2167 lines
80 KiB
Python
2167 lines
80 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# pylint: disable=g-short-docstring-punctuation
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"""Sparse Tensor Representation. See the @{$python/sparse_ops} guide."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import numbers
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import numpy as np
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import gen_sparse_ops
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from tensorflow.python.ops import math_ops
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# go/tf-wildcard-import
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# pylint: disable=wildcard-import
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from tensorflow.python.ops.gen_sparse_ops import *
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# pylint: enable=wildcard-import
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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def _convert_to_sparse_tensor(sp_input):
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"""Convert `sp_input` to `SparseTensor` and return it.
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Args:
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sp_input: `SparseTensor` or `SparseTensorValue`.
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Returns:
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`sp_input` converted to `SparseTensor`.
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Raises:
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ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
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"""
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if isinstance(sp_input, sparse_tensor.SparseTensorValue):
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return sparse_tensor.SparseTensor.from_value(sp_input)
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if not isinstance(sp_input, sparse_tensor.SparseTensor):
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raise TypeError("Input must be a SparseTensor.")
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return sp_input
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def _convert_to_sparse_tensors(sp_inputs):
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"""Convert `sp_inputs` to `SparseTensor` objects and return them.
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Args:
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sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
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objects.
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Returns:
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`sp_inputs` converted to `SparseTensor` objects.
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Raises:
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ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
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`SparseTensorValue`.
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"""
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if isinstance(sp_inputs, list):
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return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
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if isinstance(sp_inputs, tuple):
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return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
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raise TypeError("Inputs must be a list or tuple.")
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# pylint: disable=protected-access
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@tf_export("sparse_concat")
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@deprecation.deprecated_args(
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None, "concat_dim is deprecated, use axis instead", "concat_dim")
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def sparse_concat(axis,
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sp_inputs,
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name=None,
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expand_nonconcat_dim=False,
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concat_dim=None):
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"""Concatenates a list of `SparseTensor` along the specified dimension.
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Concatenation is with respect to the dense versions of each sparse input.
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It is assumed that each inputs is a `SparseTensor` whose elements are ordered
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along increasing dimension number.
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If expand_nonconcat_dim is False, all inputs' shapes must match, except for
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the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are
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allowed to vary among all inputs.
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The `indices`, `values`, and `shapes` lists must have the same length.
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If expand_nonconcat_dim is False, then the output shape is identical to the
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inputs', except along the concat dimension, where it is the sum of the inputs'
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sizes along that dimension.
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If expand_nonconcat_dim is True, then the output shape along the non-concat
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dimensions will be expand to be the largest among all inputs, and it is the
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sum of the inputs sizes along the concat dimension.
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The output elements will be resorted to preserve the sort order along
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increasing dimension number.
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This op runs in `O(M log M)` time, where `M` is the total number of non-empty
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values across all inputs. This is due to the need for an internal sort in
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order to concatenate efficiently across an arbitrary dimension.
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For example, if `axis = 1` and the inputs are
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sp_inputs[0]: shape = [2, 3]
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[0, 2]: "a"
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[1, 0]: "b"
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[1, 1]: "c"
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sp_inputs[1]: shape = [2, 4]
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[0, 1]: "d"
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[0, 2]: "e"
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then the output will be
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shape = [2, 7]
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[0, 2]: "a"
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[0, 4]: "d"
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[0, 5]: "e"
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[1, 0]: "b"
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[1, 1]: "c"
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Graphically this is equivalent to doing
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[ a] concat [ d e ] = [ a d e ]
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[b c ] [ ] [b c ]
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Another example, if 'axis = 1' and the inputs are
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sp_inputs[0]: shape = [3, 3]
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[0, 2]: "a"
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[1, 0]: "b"
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[2, 1]: "c"
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sp_inputs[1]: shape = [2, 4]
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[0, 1]: "d"
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[0, 2]: "e"
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if expand_nonconcat_dim = False, this will result in an error. But if
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expand_nonconcat_dim = True, this will result in:
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shape = [3, 7]
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[0, 2]: "a"
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[0, 4]: "d"
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[0, 5]: "e"
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[1, 0]: "b"
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[2, 1]: "c"
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Graphically this is equivalent to doing
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[ a] concat [ d e ] = [ a d e ]
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[b ] [ ] [b ]
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[ c ] [ c ]
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Args:
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axis: Dimension to concatenate along. Must be in range [-rank, rank),
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where rank is the number of dimensions in each input `SparseTensor`.
