348 lines
11 KiB
Python
348 lines
11 KiB
Python
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# Copyright 2016 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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"""Implementation of tf.sets."""
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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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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.ops import gen_set_ops
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from tensorflow.python.util.tf_export import tf_export
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_VALID_DTYPES = set([
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dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
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dtypes.uint8, dtypes.uint16, dtypes.string])
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@tf_export("sets.set_size")
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def set_size(a, validate_indices=True):
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"""Compute number of unique elements along last dimension of `a`.
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Args:
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a: `SparseTensor`, with indices sorted in row-major order.
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validate_indices: Whether to validate the order and range of sparse indices
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in `a`.
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Returns:
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`int32` `Tensor` of set sizes. For `a` ranked `n`, this is a `Tensor` with
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rank `n-1`, and the same 1st `n-1` dimensions as `a`. Each value is the
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number of unique elements in the corresponding `[0...n-1]` dimension of `a`.
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Raises:
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TypeError: If `a` is an invalid types.
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"""
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a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
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if not isinstance(a, sparse_tensor.SparseTensor):
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raise TypeError("Expected `SparseTensor`, got %s." % a)
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if a.values.dtype.base_dtype not in _VALID_DTYPES:
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raise TypeError("Invalid dtype %s." % a.values.dtype)
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# pylint: disable=protected-access
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return gen_set_ops.set_size(
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a.indices, a.values, a.dense_shape, validate_indices)
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ops.NotDifferentiable("SetSize")
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ops.NotDifferentiable("DenseToDenseSetOperation")
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ops.NotDifferentiable("DenseToSparseSetOperation")
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ops.NotDifferentiable("SparseToSparseSetOperation")
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def _convert_to_tensors_or_sparse_tensors(a, b):
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"""Convert to tensor types, and flip order if necessary.
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Args:
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a: `Tensor` or `SparseTensor` of the same type as `b`.
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b: `Tensor` or `SparseTensor` of the same type as `a`.
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Returns:
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Tuple of `(a, b, flipped)`, where `a` and `b` have been converted to
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`Tensor` or `SparseTensor`, and `flipped` indicates whether the order has
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been flipped to make it dense,sparse instead of sparse,dense (since the set
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ops do not support the latter).
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"""
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a = sparse_tensor.convert_to_tensor_or_sparse_tensor(a, name="a")
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if a.dtype.base_dtype not in _VALID_DTYPES:
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raise TypeError("'a' invalid dtype %s." % a.dtype)
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b = sparse_tensor.convert_to_tensor_or_sparse_tensor(b, name="b")
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if b.dtype.base_dtype != a.dtype.base_dtype:
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raise TypeError("Types don't match, %s vs %s." % (a.dtype, b.dtype))
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if (isinstance(a, sparse_tensor.SparseTensor) and
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not isinstance(b, sparse_tensor.SparseTensor)):
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return b, a, True
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return a, b, False
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def _set_operation(a, b, set_operation, validate_indices=True):
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"""Compute set operation of elements in last dimension of `a` and `b`.
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All but the last dimension of `a` and `b` must match.
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Args:
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a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
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must be sorted in row-major order.
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b: `Tensor` or `SparseTensor` of the same type as `a`. Must be
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`SparseTensor` if `a` is `SparseTensor`. If sparse, indices must be
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sorted in row-major order.
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set_operation: String indicating set operation. See
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SetOperationOp::SetOperationFromContext for valid values.
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validate_indices: Whether to validate the order and range of sparse indices
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in `a` and `b`.
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Returns:
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A `SparseTensor` with the same rank as `a` and `b`, and all but the last
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dimension the same. Elements along the last dimension contain the results
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of the set operation.
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Raises:
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TypeError: If inputs are invalid types.
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ValueError: If `a` is sparse and `b` is dense.
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"""
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if isinstance(a, sparse_tensor.SparseTensor):
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if isinstance(b, sparse_tensor.SparseTensor):
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indices, values, shape = gen_set_ops.sparse_to_sparse_set_operation(
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a.indices, a.values, a.dense_shape,
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b.indices, b.values, b.dense_shape,
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set_operation, validate_indices)
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else:
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raise ValueError("Sparse,Dense is not supported, but Dense,Sparse is. "
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"Please flip the order of your inputs.")
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elif isinstance(b, sparse_tensor.SparseTensor):
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indices, values, shape = gen_set_ops.dense_to_sparse_set_operation(
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a, b.indices, b.values, b.dense_shape, set_operation, validate_indices)
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else:
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indices, values, shape = gen_set_ops.dense_to_dense_set_operation(
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a, b, set_operation, validate_indices)
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return sparse_tensor.SparseTensor(indices, values, shape)
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@tf_export("sets.set_intersection")
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def set_intersection(a, b, validate_indices=True):
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"""Compute set intersection of elements in last dimension of `a` and `b`.
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All but the last dimension of `a` and `b` must match.
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Example:
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```python
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import tensorflow as tf
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import collections
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# Represent the following array of sets as a sparse tensor:
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# a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]])
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a = collections.OrderedDict([
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((0, 0, 0), 1),
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((0, 0, 1), 2),
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((0, 1, 0), 3),
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((1, 0, 0), 4),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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])
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a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])
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# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]])
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b = collections.OrderedDict([
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((0, 0, 0), 1),
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((1, 0, 0), 4),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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((1, 1, 2), 7),
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((1, 1, 3), 8),
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])
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b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
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# `tf.sets.set_intersection` is applied to each aligned pair of sets.
