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tf.SparseTensor to tf.sparse.SparseTensor

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Angus-Luo 2020-03-24 13:40:16 +08:00
parent f1efe7d568
commit 652a4b64be
29 changed files with 81 additions and 81 deletions
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2
tensorflow/core/api_def/base_api/api_def_SparseMatrixOrderingAMD.pbtxt
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@ -45,7 +45,7 @@ Usage example:
with tf.Session() as sess:
# Define (COO format) SparseTensor over Numpy array.
a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
# Convert SparseTensors to CSR SparseMatrix.
a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(

2
tensorflow/core/api_def/base_api/api_def_SparseMatrixSparseCholesky.pbtxt
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@ -58,7 +58,7 @@ Usage example:
with tf.Session() as sess:
# Define (COO format) SparseTensor over Numpy array.
a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
# Convert SparseTensors to CSR SparseMatrix.
a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(

4
tensorflow/core/api_def/base_api/api_def_SparseMatrixSparseMatMul.pbtxt
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@ -71,8 +71,8 @@ Usage example:
with tf.Session() as sess:
# Define (COO format) Sparse Tensors over Numpy arrays
a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
b_st = tf.SparseTensor(b_indices, b_values, b_dense_shape)
a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
b_st = tf.sparse.SparseTensor(b_indices, b_values, b_dense_shape)
# Convert SparseTensors to CSR SparseMatrix
a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(

2
tensorflow/examples/saved_model/integration_tests/export_simple_text_embedding.py
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@ -87,7 +87,7 @@ class TextEmbeddingModel(tf.train.Checkpoint):
return tf.nn.safe_embedding_lookup_sparse(
embedding_weights=self.embeddings,
sparse_ids=tf.SparseTensor(token_ids, token_values, token_dense_shape),
sparse_ids=tf.sparse.SparseTensor(token_ids, token_values, token_dense_shape),
sparse_weights=None,
combiner="sqrtn")

8
tensorflow/go/op/wrappers.go
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@ -20740,7 +20740,7 @@ func Print(scope *Scope, input tf.Output, data []tf.Output, optional ...PrintAtt
//
// with tf.Session() as sess:
// # Define (COO format) SparseTensor over Numpy array.
// a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
//
// # Convert SparseTensors to CSR SparseMatrix.
// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
@ -32089,8 +32089,8 @@ func SparseMatrixSparseMatMulAdjointB(value bool) SparseMatrixSparseMatMulAttr {
//
// with tf.Session() as sess:
// # Define (COO format) Sparse Tensors over Numpy arrays
// a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
// b_st = tf.SparseTensor(b_indices, b_values, b_dense_shape)
// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
// b_st = tf.sparse.SparseTensor(b_indices, b_values, b_dense_shape)
//
// # Convert SparseTensors to CSR SparseMatrix
// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
@ -37606,7 +37606,7 @@ func RecvTPUEmbeddingActivations(scope *Scope, num_outputs int64, config string)
//
// with tf.Session() as sess:
// # Define (COO format) SparseTensor over Numpy array.
// a_st = tf.SparseTensor(a_indices, a_values, a_dense_shape)
// a_st = tf.sparse.SparseTensor(a_indices, a_values, a_dense_shape)
//
// # Convert SparseTensors to CSR SparseMatrix.
// a_sm = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(

4
tensorflow/python/client/session.py
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@ -862,7 +862,7 @@ class BaseSession(SessionInterface):
* A `tf.Tensor`.
The corresponding fetched value will be a numpy ndarray containing the
value of that tensor.
* A `tf.SparseTensor`.
* A `tf.sparse.SparseTensor`.
The corresponding fetched value will be a
`tf.compat.v1.SparseTensorValue`
containing the value of that sparse tensor.
@ -907,7 +907,7 @@ class BaseSession(SessionInterface):
`tf.compat.v1.placeholder`, the shape of
the value will be checked for compatibility with the placeholder.
* If the key is a
`tf.SparseTensor`,
`tf.sparse.SparseTensor`,
the value should be a
`tf.compat.v1.SparseTensorValue`.
* If the key is a nested tuple of `Tensor`s or `SparseTensor`s, the value

