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TensorFlow稀疏张量表示
2018-01-19 10:48 更新
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＃==============================================================================
＃pylint: disable=g-short-docstring-punctuation
""稀疏张量表示.请参阅@ {$ python / sparse_ops}指南.""
@@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.base_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
tensor with False everywhere except at positions
(0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150),
(1, 2, 121).
Note that repeats are allowed in the input SparseTensor.
This op is useful for converting `SparseTensor`s into dense formats for
compatibility with ops that expect dense tensors.
The input `SparseTensor` must be in row-major order.
Args:
sp_input: A `SparseTensor` with `values` property of type `int32` or
`int64`.
vocab_size: A scalar int64 Tensor (or Python int) containing the new size
of the last dimension, `all(0 <= sp_input.values < vocab_size)`.
name: A name prefix for the returned tensors (optional)
Returns:
A dense bool indicator tensor representing the indices with specified value.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
with ops.name_scope(name, "SparseToIndicator", [sp_input]) as name:
num_entries = array_ops.shape(sp_input.indices)[0]
new_values = array_ops.fill(array_ops.expand_dims(num_entries, 0), True)
sp_values = sparse_tensor.SparseTensor(
sp_input.indices, new_values, sp_input.dense_shape)
sp_new = sparse_merge(sp_input, sp_values, vocab_size, name)
# validate_indices may be False because we allow duplicates in new_indices:
# repeated indices are allowed when creating an indicator matrix.
return sparse_tensor_to_dense(
sp_new, default_value=False, validate_indices=False, name=name)
def sparse_merge(sp_ids, sp_values, vocab_size, name=None,
already_sorted=False):
"""Combines a batch of feature ids and values into a single `SparseTensor`.
The most common use case for this function occurs when feature ids and
their corresponding values are stored in `Example` protos on disk.
`parse_example` will return a batch of ids and a batch of values, and this
function joins them into a single logical `SparseTensor` for use in
functions such as `sparse_tensor_dense_matmul`, `sparse_to_dense`, etc.
The `SparseTensor` returned by this function has the following properties:
- `indices` is equivalent to `sp_ids.indices` with the last
dimension discarded and replaced with `sp_ids.values`.
- `values` is simply `sp_values.values`.
- If `sp_ids.dense_shape = [D0, D1, ..., Dn, K]`, then
`output.shape = [D0, D1, ..., Dn, vocab_size]`.
For example, consider the following feature vectors:
```python
vector1 = [-3, 0, 0, 0, 0, 0]
vector2 = [ 0, 1, 0, 4, 1, 0]
vector3 = [ 5, 0, 0, 9, 0, 0]
```
These might be stored sparsely in the following Example protos by storing
only the feature ids (column number if the vectors are treated as a matrix)
of the non-zero elements and the corresponding values:
```python
examples = [Example(features={
"ids": Feature(int64_list=Int64List(value=[0])),
"values": Feature(float_list=FloatList(value=[-3]))}),
Example(features={
"ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
"values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
Example(features={
"ids": Feature(int64_list=Int64List(value=[0, 3])),
"values": Feature(float_list=FloatList(value=[5, 9]))})]
```
The result of calling parse_example on these examples will produce a
dictionary with entries for "ids" and "values". Passing those two objects
to this function along with vocab_size=6, will produce a `SparseTensor` that
sparsely represents all three instances. Namely, the `indices` property will
contain the coordinates of the non-zero entries in the feature matrix (the
first dimension is the row number in the matrix, i.e., the index within the
batch, and the second dimension is the column number, i.e., the feature id);
`values` will contain the actual values. `shape` will be the shape of the
original matrix, i.e., (3, 6). For our example above, the output will be
equal to:
```python
SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
values=[-3, 1, 4, 1, 5, 9],
dense_shape=[3, 6])
```
This method generalizes to higher-dimensions by simply providing a list for
both the sp_ids as well as the vocab_size.
In this case the resulting `SparseTensor` has the following properties:
- `indices` is equivalent to `sp_ids[0].indices` with the last
dimension discarded and concatenated with
`sp_ids[0].values, sp_ids[1].values, ...`.
- `values` is simply `sp_values.values`.
- If `sp_ids.dense_shape = [D0, D1, ..., Dn, K]`, then
`output.shape = [D0, D1, ..., Dn] + vocab_size`.