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tf.sets.set_intersection(a, b)
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# The result will be equivalent to either of:
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#
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# np.array([[{1}, {}], [{4}, {5, 6}]])
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#
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# collections.OrderedDict([
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# ((0, 0, 0), 1),
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# ((1, 0, 0), 4),
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# ((1, 1, 0), 5),
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# ((1, 1, 1), 6),
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# ])
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```
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Args:
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a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
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must be sorted in row-major order.
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b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices
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must be sorted in row-major order.
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validate_indices: Whether to validate the order and range of sparse indices
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in `a` and `b`.
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Returns:
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A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but
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the last dimension the same. Elements along the last dimension contain the
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intersections.
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"""
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a, b, _ = _convert_to_tensors_or_sparse_tensors(a, b)
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return _set_operation(a, b, "intersection", validate_indices)
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@tf_export("sets.set_difference")
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def set_difference(a, b, aminusb=True, validate_indices=True):
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"""Compute set difference of elements in last dimension of `a` and `b`.
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All but the last dimension of `a` and `b` must match.
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Example:
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```python
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import tensorflow as tf
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import collections
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# Represent the following array of sets as a sparse tensor:
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# a = np.array([[{1, 2}, {3}], [{4}, {5, 6}]])
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a = collections.OrderedDict([
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((0, 0, 0), 1),
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((0, 0, 1), 2),
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((0, 1, 0), 3),
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((1, 0, 0), 4),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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])
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a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
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# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]])
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b = collections.OrderedDict([
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((0, 0, 0), 1),
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((0, 0, 1), 3),
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((0, 1, 0), 2),
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((1, 0, 0), 4),
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((1, 0, 1), 5),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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((1, 1, 2), 7),
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((1, 1, 3), 8),
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])
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b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
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# `set_difference` is applied to each aligned pair of sets.
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tf.sets.set_difference(a, b)
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# The result will be equivalent to either of:
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#
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# np.array([[{2}, {3}], [{}, {}]])
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#
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# collections.OrderedDict([
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# ((0, 0, 0), 2),
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# ((0, 1, 0), 3),
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# ])
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```
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Args:
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a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
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must be sorted in row-major order.
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b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices
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must be sorted in row-major order.
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aminusb: Whether to subtract `b` from `a`, vs vice versa.
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validate_indices: Whether to validate the order and range of sparse indices
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in `a` and `b`.
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Returns:
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A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but
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the last dimension the same. Elements along the last dimension contain the
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differences.
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"""
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a, b, flipped = _convert_to_tensors_or_sparse_tensors(a, b)
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if flipped:
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aminusb = not aminusb
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return _set_operation(a, b, "a-b" if aminusb else "b-a", validate_indices)
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@tf_export("sets.set_union")
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def set_union(a, b, validate_indices=True):
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"""Compute set union of elements in last dimension of `a` and `b`.
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All but the last dimension of `a` and `b` must match.
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Example:
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```python
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import tensorflow as tf
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import collections
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# [[{1, 2}, {3}], [{4}, {5, 6}]]
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a = collections.OrderedDict([
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((0, 0, 0), 1),
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((0, 0, 1), 2),
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((0, 1, 0), 3),
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((1, 0, 0), 4),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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])
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a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
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# [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]
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b = collections.OrderedDict([
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((0, 0, 0), 1),
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((0, 0, 1), 3),
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((0, 1, 0), 2),
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((1, 0, 0), 4),
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((1, 0, 1), 5),
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((1, 1, 0), 5),
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((1, 1, 1), 6),
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((1, 1, 2), 7),
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((1, 1, 3), 8),
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])
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b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
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# `set_union` is applied to each aligned pair of sets.
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tf.sets.set_union(a, b)
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# The result will be a equivalent to either of:
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#
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# np.array([[{1, 2, 3}, {2, 3}], [{4, 5}, {5, 6, 7, 8}]])
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#
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# collections.OrderedDict([
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# ((0, 0, 0), 1),
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# ((0, 0, 1), 2),
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# ((0, 0, 2), 3),
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# ((0, 1, 0), 2),
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# ((0, 1, 1), 3),
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# ((1, 0, 0), 4),
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# ((1, 0, 1), 5),
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# ((1, 1, 0), 5),
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# ((1, 1, 1), 6),
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# ((1, 1, 2), 7),
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# ((1, 1, 3), 8),
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# ])
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```
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Args:
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a: `Tensor` or `SparseTensor` of the same type as `b`. If sparse, indices
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must be sorted in row-major order.
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b: `Tensor` or `SparseTensor` of the same type as `a`. If sparse, indices
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must be sorted in row-major order.
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validate_indices: Whether to validate the order and range of sparse indices
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in `a` and `b`.
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Returns:
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A `SparseTensor` whose shape is the same rank as `a` and `b`, and all but
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the last dimension the same. Elements along the last dimension contain the
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unions.
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"""
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a, b, _ = _convert_to_tensors_or_sparse_tensors(a, b)
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return _set_operation(a, b, "union", validate_indices)
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