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sparse_ops.py
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2034 lines (1637 loc) · 75.4 KB
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
"""Sparse Tensor Representation. See the @{$python/sparse_ops} guide.
@@SparseTensor
@@SparseTensorValue
@@sparse_to_dense
@@sparse_tensor_to_dense
@@sparse_to_indicator
@@sparse_merge
@@sparse_concat
@@sparse_reorder
@@sparse_reshape
@@sparse_slice
@@sparse_split
@@sparse_retain
@@sparse_reset_shape
@@sparse_fill_empty_rows
@@sparse_transpose
@@sparse_reduce_max
@@sparse_reduce_max_sparse
@@sparse_reduce_sum
@@sparse_reduce_sum_sparse
@@sparse_add
@@sparse_softmax
@@sparse_tensor_dense_matmul
@@sparse_maximum
@@sparse_minimum
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numbers
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_sparse_ops
from tensorflow.python.ops import math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_sparse_ops import *
# pylint: enable=wildcard-import
from tensorflow.python.util import deprecation
def _convert_to_sparse_tensor(sp_input):
"""Convert `sp_input` to `SparseTensor` and return it.
Args:
sp_input: `SparseTensor` or `SparseTensorValue`.
Returns:
`sp_input` converted to `SparseTensor`.
Raises:
ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
"""
if isinstance(sp_input, sparse_tensor.SparseTensorValue):
return sparse_tensor.SparseTensor.from_value(sp_input)
if not isinstance(sp_input, sparse_tensor.SparseTensor):
raise TypeError("Input must be a SparseTensor.")
return sp_input
def _convert_to_sparse_tensors(sp_inputs):
"""Convert `sp_inputs` to `SparseTensor` objects and return them.
Args:
sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
objects.
Returns:
`sp_inputs` converted to `SparseTensor` objects.
Raises:
ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
`SparseTensorValue`.
"""
if isinstance(sp_inputs, list):
return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
if isinstance(sp_inputs, tuple):
return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
raise TypeError("Inputs must be a list or tuple.")
# pylint: disable=protected-access
def sparse_concat(axis,
sp_inputs,
name=None,
expand_nonconcat_dim=False,
concat_dim=None):
"""Concatenates a list of `SparseTensor` along the specified dimension.
Concatenation is with respect to the dense versions of each sparse input.
It is assumed that each inputs is a `SparseTensor` whose elements are ordered
along increasing dimension number.
If expand_nonconcat_dim is False, all inputs' shapes must match, except for
the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are
allowed to vary among all inputs.
The `indices`, `values`, and `shapes` lists must have the same length.
If expand_nonconcat_dim is False, then 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.
If expand_nonconcat_dim is True, then the output shape along the non-concat
dimensions will be expand to be the largest among all inputs, and it is the
sum of the inputs sizes along the concat 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 `axis = 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 ]
Another example, if 'axis = 1' and the inputs are
sp_inputs[0]: shape = [3, 3]
[0, 2]: "a"
[1, 0]: "b"
[2, 1]: "c"
sp_inputs[1]: shape = [2, 4]
[0, 1]: "d"
[0, 2]: "e"
if expand_nonconcat_dim = False, this will result in an error. But if
expand_nonconcat_dim = True, this will result in:
shape = [3, 7]
[0, 2]: "a"
[0, 4]: "d"
[0, 5]: "e"
[1, 0]: "b"
[2, 1]: "c"
Graphically this is equivalent to doing
[ a] concat [ d e ] = [ a d e ]
[b ] [ ] [b ]
[ c ] [ c ]
Args:
axis: Dimension to concatenate along. Must be in range [-rank, rank),
where rank is the number of dimensions in each input `SparseTensor`.
sp_inputs: List of `SparseTensor` to concatenate.
name: A name prefix for the returned tensors (optional).
expand_nonconcat_dim: Whether to allow the expansion in the non-concat
dimensions. Defaulted to False.
concat_dim: The old (deprecated) name for axis.