Args:
sp_ids: A single `SparseTensor` with `values` property of type `int32`
or `int64` or a Python list of such `SparseTensor`s or a list thereof.
sp_values: A `SparseTensor` of any type.
vocab_size: A scalar `int64` Tensor (or Python int) containing the new size
of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
Or a list thereof with `all(0 <= sp_ids[i].values < vocab_size[i])` for
all `i`.
name: A name prefix for the returned tensors (optional)
already_sorted: A boolean to specify whether the per-batch values in
`sp_values` are already sorted. If so skip sorting, False by default
(optional).
Returns:
A `SparseTensor` compactly representing a batch of feature ids and values,
useful for passing to functions that expect such a `SparseTensor`.
Raises:
TypeError: If `sp_values` is not a `SparseTensor`. Or if `sp_ids` is neither
a `SparseTensor` nor a list thereof. Or if `vocab_size` is not a
`Tensor` or a Python int and `sp_ids` is a `SparseTensor`. Or if
`vocab_size` is not a or list thereof and `sp_ids` is a list.
ValueError: If `sp_ids` and `vocab_size` are lists of different lengths.
"""
if isinstance(sp_ids, sparse_tensor.SparseTensorValue) or isinstance(
sp_ids, sparse_tensor.SparseTensor):
sp_ids = [sp_ids]
if not (isinstance(vocab_size, ops.Tensor) or
isinstance(vocab_size, numbers.Integral)):
raise TypeError("vocab_size has to be a Tensor or Python int. Found %s" %
type(vocab_size))
vocab_size = [vocab_size]
else:
if not isinstance(sp_ids, collections.Iterable):
raise TypeError("sp_ids has to be a SparseTensor or list thereof. "
"Found %s" % type(sp_ids))
if not isinstance(vocab_size, collections.Iterable):
raise TypeError("vocab_size has to be a list of Tensors or Python ints. "
"Found %s" % type(vocab_size))
for dim in vocab_size:
if not (isinstance(dim, ops.Tensor) or
isinstance(dim, numbers.Integral)):
raise TypeError(
"vocab_size has to be a list of Tensors or Python ints. Found %s" %
type(dim))
if len(sp_ids) != len(vocab_size):
raise ValueError("sp_ids and vocab_size have to have equal lengths.")
with ops.name_scope(name, "SparseMerge", [sp_ids, sp_values]):
sp_ids = [_convert_to_sparse_tensor(sp_ids_dim) for sp_ids_dim in sp_ids]
sp_values = _convert_to_sparse_tensor(sp_values)
ids = []
for sp_ids_dim in sp_ids:
ids_dim = sp_ids_dim.values
if sp_ids_dim.dtype != dtypes.int64:
ids_dim = math_ops.cast(ids_dim, dtypes.int64)
ids += [array_ops.expand_dims(ids_dim, axis=1)]
vocab_size = [math_ops.cast(x, dtypes.int64) for x in vocab_size]
# Slice off the last dimension of indices, then tack on the ids
indices_columns_to_preserve = sp_ids[0].indices[:, :-1]
new_indices = array_ops.concat([indices_columns_to_preserve] + ids, 1)
new_values = sp_values.values
new_shape = array_ops.concat([sp_ids[0].dense_shape[:-1], vocab_size], 0)
result = sparse_tensor.SparseTensor(new_indices, new_values, new_shape)
return result if already_sorted else sparse_reorder(result)
def sparse_retain(sp_input, to_retain):
"""Retains specified non-empty values within a `SparseTensor`.
For example, if `sp_input` has shape `[4, 5]` and 4 non-empty string values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
and `to_retain = [True, False, False, True]`, then the output will
be a `SparseTensor` of shape `[4, 5]` with 2 non-empty values:
[0, 1]: a
[3, 1]: d
Args:
sp_input: The input `SparseTensor` with `N` non-empty elements.
to_retain: A bool vector of length `N` with `M` true values.
Returns:
A `SparseTensor` with the same shape as the input and `M` non-empty
elements corresponding to the true positions in `to_retain`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
to_retain = ops.convert_to_tensor(to_retain)
# Shape checking, if shape is known at graph construction time
retain_shape = to_retain.get_shape()
retain_shape.assert_has_rank(1)
sp_input.values.get_shape()[0].merge_with(retain_shape[0])
where_true = array_ops.reshape(array_ops.where(to_retain), [-1])
new_indices = array_ops.gather(sp_input.indices, where_true)
new_values = array_ops.gather(sp_input.values, where_true)
return sparse_tensor.SparseTensor(new_indices, new_values,
array_ops.identity(sp_input.dense_shape))
def sparse_reset_shape(sp_input, new_shape=None):
"""Resets the shape of a `SparseTensor` with indices and values unchanged.
