make(_result)
return _result
_SparseAddGradOutput = collections.namedtuple(
"SparseAddGrad",
["a_val_grad", "b_val_grad"])
def sparse_add_grad(backprop_val_grad, a_indices, b_indices, sum_indices, name=None):
r"""The gradient operator for the SparseAdd op.
The SparseAdd op calculates A + B, where A, B, and the sum are all represented
as `SparseTensor` objects. This op takes in the upstream gradient w.r.t.
non-empty values of the sum, and outputs the gradients w.r.t. the non-empty
values of A and B.
Args:
backprop_val_grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D with shape `[nnz(sum)]`. The gradient with respect to
the non-empty values of the sum.
a_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
b_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
sum_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the sum `SparseTensor`, size
`[nnz(sum), ndims]`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (a_val_grad, b_val_grad).
a_val_grad: A `Tensor`. Has the same type as `backprop_val_grad`.
b_val_grad: A `Tensor`. Has the same type as `backprop_val_grad`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseAddGrad", name, backprop_val_grad, a_indices, b_indices,
sum_indices)
_result = _SparseAddGradOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_add_grad_eager_fallback(
backprop_val_grad, a_indices, b_indices, sum_indices, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseAddGrad", backprop_val_grad=backprop_val_grad,
a_indices=a_indices, b_indices=b_indices,
sum_indices=sum_indices, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseAddGrad", _inputs_flat, _attrs, _result)
_result = _SparseAddGradOutput._make(_result)
return _result
SparseAddGrad = tf_export("raw_ops.SparseAddGrad")(_ops.to_raw_op(sparse_add_grad))
def sparse_add_grad_eager_fallback(backprop_val_grad, a_indices, b_indices, sum_indices, name, ctx):
_attr_T, (backprop_val_grad,) = _execute.args_to_matching_eager([backprop_val_grad], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
a_indices = _ops.convert_to_tensor(a_indices, _dtypes.int64)
b_indices = _ops.convert_to_tensor(b_indices, _dtypes.int64)
sum_indices = _ops.convert_to_tensor(sum_indices, _dtypes.int64)
_inputs_flat = [backprop_val_grad, a_indices, b_indices, sum_indices]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseAddGrad", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseAddGrad", _inputs_flat, _attrs, _result)
_result = _SparseAddGradOutput._make(_result)
return _result
_SparseConcatOutput = collections.namedtuple(
"SparseConcat",
["output_indices", "output_values", "output_shape"])
def sparse_concat(indices, values, shapes, concat_dim, name=None):
r"""Concatenates a list of `SparseTensor` along the specified dimension.
Concatenation is with respect to the dense versions of these sparse tensors.
It is assumed that each input is a `SparseTensor` whose elements are ordered
along increasing dimension number.
All inputs' shapes must match, except for the concat dimension. The
`indices`, `values`, and `shapes` lists must have the same length.
The output shape is identical to the inputs', except along the concat
dimension, where it is the sum of the inputs' sizes along that dimension.
The output elements will be resorted to preserve the sort order along
increasing dimension number.
This op runs in `O(M log M)` time, where `M` is the total number of non-empty
values across all inputs. This is due to the need for an internal sort in
order to concatenate efficiently across an arbitrary dimension.
For example, if `concat_dim = 1` and the inputs are
sp_inputs[0]: shape = [2, 3]
[0, 2]: "a"
[1, 0]: "b"
[1, 1]: "c"
sp_inputs[1]: shape = [2, 4]
[0, 1]: "d"
[0, 2]: "e"
then the output will be
shape = [2, 7]
[0, 2]: "a"
[0, 4]: "d"
[0, 5]: "e"
[1, 0]: "b"
[1, 1]: "c"
Graphically this is equivalent to doing
[ a] concat [ d e ] = [ a d e ]
[b c ] [ ] [b c ]
Args:
indices: A list of at least 2 `Tensor` objects with type `int64`.
2-D. Indices of each input `SparseTensor`.
values: A list with the same length as `indices` of `Tensor` objects with the same type.
1-D. Non-empty values of each `SparseTensor`.
shapes: A list with the same length as `indices` of `Tensor` objects with type `int64`.
1-D. Shapes of each `SparseTensor`.
concat_dim: An `int`.
Dimension to concatenate along. Must be in range [-rank, rank),
where rank is the number of dimensions in each input `SparseTensor`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `values`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseConcat", name, indices, values, shapes, "concat_dim",
concat_dim)
_result = _SparseConcatOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_concat_eager_fallback(
indices, values, shapes, concat_dim=concat_dim, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_concat' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'sparse_concat' Op, not %r." % values)
if len(values) != _attr_N:
raise ValueError(
"List argument 'values' to 'sparse_concat' Op with length %d "
"must match length %d of argument 'indices'." %
(len(values), _attr_N))
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_concat' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_concat' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
concat_dim = _execute.make_int(concat_dim, "concat_dim")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseConcat", indices=indices, values=values, shapes=shapes,
concat_dim=concat_dim, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("concat_dim", _op._get_attr_int("concat_dim"), "N",
_op._get_attr_int("N"), "T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseConcat", _inputs_flat, _attrs, _result)
_result = _SparseConcatOutput._make(_result)
return _result
SparseConcat = tf_export("raw_ops.SparseConcat")(_ops.to_raw_op(sparse_concat))
def sparse_concat_eager_fallback(indices, values, shapes, concat_dim, name, ctx):
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_concat' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'sparse_concat' Op, not %r." % values)
if len(values) != _attr_N:
raise ValueError(
"List argument 'values' to 'sparse_concat' Op with length %d "
"must match length %d of argument 'indices'." %
(len(values), _attr_N))
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_concat' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_concat' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
concat_dim = _execute.make_int(concat_dim, "concat_dim")
_attr_T, values = _execute.args_to_matching_eager(list(values), ctx, [])
indices = _ops.convert_n_to_tensor(indices, _dtypes.int64)
shapes = _ops.convert_n_to_tensor(shapes, _dtypes.int64)
_inputs_flat = list(indices) + list(values) + list(shapes)
_attrs = ("concat_dim", concat_dim, "N", _attr_N, "T", _attr_T)
_result = _execute.execute(b"SparseConcat", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseConcat", _inputs_flat, _attrs, _result)
_result = _SparseConcatOutput._make(_result)
return _result
_SparseCrossOutput = collections.namedtuple(
"SparseCross",
["output_indices", "output_values", "output_shape"])
def sparse_cross(indices, values, shapes, dense_inputs, hashed_output, num_buckets, hash_key, out_type, internal_type, name=None):
r"""Generates sparse cross from a list of sparse and dense tensors.
The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
representing features of one feature column. It outputs a 2D `SparseTensor` with
the batchwise crosses of these features.
For example, if the inputs are
inputs[0]: SparseTensor with shape = [2, 2]
[0, 0]: "a"
[1, 0]: "b"
[1, 1]: "c"
inputs[1]: SparseTensor with shape = [2, 1]
[0, 0]: "d"
[1, 0]: "e"
inputs[2]: Tensor [["f"], ["g"]]
then the output will be
shape = [2, 2]
[0, 0]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
if hashed_output=true then the output will be
shape = [2, 2]
[0, 0]: FingerprintCat64(
Fingerprint64("f"), FingerprintCat64(
Fingerprint64("d"), Fingerprint64("a")))
[1, 0]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("b")))
[1, 1]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("c")))
Args:
indices: A list of `Tensor` objects with type `int64`.
2-D. Indices of each input `SparseTensor`.
values: A list of `Tensor` objects with types from: `int64`, `string`.
1-D. values of each `SparseTensor`.
shapes: A list with the same length as `indices` of `Tensor` objects with type `int64`.
1-D. Shapes of each `SparseTensor`.
dense_inputs: A list of `Tensor` objects with types from: `int64`, `string`.
2-D. Columns represented by dense `Tensor`.
hashed_output: A `bool`.
If true, returns the hash of the cross instead of the string.
This will allow us avoiding string manipulations.
num_buckets: An `int` that is `>= 0`.
It is used if hashed_output is true.
output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
hash_key: An `int`.