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sp_inputs: List of `SparseTensor` to concatenate.
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name: A name prefix for the returned tensors (optional).
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expand_nonconcat_dim: Whether to allow the expansion in the non-concat
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dimensions. Defaulted to False.
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concat_dim: The old (deprecated) name for axis.
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Returns:
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A `SparseTensor` with the concatenated output.
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Raises:
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TypeError: If `sp_inputs` is not a list of `SparseTensor`.
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"""
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axis = deprecation.deprecated_argument_lookup("axis", axis, "concat_dim",
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concat_dim)
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sp_inputs = _convert_to_sparse_tensors(sp_inputs)
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if len(sp_inputs) == 1: # Degenerate case of one tensor.
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return sp_inputs[0]
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inds = [sp_input.indices for sp_input in sp_inputs]
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vals = [sp_input.values for sp_input in sp_inputs]
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shapes = [sp_input.dense_shape for sp_input in sp_inputs]
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if expand_nonconcat_dim:
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max_shape = math_ops.reduce_max(
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array_ops.concat(
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[array_ops.reshape(shape, [1, -1]) for shape in shapes], 0), 0)
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shapes = [
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array_ops.concat([
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max_shape[:axis], shape[-1:]
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if axis == -1 else shape[axis:axis + 1], []
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if axis == -1 else max_shape[axis + 1:]
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], 0) for shape in shapes
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]
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output_ind, output_val, output_shape = (
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gen_sparse_ops.sparse_concat(inds, vals, shapes, axis, name=name))
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return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
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@tf_export("sparse_add")
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def sparse_add(a, b, thresh=0):
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"""Adds two tensors, at least one of each is a `SparseTensor`.
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If one `SparseTensor` and one `Tensor` are passed in, returns a `Tensor`. If
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both arguments are `SparseTensor`s, this returns a `SparseTensor`. The order
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of arguments does not matter. Use vanilla `tf.add()` for adding two dense
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`Tensor`s.
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The shapes of the two operands must match: broadcasting is not supported.
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The indices of any input `SparseTensor` are assumed ordered in standard
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lexicographic order. If this is not the case, before this step run
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`SparseReorder` to restore index ordering.
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If both arguments are sparse, we perform "clipping" as follows. By default,
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if two values sum to zero at some index, the output `SparseTensor` would still
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include that particular location in its index, storing a zero in the
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corresponding value slot. To override this, callers can specify `thresh`,
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indicating that if the sum has a magnitude strictly smaller than `thresh`, its
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corresponding value and index would then not be included. In particular,
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`thresh == 0.0` (default) means everything is kept and actual thresholding
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happens only for a positive value.
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For example, suppose the logical sum of two sparse operands is (densified):
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[ 2]
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[.1 0]
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[ 6 -.2]
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Then,
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* `thresh == 0` (the default): all 5 index/value pairs will be returned.
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* `thresh == 0.11`: only .1 and 0 will vanish, and the remaining three
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index/value pairs will be returned.
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* `thresh == 0.21`: .1, 0, and -.2 will vanish.
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Args:
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a: The first operand; `SparseTensor` or `Tensor`.
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b: The second operand; `SparseTensor` or `Tensor`. At least one operand
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must be sparse.
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thresh: A 0-D `Tensor`. The magnitude threshold that determines if an
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output value/index pair takes space. Its dtype should match that of the
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values if they are real; if the latter are complex64/complex128, then the
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dtype should be float32/float64, correspondingly.
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Returns:
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A `SparseTensor` or a `Tensor`, representing the sum.
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Raises:
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TypeError: If both `a` and `b` are `Tensor`s. Use `tf.add()` instead.
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"""
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sparse_classes = (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)
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if not any(isinstance(inp, sparse_classes) for inp in [a, b]):
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raise TypeError("At least one input should be SparseTensor; do you mean to"
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" use tf.add()?")
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if all(isinstance(inp, sparse_classes) for inp in [a, b]):
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a = _convert_to_sparse_tensor(a)
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b = _convert_to_sparse_tensor(b)
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thresh = ops.convert_to_tensor(
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thresh, dtype=a.values.dtype.real_dtype.base_dtype, name="thresh")
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output_ind, output_val, output_shape = (
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gen_sparse_ops.sparse_add(a.indices, a.values, a.dense_shape,
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b.indices, b.values, b.dense_shape, thresh))
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# Attempt to get output_shape statically.
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a.get_shape().assert_is_compatible_with(b.get_shape())
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static_shape = array_ops.broadcast_static_shape(a.get_shape(),
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b.get_shape())
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if static_shape.is_fully_defined():
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output_shape = static_shape.as_list()
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return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
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else:
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# swap to make `a` the SparseTensor.
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if isinstance(b, sparse_classes):
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a, b = b, a
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return gen_sparse_ops.sparse_tensor_dense_add(a.indices, a.values,
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a.dense_shape, b)
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@tf_export("sparse.cross")
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def sparse_cross(inputs, name=None):
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"""Generates sparse cross from a list of sparse and dense tensors.
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For example, if the inputs are
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* inputs[0]: SparseTensor with shape = [2, 2]
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[0, 0]: "a"
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[1, 0]: "b"
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[1, 1]: "c"
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* inputs[1]: SparseTensor with shape = [2, 1]
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[0, 0]: "d"
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[1, 0]: "e"
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* inputs[2]: Tensor [["f"], ["g"]]
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then the output will be:
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shape = [2, 2]
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[0, 0]: "a_X_d_X_f"
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[1, 0]: "b_X_e_X_g"
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[1, 1]: "c_X_e_X_g"
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Args:
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inputs: An iterable of `Tensor` or `SparseTensor`.
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name: Optional name for the op.
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Returns:
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A `SparseTensor` of type `string`.
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"""
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return _sparse_cross_internal(inputs=inputs, hashed_output=False, name=name)
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_sparse_cross = sparse_cross
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@tf_export("sparse.cross_hashed")
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def sparse_cross_hashed(inputs, num_buckets=0, hash_key=None, name=None):
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"""Generates hashed sparse cross from a list of sparse and dense tensors.
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For example, if the inputs are
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* inputs[0]: SparseTensor with shape = [2, 2]
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[0, 0]: "a"
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[1, 0]: "b"
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[1, 1]: "c"
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* inputs[1]: SparseTensor with shape = [2, 1]
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[0, 0]: "d"
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[1, 0]: "e"
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* inputs[2]: Tensor [["f"], ["g"]]
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then the output will be:
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shape = [2, 2]
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[0, 0]: FingerprintCat64(
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Fingerprint64("f"), FingerprintCat64(
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Fingerprint64("d"), Fingerprint64("a")))
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[1, 0]: FingerprintCat64(
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Fingerprint64("g"), FingerprintCat64(
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Fingerprint64("e"), Fingerprint64("b")))
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[1, 1]: FingerprintCat64(
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Fingerprint64("g"), FingerprintCat64(
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Fingerprint64("e"), Fingerprint64("c")))
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Args:
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inputs: An iterable of `Tensor` or `SparseTensor`.
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num_buckets: An `int` that is `>= 0`.
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output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
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hash_key: Integer hash_key that will be used by the `FingerprintCat64`
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function. If not given, will use a default key.