If `new_shape` is None, returns a copy of `sp_input` with its shape reset
to the tight bounding box of `sp_input`. This will be a shape consisting of
all zeros if sp_input has no values.
If `new_shape` is provided, then it must be larger or equal in all dimensions
compared to the shape of `sp_input`. When this condition is met, the returned
SparseTensor will have its shape reset to `new_shape` and its indices and
values unchanged from that of `sp_input.`
For example:
Consider a `sp_input` with shape [2, 3, 5]:
[0, 0, 1]: a
[0, 1, 0]: b
[0, 2, 2]: c
[1, 0, 3]: d
- It is an error to set `new_shape` as [3, 7] since this represents a
rank-2 tensor while `sp_input` is rank-3. This is either a ValueError
during graph construction (if both shapes are known) or an OpError during
run time.
- Setting `new_shape` as [2, 3, 6] will be fine as this shape is larger or
equal in every dimension compared to the original shape [2, 3, 5].
- On the other hand, setting new_shape as [2, 3, 4] is also an error: The
third dimension is smaller than the original shape [2, 3, 5] (and an
`InvalidArgumentError` will be raised).
- If `new_shape` is None, the returned SparseTensor will have a shape
[2, 3, 4], which is the tight bounding box of `sp_input`.
Args:
sp_input: The input `SparseTensor`.
new_shape: None or a vector representing the new shape for the returned
`SparseTensor`.
Returns:
A `SparseTensor` indices and values unchanged from `input_sp`. Its shape is
`new_shape` if that is set. Otherwise it is the tight bounding box of
`input_sp`
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
ValueError: If `new_shape` represents a tensor with a different rank from
that of `sp_input` (if shapes are known when graph is constructed).
ValueError: If `new_shape` is determined during graph build to have
dimension sizes that are too small.
OpError:
- If `new_shape` has dimension sizes that are too small.
- If shapes are not known during graph construction time, and during run
time it is found out that the ranks do not match.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
in_indices = array_ops.identity(sp_input.indices)
in_values = array_ops.identity(sp_input.values)
in_shape = array_ops.identity(sp_input.dense_shape)
if new_shape is None:
dim_low_bound = math_ops.reduce_max(in_indices, axis=0)
output_shape_tensor = math_ops.maximum(
array_ops.constant(0, dtype=dtypes.int64),
math_ops.add(dim_low_bound, array_ops.ones_like(in_shape)))
else:
output_shape_tensor = ops.convert_to_tensor(new_shape)
output_shape_tensor.get_shape().assert_has_rank(1)
output_shape_tensor = math_ops.cast(output_shape_tensor, dtypes.int64)
# For cases when shape is known during graph construction, this catches the
# error before the sparse_tensor.SparseTensor catches it.
output_shape_tensor.get_shape()[0].merge_with(in_shape.get_shape()[0])
output_shape_tensor_const = tensor_util.constant_value(
output_shape_tensor)
# For cases where all shapes are known during graph construction
if (output_shape_tensor_const is not None
and sp_input.get_shape().is_fully_defined()):
in_shape_const = np.array(sp_input.get_shape().as_list())
if not np.all(in_shape_const <= output_shape_tensor_const):
raise ValueError(
"Requested new_shape should have dimension sizes >= sp_input.shape."
" Found new_shape (%s), sp_input.shape (%s)."
% (in_shape_const, output_shape_tensor_const))
output_shape_tensor = output_shape_tensor_const
else:
# For cases where shape is not known during graph construction.
output_shape_tensor = control_flow_ops.with_dependencies(
[check_ops.assert_equal(
array_ops.shape(in_shape), array_ops.shape(output_shape_tensor))],
output_shape_tensor)
output_shape_tensor = control_flow_ops.with_dependencies(
[check_ops.assert_less_equal(in_shape, output_shape_tensor)],
output_shape_tensor)
return sparse_tensor.SparseTensor(in_indices, in_values, output_shape_tensor)
def sparse_fill_empty_rows(sp_input, default_value, name=None):
"""Fills empty rows in the input 2-D `SparseTensor` with a default value.
This op adds entries with the specified `default_value` at index
`[row, 0]` for any row in the input that does not already have a 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
Note that the input may have empty columns at the end, with no effect on
this op.
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 such that
empty_row_indicator[i] = True iff row i was an empty row.