Specify the hash_key that will be used by the `FingerprintCat64`
function to combine the crosses fingerprints.
out_type: A `tf.DType` from: `tf.int64, tf.string`.
internal_type: A `tf.DType` from: `tf.int64, tf.string`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor` of type `out_type`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseCross", name, indices, values, shapes, dense_inputs,
"hashed_output", hashed_output, "num_buckets", num_buckets,
"hash_key", hash_key, "out_type", out_type, "internal_type",
internal_type)
_result = _SparseCrossOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_cross_eager_fallback(
indices, values, shapes, dense_inputs, hashed_output=hashed_output,
num_buckets=num_buckets, hash_key=hash_key, out_type=out_type,
internal_type=internal_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
hashed_output = _execute.make_bool(hashed_output, "hashed_output")
num_buckets = _execute.make_int(num_buckets, "num_buckets")
hash_key = _execute.make_int(hash_key, "hash_key")
out_type = _execute.make_type(out_type, "out_type")
internal_type = _execute.make_type(internal_type, "internal_type")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseCross", indices=indices, values=values, shapes=shapes,
dense_inputs=dense_inputs, hashed_output=hashed_output,
num_buckets=num_buckets, hash_key=hash_key,
out_type=out_type, internal_type=internal_type,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("N", _op._get_attr_int("N"), "hashed_output",
_op._get_attr_bool("hashed_output"), "num_buckets",
_op._get_attr_int("num_buckets"), "hash_key",
_op._get_attr_int("hash_key"), "sparse_types",
_op.get_attr("sparse_types"), "dense_types",
_op.get_attr("dense_types"), "out_type",
_op._get_attr_type("out_type"), "internal_type",
_op._get_attr_type("internal_type"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseCross", _inputs_flat, _attrs, _result)
_result = _SparseCrossOutput._make(_result)
return _result
SparseCross = tf_export("raw_ops.SparseCross")(_ops.to_raw_op(sparse_cross))
def sparse_cross_eager_fallback(indices, values, shapes, dense_inputs, hashed_output, num_buckets, hash_key, out_type, internal_type, name, ctx):
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
hashed_output = _execute.make_bool(hashed_output, "hashed_output")
num_buckets = _execute.make_int(num_buckets, "num_buckets")
hash_key = _execute.make_int(hash_key, "hash_key")
out_type = _execute.make_type(out_type, "out_type")
internal_type = _execute.make_type(internal_type, "internal_type")
_attr_sparse_types, values = _execute.convert_to_mixed_eager_tensors(values, ctx)
_attr_dense_types, dense_inputs = _execute.convert_to_mixed_eager_tensors(dense_inputs, ctx)
indices = _ops.convert_n_to_tensor(indices, _dtypes.int64)
shapes = _ops.convert_n_to_tensor(shapes, _dtypes.int64)
_inputs_flat = list(indices) + list(values) + list(shapes) + list(dense_inputs)
_attrs = ("N", _attr_N, "hashed_output", hashed_output, "num_buckets",
num_buckets, "hash_key", hash_key, "sparse_types", _attr_sparse_types,
"dense_types", _attr_dense_types, "out_type", out_type, "internal_type",
internal_type)
_result = _execute.execute(b"SparseCross", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseCross", _inputs_flat, _attrs, _result)
_result = _SparseCrossOutput._make(_result)
return _result
_SparseCrossHashedOutput = collections.namedtuple(
"SparseCrossHashed",
["output_indices", "output_values", "output_shape"])
def sparse_cross_hashed(indices, values, shapes, dense_inputs, num_buckets, strong_hash, salt, name=None):
r"""Generates sparse cross from a list of sparse and dense tensors.
The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
representing features of one feature column. It outputs a 2D `SparseTensor` with
the batchwise crosses of these features.
For example, if the inputs are
inputs[0]: SparseTensor with shape = [2, 2]
[0, 0]: "a"
[1, 0]: "b"
[1, 1]: "c"
inputs[1]: SparseTensor with shape = [2, 1]
[0, 0]: "d"
[1, 0]: "e"
inputs[2]: Tensor [["f"], ["g"]]
then the output will be
shape = [2, 2]
[0, 0]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
if hashed_output=true then the output will be
shape = [2, 2]
[0, 0]: FingerprintCat64(
Fingerprint64("f"), FingerprintCat64(
Fingerprint64("d"), Fingerprint64("a")))
[1, 0]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("b")))
[1, 1]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("c")))
Args:
indices: A list of `Tensor` objects with type `int64`.
2-D. Indices of each input `SparseTensor`.
values: A list of `Tensor` objects with types from: `int64`, `string`.
1-D. values of each `SparseTensor`.
shapes: A list with the same length as `indices` of `Tensor` objects with type `int64`.
1-D. Shapes of each `SparseTensor`.
dense_inputs: A list of `Tensor` objects with types from: `int64`, `string`.
2-D. Columns represented by dense `Tensor`.
num_buckets: A `Tensor` of type `int64`.
It is used if hashed_output is true.
output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
strong_hash: A `Tensor` of type `bool`.
boolean, if true, siphash with salt will be used instead of farmhash.
salt: A `Tensor` of type `int64`.
Specify the salt that will be used by the siphash function.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor` of type `int64`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseCrossHashed", name, indices, values, shapes,
dense_inputs, num_buckets, strong_hash, salt)
_result = _SparseCrossHashedOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_cross_hashed_eager_fallback(
indices, values, shapes, dense_inputs, num_buckets, strong_hash,
salt, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross_hashed' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross_hashed' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross_hashed' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseCrossHashed", indices=indices, values=values, shapes=shapes,
dense_inputs=dense_inputs,
num_buckets=num_buckets, strong_hash=strong_hash,
salt=salt, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("N", _op._get_attr_int("N"), "sparse_types",
_op.get_attr("sparse_types"), "dense_types",
_op.get_attr("dense_types"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseCrossHashed", _inputs_flat, _attrs, _result)
_result = _SparseCrossHashedOutput._make(_result)
return _result
SparseCrossHashed = tf_export("raw_ops.SparseCrossHashed")(_ops.to_raw_op(sparse_cross_hashed))
def sparse_cross_hashed_eager_fallback(indices, values, shapes, dense_inputs, num_buckets, strong_hash, salt, name, ctx):
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross_hashed' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross_hashed' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross_hashed' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
_attr_sparse_types, values = _execute.convert_to_mixed_eager_tensors(values, ctx)
_attr_dense_types, dense_inputs = _execute.convert_to_mixed_eager_tensors(dense_inputs, ctx)
indices = _ops.convert_n_to_tensor(indices, _dtypes.int64)
shapes = _ops.convert_n_to_tensor(shapes, _dtypes.int64)
num_buckets = _ops.convert_to_tensor(num_buckets, _dtypes.int64)
strong_hash = _ops.convert_to_tensor(strong_hash, _dtypes.bool)
salt = _ops.convert_to_tensor(salt, _dtypes.int64)
_inputs_flat = list(indices) + list(values) + list(shapes) + list(dense_inputs) + [num_buckets, strong_hash, salt]
_attrs = ("N", _attr_N, "sparse_types", _attr_sparse_types, "dense_types",
_attr_dense_types)
_result = _execute.execute(b"SparseCrossHashed", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseCrossHashed", _inputs_flat, _attrs, _result)
_result = _SparseCrossHashedOutput._make(_result)
return _result
_SparseCrossV2Output = collections.namedtuple(
"SparseCrossV2",
["output_indices", "output_values", "output_shape"])
def sparse_cross_v2(indices, values, shapes, dense_inputs, sep, name=None):
r"""Generates sparse cross from a list of sparse and dense tensors.
The op takes two lists, one of 2D `SparseTensor` and one of 2D `Tensor`, each
representing features of one feature column. It outputs a 2D `SparseTensor` with
the batchwise crosses of these features.
For example, if the inputs are
inputs[0]: SparseTensor with shape = [2, 2]
[0, 0]: "a"
[1, 0]: "b"
[1, 1]: "c"
inputs[1]: SparseTensor with shape = [2, 1]
[0, 0]: "d"
[1, 0]: "e"
inputs[2]: Tensor [["f"], ["g"]]
then the output will be
shape = [2, 2]
[0, 0]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
if hashed_output=true then the output will be
shape = [2, 2]
[0, 0]: FingerprintCat64(
Fingerprint64("f"), FingerprintCat64(
Fingerprint64("d"), Fingerprint64("a")))
[1, 0]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("b")))
[1, 1]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("c")))
Args:
indices: A list of `Tensor` objects with type `int64`.