10
tensorflow/python/data/experimental/ops/batching.py
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@ -88,15 +88,15 @@ def dense_to_ragged_batch(batch_size,
@tf_export("data.experimental.dense_to_sparse_batch")
def dense_to_sparse_batch(batch_size, row_shape):
"""A transformation that batches ragged elements into `tf.SparseTensor`s.
"""A transformation that batches ragged elements into `tf.sparse.SparseTensor`s.
Like `Dataset.padded_batch()`, this transformation combines multiple
consecutive elements of the dataset, which might have different
shapes, into a single element. The resulting element has three
components (`indices`, `values`, and `dense_shape`), which
comprise a `tf.SparseTensor` that represents the same data. The
comprise a `tf.sparse.SparseTensor` that represents the same data. The
`row_shape` represents the dense shape of each row in the
resulting `tf.SparseTensor`, to which the effective batch size is
resulting `tf.sparse.SparseTensor`, to which the effective batch size is
prepended. For example:
```python
@ -121,7 +121,7 @@ def dense_to_sparse_batch(batch_size, row_shape):
consecutive elements of this dataset to combine in a single batch.
row_shape: A `tf.TensorShape` or `tf.int64` vector tensor-like object
representing the equivalent dense shape of a row in the resulting
`tf.SparseTensor`. Each element of this dataset must have the same rank as
`tf.sparse.SparseTensor`. Each element of this dataset must have the same rank as
`row_shape`, and must have size less than or equal to `row_shape` in each
dimension.
@ -283,7 +283,7 @@ def unbatch():
class _DenseToSparseBatchDataset(dataset_ops.UnaryDataset):
"""A `Dataset` that batches ragged dense elements into `tf.SparseTensor`s."""
"""A `Dataset` that batches ragged dense elements into `tf.sparse.SparseTensor`s."""
def __init__(self, input_dataset, batch_size, row_shape):
"""See `Dataset.dense_to_sparse_batch()` for more details."""

2
tensorflow/python/data/experimental/ops/grouping.py
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@ -161,7 +161,7 @@ def bucket_by_sequence_length(element_length_func,
bucket), and caller must ensure that the source `Dataset` does not contain
any elements with length longer than `max(bucket_boundaries)`.
no_padding: `bool`, indicates whether to pad the batch features (features
need to be either of type `tf.SparseTensor` or of same shape).
need to be either of type `tf.sparse.SparseTensor` or of same shape).
drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
whether the last batch should be dropped in the case it has fewer than
`batch_size` elements; the default behavior is not to drop the smaller

2
tensorflow/python/data/kernel_tests/from_sparse_tensor_slices_test.py
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@ -37,7 +37,7 @@ class FromSparseTensorSlicesTest(test_base.DatasetTestBase,
@combinations.generate(
combinations.combine(tf_api_version=1, mode=["graph"]))
def testFromSparseTensorSlices(self):
"""Test a dataset based on slices of a `tf.SparseTensor`."""
"""Test a dataset based on slices of a `tf.sparse.SparseTensor`."""
st = array_ops.sparse_placeholder(dtypes.float64)
iterator = dataset_ops.make_initializable_iterator(
dataset_ops.Dataset.from_sparse_tensor_slices(st))

12
tensorflow/python/data/ops/dataset_ops.py
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@ -158,7 +158,7 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
Elements can be nested structures of tuples, named tuples, and dictionaries.
Element components can be of any type representable by `tf.TypeSpec`,
including `tf.Tensor`, `tf.data.Dataset`, `tf.SparseTensor`,
including `tf.Tensor`, `tf.data.Dataset`, `tf.sparse.SparseTensor`,
`tf.RaggedTensor`, and `tf.TensorArray`.
>>> a = 1 # Integer element
@ -1486,7 +1486,7 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
array([[ 10, 100], [ 11, 12]], dtype=int32))]
See also `tf.data.experimental.dense_to_sparse_batch`, which combines