Args:
sp_input: A `SparseTensor` with shape `[N, M]`.
default_value: The value to fill for empty rows, with the same type as
`sp_input.`
name: A name prefix for the returned tensors (optional)
Returns:
sp_ordered_output: A `SparseTensor` with shape `[N, M]`, and with all empty
rows filled in with `default_value`.
empty_row_indicator: A bool vector of length `N` indicating whether each
input row was empty.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
with ops.name_scope(name, "SparseFillEmptyRows", [sp_input]):
default_value = ops.convert_to_tensor(
default_value, dtype=sp_input.values.dtype)
(output_indices, output_values, empty_row_indicator,
unused_reverse_index_map) = gen_sparse_ops._sparse_fill_empty_rows(
indices=sp_input.indices,
values=sp_input.values,
dense_shape=sp_input.dense_shape,
default_value=default_value)
return (sparse_tensor.SparseTensor(indices=output_indices,
values=output_values,
dense_shape=sp_input.dense_shape),
empty_row_indicator)
def serialize_sparse(sp_input, name=None):
"""Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object.
Args:
sp_input: The input `SparseTensor`.
name: A name prefix for the returned tensors (optional).
Returns:
A string 3-vector (1D `Tensor`), with each column representing the
serialized `SparseTensor`'s indices, values, and shape (respectively).
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
return gen_sparse_ops._serialize_sparse(
sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)
def serialize_many_sparse(sp_input, name=None):
"""Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`.
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 the output `Tensor` will have
rank `R-1`.
The minibatch size `N` is extracted from `sparse_shape[0]`.
Args:
sp_input: The input rank `R` `SparseTensor`.
name: A name prefix for the returned tensors (optional).
Returns:
A string matrix (2-D `Tensor`) with `N` rows and `3` columns.
Each column represents serialized `SparseTensor`'s indices, values, and
shape (respectively).
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
return gen_sparse_ops._serialize_many_sparse(
sp_input.indices, sp_input.values, sp_input.dense_shape, name=name)
def deserialize_many_sparse(serialized_sparse, dtype, rank=None, name=None):
"""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
`serialize_sparse`. 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 `sparse_reorder` to restore index ordering.
For example, if the serialized input 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 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: 2-D `Tensor` of type `string` of shape `[N, 3]`.
The serialized and packed `SparseTensor` objects.
dtype: The `dtype` of the serialized `SparseTensor` objects.
rank: (optional) Python int, the rank of the `SparseTensor` objects.
name: A name prefix for the returned tensors (optional)
Returns:
A `SparseTensor` representing the deserialized `SparseTensor`s,
concatenated along the `SparseTensor`s' first dimension.
All of the serialized `SparseTensor`s must have had the same rank and type.
"""
output_indices, output_values, output_shape = (
gen_sparse_ops._deserialize_many_sparse(
serialized_sparse, dtype, name=name))
# Feed rank data back in, if available
output_indices.set_shape([None, rank])
output_shape.set_shape([rank])
return sparse_tensor.SparseTensor(output_indices, output_values, output_shape)
def sparse_tensor_dense_matmul(sp_a,
b,
adjoint_a=False,
adjoint_b=False,
name=None):
# pylint: disable=line-too-long
"""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 `sparse_reorder` 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).
Using `tf.nn.embedding_lookup_sparse` for sparse multiplication:
It's not obvious but you can consider `embedding_lookup_sparse` as another
sparse and dense multiplication. In some situations, you may prefer to use
`embedding_lookup_sparse` even though you're not dealing with embeddings.
There are two questions to ask in the decision process: Do you need gradients
computed as sparse too? Is your sparse data represented as two
`SparseTensor`s: ids and values? There is more explanation about data format
below. If you answer any of these questions as yes, consider using
`tf.nn.embedding_lookup_sparse`.
Following explains differences between the expected SparseTensors:
For example if dense form of your sparse data has shape `[3, 5]` and values:
[[ a ]
[b c]
[ d ]]
`SparseTensor` format expected by `sparse_tensor_dense_matmul`:
`sp_a` (indices, values):
[0, 1]: a
[1, 0]: b
[1, 4]: c
[2, 2]: d
`SparseTensor` format expected by `embedding_lookup_sparse`:
`sp_ids` `sp_weights`
[0, 0]: 1 [0, 0]: a
[1, 0]: 0 [1, 0]: b
[1, 1]: 4 [1, 1]: c
[2, 0]: 2 [2, 0]: d
Deciding when to use `sparse_tensor_dense_matmul` vs.