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name: Optional name for the op.
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Returns:
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A `SparseTensor` of type `int64`.
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"""
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return _sparse_cross_internal(
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inputs=inputs,
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hashed_output=True,
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num_buckets=num_buckets,
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hash_key=hash_key,
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name=name)
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_sparse_cross_hashed = sparse_cross_hashed
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_DEFAULT_HASH_KEY = 0xDECAFCAFFE
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def _sparse_cross_internal(inputs,
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hashed_output=False,
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num_buckets=0,
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hash_key=None,
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name=None):
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"""See gen_sparse_ops.sparse_cross."""
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if not isinstance(inputs, list):
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raise TypeError("Inputs must be a list")
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if not all(
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isinstance(i, sparse_tensor.SparseTensor) or isinstance(i, ops.Tensor)
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for i in inputs):
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raise TypeError("All inputs must be SparseTensors")
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sparse_inputs = [
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i for i in inputs if isinstance(i, sparse_tensor.SparseTensor)
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]
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dense_inputs = [
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i for i in inputs if not isinstance(i, sparse_tensor.SparseTensor)
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]
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indices = [sp_input.indices for sp_input in sparse_inputs]
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values = [sp_input.values for sp_input in sparse_inputs]
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shapes = [sp_input.dense_shape for sp_input in sparse_inputs]
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out_type = dtypes.int64 if hashed_output else dtypes.string
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internal_type = dtypes.string
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for i in range(len(values)):
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if values[i].dtype != dtypes.string:
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values[i] = math_ops.to_int64(values[i])
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internal_type = dtypes.int64
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||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reorder")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reshape")
|
||
|
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`.
|
||
|
ValueError: If `shape` has more than one inferred (== -1) dimension.
|
||
|
"""
|
||
|
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()):
|
||
|
num_implied = sum((dim == -1) for dim in reshaped_shape_const)
|
||
|
if num_implied > 1:
|
||
|
raise ValueError("At most one dimension can be inferred (-1). Found: %s"
|
||
|
% reshaped_shape_const)
|
||
|
original_reshaped_shape = list(reshaped_shape_const) # Copy.
|
||
|
in_shape_size = np.prod(sp_input.get_shape().as_list())
|
||
|
if num_implied:
|
||
|
implied_idx = original_reshaped_shape.index(-1)
|
||
|
non_implied_idx = (
|
||
|
original_reshaped_shape[:implied_idx] +
|
||
|
original_reshaped_shape[implied_idx + 1:])
|
||
|
reshaped_shape_const[implied_idx] = (
|
||
|
in_shape_size // np.prod(non_implied_idx))
|
||
|
reshaped_size = np.prod(reshaped_shape_const)
|
||
|
if reshaped_size != in_shape_size:
|
||
|
raise ValueError("Cannot reshape a tensor with %d elements to shape %s "
|
||
|
"(%d elements)." %
|
||
|
(in_shape_size, original_reshaped_shape,
|
||
|
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()"
|
||
|
|
||
|
|
||
|
@tf_export("sparse_split")
|
||
|
@deprecation.deprecated_args(
|
||
|
None, "split_dim is deprecated, use axis instead", "split_dim")
|
||
|
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
|
||
|
|
||
|
|
||
|
@tf_export("sparse_slice")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_to_dense")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reduce_max")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reduce_max_sparse")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reduce_sum")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reduce_sum_sparse")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_tensor_to_dense")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_to_indicator")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_merge")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_retain")
|
||
|
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))
|
||
|
|
||
|
|
||
|
@tf_export("sparse_reset_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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_fill_empty_rows")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("serialize_sparse")
|
||
|
def serialize_sparse(sp_input, name=None, out_type=dtypes.string):
|
||
|
"""Serialize a `SparseTensor` into a 3-vector (1-D `Tensor`) object.
|
||
|
|
||
|
Args:
|
||
|
sp_input: The input `SparseTensor`.
|
||
|
name: A name prefix for the returned tensors (optional).
|
||
|
out_type: The `dtype` to use for serialization.
|
||
|
|
||
|
Returns:
|
||
|
A 3-vector (1-D `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,
|
||
|
out_type=out_type)
|
||
|
|
||
|
|
||
|
@tf_export("serialize_many_sparse")
|
||
|
def serialize_many_sparse(sp_input, name=None, out_type=dtypes.string):
|
||
|
"""Serialize `N`-minibatch `SparseTensor` into an `[N, 3]` `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).
|
||
|
out_type: The `dtype` to use for serialization.
|
||
|
|
||
|
Returns:
|
||
|
A 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,
|
||
|
out_type=out_type)
|
||
|
|
||
|
|
||
|
def deserialize_sparse(serialized_sparse, dtype, rank=None, name=None):
|
||
|
"""Deserialize `SparseTensor` objects.
|
||
|
|
||
|
The input `serialized_sparse` must have the shape `[?, ?, ..., ?, 3]` where
|
||
|
the last dimension stores serialized `SparseTensor` objects and the other N
|
||
|
dimensions (N >= 0) correspond to a batch. The ranks of the original
|
||
|
`SparseTensor` objects must all match. When the final `SparseTensor` is
|
||
|
created, its rank is the rank of the incoming `SparseTensor` objects plus N;
|
||
|
the sparse tensors have been concatenated along new dimensions, one for each
|
||
|
batch.
|
||
|
|
||
|
The output `SparseTensor` object's shape values for the original dimensions
|
||
|
are the max across the input `SparseTensor` objects' shape values for the
|
||
|
corresponding dimensions. The new dimensions match the size of the batch.
|
||
|
|
||
|
The input `SparseTensor` objects' indices are assumed ordered in
|
||
|
standard lexicographic order. If this is not the case, after this
|
||
|
step run `SparseReorder` to restore index ordering.
|
||
|
|
||
|
For example, if the serialized input is a `[2 x 3]` matrix representing two
|
||
|
original `SparseTensor` objects:
|
||
|
|
||
|
index = [ 0]
|
||
|
[10]
|
||
|
[20]
|
||
|
values = [1, 2, 3]
|
||
|
shape = [50]
|
||
|
|
||
|
and
|
||
|
|
||
|
index = [ 2]
|
||
|
[10]
|
||
|
values = [4, 5]
|
||
|
shape = [30]
|
||
|
|
||
|
then the final deserialized `SparseTensor` will be:
|
||
|
|
||
|
index = [0 0]
|
||
|
[0 10]
|
||
|
[0 20]
|
||
|
[1 2]
|
||
|
[1 10]
|
||
|
values = [1, 2, 3, 4, 5]
|
||
|
shape = [2 50]
|
||
|
|
||
|
Args:
|
||
|
serialized_sparse: The serialized `SparseTensor` objects.
|
||
|
The last dimension must have 3 columns.
|
||
|
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` objects.
|
||
|
|
||
|
"""
|
||
|
output_indices, output_values, output_shape = (
|
||
|
gen_sparse_ops.deserialize_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)
|
||
|
|
||
|
|
||
|
@tf_export("deserialize_many_sparse")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_tensor_dense_matmul")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_softmax")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_maximum")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_minimum")
|
||
|
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)
|
||
|
|
||
|
|
||
|
@tf_export("sparse_transpose")
|
||
|
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))
|
||
|
|
||
|
perm_ = tensor_util.constant_value(ops.convert_to_tensor(perm))
|
||
|
if perm_ is not None and sp_input.get_shape().is_fully_defined():
|
||
|
old_shape_ = sp_input.get_shape().as_list()
|
||
|
transposed_dense_shape = list(old_shape_) # Copy.
|
||
|
for i, p in enumerate(perm_):
|
||
|
transposed_dense_shape[i] = old_shape_[p]
|
||
|
else:
|
||
|
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)
|