elements that may have different shapes into a `tf.SparseTensor`.
elements that may have different shapes into a `tf.sparse.SparseTensor`.
Args:
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
@ -2296,10 +2296,10 @@ class DatasetV1(DatasetV2):
@staticmethod
@deprecation.deprecated(None, "Use `tf.data.Dataset.from_tensor_slices()`.")
def from_sparse_tensor_slices(sparse_tensor):
"""Splits each rank-N `tf.SparseTensor` in this dataset row-wise.
"""Splits each rank-N `tf.sparse.SparseTensor` in this dataset row-wise.
Args:
sparse_tensor: A `tf.SparseTensor`.
sparse_tensor: A `tf.sparse.SparseTensor`.
Returns:
Dataset: A `Dataset` of rank-(N-1) sparse tensors.
@ -2909,13 +2909,13 @@ class TensorSliceDataset(DatasetSource):
class SparseTensorSliceDataset(DatasetSource):
"""A `Dataset` that splits a rank-N `tf.SparseTensor` into its rows."""
"""A `Dataset` that splits a rank-N `tf.sparse.SparseTensor` into its rows."""
def __init__(self, sparse_tensor):
"""See `Dataset.from_sparse_tensor_slices()` for details."""
if not isinstance(sparse_tensor, sparse_tensor_lib.SparseTensor):
raise TypeError(
"`sparse_tensor` must be a `tf.SparseTensor` object. Was {}.".format(
"`sparse_tensor` must be a `tf.sparse.SparseTensor` object. Was {}.".format(
sparse_tensor))
self._sparse_tensor = sparse_tensor

4
tensorflow/python/data/ops/iterator_ops.py
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@ -448,7 +448,7 @@ class Iterator(trackable.Trackable):
def output_classes(self):
"""Returns the class of each component of an element of this iterator.
The expected values are `tf.Tensor` and `tf.SparseTensor`.
The expected values are `tf.Tensor` and `tf.sparse.SparseTensor`.
Returns:
A nested structure of Python `type` objects corresponding to each
@ -677,7 +677,7 @@ class OwnedIterator(trackable.Trackable, composite_tensor.CompositeTensor):
def output_classes(self):
"""Returns the class of each component of an element of this iterator.
The expected values are `tf.Tensor` and `tf.SparseTensor`.
The expected values are `tf.Tensor` and `tf.sparse.SparseTensor`.
Returns:
A nested structure of Python `type` objects corresponding to each

6
tensorflow/python/data/util/sparse.py
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@ -47,7 +47,7 @@ def as_dense_shapes(shapes, classes):
Returns:
a structure matching the nested structure of `shapes`, containing
`tensor_shape.unknown_shape()` at positions where `classes` contains
`tf.SparseTensor` and matching contents of `shapes` otherwise
`tf.sparse.SparseTensor` and matching contents of `shapes` otherwise
"""
ret = nest.pack_sequence_as(shapes, [
tensor_shape.unknown_shape() if c is sparse_tensor.SparseTensor else shape
@ -65,7 +65,7 @@ def as_dense_types(types, classes):
Returns:
a structure matching the nested structure of `types`, containing
`dtypes.variant` at positions where `classes` contains `tf.SparseTensor` and
`dtypes.variant` at positions where `classes` contains `tf.sparse.SparseTensor` and
matching contents of `types` otherwise
"""
ret = nest.pack_sequence_as(types, [
@ -106,7 +106,7 @@ def get_classes(tensors):
Returns:
a structure matching the nested structure of `tensors`, containing
`tf.SparseTensor` at positions where `tensors` contains a sparse tensor and
`tf.sparse.SparseTensor` at positions where `tensors` contains a sparse tensor and
`tf.Tensor` otherwise
"""
return nest.pack_sequence_as(tensors, [

2
tensorflow/python/feature_column/feature_column.py
View File

@ -1969,7 +1969,7 @@ class _CategoricalColumn(_FeatureColumn):
WARNING: Do not subclass this layer unless you know what you are doing:
the API is subject to future changes.
A categorical feature typically handled with a `tf.SparseTensor` of IDs.
A categorical feature typically handled with a `tf.sparse.SparseTensor` of IDs.
"""
IdWeightPair = collections.namedtuple( # pylint: disable=invalid-name

2