`matmul`(a_is_sparse=True):
There are a number of questions to ask in the decision process, including:
* Will the SparseTensor `A` fit in memory if densified?
* Is the column count of the product large (>> 1)?
* Is the density of `A` larger than approximately 15%?
If the answer to several of these questions is yes, consider
converting the `SparseTensor` to a dense one and using `tf.matmul` with
`a_is_sparse=True`.
This operation tends to perform well when `A` is more sparse, if the column
size of the product is small (e.g. matrix-vector multiplication), if
`sp_a.dense_shape` takes on large values.
Below is a rough speed comparison between `sparse_tensor_dense_matmul`,
labeled 'sparse', and `matmul`(a_is_sparse=True), labeled 'dense'. For
purposes of the comparison, the time spent converting from a `SparseTensor` to
a dense `Tensor` is not included, so it is overly conservative with respect to
the time ratio.
Benchmark system:
CPU: Intel Ivybridge with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:12MB
GPU: NVidia Tesla k40c
Compiled with:
`-c opt --config=cuda --copt=-mavx`
```
tensorflow/python/sparse_tensor_dense_matmul_op_test --benchmarks
A sparse [m, k] with % nonzero values between 1% and 80%
B dense [k, n]
% nnz n gpu m k dt(dense) dt(sparse) dt(sparse)/dt(dense)
0.01 1 True 100 100 0.000221166 0.00010154 0.459112
0.01 1 True 100 1000 0.00033858 0.000109275 0.322745
0.01 1 True 1000 100 0.000310557 9.85661e-05 0.317385
0.01 1 True 1000 1000 0.0008721 0.000100875 0.115669
0.01 1 False 100 100 0.000208085 0.000107603 0.51711
0.01 1 False 100 1000 0.000327112 9.51118e-05 0.290762
0.01 1 False 1000 100 0.000308222 0.00010345 0.335635
0.01 1 False 1000 1000 0.000865721 0.000101397 0.117124
0.01 10 True 100 100 0.000218522 0.000105537 0.482958
0.01 10 True 100 1000 0.000340882 0.000111641 0.327506
0.01 10 True 1000 100 0.000315472 0.000117376 0.372064
0.01 10 True 1000 1000 0.000905493 0.000123263 0.136128
0.01 10 False 100 100 0.000221529 9.82571e-05 0.44354
0.01 10 False 100 1000 0.000330552 0.000112615 0.340687
0.01 10 False 1000 100 0.000341277 0.000114097 0.334324
0.01 10 False 1000 1000 0.000819944 0.000120982 0.147549
0.01 25 True 100 100 0.000207806 0.000105977 0.509981
0.01 25 True 100 1000 0.000322879 0.00012921 0.400181
0.01 25 True 1000 100 0.00038262 0.00014158 0.370035
0.01 25 True 1000 1000 0.000865438 0.000202083 0.233504
0.01 25 False 100 100 0.000209401 0.000104696 0.499979
0.01 25 False 100 1000 0.000321161 0.000130737 0.407076
0.01 25 False 1000 100 0.000377012 0.000136801 0.362856
0.01 25 False 1000 1000 0.000861125 0.00020272 0.235413
0.2 1 True 100 100 0.000206952 9.69219e-05 0.46833
0.2 1 True 100 1000 0.000348674 0.000147475 0.422959
0.2 1 True 1000 100 0.000336908 0.00010122 0.300439
0.2 1 True 1000 1000 0.001022 0.000203274 0.198898
0.2 1 False 100 100 0.000207532 9.5412e-05 0.459746
0.2 1 False 100 1000 0.000356127 0.000146824 0.41228
0.2 1 False 1000 100 0.000322664 0.000100918 0.312764
0.2 1 False 1000 1000 0.000998987 0.000203442 0.203648
0.2 10 True 100 100 0.000211692 0.000109903 0.519165
0.2 10 True 100 1000 0.000372819 0.000164321 0.440753
0.2 10 True 1000 100 0.000338651 0.000144806 0.427596
0.2 10 True 1000 1000 0.00108312 0.000758876 0.70064
0.2 10 False 100 100 0.000215727 0.000110502 0.512231
0.2 10 False 100 1000 0.000375419 0.0001613 0.429653
0.2 10 False 1000 100 0.000336999 0.000145628 0.432132
0.2 10 False 1000 1000 0.00110502 0.000762043 0.689618
0.2 25 True 100 100 0.000218705 0.000129913 0.594009
0.2 25 True 100 1000 0.000394794 0.00029428 0.745402
0.2 25 True 1000 100 0.000404483 0.0002693 0.665788
0.2 25 True 1000 1000 0.0012002 0.00194494 1.62052
0.2 25 False 100 100 0.000221494 0.0001306 0.589632
0.2 25 False 100 1000 0.000396436 0.000297204 0.74969
0.2 25 False 1000 100 0.000409346 0.000270068 0.659754
0.2 25 False 1000 1000 0.00121051 0.00193737 1.60046
0.5 1 True 100 100 0.000214981 9.82111e-05 0.456836
0.5 1 True 100 1000 0.000415328 0.000223073 0.537101
0.5 1 True 1000 100 0.000358324 0.00011269 0.314492
0.5 1 True 1000 1000 0.00137612 0.000437401 0.317851
0.5 1 False 100 100 0.000224196 0.000101423 0.452386
0.5 1 False 100 1000 0.000400987 0.000223286 0.556841
0.5 1 False 1000 100 0.000368825 0.00011224 0.304318
0.5 1 False 1000 1000 0.00136036 0.000429369 0.31563
0.5 10 True 100 100 0.000222125 0.000112308 0.505608
0.5 10 True 100 1000 0.000461088 0.00032357 0.701753
0.5 10 True 1000 100 0.000394624 0.000225497 0.571422
0.5 10 True 1000 1000 0.00158027 0.00190898 1.20801
0.5 10 False 100 100 0.000232083 0.000114978 0.495418
0.5 10 False 100 1000 0.000454574 0.000324632 0.714146
0.5 10 False 1000 100 0.000379097 0.000227768 0.600817
0.5 10 False 1000 1000 0.00160292 0.00190168 1.18638
0.5 25 True 100 100 0.00023429 0.000151703 0.647501
0.5 25 True 100 1000 0.000497462 0.000598873 1.20386
0.5 25 True 1000 100 0.000460778 0.000557038 1.20891
0.5 25 True 1000 1000 0.00170036 0.00467336 2.74845
0.5 25 False 100 100 0.000228981 0.000155334 0.678371
0.5 25 False 100 1000 0.000496139 0.000620789 1.25124
0.5 25 False 1000 100 0.00045473 0.000551528 1.21287
0.5 25 False 1000 1000 0.00171793 0.00467152 2.71927
0.8 1 True 100 100 0.000222037 0.000105301 0.47425
0.8 1 True 100 1000 0.000410804 0.000329327 0.801664
0.8 1 True 1000 100 0.000349735 0.000131225 0.375212
0.8 1 True 1000 1000 0.00139219 0.000677065 0.48633
0.8 1 False 100 100 0.000214079 0.000107486 0.502085
0.8 1 False 100 1000 0.000413746 0.000323244 0.781261
0.8 1 False 1000 100 0.000348983 0.000131983 0.378193
0.8 1 False 1000 1000 0.00136296 0.000685325 0.50282
0.8 10 True 100 100 0.000229159 0.00011825 0.516017
0.8 10 True 100 1000 0.000498845 0.000532618 1.0677
0.8 10 True 1000 100 0.000383126 0.00029935 0.781336
0.8 10 True 1000 1000 0.00162866 0.00307312 1.88689
0.8 10 False 100 100 0.000230783 0.000124958 0.541452
0.8 10 False 100 1000 0.000493393 0.000550654 1.11606
0.8 10 False 1000 100 0.000377167 0.000298581 0.791642
0.8 10 False 1000 1000 0.00165795 0.00305103 1.84024
0.8 25 True 100 100 0.000233496 0.000175241 0.75051
0.8 25 True 100 1000 0.00055654 0.00102658 1.84458
0.8 25 True 1000 100 0.000463814 0.000783267 1.68875
0.8 25 True 1000 1000 0.00186905 0.00755344 4.04132
0.8 25 False 100 100 0.000240243 0.000175047 0.728625
0.8 25 False 100 1000 0.000578102 0.00104499 1.80763
0.8 25 False 1000 100 0.000485113 0.000776849 1.60138
0.8 25 False 1000 1000 0.00211448 0.00752736 3.55992
```
Args:
sp_a: SparseTensor A, of rank 2.
b: A dense Matrix with the same dtype as sp_a.