2-D. Indices of each input `SparseTensor`.
values: A list of `Tensor` objects with types from: `int64`, `string`.
1-D. values of each `SparseTensor`.
shapes: A list with the same length as `indices` of `Tensor` objects with type `int64`.
1-D. Shapes of each `SparseTensor`.
dense_inputs: A list of `Tensor` objects with types from: `int64`, `string`.
2-D. Columns represented by dense `Tensor`.
sep: A `Tensor` of type `string`.
string used when joining a list of string inputs, can be used as separator later.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor` of type `string`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseCrossV2", name, indices, values, shapes, dense_inputs,
sep)
_result = _SparseCrossV2Output._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_cross_v2_eager_fallback(
indices, values, shapes, dense_inputs, sep, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross_v2' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross_v2' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross_v2' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseCrossV2", indices=indices, values=values, shapes=shapes,
dense_inputs=dense_inputs, sep=sep, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("N", _op._get_attr_int("N"), "sparse_types",
_op.get_attr("sparse_types"), "dense_types",
_op.get_attr("dense_types"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseCrossV2", _inputs_flat, _attrs, _result)
_result = _SparseCrossV2Output._make(_result)
return _result
SparseCrossV2 = tf_export("raw_ops.SparseCrossV2")(_ops.to_raw_op(sparse_cross_v2))
def sparse_cross_v2_eager_fallback(indices, values, shapes, dense_inputs, sep, name, ctx):
if not isinstance(indices, (list, tuple)):
raise TypeError(
"Expected list for 'indices' argument to "
"'sparse_cross_v2' Op, not %r." % indices)
_attr_N = len(indices)
if not isinstance(shapes, (list, tuple)):
raise TypeError(
"Expected list for 'shapes' argument to "
"'sparse_cross_v2' Op, not %r." % shapes)
if len(shapes) != _attr_N:
raise ValueError(
"List argument 'shapes' to 'sparse_cross_v2' Op with length %d "
"must match length %d of argument 'indices'." %
(len(shapes), _attr_N))
_attr_sparse_types, values = _execute.convert_to_mixed_eager_tensors(values, ctx)
_attr_dense_types, dense_inputs = _execute.convert_to_mixed_eager_tensors(dense_inputs, ctx)
indices = _ops.convert_n_to_tensor(indices, _dtypes.int64)
shapes = _ops.convert_n_to_tensor(shapes, _dtypes.int64)
sep = _ops.convert_to_tensor(sep, _dtypes.string)
_inputs_flat = list(indices) + list(values) + list(shapes) + list(dense_inputs) + [sep]
_attrs = ("N", _attr_N, "sparse_types", _attr_sparse_types, "dense_types",
_attr_dense_types)
_result = _execute.execute(b"SparseCrossV2", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseCrossV2", _inputs_flat, _attrs, _result)
_result = _SparseCrossV2Output._make(_result)
return _result
def sparse_dense_cwise_add(sp_indices, sp_values, sp_shape, dense, name=None):
r"""Adds up a SparseTensor and a dense Tensor, using these special rules:
(1) Broadcasts the dense side to have the same shape as the sparse side, if
eligible;
(2) Then, only the dense values pointed to by the indices of the SparseTensor
participate in the cwise addition.
By these rules, the result is a logical SparseTensor with exactly the same
indices and shape, but possibly with different non-zero values. The output of
this Op is the resultant non-zero values.
Args:
sp_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
sp_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `sp_indices`.
sp_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
dense: A `Tensor`. Must have the same type as `sp_values`.
`R`-D. The dense Tensor operand.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `sp_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseDenseCwiseAdd", name, sp_indices, sp_values, sp_shape,
dense)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_dense_cwise_add_eager_fallback(
sp_indices, sp_values, sp_shape, dense, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseDenseCwiseAdd", sp_indices=sp_indices, sp_values=sp_values,
sp_shape=sp_shape, dense=dense, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseDenseCwiseAdd", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseDenseCwiseAdd = tf_export("raw_ops.SparseDenseCwiseAdd")(_ops.to_raw_op(sparse_dense_cwise_add))
def sparse_dense_cwise_add_eager_fallback(sp_indices, sp_values, sp_shape, dense, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([sp_values, dense], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(sp_values, dense) = _inputs_T
sp_indices = _ops.convert_to_tensor(sp_indices, _dtypes.int64)
sp_shape = _ops.convert_to_tensor(sp_shape, _dtypes.int64)
_inputs_flat = [sp_indices, sp_values, sp_shape, dense]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseDenseCwiseAdd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseDenseCwiseAdd", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def sparse_dense_cwise_div(sp_indices, sp_values, sp_shape, dense, name=None):
r"""Component-wise divides a SparseTensor by a dense Tensor.
*Limitation*: this Op only broadcasts the dense side to the sparse side, but not
the other direction.
Args:
sp_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
sp_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `sp_indices`.
sp_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
dense: A `Tensor`. Must have the same type as `sp_values`.
`R`-D. The dense Tensor operand.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `sp_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseDenseCwiseDiv", name, sp_indices, sp_values, sp_shape,
dense)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_dense_cwise_div_eager_fallback(
sp_indices, sp_values, sp_shape, dense, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseDenseCwiseDiv", sp_indices=sp_indices, sp_values=sp_values,
sp_shape=sp_shape, dense=dense, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseDenseCwiseDiv", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseDenseCwiseDiv = tf_export("raw_ops.SparseDenseCwiseDiv")(_ops.to_raw_op(sparse_dense_cwise_div))
def sparse_dense_cwise_div_eager_fallback(sp_indices, sp_values, sp_shape, dense, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([sp_values, dense], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(sp_values, dense) = _inputs_T
sp_indices = _ops.convert_to_tensor(sp_indices, _dtypes.int64)
sp_shape = _ops.convert_to_tensor(sp_shape, _dtypes.int64)
_inputs_flat = [sp_indices, sp_values, sp_shape, dense]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseDenseCwiseDiv", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseDenseCwiseDiv", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def sparse_dense_cwise_mul(sp_indices, sp_values, sp_shape, dense, name=None):
r"""Component-wise multiplies a SparseTensor by a dense Tensor.
The output locations corresponding to the implicitly zero elements in the sparse
tensor will be zero (i.e., will not take up storage space), regardless of the
contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN).
*Limitation*: this Op only broadcasts the dense side to the sparse side, but not
the other direction.
Args:
sp_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
sp_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `sp_indices`.
sp_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
dense: A `Tensor`. Must have the same type as `sp_values`.
`R`-D. The dense Tensor operand.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `sp_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseDenseCwiseMul", name, sp_indices, sp_values, sp_shape,
dense)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_dense_cwise_mul_eager_fallback(
sp_indices, sp_values, sp_shape, dense, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseDenseCwiseMul", sp_indices=sp_indices, sp_values=sp_values,
sp_shape=sp_shape, dense=dense, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseDenseCwiseMul", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseDenseCwiseMul = tf_export("raw_ops.SparseDenseCwiseMul")(_ops.to_raw_op(sparse_dense_cwise_mul))
def sparse_dense_cwise_mul_eager_fallback(sp_indices, sp_values, sp_shape, dense, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([sp_values, dense], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(sp_values, dense) = _inputs_T
sp_indices = _ops.convert_to_tensor(sp_indices, _dtypes.int64)
sp_shape = _ops.convert_to_tensor(sp_shape, _dtypes.int64)
_inputs_flat = [sp_indices, sp_values, sp_shape, dense]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseDenseCwiseMul", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseDenseCwiseMul", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SparseFillEmptyRowsOutput = collections.namedtuple(
"SparseFillEmptyRows",
["output_indices", "output_values", "empty_row_indicator", "reverse_index_map"])
def sparse_fill_empty_rows(indices, values, dense_shape, default_value, name=None):
r"""Fills empty rows in the input 2-D `SparseTensor` with a default value.
The input `SparseTensor` is represented via the tuple of inputs
(`indices`, `values`, `dense_shape`). The output `SparseTensor` has the
same `dense_shape` but with indices `output_indices` and values
`output_values`.
This op inserts a single entry for every row that doesn't have any values.
The index is created as `[row, 0, ..., 0]` and the inserted value
is `default_value`.