tensorflow/python/feature_column/feature_column_v2.py
View File

@ -2515,7 +2515,7 @@ def _create_dense_column_weighted_sum(column, transformation_cache,
class CategoricalColumn(FeatureColumn):
"""Represents a categorical feature.
A categorical feature typically handled with a `tf.SparseTensor` of IDs.
A categorical feature typically handled with a `tf.sparse.SparseTensor` of IDs.
"""
IdWeightPair = collections.namedtuple( # pylint: disable=invalid-name

2
tensorflow/python/framework/indexed_slices.py
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@ -84,7 +84,7 @@ class IndexedSlices(_TensorLike, composite_tensor.CompositeTensor):
(e.g. `tf.gather`).
Contrast this representation with
`tf.SparseTensor`,
`tf.sparse.SparseTensor`,
which uses multi-dimensional indices and scalar values.
"""

2
tensorflow/python/framework/ops.py
View File

@ -338,7 +338,7 @@ class Tensor(_TensorLike):
shape of a tensor at execution time.
There are specialized tensors; for these, see `tf.Variable`, `tf.constant`,
`tf.placeholder`, `tf.SparseTensor`, and `tf.RaggedTensor`.
`tf.placeholder`, `tf.sparse.SparseTensor`, and `tf.RaggedTensor`.
For more on Tensors, see the [guide](https://tensorflow.org/guide/tensor`).
"""

8
tensorflow/python/framework/sparse_tensor.py
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@ -298,14 +298,14 @@ _pywrap_utils.RegisterType("SparseTensorValue", SparseTensorValue)
@tf_export("SparseTensorSpec")
class SparseTensorSpec(type_spec.BatchableTypeSpec):
"""Type specification for a `tf.SparseTensor`."""
"""Type specification for a `tf.sparse.SparseTensor`."""
__slots__ = ["_shape", "_dtype"]
value_type = property(lambda self: SparseTensor)
def __init__(self, shape=None, dtype=dtypes.float32):
"""Constructs a type specification for a `tf.SparseTensor`.
"""Constructs a type specification for a `tf.sparse.SparseTensor`.
Args:
shape: The dense shape of the `SparseTensor`, or `None` to allow
@ -473,13 +473,13 @@ def convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None):
def is_sparse(x):
"""Check whether `x` is sparse.
Check whether an object is a `tf.SparseTensor` or
Check whether an object is a `tf.sparse.SparseTensor` or
`tf.compat.v1.SparseTensorValue`.
Args:
x: A python object to check.
Returns:
`True` iff `x` is a `tf.SparseTensor` or `tf.compat.v1.SparseTensorValue`.
`True` iff `x` is a `tf.sparse.SparseTensor` or `tf.compat.v1.SparseTensorValue`.
"""
return isinstance(x, (SparseTensor, SparseTensorValue))

2
tensorflow/python/keras/layers/local.py
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@ -782,7 +782,7 @@ def local_conv_sparse_matmul(inputs, kernel, kernel_idxs, kernel_shape,
output_shape):
"""Apply N-D convolution with un-shared weights using a single sparse matmul.
This method outputs `inputs . tf.SparseTensor(indices=kernel_idxs,
This method outputs `inputs . tf.sparse.SparseTensor(indices=kernel_idxs,
values=kernel, dense_shape=kernel_shape)`, with `.` standing for
matrix-multiply. It also reshapes `inputs` to 2-D and `output` to (N+2)-D.

6
tensorflow/python/ops/array_ops.py
View File

@ -3069,7 +3069,7 @@ def sparse_placeholder(dtype, shape=None, name=None):
print(sess.run(y, feed_dict={
x: (indices, values, shape)})) # Will succeed.
sp = tf.SparseTensor(indices=indices, values=values, dense_shape=shape)
sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=shape)
sp_value = sp.eval(session=sess)
print(sess.run(y, feed_dict={x: sp_value})) # Will succeed.
```
@ -3471,7 +3471,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
# 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
# (0,0) = ["a"]
# (1,0) = ["b"]
hypothesis = tf.SparseTensor(
hypothesis = tf.sparse.SparseTensor(
[[0, 0, 0],
[1, 0, 0]],
["a", "b"],
@ -3482,7 +3482,7 @@ def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
# (0,1) = ["a"]
# (1,0) = ["b", "c"]
# (1,1) = ["a"]
truth = tf.SparseTensor(
truth = tf.sparse.SparseTensor(
[[0, 1, 0],
[1, 0, 0],
[1, 0, 1],