adjoint_a: 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: 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 prefix for the returned tensors (optional)
Returns:
A dense matrix (pseudo-code in dense np.matrix notation):
`A = A.H if adjoint_a else A`
`B = B.H if adjoint_b else B`
`return A*B`
"""
# pylint: enable=line-too-long
sp_a = _convert_to_sparse_tensor(sp_a)
with ops.name_scope(name, "SparseTensorDenseMatMul",
[sp_a.indices, sp_a.values, b]) as name:
b = ops.convert_to_tensor(b, name="b")
return gen_sparse_ops._sparse_tensor_dense_mat_mul(
a_indices=sp_a.indices,
a_values=sp_a.values,
a_shape=sp_a.dense_shape,
b=b,
adjoint_a=adjoint_a,
adjoint_b=adjoint_b)
def sparse_softmax(sp_input, name=None):
"""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:
(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.
Example:
```python
# First batch:
# [? e.]
# [1. ? ]
# Second batch:
# [e ? ]
# [e e ]
shape = [2, 2, 2] # 3-D SparseTensor
values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]])
indices = np.vstack(np.where(values)).astype(np.int64).T
result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape))
# ...returning a 3-D SparseTensor, equivalent to:
# [? 1.] [1 ?]
# [1. ? ] and [.5 .5]
# where ? means implicitly zero.
```
Args:
sp_input: N-D `SparseTensor`, where `N >= 2`.
name: optional name of the operation.
Returns:
output: N-D `SparseTensor` representing the results.
"""
with ops.name_scope(name, "SparseSoftmax",
[sp_input.indices, sp_input.values]) as name:
out_vals = gen_sparse_ops.sparse_softmax(sp_input.indices, sp_input.values,
sp_input.dense_shape)
return sparse_tensor.SparseTensor(
sp_input.indices, out_vals, sp_input.dense_shape)
def sparse_maximum(sp_a, sp_b, name=None):
"""Returns the element-wise max of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse_maximum(sp_zero, sp_one).eval()
# "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]).
```
Args:
sp_a: a `SparseTensor` operand whose dtype is real, and indices
lexicographically ordered.
sp_b: the other `SparseTensor` operand with the same requirements (and the
same shape).
name: optional name of the operation.
Returns:
output: the output SparseTensor.
"""
with ops.name_scope(name, "SparseSparseMaximum", [sp_a.indices, sp_a.values,
sp_b.indices,
sp_b.values]) as name:
out_indices, out_values = gen_sparse_ops.sparse_sparse_maximum(
sp_a.indices,
sp_a.values,
sp_a.dense_shape,
sp_b.indices,
sp_b.values,
sp_b.dense_shape,
name=name)
return sparse_tensor.SparseTensor(out_indices, out_values, sp_a.dense_shape)
def sparse_minimum(sp_a, sp_b, name=None):
"""Returns the element-wise min of two SparseTensors.
Assumes the two SparseTensors have the same shape, i.e., no broadcasting.
Example:
```python
sp_zero = sparse_tensor.SparseTensor([[0]], [0], [7])
sp_one = sparse_tensor.SparseTensor([[1]], [1], [7])
res = tf.sparse_minimum(sp_zero, sp_one).eval()
# "res" should be equal to SparseTensor([[0], [1]], [0, 0], [7]).
```
Args:
sp_a: a `SparseTensor` operand whose dtype is real, and indices
lexicographically ordered.
sp_b: the other `SparseTensor` operand with the same requirements (and the
same shape).
name: optional name of the operation.
Returns:
output: the output SparseTensor.
"""
with ops.name_scope(name, "SparseSparseMinimum", [sp_a.indices, sp_a.values,
sp_b.indices,
sp_b.values]) as name:
out_indices, out_values = gen_sparse_ops.sparse_sparse_minimum(
sp_a.indices,
sp_a.values,
sp_a.dense_shape,
sp_b.indices,
sp_b.values,
sp_b.dense_shape,
name=name)
return sparse_tensor.SparseTensor(out_indices, out_values, sp_a.dense_shape)
def sparse_transpose(sp_input, perm=None, name=None):
"""Transposes a `SparseTensor`
The returned tensor's dimension i will correspond to the input dimension
`perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
the rank of the input tensor. Hence by default, this operation performs a
regular matrix transpose on 2-D input Tensors.