For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
[0, 1]: a
[0, 3]: b
[1, 0]: default_value
[2, 0]: c
[3, 1]: d
[4, 0]: default_value
The output `SparseTensor` will be in row-major order and will have the
same shape as the input.
This op also returns an indicator vector shaped `[dense_shape[0]]` such that
empty_row_indicator[i] = True iff row i was an empty row.
And a reverse index map vector shaped `[indices.shape[0]]` that is used during
backpropagation,
reverse_index_map[j] = out_j s.t. indices[j, :] == output_indices[out_j, :]
Args:
indices: A `Tensor` of type `int64`.
2-D. the indices of the sparse tensor.
values: A `Tensor`. 1-D. the values of the sparse tensor.
dense_shape: A `Tensor` of type `int64`.
1-D. the shape of the sparse tensor.
default_value: A `Tensor`. Must have the same type as `values`.
0-D. default value to insert into location `[row, 0, ..., 0]`
for rows missing from the input sparse tensor.
output indices: 2-D. the indices of the filled sparse tensor.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, empty_row_indicator, reverse_index_map).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `values`.
empty_row_indicator: A `Tensor` of type `bool`.
reverse_index_map: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseFillEmptyRows", name, indices, values, dense_shape,
default_value)
_result = _SparseFillEmptyRowsOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_fill_empty_rows_eager_fallback(
indices, values, dense_shape, default_value, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseFillEmptyRows", indices=indices, values=values,
dense_shape=dense_shape,
default_value=default_value, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseFillEmptyRows", _inputs_flat, _attrs, _result)
_result = _SparseFillEmptyRowsOutput._make(_result)
return _result
SparseFillEmptyRows = tf_export("raw_ops.SparseFillEmptyRows")(_ops.to_raw_op(sparse_fill_empty_rows))
def sparse_fill_empty_rows_eager_fallback(indices, values, dense_shape, default_value, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([values, default_value], ctx, [])
(values, default_value) = _inputs_T
indices = _ops.convert_to_tensor(indices, _dtypes.int64)
dense_shape = _ops.convert_to_tensor(dense_shape, _dtypes.int64)
_inputs_flat = [indices, values, dense_shape, default_value]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseFillEmptyRows", 4, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseFillEmptyRows", _inputs_flat, _attrs, _result)
_result = _SparseFillEmptyRowsOutput._make(_result)
return _result
_SparseFillEmptyRowsGradOutput = collections.namedtuple(
"SparseFillEmptyRowsGrad",
["d_values", "d_default_value"])
def sparse_fill_empty_rows_grad(reverse_index_map, grad_values, name=None):
r"""The gradient of SparseFillEmptyRows.
Takes vectors reverse_index_map, shaped `[N]`, and grad_values,
shaped `[N_full]`, where `N_full >= N` and copies data into either
`d_values` or `d_default_value`. Here `d_values` is shaped `[N]` and
`d_default_value` is a scalar.
d_values[j] = grad_values[reverse_index_map[j]]
d_default_value = sum_{k : 0 .. N_full - 1} (
grad_values[k] * 1{k not in reverse_index_map})
Args:
reverse_index_map: A `Tensor` of type `int64`.
1-D. The reverse index map from SparseFillEmptyRows.
grad_values: A `Tensor`. 1-D. The gradients from backprop.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (d_values, d_default_value).
d_values: A `Tensor`. Has the same type as `grad_values`.
d_default_value: A `Tensor`. Has the same type as `grad_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseFillEmptyRowsGrad", name, reverse_index_map, grad_values)
_result = _SparseFillEmptyRowsGradOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_fill_empty_rows_grad_eager_fallback(
reverse_index_map, grad_values, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseFillEmptyRowsGrad", reverse_index_map=reverse_index_map,
grad_values=grad_values, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseFillEmptyRowsGrad", _inputs_flat, _attrs, _result)
_result = _SparseFillEmptyRowsGradOutput._make(_result)
return _result
SparseFillEmptyRowsGrad = tf_export("raw_ops.SparseFillEmptyRowsGrad")(_ops.to_raw_op(sparse_fill_empty_rows_grad))
def sparse_fill_empty_rows_grad_eager_fallback(reverse_index_map, grad_values, name, ctx):
_attr_T, (grad_values,) = _execute.args_to_matching_eager([grad_values], ctx, [])
reverse_index_map = _ops.convert_to_tensor(reverse_index_map, _dtypes.int64)
_inputs_flat = [reverse_index_map, grad_values]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseFillEmptyRowsGrad", 2,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseFillEmptyRowsGrad", _inputs_flat, _attrs, _result)
_result = _SparseFillEmptyRowsGradOutput._make(_result)
return _result
def sparse_reduce_max(input_indices, input_values, input_shape, reduction_axes, keep_dims=False, name=None):
r"""Computes the max of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_max()`. In particular, this Op also returns a dense `Tensor`
instead of a sparse one.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
which are interpreted according to the indexing rules in Python.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `input_indices`.
input_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
reduction_axes: A `Tensor` of type `int32`.
1-D. Length-`K` vector containing the reduction axes.
keep_dims: An optional `bool`. Defaults to `False`.
If true, retain reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReduceMax", name, input_indices, input_values,
input_shape, reduction_axes, "keep_dims", keep_dims)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reduce_max_eager_fallback(
input_indices, input_values, input_shape, reduction_axes,
keep_dims=keep_dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReduceMax", input_indices=input_indices,
input_values=input_values, input_shape=input_shape,
reduction_axes=reduction_axes, keep_dims=keep_dims,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
_op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReduceMax", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseReduceMax = tf_export("raw_ops.SparseReduceMax")(_ops.to_raw_op(sparse_reduce_max))
def sparse_reduce_max_eager_fallback(input_indices, input_values, input_shape, reduction_axes, keep_dims, name, ctx):
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_attr_T, (input_values,) = _execute.args_to_matching_eager([input_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
reduction_axes = _ops.convert_to_tensor(reduction_axes, _dtypes.int32)
_inputs_flat = [input_indices, input_values, input_shape, reduction_axes]
_attrs = ("keep_dims", keep_dims, "T", _attr_T)
_result = _execute.execute(b"SparseReduceMax", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReduceMax", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SparseReduceMaxSparseOutput = collections.namedtuple(
"SparseReduceMaxSparse",
["output_indices", "output_values", "output_shape"])
def sparse_reduce_max_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims=False, name=None):
r"""Computes the max of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_max()`. In contrast to SparseReduceMax, this Op returns a
SparseTensor.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
which are interpreted according to the indexing rules in Python.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `input_indices`.
input_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
reduction_axes: A `Tensor` of type `int32`.
1-D. Length-`K` vector containing the reduction axes.
keep_dims: An optional `bool`. Defaults to `False`.
If true, retain reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `input_values`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReduceMaxSparse", name, input_indices, input_values,
input_shape, reduction_axes, "keep_dims", keep_dims)
_result = _SparseReduceMaxSparseOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reduce_max_sparse_eager_fallback(
input_indices, input_values, input_shape, reduction_axes,
keep_dims=keep_dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReduceMaxSparse", input_indices=input_indices,
input_values=input_values,
input_shape=input_shape,
reduction_axes=reduction_axes,
keep_dims=keep_dims, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
_op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReduceMaxSparse", _inputs_flat, _attrs, _result)
_result = _SparseReduceMaxSparseOutput._make(_result)
return _result
SparseReduceMaxSparse = tf_export("raw_ops.SparseReduceMaxSparse")(_ops.to_raw_op(sparse_reduce_max_sparse))
def sparse_reduce_max_sparse_eager_fallback(input_indices, input_values, input_shape, reduction_axes, keep_dims, name, ctx):
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_attr_T, (input_values,) = _execute.args_to_matching_eager([input_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
reduction_axes = _ops.convert_to_tensor(reduction_axes, _dtypes.int32)
_inputs_flat = [input_indices, input_values, input_shape, reduction_axes]
_attrs = ("keep_dims", keep_dims, "T", _attr_T)
_result = _execute.execute(b"SparseReduceMaxSparse", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReduceMaxSparse", _inputs_flat, _attrs, _result)
_result = _SparseReduceMaxSparseOutput._make(_result)
return _result
def sparse_reduce_sum(input_indices, input_values, input_shape, reduction_axes, keep_dims=False, name=None):
r"""Computes the sum of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor`
instead of a sparse one.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
which are interpreted according to the indexing rules in Python.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `input_indices`.
input_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
reduction_axes: A `Tensor` of type `int32`.
1-D. Length-`K` vector containing the reduction axes.
keep_dims: An optional `bool`. Defaults to `False`.
If true, retain reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReduceSum", name, input_indices, input_values,
input_shape, reduction_axes, "keep_dims", keep_dims)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reduce_sum_eager_fallback(
input_indices, input_values, input_shape, reduction_axes,
keep_dims=keep_dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReduceSum", input_indices=input_indices,
input_values=input_values, input_shape=input_shape,
reduction_axes=reduction_axes, keep_dims=keep_dims,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
_op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReduceSum", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseReduceSum = tf_export("raw_ops.SparseReduceSum")(_ops.to_raw_op(sparse_reduce_sum))
def sparse_reduce_sum_eager_fallback(input_indices, input_values, input_shape, reduction_axes, keep_dims, name, ctx):
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_attr_T, (input_values,) = _execute.args_to_matching_eager([input_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
reduction_axes = _ops.convert_to_tensor(reduction_axes, _dtypes.int32)
_inputs_flat = [input_indices, input_values, input_shape, reduction_axes]
_attrs = ("keep_dims", keep_dims, "T", _attr_T)
_result = _execute.execute(b"SparseReduceSum", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReduceSum", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SparseReduceSumSparseOutput = collections.namedtuple(
"SparseReduceSumSparse",
["output_indices", "output_values", "output_shape"])
def sparse_reduce_sum_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims=False, name=None):
r"""Computes the sum of elements across dimensions of a SparseTensor.
This Op takes a SparseTensor and is the sparse counterpart to
`tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a
SparseTensor.
Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless
`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
`reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
with length 1.
If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
which are interpreted according to the indexing rules in Python.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `input_indices`.
input_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
reduction_axes: A `Tensor` of type `int32`.
1-D. Length-`K` vector containing the reduction axes.
keep_dims: An optional `bool`. Defaults to `False`.
If true, retain reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `input_values`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReduceSumSparse", name, input_indices, input_values,
input_shape, reduction_axes, "keep_dims", keep_dims)
_result = _SparseReduceSumSparseOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reduce_sum_sparse_eager_fallback(
input_indices, input_values, input_shape, reduction_axes,
keep_dims=keep_dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReduceSumSparse", input_indices=input_indices,
input_values=input_values,
input_shape=input_shape,
reduction_axes=reduction_axes,
keep_dims=keep_dims, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
_op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReduceSumSparse", _inputs_flat, _attrs, _result)
_result = _SparseReduceSumSparseOutput._make(_result)
return _result
SparseReduceSumSparse = tf_export("raw_ops.SparseReduceSumSparse")(_ops.to_raw_op(sparse_reduce_sum_sparse))
def sparse_reduce_sum_sparse_eager_fallback(input_indices, input_values, input_shape, reduction_axes, keep_dims, name, ctx):
if keep_dims is None:
keep_dims = False
keep_dims = _execute.make_bool(keep_dims, "keep_dims")
_attr_T, (input_values,) = _execute.args_to_matching_eager([input_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
reduction_axes = _ops.convert_to_tensor(reduction_axes, _dtypes.int32)
_inputs_flat = [input_indices, input_values, input_shape, reduction_axes]
_attrs = ("keep_dims", keep_dims, "T", _attr_T)
_result = _execute.execute(b"SparseReduceSumSparse", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReduceSumSparse", _inputs_flat, _attrs, _result)
_result = _SparseReduceSumSparseOutput._make(_result)
return _result
_SparseReorderOutput = collections.namedtuple(
"SparseReorder",
["output_indices", "output_values"])
def sparse_reorder(input_indices, input_values, input_shape, name=None):
r"""Reorders a SparseTensor into the canonical, row-major ordering.
Note that by convention, all sparse ops preserve the canonical ordering along
increasing dimension number. The only time ordering can be violated is during
manual manipulation of the indices and values vectors to add entries.
Reordering does not affect the shape of the SparseTensor.
If the tensor has rank `R` and `N` non-empty values, `input_indices` has
shape `[N, R]`, input_values has length `N`, and input_shape has length `R`.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values: A `Tensor`.
1-D. `N` non-empty values corresponding to `input_indices`.
input_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `input_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReorder", name, input_indices, input_values, input_shape)
_result = _SparseReorderOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reorder_eager_fallback(
input_indices, input_values, input_shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReorder", input_indices=input_indices,
input_values=input_values, input_shape=input_shape,
name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReorder", _inputs_flat, _attrs, _result)
_result = _SparseReorderOutput._make(_result)
return _result
SparseReorder = tf_export("raw_ops.SparseReorder")(_ops.to_raw_op(sparse_reorder))
def sparse_reorder_eager_fallback(input_indices, input_values, input_shape, name, ctx):
_attr_T, (input_values,) = _execute.args_to_matching_eager([input_values], ctx, [])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
_inputs_flat = [input_indices, input_values, input_shape]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseReorder", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReorder", _inputs_flat, _attrs, _result)
_result = _SparseReorderOutput._make(_result)
return _result
_SparseReshapeOutput = collections.namedtuple(
"SparseReshape",
["output_indices", "output_shape"])
def sparse_reshape(input_indices, input_shape, new_shape, name=None):
r"""Reshapes a SparseTensor to represent values in a new dense shape.
This operation has the same semantics as reshape on the represented dense
tensor. The `input_indices` are recomputed based on the requested `new_shape`.
If one component of `new_shape` is the special value -1, the size of that
dimension is computed so that the total dense size remains constant. At
most one component of `new_shape` can be -1. The number of dense elements
implied by `new_shape` must be the same as the number of dense elements
originally implied by `input_shape`.
Reshaping does not affect the order of values in the SparseTensor.
If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape`
has length `R_out`, then `input_indices` has shape `[N, R_in]`,
`input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and
`output_shape` has length `R_out`.
Args:
input_indices: A `Tensor` of type `int64`.
2-D. `N x R_in` matrix with the indices of non-empty values in a
SparseTensor.
input_shape: A `Tensor` of type `int64`.
1-D. `R_in` vector with the input SparseTensor's dense shape.
new_shape: A `Tensor` of type `int64`.
1-D. `R_out` vector with the requested new dense shape.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_shape).
output_indices: A `Tensor` of type `int64`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseReshape", name, input_indices, input_shape, new_shape)
_result = _SparseReshapeOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_reshape_eager_fallback(
input_indices, input_shape, new_shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseReshape", input_indices=input_indices, input_shape=input_shape,
new_shape=new_shape, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ()
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseReshape", _inputs_flat, _attrs, _result)
_result = _SparseReshapeOutput._make(_result)
return _result
SparseReshape = tf_export("raw_ops.SparseReshape")(_ops.to_raw_op(sparse_reshape))
def sparse_reshape_eager_fallback(input_indices, input_shape, new_shape, name, ctx):
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_shape = _ops.convert_to_tensor(input_shape, _dtypes.int64)
new_shape = _ops.convert_to_tensor(new_shape, _dtypes.int64)
_inputs_flat = [input_indices, input_shape, new_shape]
_attrs = None
_result = _execute.execute(b"SparseReshape", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseReshape", _inputs_flat, _attrs, _result)
_result = _SparseReshapeOutput._make(_result)
return _result
_SparseSliceOutput = collections.namedtuple(
"SparseSlice",
["output_indices", "output_values", "output_shape"])
def sparse_slice(indices, values, shape, start, size, name=None):
r"""Slice a `SparseTensor` based on the `start` and `size`.
For example, if the input is
input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are:
sparse_slice([0, 0], [2, 4]) = shape = [2, 4]
[ a ]
[b c ]
sparse_slice([0, 4], [2, 3]) = shape = [2, 3]
[ d e ]
[ ]
Args:
indices: A `Tensor` of type `int64`.
2-D tensor represents the indices of the sparse tensor.
values: A `Tensor`.
1-D tensor represents the values of the sparse tensor.
shape: A `Tensor` of type `int64`.
1-D. tensor represents the shape of the sparse tensor.
start: A `Tensor` of type `int64`.
1-D. tensor represents the start of the slice.
size: A `Tensor` of type `int64`.
1-D. tensor represents the size of the slice.
output indices: A list of 1-D tensors represents the indices of the output
sparse tensors.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `values`.
output_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSlice", name, indices, values, shape, start, size)
_result = _SparseSliceOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_slice_eager_fallback(
indices, values, shape, start, size, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSlice", indices=indices, values=values, shape=shape,
start=start, size=size, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSlice", _inputs_flat, _attrs, _result)
_result = _SparseSliceOutput._make(_result)
return _result
SparseSlice = tf_export("raw_ops.SparseSlice")(_ops.to_raw_op(sparse_slice))
def sparse_slice_eager_fallback(indices, values, shape, start, size, name, ctx):
_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [])
indices = _ops.convert_to_tensor(indices, _dtypes.int64)
shape = _ops.convert_to_tensor(shape, _dtypes.int64)
start = _ops.convert_to_tensor(start, _dtypes.int64)
size = _ops.convert_to_tensor(size, _dtypes.int64)
_inputs_flat = [indices, values, shape, start, size]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseSlice", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSlice", _inputs_flat, _attrs, _result)
_result = _SparseSliceOutput._make(_result)
return _result
def sparse_slice_grad(backprop_val_grad, input_indices, input_start, output_indices, name=None):
r"""The gradient operator for the SparseSlice op.
This op takes in the upstream gradient w.r.t. non-empty values of
the sliced `SparseTensor`, and outputs the gradients w.r.t.
the non-empty values of input `SparseTensor`.
Args:
backprop_val_grad: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. The gradient with respect to
the non-empty values of the sliced `SparseTensor`.
input_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the input `SparseTensor`.
input_start: A `Tensor` of type `int64`.
1-D. tensor represents the start of the slice.
output_indices: A `Tensor` of type `int64`.
2-D. The `indices` of the sliced `SparseTensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `backprop_val_grad`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSliceGrad", name, backprop_val_grad, input_indices,
input_start, output_indices)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_slice_grad_eager_fallback(
backprop_val_grad, input_indices, input_start, output_indices,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSliceGrad", backprop_val_grad=backprop_val_grad,
input_indices=input_indices,
input_start=input_start,
output_indices=output_indices, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSliceGrad", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseSliceGrad = tf_export("raw_ops.SparseSliceGrad")(_ops.to_raw_op(sparse_slice_grad))
def sparse_slice_grad_eager_fallback(backprop_val_grad, input_indices, input_start, output_indices, name, ctx):
_attr_T, (backprop_val_grad,) = _execute.args_to_matching_eager([backprop_val_grad], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
input_indices = _ops.convert_to_tensor(input_indices, _dtypes.int64)
input_start = _ops.convert_to_tensor(input_start, _dtypes.int64)
output_indices = _ops.convert_to_tensor(output_indices, _dtypes.int64)
_inputs_flat = [backprop_val_grad, input_indices, input_start, output_indices]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseSliceGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSliceGrad", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def sparse_softmax(sp_indices, sp_values, sp_shape, name=None):
r"""Applies softmax to a batched N-D `SparseTensor`.
The inputs represent an N-D SparseTensor with logical shape `[..., B, C]`
(where `N >= 2`), and with indices sorted in the canonical lexicographic order.
This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost
logical submatrix with shape `[B, C]`, but with the catch that *the implicitly
zero elements do not participate*. Specifically, the algorithm is equivalent
to the following:
(1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix
with shape `[B, C]`, along the size-C dimension;
(2) Masks out the original implicitly-zero locations;
(3) Renormalizes the remaining elements.
Hence, the `SparseTensor` result has exactly the same non-zero indices and
shape.
Args:
sp_indices: A `Tensor` of type `int64`.
2-D. `NNZ x R` matrix with the indices of non-empty values in a
SparseTensor, in canonical ordering.
sp_values: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
1-D. `NNZ` non-empty values corresponding to `sp_indices`.
sp_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `sp_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSoftmax", name, sp_indices, sp_values, sp_shape)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_softmax_eager_fallback(
sp_indices, sp_values, sp_shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSoftmax", sp_indices=sp_indices, sp_values=sp_values,
sp_shape=sp_shape, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSoftmax", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseSoftmax = tf_export("raw_ops.SparseSoftmax")(_ops.to_raw_op(sparse_softmax))
def sparse_softmax_eager_fallback(sp_indices, sp_values, sp_shape, name, ctx):
_attr_T, (sp_values,) = _execute.args_to_matching_eager([sp_values], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, ])
sp_indices = _ops.convert_to_tensor(sp_indices, _dtypes.int64)
sp_shape = _ops.convert_to_tensor(sp_shape, _dtypes.int64)
_inputs_flat = [sp_indices, sp_values, sp_shape]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseSoftmax", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSoftmax", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_SparseSparseMaximumOutput = collections.namedtuple(
"SparseSparseMaximum",
["output_indices", "output_values"])
def sparse_sparse_maximum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, name=None):
r"""Returns the element-wise max of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Args:
a_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, in the canonical lexicographic ordering.
a_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `a_indices`.
a_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
b_indices: A `Tensor` of type `int64`.
counterpart to `a_indices` for the other operand.
b_values: A `Tensor`. Must have the same type as `a_values`.
counterpart to `a_values` for the other operand; must be of the same dtype.
b_shape: A `Tensor` of type `int64`.
counterpart to `a_shape` for the other operand; the two shapes must be equal.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `a_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSparseMaximum", name, a_indices, a_values, a_shape,
b_indices, b_values, b_shape)
_result = _SparseSparseMaximumOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_sparse_maximum_eager_fallback(
a_indices, a_values, a_shape, b_indices, b_values, b_shape,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSparseMaximum", a_indices=a_indices, a_values=a_values,
a_shape=a_shape, b_indices=b_indices,
b_values=b_values, b_shape=b_shape, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSparseMaximum", _inputs_flat, _attrs, _result)
_result = _SparseSparseMaximumOutput._make(_result)
return _result
SparseSparseMaximum = tf_export("raw_ops.SparseSparseMaximum")(_ops.to_raw_op(sparse_sparse_maximum))
def sparse_sparse_maximum_eager_fallback(a_indices, a_values, a_shape, b_indices, b_values, b_shape, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([a_values, b_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(a_values, b_values) = _inputs_T
a_indices = _ops.convert_to_tensor(a_indices, _dtypes.int64)
a_shape = _ops.convert_to_tensor(a_shape, _dtypes.int64)
b_indices = _ops.convert_to_tensor(b_indices, _dtypes.int64)
b_shape = _ops.convert_to_tensor(b_shape, _dtypes.int64)
_inputs_flat = [a_indices, a_values, a_shape, b_indices, b_values, b_shape]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseSparseMaximum", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSparseMaximum", _inputs_flat, _attrs, _result)
_result = _SparseSparseMaximumOutput._make(_result)
return _result
_SparseSparseMinimumOutput = collections.namedtuple(
"SparseSparseMinimum",
["output_indices", "output_values"])
def sparse_sparse_minimum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, name=None):
r"""Returns the element-wise min of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Args:
a_indices: A `Tensor` of type `int64`.
2-D. `N x R` matrix with the indices of non-empty values in a
SparseTensor, in the canonical lexicographic ordering.
a_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. `N` non-empty values corresponding to `a_indices`.
a_shape: A `Tensor` of type `int64`.
1-D. Shape of the input SparseTensor.
b_indices: A `Tensor` of type `int64`.
counterpart to `a_indices` for the other operand.
b_values: A `Tensor`. Must have the same type as `a_values`.
counterpart to `a_values` for the other operand; must be of the same dtype.
b_shape: A `Tensor` of type `int64`.
counterpart to `a_shape` for the other operand; the two shapes must be equal.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values).
output_indices: A `Tensor` of type `int64`.
output_values: A `Tensor`. Has the same type as `a_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSparseMinimum", name, a_indices, a_values, a_shape,
b_indices, b_values, b_shape)
_result = _SparseSparseMinimumOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_sparse_minimum_eager_fallback(
a_indices, a_values, a_shape, b_indices, b_values, b_shape,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSparseMinimum", a_indices=a_indices, a_values=a_values,
a_shape=a_shape, b_indices=b_indices,
b_values=b_values, b_shape=b_shape, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSparseMinimum", _inputs_flat, _attrs, _result)
_result = _SparseSparseMinimumOutput._make(_result)
return _result
SparseSparseMinimum = tf_export("raw_ops.SparseSparseMinimum")(_ops.to_raw_op(sparse_sparse_minimum))
def sparse_sparse_minimum_eager_fallback(a_indices, a_values, a_shape, b_indices, b_values, b_shape, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([a_values, b_values], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(a_values, b_values) = _inputs_T
a_indices = _ops.convert_to_tensor(a_indices, _dtypes.int64)
a_shape = _ops.convert_to_tensor(a_shape, _dtypes.int64)
b_indices = _ops.convert_to_tensor(b_indices, _dtypes.int64)
b_shape = _ops.convert_to_tensor(b_shape, _dtypes.int64)
_inputs_flat = [a_indices, a_values, a_shape, b_indices, b_values, b_shape]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"SparseSparseMinimum", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSparseMinimum", _inputs_flat, _attrs, _result)
_result = _SparseSparseMinimumOutput._make(_result)
return _result
_SparseSplitOutput = collections.namedtuple(
"SparseSplit",
["output_indices", "output_values", "output_shape"])
def sparse_split(split_dim, indices, values, shape, num_split, name=None):
r"""Split a `SparseTensor` into `num_split` tensors along one dimension.
If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices
`[0 : shape[split_dim] % num_split]` gets one extra dimension.
For example, if `split_dim = 1` and `num_split = 2` and the input is
input_tensor = shape = [2, 7]
[ a d e ]
[b c ]
Graphically the output tensors are:
output_tensor[0] = shape = [2, 4]
[ a ]
[b c ]
output_tensor[1] = shape = [2, 3]
[ d e ]
[ ]
Args:
split_dim: A `Tensor` of type `int64`.
0-D. The dimension along which to split. Must be in the range
`[0, rank(shape))`.
indices: A `Tensor` of type `int64`.
2-D tensor represents the indices of the sparse tensor.
values: A `Tensor`.
1-D tensor represents the values of the sparse tensor.
shape: A `Tensor` of type `int64`.
1-D. tensor represents the shape of the sparse tensor.
output indices: A list of 1-D tensors represents the indices of the output
sparse tensors.
num_split: An `int` that is `>= 1`. The number of ways to split.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output_indices, output_values, output_shape).
output_indices: A list of `num_split` `Tensor` objects with type `int64`.
output_values: A list of `num_split` `Tensor` objects with the same type as `values`.
output_shape: A list of `num_split` `Tensor` objects with type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseSplit", name, split_dim, indices, values, shape,
"num_split", num_split)
_result = _SparseSplitOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_split_eager_fallback(
split_dim, indices, values, shape, num_split=num_split, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
num_split = _execute.make_int(num_split, "num_split")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseSplit", split_dim=split_dim, indices=indices, values=values,
shape=shape, num_split=num_split, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("num_split", _op._get_attr_int("num_split"), "T",
_op._get_attr_type("T"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseSplit", _inputs_flat, _attrs, _result)
_result = [_result[:num_split]] + _result[num_split:]
_result = _result[:1] + [_result[1:1 + num_split]] + _result[1 + num_split:]
_result = _result[:2] + [_result[2:]]
_result = _SparseSplitOutput._make(_result)
return _result
SparseSplit = tf_export("raw_ops.SparseSplit")(_ops.to_raw_op(sparse_split))
def sparse_split_eager_fallback(split_dim, indices, values, shape, num_split, name, ctx):
num_split = _execute.make_int(num_split, "num_split")
_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [])
split_dim = _ops.convert_to_tensor(split_dim, _dtypes.int64)
indices = _ops.convert_to_tensor(indices, _dtypes.int64)
shape = _ops.convert_to_tensor(shape, _dtypes.int64)
_inputs_flat = [split_dim, indices, values, shape]
_attrs = ("num_split", num_split, "T", _attr_T)
_result = _execute.execute(b"SparseSplit", num_split + num_split +
num_split, inputs=_inputs_flat, attrs=_attrs,
ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseSplit", _inputs_flat, _attrs, _result)
_result = [_result[:num_split]] + _result[num_split:]
_result = _result[:1] + [_result[1:1 + num_split]] + _result[1 + num_split:]
_result = _result[:2] + [_result[2:]]
_result = _SparseSplitOutput._make(_result)
return _result
def sparse_tensor_dense_add(a_indices, a_values, a_shape, b, name=None):
r"""Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
This Op does not require `a_indices` be sorted in standard lexicographic order.
Args:
a_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
a_values: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
1-D. The `values` of the `SparseTensor`, with shape `[nnz]`.
a_shape: A `Tensor`. Must have the same type as `a_indices`.
1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`.
b: A `Tensor`. Must have the same type as `a_values`.
`ndims`-D Tensor. With shape `a_shape`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `a_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseTensorDenseAdd", name, a_indices, a_values, a_shape, b)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_tensor_dense_add_eager_fallback(
a_indices, a_values, a_shape, b, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseTensorDenseAdd", a_indices=a_indices, a_values=a_values,
a_shape=a_shape, b=b, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "Tindices",
_op._get_attr_type("Tindices"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseTensorDenseAdd", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseTensorDenseAdd = tf_export("raw_ops.SparseTensorDenseAdd")(_ops.to_raw_op(sparse_tensor_dense_add))
def sparse_tensor_dense_add_eager_fallback(a_indices, a_values, a_shape, b, name, ctx):
_attr_T, _inputs_T = _execute.args_to_matching_eager([a_values, b], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
(a_values, b) = _inputs_T
_attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([a_indices, a_shape], ctx, [_dtypes.int32, _dtypes.int64, ])
(a_indices, a_shape) = _inputs_Tindices
_inputs_flat = [a_indices, a_values, a_shape, b]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"SparseTensorDenseAdd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseTensorDenseAdd", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def sparse_tensor_dense_mat_mul(a_indices, a_values, a_shape, b, adjoint_a=False, adjoint_b=False, name=None):
r"""Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
No validity checking is performed on the indices of A. However, the following
input format is recommended for optimal behavior:
if adjoint_a == false:
A should be sorted in lexicographically increasing order. Use SparseReorder
if you're not sure.
if adjoint_a == true:
A should be sorted in order of increasing dimension 1 (i.e., "column major"
order instead of "row major" order).
Args:
a_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
a_values: A `Tensor`.
1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.
a_shape: A `Tensor` of type `int64`.
1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.
b: A `Tensor`. Must have the same type as `a_values`.
2-D. A dense Matrix.
adjoint_a: An optional `bool`. Defaults to `False`.
Use the adjoint of A in the matrix multiply. If A is complex, this
is transpose(conj(A)). Otherwise it's transpose(A).
adjoint_b: An optional `bool`. Defaults to `False`.
Use the adjoint of B in the matrix multiply. If B is complex, this
is transpose(conj(B)). Otherwise it's transpose(B).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `a_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseTensorDenseMatMul", name, a_indices, a_values, a_shape,
b, "adjoint_a", adjoint_a, "adjoint_b", adjoint_b)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_tensor_dense_mat_mul_eager_fallback(
a_indices, a_values, a_shape, b, adjoint_a=adjoint_a,
adjoint_b=adjoint_b, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if adjoint_a is None:
adjoint_a = False
adjoint_a = _execute.make_bool(adjoint_a, "adjoint_a")
if adjoint_b is None:
adjoint_b = False
adjoint_b = _execute.make_bool(adjoint_b, "adjoint_b")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseTensorDenseMatMul", a_indices=a_indices, a_values=a_values,
a_shape=a_shape, b=b, adjoint_a=adjoint_a,
adjoint_b=adjoint_b, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("T", _op._get_attr_type("T"), "Tindices",
_op._get_attr_type("Tindices"), "adjoint_a",
_op._get_attr_bool("adjoint_a"), "adjoint_b",
_op._get_attr_bool("adjoint_b"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseTensorDenseMatMul", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseTensorDenseMatMul = tf_export("raw_ops.SparseTensorDenseMatMul")(_ops.to_raw_op(sparse_tensor_dense_mat_mul))
def sparse_tensor_dense_mat_mul_eager_fallback(a_indices, a_values, a_shape, b, adjoint_a, adjoint_b, name, ctx):
if adjoint_a is None:
adjoint_a = False
adjoint_a = _execute.make_bool(adjoint_a, "adjoint_a")
if adjoint_b is None:
adjoint_b = False
adjoint_b = _execute.make_bool(adjoint_b, "adjoint_b")
_attr_T, _inputs_T = _execute.args_to_matching_eager([a_values, b], ctx, [])
(a_values, b) = _inputs_T
_attr_Tindices, (a_indices,) = _execute.args_to_matching_eager([a_indices], ctx, [_dtypes.int32, _dtypes.int64, ], _dtypes.int64)
a_shape = _ops.convert_to_tensor(a_shape, _dtypes.int64)
_inputs_flat = [a_indices, a_values, a_shape, b]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "adjoint_a", adjoint_a,
"adjoint_b", adjoint_b)
_result = _execute.execute(b"SparseTensorDenseMatMul", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseTensorDenseMatMul", _inputs_flat, _attrs, _result)
_result, = _result
return _result
def sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value, validate_indices=True, name=None):
r"""Converts a sparse representation into a dense tensor.
Builds an array `dense` with shape `output_shape` such that
```
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)
# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]
# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
```
All other values in `dense` are set to `default_value`. If `sparse_values` is a
scalar, all sparse indices are set to this single value.
Indices should be sorted in lexicographic order, and indices must not
contain any repeats. If `validate_indices` is true, these properties
are checked during execution.
Args:
sparse_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete
index where `sparse_values[i]` will be placed.
output_shape: A `Tensor`. Must have the same type as `sparse_indices`.
1-D. Shape of the dense output tensor.
sparse_values: A `Tensor`.
1-D. Values corresponding to each row of `sparse_indices`,
or a scalar value to be used for all sparse indices.
default_value: A `Tensor`. Must have the same type as `sparse_values`.
Scalar value to set for indices not specified in
`sparse_indices`.
validate_indices: An optional `bool`. Defaults to `True`.
If true, indices are checked to make sure they are sorted in
lexicographic order and that there are no repeats.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `sparse_values`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "SparseToDense", name, sparse_indices, output_shape,
sparse_values, default_value, "validate_indices", validate_indices)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return sparse_to_dense_eager_fallback(
sparse_indices, output_shape, sparse_values, default_value,
validate_indices=validate_indices, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
if validate_indices is None:
validate_indices = True
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
_, _, _op, _outputs = _op_def_library._apply_op_helper(
"SparseToDense", sparse_indices=sparse_indices,
output_shape=output_shape,
sparse_values=sparse_values,
default_value=default_value,
validate_indices=validate_indices, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("validate_indices", _op._get_attr_bool("validate_indices"), "T",
_op._get_attr_type("T"), "Tindices",
_op._get_attr_type("Tindices"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"SparseToDense", _inputs_flat, _attrs, _result)
_result, = _result
return _result
SparseToDense = tf_export("raw_ops.SparseToDense")(_ops.to_raw_op(sparse_to_dense))
def sparse_to_dense_eager_fallback(sparse_indices, output_shape, sparse_values, default_value, validate_indices, name, ctx):
if validate_indices is None:
validate_indices = True
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
_attr_T, _inputs_T = _execute.args_to_matching_eager([sparse_values, default_value], ctx, [])
(sparse_values, default_value) = _inputs_T
_attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([sparse_indices, output_shape], ctx, [_dtypes.int32, _dtypes.int64, ])
(sparse_indices, output_shape) = _inputs_Tindices
_inputs_flat = [sparse_indices, output_shape, sparse_values, default_value]
_attrs = ("validate_indices", validate_indices, "T", _attr_T, "Tindices",
_attr_Tindices)
_result = _execute.execute(b"SparseToDense", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=ctx, name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"SparseToDense", _inputs_flat, _attrs, _result)
_result, = _result
return _result
_TakeManySparseFromTensorsMapOutput = collections.namedtuple(
"TakeManySparseFromTensorsMap",
["sparse_indices", "sparse_values", "sparse_shape"])
def take_many_sparse_from_tensors_map(sparse_handles, dtype, container="", shared_name="", name=None):
r"""Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where
`N` is the minibatch size and the rows correspond to the output handles of
`AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the
original `SparseTensor` objects that went into the given input ops must all
match. When the final `SparseTensor` is created, it has rank one
higher than the ranks of the incoming `SparseTensor` objects
(they have been concatenated along a new row dimension on the left).
The output `SparseTensor` object's shape values for all dimensions but the
first are the max across the input `SparseTensor` objects' shape values
for the corresponding dimensions. Its first shape value is `N`, the minibatch
size.
The input `SparseTensor` objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run `SparseReorder` to restore index ordering.
For example, if the handles represent an input, which is a `[2, 3]` matrix
representing two original `SparseTensor` objects:
```
index = [ 0]
[10]
[20]
values = [1, 2, 3]
shape = [50]
```
and
```
index = [ 2]
[10]
values = [4, 5]
shape = [30]
```
then the final `SparseTensor` will be:
```
index = [0 0]
[0 10]
[0 20]
[1 2]
[1 10]
values = [1, 2, 3, 4, 5]
shape = [2 50]
```
Args:
sparse_handles: A `Tensor` of type `int64`.
1-D, The `N` serialized `SparseTensor` objects.
Shape: `[N]`.
dtype: A `tf.DType`.
The `dtype` of the `SparseTensor` objects stored in the
`SparseTensorsMap`.
container: An optional `string`. Defaults to `""`.
The container name for the `SparseTensorsMap` read by this op.
shared_name: An optional `string`. Defaults to `""`.
The shared name for the `SparseTensorsMap` read by this op.
It should not be blank; rather the `shared_name` or unique Operation name
of the Op that created the original `SparseTensorsMap` should be used.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (sparse_indices, sparse_values, sparse_shape).
sparse_indices: A `Tensor` of type `int64`.
sparse_values: A `Tensor` of type `dtype`.
sparse_shape: A `Tensor` of type `int64`.
"""
_ctx = _context._context or _context.context()
tld = _ctx._thread_local_data
if tld.is_eager:
try:
_result = pywrap_tfe.TFE_Py_FastPathExecute(
_ctx, "TakeManySparseFromTensorsMap", name, sparse_handles, "dtype",
dtype, "container", container, "shared_name", shared_name)
_result = _TakeManySparseFromTensorsMapOutput._make(_result)
return _result
except _core._NotOkStatusException as e:
_ops.raise_from_not_ok_status(e, name)
except _core._FallbackException:
pass
try:
return take_many_sparse_from_tensors_map_eager_fallback(
sparse_handles, dtype=dtype, container=container,
shared_name=shared_name, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
# Add nodes to the TensorFlow graph.
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, _outputs = _op_def_library._apply_op_helper(
"TakeManySparseFromTensorsMap", sparse_handles=sparse_handles,
dtype=dtype, container=container,
shared_name=shared_name, name=name)
_result = _outputs[:]
if _execute.must_record_gradient():
_attrs = ("dtype", _op._get_attr_type("dtype"), "container",
_op.get_attr("container"), "shared_name",
_op.get_attr("shared_name"))
_inputs_flat = _op.inputs
_execute.record_gradient(
"TakeManySparseFromTensorsMap", _inputs_flat, _attrs, _result)
_result = _TakeManySparseFromTensorsMapOutput._make(_result)
return _result
TakeManySparseFromTensorsMap = tf_export("raw_ops.TakeManySparseFromTensorsMap")(_ops.to_raw_op(take_many_sparse_from_tensors_map))
def take_many_sparse_from_tensors_map_eager_fallback(sparse_handles, dtype, container, shared_name, name, ctx):
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")
sparse_handles = _ops.convert_to_tensor(sparse_handles, _dtypes.int64)
_inputs_flat = [sparse_handles]
_attrs = ("dtype", dtype, "container", container, "shared_name",
shared_name)
_result = _execute.execute(b"TakeManySparseFromTensorsMap", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
name=name)
if _execute.must_record_gradient():
_execute.record_gradient(
"TakeManySparseFromTensorsMap", _inputs_flat, _attrs, _result)
_result = _TakeManySparseFromTensorsMapOutput._make(_result)
return _result
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