2
tensorflow/python/ops/ctc_ops.py
View File

@ -1126,7 +1126,7 @@ def dense_labels_to_sparse(dense, length):
length: int tensor of shape [batch] The length of each sequence in dense.
Returns:
tf.SparseTensor with values only for the valid elements of sequences.
tf.sparse.SparseTensor with values only for the valid elements of sequences.
"""
flat_values = array_ops.reshape(dense, [-1])

14
tensorflow/python/ops/map_fn.py
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@ -106,7 +106,7 @@ def map_fn(fn,
* A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)
* A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.SparseTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)
* A (possibly nested) tuple, list, or dict containing the above types.
#### RaggedTensors
@ -159,11 +159,11 @@ def map_fn(fn,
#### SparseTensors
`map_fn` supports `tf.SparseTensor` inputs and outputs. In particular:
`map_fn` supports `tf.sparse.SparseTensor` inputs and outputs. In particular:
* If `elems` is a `SparseTensor`, then `fn` will be called with each row
of that sparse tensor. In particular, the value passed to `fn` will be a
`tf.SparseTensor` with one fewer dimension than `elems`.
`tf.sparse.SparseTensor` with one fewer dimension than `elems`.
* If the result of `map_fn` should be a `SparseTensor`, then use a
`tf.SparseTensorSpec` to specify `fn_output_signature`. The individual
@ -171,7 +171,7 @@ def map_fn(fn,
`SparseTensor` with one more dimension.
>>> # Example: SparseTensor input
>>> st = tf.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4])
>>> st = tf.sparse.SparseTensor([[0, 0], [2, 0], [2, 1]], [2, 3, 4], [4, 4])
>>> tf.map_fn(tf.sparse.reduce_sum, st, fn_output_signature=tf.int32)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([2, 0, 7, 0], dtype=int32)>
@ -191,9 +191,9 @@ def map_fn(fn,
*rows* of a `SparseTensor`. If you wish to map a function over the nonzero
values, then you should use:
* `tf.SparseTensor(st.indices, fn(st.values), st.dense_shape)`
* `tf.sparse.SparseTensor(st.indices, fn(st.values), st.dense_shape)`
(if the function is expressible as TensorFlow ops)
* `tf.SparseTensor(st.indices, tf.map_fn(fn, st.values), st.dense_shape)`
* `tf.sparse.SparseTensor(st.indices, tf.map_fn(fn, st.values), st.dense_shape)`
(otherwise).
#### `map_fn` vs. vectorized operations
@ -276,7 +276,7 @@ def map_fn(fn,
* A `tf.DType` or `tf.TensorSpec` (to describe a `tf.Tensor`)
* A `tf.RaggedTensorSpec` (to describe a `tf.RaggedTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.SparseTensor`)
* A `tf.SparseTensorSpec` (to describe a `tf.sparse.SparseTensor`)
* A (possibly nested) tuple, list, or dict containing the above types.
Returns:

12
tensorflow/python/ops/math_ops.py
View File

@ -1432,8 +1432,8 @@ def equal(x, y, name=None):
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([ True, True])>
Args:
x: A `tf.Tensor` or `tf.SparseTensor` or `tf.IndexedSlices`.
y: A `tf.Tensor` or `tf.SparseTensor` or `tf.IndexedSlices`.
x: A `tf.Tensor` or `tf.sparse.SparseTensor` or `tf.IndexedSlices`.
y: A `tf.Tensor` or `tf.sparse.SparseTensor` or `tf.IndexedSlices`.
name: A name for the operation (optional).
Returns:
@ -1468,8 +1468,8 @@ def not_equal(x, y, name=None):
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, False])>
Args:
x: A `tf.Tensor` or `tf.SparseTensor` or `tf.IndexedSlices`.
y: A `tf.Tensor` or `tf.SparseTensor` or `tf.IndexedSlices`.
x: A `tf.Tensor` or `tf.sparse.SparseTensor` or `tf.IndexedSlices`.
y: A `tf.Tensor` or `tf.sparse.SparseTensor` or `tf.IndexedSlices`.
name: A name for the operation (optional).
Returns:
@ -2907,12 +2907,12 @@ def matmul(a,
**does not support `tf.sparse.SparseTensor`**, it just makes optimizations
that assume most values in `a` are zero.
See `tf.sparse.sparse_dense_matmul`
for some support for `tf.SparseTensor` multiplication.
for some support for `tf.sparse.SparseTensor` multiplication.
b_is_sparse: If `True`, `b` is treated as a sparse matrix. Notice, this
**does not support `tf.sparse.SparseTensor`**, it just makes optimizations
that assume most values in `a` are zero.
See `tf.sparse.sparse_dense_matmul`
for some support for `tf.SparseTensor` multiplication.
for some support for `tf.sparse.SparseTensor` multiplication.
name: Name for the operation (optional).
Returns:

4
tensorflow/python/ops/parallel_for/pfor.py
View File

@ -1217,10 +1217,10 @@ class PFor(object):
the new dense shape will be (N, max_i(x_i), max_i(y_i), max_i(z_i)).
Args:
y: A tf.SparseTensor.
y: A tf.sparse.SparseTensor.
Returns:
A tf.SparseTensor that is the converted value corresponding to y.
A tf.sparse.SparseTensor that is the converted value corresponding to y.
"""
outputs = [
self._convert_helper(t) for t in (y.indices, y.values, y.dense_shape)

6
tensorflow/python/ops/ragged/ragged_tensor.py
View File

@ -1629,7 +1629,7 @@ class RaggedTensor(composite_tensor.CompositeTensor):
@classmethod
def from_sparse(cls, st_input, name=None, row_splits_dtype=dtypes.int64):
"""Converts a 2D `tf.SparseTensor` to a `RaggedTensor`.
"""Converts a 2D `tf.sparse.SparseTensor` to a `RaggedTensor`.
Each row of the `output` `RaggedTensor` will contain the explicit values
from the same row in `st_input`. `st_input` must be ragged-right. If not
@ -1637,7 +1637,7 @@ class RaggedTensor(composite_tensor.CompositeTensor):
Example:
>>> st = tf.SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0]],
>>> st = tf.sparse.SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0]],
... values=[1, 2, 3, 4, 5],
... dense_shape=[4, 3])
>>> tf.RaggedTensor.from_sparse(st).to_list()
@ -1690,7 +1690,7 @@ class RaggedTensor(composite_tensor.CompositeTensor):
st_input.values, segment_ids, num_segments, validate=False)
def to_sparse(self, name=None):
"""Converts this `RaggedTensor` into a `tf.SparseTensor`.
"""Converts this `RaggedTensor` into a `tf.sparse.SparseTensor`.
Example:

12
tensorflow/python/ops/sets_impl.py
View File

@ -156,7 +156,7 @@ def set_intersection(a, b, validate_indices=True):
((1, 1, 0), 5),
((1, 1, 1), 6),
])
a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])
a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2,2,2])
# b = np.array([[{1}, {}], [{4}, {5, 6, 7, 8}]])
b = collections.OrderedDict([
@ -167,7 +167,7 @@ def set_intersection(a, b, validate_indices=True):
((1, 1, 2), 7),
((1, 1, 3), 8),
])
b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
# `tf.sets.intersection` is applied to each aligned pair of sets.
tf.sets.intersection(a, b)
@ -224,7 +224,7 @@ def set_difference(a, b, aminusb=True, validate_indices=True):
((1, 1, 0), 5),
((1, 1, 1), 6),
])
a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
# np.array([[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]])
b = collections.OrderedDict([
@ -238,7 +238,7 @@ def set_difference(a, b, aminusb=True, validate_indices=True):
((1, 1, 2), 7),
((1, 1, 3), 8),
])
b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
# `set_difference` is applied to each aligned pair of sets.
tf.sets.difference(a, b)
@ -302,7 +302,7 @@ def set_union(a, b, validate_indices=True):
((1, 1, 0), 5),
((1, 1, 1), 6),
])
a = tf.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
a = tf.sparse.SparseTensor(list(a.keys()), list(a.values()), dense_shape=[2, 2, 2])
# [[{1, 3}, {2}], [{4, 5}, {5, 6, 7, 8}]]
b = collections.OrderedDict([
@ -316,7 +316,7 @@ def set_union(a, b, validate_indices=True):
((1, 1, 2), 7),
((1, 1, 3), 8),
])
b = tf.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
b = tf.sparse.SparseTensor(list(b.keys()), list(b.values()), dense_shape=[2, 2, 4])
# `set_union` is applied to each aligned pair of sets.
tf.sets.union(a, b)

4
tensorflow/python/ops/sparse_ops.py
View File

@ -16,7 +16,7 @@
# pylint: disable=g-short-docstring-punctuation
"""Sparse Tensor Representation.
See also `tf.SparseTensor`.
See also `tf.sparse.SparseTensor`.
"""
from __future__ import absolute_import
@ -2460,7 +2460,7 @@ def sparse_softmax(sp_input, name=None):
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))
result = tf.sparse.softmax(tf.sparse.SparseTensor(indices, values, shape))
# ...returning a 3-D SparseTensor, equivalent to:
# [? 1.] [1 ?]
# [1. ? ] and [.5 .5]