Returns:
A `SparseTensor` with the concatenated output.
Raises:
TypeError: If `sp_inputs` is not a list of `SparseTensor`.
"""
axis = deprecation.deprecated_argument_lookup("axis", axis, "concat_dim",
concat_dim)
sp_inputs = _convert_to_sparse_tensors(sp_inputs)
if len(sp_inputs) == 1: # Degenerate case of one tensor.
return sp_inputs[0]
inds = [sp_input.indices for sp_input in sp_inputs]
vals = [sp_input.values for sp_input in sp_inputs]
shapes = [sp_input.dense_shape for sp_input in sp_inputs]
if expand_nonconcat_dim:
max_shape = math_ops.reduce_max(
array_ops.concat(
[array_ops.reshape(shape, [1, -1]) for shape in shapes], 0), 0)
shapes = [
array_ops.concat([
max_shape[:axis], shape[-1:] if axis == -1 else
shape[axis:axis + 1], [] if axis == -1 else max_shape[axis + 1:]
], 0) for shape in shapes
]
output_ind, output_val, output_shape = (gen_sparse_ops._sparse_concat(
inds, vals, shapes, axis, name=name))
return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
def sparse_add(a, b, thresh=0):
"""Adds two tensors, at least one of each is a `SparseTensor`.
If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`. If
both arguments are `SparseTensor`s, this returns a `SparseTensor`. The order
of arguments does not matter. Use vanilla `tf.add()` for adding two dense
`Tensor`s.
The shapes of the two operands must match: broadcasting is not supported.
The indices of any input `SparseTensor` are assumed ordered in standard
lexicographic order. If this is not the case, before this step run
`SparseReorder` to restore index ordering.
If both arguments are sparse, we perform "clipping" as follows. 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 == 0.0` (default) means everything is kept and actual thresholding
happens only for a positive value.
For example, suppose the logical sum of two sparse operands is (densified):
[ 2]
[.1 0]
[ 6 -.2]
Then,
* `thresh == 0` (the default): all 5 index/value pairs will be returned.
* `thresh == 0.11`: only .1 and 0 will vanish, and the remaining three
index/value pairs will be returned.
* `thresh == 0.21`: .1, 0, and -.2 will vanish.
Args:
a: The first operand; `SparseTensor` or `Tensor`.
b: The second operand; `SparseTensor` or `Tensor`. At least one operand
must be sparse.
thresh: A 0-D `Tensor`. The magnitude threshold that determines if an
output value/index pair takes space. Its dtype should match that of the
values if they are real; if the latter are complex64/complex128, then the
dtype should be float32/float64, correspondingly.
Returns:
A `SparseTensor` or a `Tensor`, representing the sum.
Raises:
TypeError: If both `a` and `b` are `Tensor`s. Use `tf.add()` instead.
"""
sparse_classes = (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)
if not any(isinstance(inp, sparse_classes) for inp in [a, b]):
raise TypeError("At least one input should be SparseTensor; do you mean to"
" use tf.add()?")
if all(isinstance(inp, sparse_classes) for inp in [a, b]):
a = _convert_to_sparse_tensor(a)
b = _convert_to_sparse_tensor(b)
thresh = ops.convert_to_tensor(
thresh, dtype=a.values.dtype.real_dtype, name="thresh")
output_ind, output_val, output_shape = (gen_sparse_ops._sparse_add(
a.indices, a.values, a.dense_shape,
b.indices, b.values, b.dense_shape,
thresh))
# Attempt to get output_shape statically.
a.get_shape().assert_is_compatible_with(b.get_shape())
static_shape = array_ops.broadcast_static_shape(
a.get_shape(), b.get_shape())
if static_shape.is_fully_defined():
output_shape = static_shape.as_list()
return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
else:
# swap to make `a` the SparseTensor.
if isinstance(b, sparse_classes):
a, b = b, a
return gen_sparse_ops._sparse_tensor_dense_add(
a.indices, a.values, a.dense_shape, b)
def _sparse_cross(inputs, name=None):
"""Generates sparse cross from a list of sparse and dense tensors.
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"
Args:
inputs: An iterable of `Tensor` or `SparseTensor`.
name: Optional name for the op.
Returns:
A `SparseTensor` of type `string`.
"""
return _sparse_cross_internal(inputs=inputs, hashed_output=False, name=name)
def _sparse_cross_hashed(inputs, num_buckets=0, hash_key=None, name=None):
"""Generates hashed sparse cross from a list of sparse and dense tensors.
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]: 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:
inputs: An iterable of `Tensor` or `SparseTensor`.
num_buckets: An `int` that is `>= 0`.
output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
hash_key: Integer hash_key that will be used by the `FingerprintCat64`
function. If not given, will use a default key.
name: Optional name for the op.
Returns:
A `SparseTensor` of type `int64`.
"""
return _sparse_cross_internal(
inputs=inputs,
hashed_output=True,
num_buckets=num_buckets,
hash_key=hash_key,
name=name)
_DEFAULT_HASH_KEY = 0xDECAFCAFFE
def _sparse_cross_internal(
inputs, hashed_output=False, num_buckets=0, hash_key=None, name=None):
"""See gen_sparse_ops._sparse_cross."""
if not isinstance(inputs, list):
raise TypeError("Inputs must be a list")
if not all(isinstance(i, sparse_tensor.SparseTensor) or
isinstance(i, ops.Tensor) for i in inputs):
raise TypeError("All inputs must be SparseTensors")
sparse_inputs = [i for i in inputs
if isinstance(i, sparse_tensor.SparseTensor)]
dense_inputs = [i for i in inputs
if not isinstance(i, sparse_tensor.SparseTensor)]
indices = [sp_input.indices for sp_input in sparse_inputs]
values = [sp_input.values for sp_input in sparse_inputs]
shapes = [sp_input.dense_shape for sp_input in sparse_inputs]
out_type = dtypes.int64 if hashed_output else dtypes.string
internal_type = dtypes.string
for i in range(len(values)):
if values[i].dtype != dtypes.string:
values[i] = math_ops.to_int64(values[i])
internal_type = dtypes.int64
for i in range(len(dense_inputs)):
if dense_inputs[i].dtype != dtypes.string:
dense_inputs[i] = math_ops.to_int64(dense_inputs[i])
internal_type = dtypes.int64
indices_out, values_out, shape_out = gen_sparse_ops._sparse_cross(
indices=indices,
values=values,
shapes=shapes,
dense_inputs=dense_inputs,
hashed_output=hashed_output,
num_buckets=num_buckets,
hash_key=hash_key or _DEFAULT_HASH_KEY,
out_type=out_type,
internal_type=internal_type,
name=name)
return sparse_tensor.SparseTensor(indices_out, values_out, shape_out)
def sparse_dense_cwise_add(sp_t, dense_t):
"""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 the 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_t: the SparseTensor operand.
dense_t: the dense Tensor operand; must have the same dtype and a
broadcast-compatible shape as `sp_t`.
Returns:
output: the SparseTensor output.
"""
result = gen_sparse_ops.sparse_dense_cwise_add(sp_t.indices, sp_t.values,
sp_t.dense_shape, dense_t)
return sparse_tensor.SparseTensor(sp_t.indices, result, sp_t.dense_shape)
def sparse_reorder(sp_input, name=None):
"""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 to add entries.
Reordering does not affect the shape of the `SparseTensor`.
For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
[0, 3]: b
[0, 1]: a
[3, 1]: d
[2, 0]: c
then the output will be a `SparseTensor` of shape `[4, 5]` and
`indices` / `values`:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Args:
sp_input: The input `SparseTensor`.
name: A name prefix for the returned tensors (optional)
Returns:
A `SparseTensor` with the same shape and non-empty values, but in
canonical ordering.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
reordered_ind, reordered_val = (gen_sparse_ops._sparse_reorder(
sp_input.indices, sp_input.values, sp_input.dense_shape, name=name))
if sp_input.get_shape().is_fully_defined():
dense_shape = sp_input.get_shape().as_list()
else:
dense_shape = array_ops.identity(sp_input.dense_shape)
return sparse_tensor.SparseTensor(reordered_ind, reordered_val, dense_shape)
def sparse_reshape(sp_input, shape, name=None):
"""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 indices of non-empty values in `sp_input` are recomputed based
on the new dense shape, and a new `SparseTensor` is returned containing the
new indices and new shape. The order of non-empty values in `sp_input` is
unchanged.
If one component of `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 `shape` can be -1. The number of dense elements
implied by `shape` must be the same as the number of dense elements
originally represented by `sp_input`.
For example, if `sp_input` has shape `[2, 3, 6]` and `indices` / `values`:
[0, 0, 0]: a
[0, 0, 1]: b
[0, 1, 0]: c
[1, 0, 0]: d
[1, 2, 3]: e
and `shape` is `[9, -1]`, then the output will be a `SparseTensor` of
shape `[9, 4]` and `indices` / `values`:
[0, 0]: a
[0, 1]: b
[1, 2]: c
[4, 2]: d
[8, 1]: e
Args:
sp_input: The input `SparseTensor`.
shape: A 1-D (vector) int64 `Tensor` specifying the new dense shape of the
represented `SparseTensor`.
name: A name prefix for the returned tensors (optional)
Returns:
A `SparseTensor` with the same non-empty values but with indices calculated
by the new dense shape.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
ValueError: If argument `shape` requests a `SparseTensor` with a different
number of elements than `sp_input`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
shape = math_ops.cast(shape, dtype=dtypes.int64)
with ops.name_scope(name, "SparseReshape", [sp_input]) as name:
reshaped_ind, reshaped_shape = gen_sparse_ops._sparse_reshape(
sp_input.indices, sp_input.dense_shape, shape, name=name)
reshaped_shape_const = tensor_util.constant_value(shape)
if (reshaped_shape_const is not None
and sp_input.get_shape().is_fully_defined()):
# Don't deal with inferred dimensions. That would add significant code.
if all(n >= 0 for n in reshaped_shape_const):
reshaped_size = np.prod(reshaped_shape_const)
in_shape_size = np.prod(sp_input.get_shape().as_list())
if reshaped_size != in_shape_size:
raise ValueError(
"Cannot reshape a tensor with %d elements to shape %s "
"(%d elements)."
% (in_shape_size, reshaped_shape_const, reshaped_size))
reshaped_shape = reshaped_shape_const
return sparse_tensor.SparseTensor(
reshaped_ind, array_ops.identity(sp_input.values),
reshaped_shape)
# TODO(aselle): Remove keyword required once for 1.0 final
class KeywordRequired(object):
def __repr__(self):
# This is needed to make documentation without fully qualified module paths
return "KeywordRequired()"
def sparse_split(keyword_required=KeywordRequired(),
sp_input=None, num_split=None, axis=None,
name=None, split_dim=None):
"""Split a `SparseTensor` into `num_split` tensors along `axis`.
If the `sp_input.dense_shape[axis]` is not an integer multiple of `num_split`
each slice starting from 0:`shape[axis] % num_split` gets extra one
dimension. For example, if `axis = 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] =
[ a ]
[b c ]
output_tensor[1] =
[ d e ]
[ ]
Args:
keyword_required: Python 2 standin for * (temporary for argument reorder)
sp_input: The `SparseTensor` to split.
num_split: A Python integer. The number of ways to split.
axis: A 0-D `int32` `Tensor`. The dimension along which to split.
name: A name for the operation (optional).
split_dim: Deprecated old name for axis.
Returns:
`num_split` `SparseTensor` objects resulting from splitting `value`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
ValueError: If the deprecated `split_dim` and `axis` are both non None.
"""
if not isinstance(keyword_required, KeywordRequired):
raise ValueError("Keyword arguments are required for this function.")
if sp_input is None:
raise ValueError("sp_input is required")
if num_split is None:
raise ValueError("num_split is required")
if axis is None:
raise ValueError("axis is required")
axis = deprecation.deprecated_argument_lookup("axis", axis, "split_dim",
split_dim)
sp_input = _convert_to_sparse_tensor(sp_input)
output_inds, output_vals, output_shapes = (gen_sparse_ops._sparse_split(
axis,
sp_input.indices,
sp_input.values,
sp_input.dense_shape,
num_split,
name=name))
sparse_tensors = []
for i in range(0, num_split):
sparse_tensors.append(
sparse_tensor.SparseTensor(
output_inds[i], output_vals[i], output_shapes[i]))
return sparse_tensors
def sparse_slice(sp_input, start, size, name=None):
"""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:
sp_input: The `SparseTensor` to split.
start: 1-D. tensor represents the start of the slice.
size: 1-D. tensor represents the size of the slice.
name: A name for the operation (optional).
Returns:
A `SparseTensor` objects resulting from splicing.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
start = ops.convert_to_tensor(start, dtypes.int64)
size = ops.convert_to_tensor(size, dtypes.int64)
with ops.name_scope(name, "SparseSlice", [sp_input]) as name:
output_indices, output_values, output_shape = gen_sparse_ops.sparse_slice(
sp_input.indices, sp_input.values, sp_input.dense_shape, start, size, name=name)
return sparse_tensor.SparseTensor(
output_indices,
output_values,
output_shape)
def sparse_to_dense(sparse_indices,
output_shape,
sparse_values,
default_value=0,
validate_indices=True,
name=None):
"""Converts a sparse representation into a dense tensor.
Builds an array `dense` with shape `output_shape` such that
```python
# 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 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
`sparse_indices[i]` contains the complete index where `sparse_values[i]`
will be placed.
output_shape: A 1-D `Tensor` of the same type as `sparse_indices`. Shape
of the dense output tensor.
sparse_values: A 0-D or 1-D `Tensor`. Values corresponding to each row of
`sparse_indices`, or a scalar value to be used for all sparse indices.
default_value: A 0-D `Tensor` of the same type as `sparse_values`. Value
to set for indices not specified in `sparse_indices`. Defaults to zero.
validate_indices: A boolean value. 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:
Dense `Tensor` of shape `output_shape`. Has the same type as
`sparse_values`.
"""
return gen_sparse_ops._sparse_to_dense(
sparse_indices,
output_shape,
sparse_values,
default_value=default_value,
validate_indices=validate_indices,
name=name)
def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
reduction_axes=None):
"""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,
similar to the indexing rules in Python.
For example:
```python
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
tf.sparse_reduce_max(x) ==> 3
tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
tf.sparse_reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis.
tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
tf.sparse_reduce_max(x, [0, 1]) ==> 3
```
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
axis: The dimensions to reduce; list or scalar. If `None` (the
default), reduces all dimensions.
keep_dims: If true, retain reduced dimensions with length 1.
reduction_axes: Deprecated name of axis.
Returns:
The reduced Tensor.
"""
return gen_sparse_ops.sparse_reduce_max(
sp_input.indices, sp_input.values,
sp_input.dense_shape,
math_ops._ReductionDims(sp_input, axis, reduction_axes),
keep_dims)
def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
reduction_axes=None):
"""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 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:
sp_input: The SparseTensor to reduce. Should have numeric type.
axis: The dimensions to reduce; list or scalar. If `None` (the
default), reduces all dimensions.
keep_dims: If true, retain reduced dimensions with length 1.
reduction_axes: Deprecated name of axis
Returns:
The reduced SparseTensor.
"""
output_ind, output_val, output_shape = (
gen_sparse_ops.sparse_reduce_max_sparse(
sp_input.indices, sp_input.values,
sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
reduction_axes),
keep_dims))
return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
reduction_axes=None):
"""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,
similar to the indexing rules in Python.
For example:
```python
# 'x' represents [[1, ?, 1]
# [?, 1, ?]]
# where ? is implicitly-zero.
tf.sparse_reduce_sum(x) ==> 3
tf.sparse_reduce_sum(x, 0) ==> [1, 1, 1]
tf.sparse_reduce_sum(x, 1) ==> [2, 1] # Can also use -1 as the axis.
tf.sparse_reduce_sum(x, 1, keep_dims=True) ==> [[2], [1]]
tf.sparse_reduce_sum(x, [0, 1]) ==> 3
```
Args:
sp_input: The SparseTensor to reduce. Should have numeric type.
axis: The dimensions to reduce; list or scalar. If `None` (the
default), reduces all dimensions.
keep_dims: If true, retain reduced dimensions with length 1.
reduction_axes: Deprecated name of axis.
Returns:
The reduced Tensor.
"""
return gen_sparse_ops.sparse_reduce_sum(
sp_input.indices, sp_input.values,
sp_input.dense_shape,
math_ops._ReductionDims(sp_input, axis, reduction_axes),
keep_dims)
def sparse_reduce_sum_sparse(sp_input, axis=None, keep_dims=False,
reduction_axes=None):
"""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:
sp_input: The SparseTensor to reduce. Should have numeric type.
axis: The dimensions to reduce; list or scalar. If `None` (the
default), reduces all dimensions.
keep_dims: If true, retain reduced dimensions with length 1.
reduction_axes: Deprecated name of axis
Returns:
The reduced SparseTensor.
"""
output_ind, output_val, output_shape = (
gen_sparse_ops.sparse_reduce_sum_sparse(
sp_input.indices, sp_input.values,
sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
reduction_axes),
keep_dims))
return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
def sparse_tensor_to_dense(sp_input,
default_value=0,
validate_indices=True,
name=None):
"""Converts a `SparseTensor` into a dense tensor.
This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
and `default_value` is `x`, then the output will be a dense `[3, 5]`
string tensor with values:
[[x a x b x]
[x x x x x]
[c x x x x]]
Indices must be without repeats. This is only
tested if validate_indices is True.
Args:
sp_input: The input `SparseTensor`.
default_value: Scalar value to set for indices not specified in
`sp_input`. Defaults to zero.
validate_indices: A boolean value. If `True`, indices are checked to make
sure they are sorted in lexicographic order and that there are no repeats.
name: A name prefix for the returned tensors (optional).
Returns:
A dense tensor with shape `sp_input.dense_shape` and values specified by
the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
`default_value`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
return sparse_to_dense(
sp_input.indices,
sp_input.dense_shape,
sp_input.values,
default_value=default_value,
validate_indices=validate_indices,
name=name)
def sparse_to_indicator(sp_input, vocab_size, name=None):
"""Converts a `SparseTensor` of ids into a dense bool indicator tensor.
The last dimension of `sp_input.indices` is discarded and replaced with
the values of `sp_input`. If `sp_input.dense_shape = [D0, D1, ..., Dn, K]`,
then `output.shape = [D0, D1, ..., Dn, vocab_size]`, where
output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True
and False elsewhere in `output`.
For example, if `sp_input.dense_shape = [2, 3, 4]` with non-empty values:
[0, 0, 0]: 0
[0, 1, 0]: 10
[1, 0, 3]: 103
[1, 1, 2]: 150
[1, 1, 3]: 149
[1, 1, 4]: 150
[1, 2, 1]: 121
and `vocab_size = 200`, then the output will be a `[2, 3, 200]` dense bool