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 `[5, 4]` and
`indices` / `values`:
[0, 2]: c
[1, 0]: a
[1, 3]: d
[3, 0]: b
Args:
sp_input: The input `SparseTensor`.
perm: A permutation of the dimensions of `sp_input`.
name: A name prefix for the returned tensors (optional)
Returns:
A transposed `SparseTensor`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
with ops.name_scope(name, "SparseTranspose", [sp_input]) as name:
if perm is None:
rank = array_ops.rank(sp_input)
perm = (rank - 1) - math_ops.range(0, rank, 1)
indices = sp_input.indices
transposed_indices = array_ops.transpose(
array_ops.gather(array_ops.transpose(indices), perm))
dense_shape = sp_input.dense_shape
transposed_dense_shape = array_ops.gather(dense_shape, perm)
transposed_st = sparse_tensor.SparseTensor(
transposed_indices, sp_input.values,
transposed_dense_shape)
transposed_st = sparse_reorder(transposed_st)
return transposed_st
def _add_sparse_to_tensors_map(sp_input, container=None,
shared_name=None, name=None):
"""Add a `SparseTensor` to a `SparseTensorsMap` and return its handle.
Args:
sp_input: The input `SparseTensor`.
container: The container for the underlying `SparseTensorsMap` (optional).
shared_name: The shared name for the underlying `SparseTensorsMap`
(optional, defaults to the name of the newly created op).
name: A name prefix for the returned tensors (optional).
Returns:
A string 1-vector (1D `Tensor`), with the single element representing the
a unique handle to a `SparseTensor` stored by the `SparseTensorMap`
underlying this op.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
return gen_sparse_ops._add_sparse_to_tensors_map(
sp_input.indices, sp_input.values, sp_input.dense_shape,
container=container, shared_name=shared_name, name=name)
def _add_many_sparse_to_tensors_map(sp_input, container=None,
shared_name=None, name=None):
"""Add a minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
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 the output `Tensor` will have
rank `R-1`.
The minibatch size `N` is extracted from `sparse_shape[0]`.
Args:
sp_input: The input rank `R` `SparseTensor`.
container: The container for the underlying `SparseTensorsMap` (optional).
shared_name: The shared name for the underlying `SparseTensorsMap`
(optional, defaults to the name of the newly created op).
name: A name prefix for the returned tensors (optional).
Returns:
A string matrix (2-D `Tensor`) with `N` rows and `1` column.
Each row represents a unique handle to a `SparseTensor` stored by
the `SparseTensorMap` underlying this op.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
sp_input = _convert_to_sparse_tensor(sp_input)
return gen_sparse_ops._add_many_sparse_to_tensors_map(
sp_input.indices, sp_input.values, sp_input.dense_shape,
container=container, shared_name=shared_name, name=name)
def _take_many_sparse_from_tensors_map(
sparse_map_op, sparse_handles, rank=None, name=None):
"""Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
The input `sparse_handles` must be a string matrix of shape `[N, 1]` where
`N` is the minibatch size and the rows correspond to packed outputs of
`add_sparse_to_tensors_map`. 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 `sparse_reorder` to restore index ordering.
For example, if the serialized input 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 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:
sparse_map_op: The `Operation` that created the original handles.
Usually this is, e.g., `add_sparse_to_tensors_map(...).op`.
sparse_handles: 2-D `Tensor` of type `string` of shape `[N, 1]`.
The serialized and packed `SparseTensor` objects.
rank: (optional) Python int, the rank of the `SparseTensor` objects.
name: A name prefix for the returned tensors (optional)
Returns:
A `SparseTensor` representing the deserialized `SparseTensor`s,
concatenated along the `SparseTensor`s' first dimension.
All of the serialized `SparseTensor`s must have had the same rank and type.
"""
if not isinstance(sparse_map_op, ops.Operation):
raise TypeError("sparse_map_op be an Operation")
if sparse_map_op.type not in ("AddSparseToTensorsMap",
"AddManySparseToTensorsMap"):
raise TypeError("sparse_map_op must be one of AddSparseToTensorsMap or "
"AddSparseToTensorsMap. Instead, found `%s`." %
sparse_map_op.type)
with ops.colocate_with(sparse_map_op):
shared_name = sparse_map_op.get_attr("shared_name") or sparse_map_op.name
output_indices, output_values, output_shape = (
gen_sparse_ops._take_many_sparse_from_tensors_map(
sparse_handles, dtype=sparse_map_op.get_attr("T"),
container=sparse_map_op.get_attr("container"),
shared_name=shared_name, name=name))
# Feed rank data back in, if available
output_indices.set_shape([None, rank])
output_shape.set_shape([rank])
return sparse_tensor.SparseTensor(output_indices, output_values, output_shape)以上内容是否对您有帮助：
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