22
tensorflow/python/util/nest.py
View File

@ -310,7 +310,7 @@ def flatten(structure, expand_composites=False):
Args:
structure: an arbitrarily nested structure. Note, numpy arrays are
considered atoms and are not flattened.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
@ -364,7 +364,7 @@ def assert_same_structure(nest1, nest2, check_types=True,
considered the same if they are both list subtypes (which allows "list"
and "_ListWrapper" from trackable dependency tracking to compare
equal).
expand_composites: If true, then composite tensors such as `tf.SparseTensor`
expand_composites: If true, then composite tensors such as `tf.sparse.SparseTensor`
and `tf.RaggedTensor` are expanded into their component tensors.
Raises:
@ -537,7 +537,7 @@ def pack_sequence_as(structure, flat_sequence, expand_composites=False):
tuples, and dicts. Note: numpy arrays and strings are considered
scalars.
flat_sequence: flat sequence to pack.
expand_composites: If true, then composite tensors such as `tf.SparseTensor`
expand_composites: If true, then composite tensors such as `tf.sparse.SparseTensor`
and `tf.RaggedTensor` are expanded into their component tensors.
Returns:
@ -574,7 +574,7 @@ def map_structure(func, *structure, **kwargs):
Note that namedtuples with identical name and fields are always
considered to have the same shallow structure.
* `expand_composites`: If set to `True`, then composite tensors such
as `tf.SparseTensor` and `tf.RaggedTensor` are expanded into their
as `tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
component tensors. If `False` (the default), then composite tensors
are not expanded.
@ -762,7 +762,7 @@ def assert_shallow_structure(shallow_tree,
`input_tree` have to be the same. Note that even with check_types==True,
this function will consider two different namedtuple classes with the same
name and _fields attribute to be the same class.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Raises:
TypeError: If `shallow_tree` is a sequence but `input_tree` is not.
@ -911,7 +911,7 @@ def flatten_up_to(shallow_tree, input_tree, check_types=True,
Note, numpy arrays are considered scalars.
check_types: bool. If True, check that each node in shallow_tree has the
same type as the corresponding node in input_tree.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
@ -1015,7 +1015,7 @@ def flatten_with_tuple_paths_up_to(shallow_tree,
Note, numpy arrays are considered scalars.
check_types: bool. If True, check that each node in shallow_tree has the
same type as the corresponding node in input_tree.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
@ -1233,7 +1233,7 @@ def get_traverse_shallow_structure(traverse_fn, structure,
shallow structure of the same type, describing which parts of the
substructure to traverse.
structure: The structure to traverse.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
@ -1313,7 +1313,7 @@ def yield_flat_paths(nest, expand_composites=False):
Args:
nest: the value to produce a flattened paths list for.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Yields:
@ -1338,7 +1338,7 @@ def flatten_with_joined_string_paths(structure, separator="/",
structure: the nested structure to flatten.
separator: string to separate levels of hierarchy in the results, defaults
to '/'.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
@ -1362,7 +1362,7 @@ def flatten_with_tuple_paths(structure, expand_composites=False):
Args:
structure: the nested structure to flatten.
expand_composites: If true, then composite tensors such as tf.SparseTensor
expand_composites: If true, then composite tensors such as tf.sparse.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:

2
tensorflow/python/util/util.h
View File

@ -234,7 +234,7 @@ PyObject* AssertSameStructure(PyObject* o1, PyObject* o2, bool check_types,
// nest: an arbitrarily nested structure or a scalar object. Note, numpy
// arrays are considered scalars.
// expand_composites: If true, then composite tensors (such as
// `tf.SparseTensor` and `tf.RaggedTensor` are flattened into their
// `tf.sparse.SparseTensor` and `tf.RaggedTensor` are flattened into their
// component tensors.
//
// Returns:

2
tensorflow/python/util/util_wrapper.cc
View File

@ -244,7 +244,7 @@ PYBIND11_MODULE(_pywrap_utils, m) {
Args:
nest: an arbitrarily nested structure or a scalar object. Note, numpy
arrays are considered scalars.
expand_composites: If true, then composite tensors such as `tf.SparseTensor`
expand_composites: If true, then composite tensors such as `tf.sparse.SparseTensor`
and `tf.RaggedTensor` are expanded into their component tensors.
Returns: