TensorFlow 2 version | View source on GitHub |
Represents a ragged tensor.
tf.RaggedTensor(
values, row_splits, cached_row_lengths=None, cached_value_rowids=None,
cached_nrows=None, internal=False
)
A RaggedTensor
is a tensor with one or more ragged dimensions, which are
dimensions whose slices may have different lengths. For example, the inner
(column) dimension of rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]
is ragged,
since the column slices (rt[0, :]
, ..., rt[4, :]
) have different lengths.
Dimensions whose slices all have the same length are called uniform
dimensions. The outermost dimension of a RaggedTensor
is always uniform,
since it consists of a single slice (and so there is no possibility for
differing slice lengths).
The total number of dimensions in a RaggedTensor
is called its rank,
and the number of ragged dimensions in a RaggedTensor
is called its
ragged-rank. A RaggedTensor
's ragged-rank is fixed at graph creation
time: it can't depend on the runtime values of Tensor
s, and can't vary
dynamically for different session runs.
Potentially Ragged Tensors
Many ops support both Tensor
s and RaggedTensor
s. The term "potentially
ragged tensor" may be used to refer to a tensor that might be either a
Tensor
or a RaggedTensor
. The ragged-rank of a Tensor
is zero.
Documenting RaggedTensor Shapes
When documenting the shape of a RaggedTensor, ragged dimensions can be
indicated by enclosing them in parentheses. For example, the shape of
a 3-D RaggedTensor
that stores the fixed-size word embedding for each
word in a sentence, for each sentence in a batch, could be written as
[num_sentences, (num_words), embedding_size]
. The parentheses around
(num_words)
indicate that dimension is ragged, and that the length
of each element list in that dimension may vary for each item.
Component Tensors
Internally, a RaggedTensor
consists of a concatenated list of values that
are partitioned into variable-length rows. In particular, each RaggedTensor
consists of:
A
values
tensor, which concatenates the variable-length rows into a flattened list. For example, thevalues
tensor for[[3, 1, 4, 1], [], [5, 9, 2], [6], []]
is[3, 1, 4, 1, 5, 9, 2, 6]
.A
row_splits
vector, which indicates how those flattened values are divided into rows. In particular, the values for rowrt[i]
are stored in the slicert.values[rt.row_splits[i]:rt.row_splits[i+1]]
.
Example:
print(tf.RaggedTensor.from_row_splits(
values=[3, 1, 4, 1, 5, 9, 2, 6],
row_splits=[0, 4, 4, 7, 8, 8]))
<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
Alternative Row-Partitioning Schemes
In addition to row_splits
, ragged tensors provide support for four other
row-partitioning schemes:
row_lengths
: a vector with shape[nrows]
, which specifies the length of each row.value_rowids
andnrows
:value_rowids
is a vector with shape[nvals]
, corresponding one-to-one withvalues
, which specifies each value's row index. In particular, the rowrt[row]
consists of the valuesrt.values[j]
wherevalue_rowids[j]==row
.nrows
is an integer scalar that specifies the number of rows in theRaggedTensor
. (nrows
is used to indicate trailing empty rows.)row_starts
: a vector with shape[nrows]
, which specifies the start offset of each row. Equivalent torow_splits[:-1]
.row_limits
: a vector with shape[nrows]
, which specifies the stop offset of each row. Equivalent torow_splits[1:]
.
Example: The following ragged tensors are equivalent, and all represent the
nested list [[3, 1, 4, 1], [], [5, 9, 2], [6], []]
.
values = [3, 1, 4, 1, 5, 9, 2, 6]
rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])
rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])
rt3 = RaggedTensor.from_value_rowids(
values, value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], nrows=5)
rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])
rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])
Multiple Ragged Dimensions
RaggedTensor
s with multiple ragged dimensions can be defined by using
a nested RaggedTensor
for the values
tensor. Each nested RaggedTensor
adds a single ragged dimension.
inner_rt = RaggedTensor.from_row_splits( # =rt1 from above
values=[3, 1, 4, 1, 5, 9, 2, 6], row_splits=[0, 4, 4, 7, 8, 8])
outer_rt = RaggedTensor.from_row_splits(
values=inner_rt, row_splits=[0, 3, 3, 5])
print outer_rt.to_list()
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
print outer_rt.ragged_rank
2
The factory function RaggedTensor.from_nested_row_splits
may be used to
construct a RaggedTensor
with multiple ragged dimensions directly, by
providing a list of row_splits
tensors:
RaggedTensor.from_nested_row_splits(
flat_values=[3, 1, 4, 1, 5, 9, 2, 6],
nested_row_splits=([0, 3, 3, 5], [0, 4, 4, 7, 8, 8])).to_list()
[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]
Uniform Inner Dimensions
RaggedTensor
s with uniform inner dimensions can be defined
by using a multidimensional Tensor
for values
.
rt = RaggedTensor.from_row_splits(values=tf.ones([5, 3]),
.. row_splits=[0, 2, 5])
print rt.to_list()
[[[1, 1, 1], [1, 1, 1]],
[[1, 1, 1], [1, 1, 1], [1, 1, 1]]]
print rt.shape
(2, ?, 3)
RaggedTensor Shape Restrictions
The shape of a RaggedTensor is currently restricted to have the following form:
- A single uniform dimension
- Followed by one or more ragged dimensions
- Followed by zero or more uniform dimensions.
This restriction follows from the fact that each nested RaggedTensor
replaces the uniform outermost dimension of its values
with a uniform
dimension followed by a ragged dimension.
Args | |
---|---|
values
|
A potentially ragged tensor of any dtype and shape [nvals, ...] .
|
row_splits
|
A 1-D integer tensor with shape [nrows+1] .
|
cached_row_lengths
|
A 1-D integer tensor with shape [nrows]
|
cached_value_rowids
|
A 1-D integer tensor with shape [nvals] .
|
cached_nrows
|
A 1-D integer scalar tensor. |
internal
|
True if the constructor is being called by one of the factory methods. If false, an exception will be raised. |
Raises | |
---|---|
TypeError
|
If a row partitioning tensor has an inappropriate dtype. |
TypeError
|
If exactly one row partitioning argument was not specified. |
ValueError
|
If a row partitioning tensor has an inappropriate shape. |
ValueError
|
If multiple partitioning arguments are specified. |
ValueError
|
If nrows is specified but value_rowids is not None. |
Attributes | |
---|---|
dtype
|
The DType of values in this tensor.
|
flat_values
|
The innermost values tensor for this ragged tensor.
Concretely, if Conceptually,
Example:
|
nested_row_splits
|
A tuple containing the row_splits for all ragged dimensions.
Example:
|
ragged_rank
|
The number of ragged dimensions in this ragged tensor. |
row_splits
|
The row-split indices for this ragged tensor's values .
Example:
|
shape
|
The statically known shape of this ragged tensor. |
values
|
The concatenated rows for this ragged tensor.
Example:
|
Methods
bounding_shape
bounding_shape(
axis=None, name=None, out_type=None
)
Returns the tight bounding box shape for this RaggedTensor
.
Args | |
---|---|
axis
|
An integer scalar or vector indicating which axes to return the bounding box for. If not specified, then the full bounding box is returned. |
name
|
A name prefix for the returned tensor (optional). |
out_type
|
dtype for the returned tensor. Defaults to
self.row_splits.dtype .
|
Returns | |
---|---|
An integer Tensor (dtype=self.row_splits.dtype ). If axis is not
specified, then output is a vector with
output.shape=[self.shape.ndims] . If axis is a scalar, then the
output is a scalar. If axis is a vector, then output is a vector,
where output[i] is the bounding size for dimension axis[i] .
|
Example:
>>> rt = ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]]) >>> rt.bounding_shape() [5, 4]
consumers
consumers()
from_nested_row_lengths
@classmethod
from_nested_row_lengths( flat_values, nested_row_lengths, name=None, validate=True )
Creates a RaggedTensor
from a nested list of row_lengths
tensors.
Equivalent to:
result = flat_values
for row_lengths in reversed(nested_row_lengths):
result = from_row_lengths(result, row_lengths)
Args | |
---|---|
flat_values
|
A potentially ragged tensor. |
nested_row_lengths
|
A list of 1-D integer tensors. The i th tensor is
used as the row_lengths for the i th ragged dimension.
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor (or flat_values if nested_row_lengths is empty).
|
from_nested_row_splits
@classmethod
from_nested_row_splits( flat_values, nested_row_splits, name=None, validate=True )
Creates a RaggedTensor
from a nested list of row_splits
tensors.
Equivalent to:
result = flat_values
for row_splits in reversed(nested_row_splits):
result = from_row_splits(result, row_splits)
Args | |
---|---|
flat_values
|
A potentially ragged tensor. |
nested_row_splits
|
A list of 1-D integer tensors. The i th tensor is
used as the row_splits for the i th ragged dimension.
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form a
valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor (or flat_values if nested_row_splits is empty).
|
from_nested_value_rowids
@classmethod
from_nested_value_rowids( flat_values, nested_value_rowids, nested_nrows=None, name=None, validate=True )
Creates a RaggedTensor
from a nested list of value_rowids
tensors.
Equivalent to:
result = flat_values
for (rowids, nrows) in reversed(zip(nested_value_rowids, nested_nrows)):
result = from_value_rowids(result, rowids, nrows)
Args | |
---|---|
flat_values
|
A potentially ragged tensor. |
nested_value_rowids
|
A list of 1-D integer tensors. The i th tensor is
used as the value_rowids for the i th ragged dimension.
|
nested_nrows
|
A list of integer scalars. The i th scalar is used as the
nrows for the i th ragged dimension.
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor (or flat_values if nested_value_rowids is empty).
|
Raises | |
---|---|
ValueError
|
If len(nested_values_rowids) != len(nested_nrows) .
|
from_row_lengths
@classmethod
from_row_lengths( values, row_lengths, name=None, validate=True )
Creates a RaggedTensor
with rows partitioned by row_lengths
.
The returned RaggedTensor
corresponds with the python list defined by:
result = [[values.pop(0) for i in range(length)]
for length in row_lengths]
Args | |
---|---|
values
|
A potentially ragged tensor with shape [nvals, ...] .
|
row_lengths
|
A 1-D integer tensor with shape [nrows] . Must be
nonnegative. sum(row_lengths) must be nvals .
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor . result.rank = values.rank + 1 .
result.ragged_rank = values.ragged_rank + 1 .
|
Example:
>>> print(tf.RaggedTensor.from_row_lengths( ... values=[3, 1, 4, 1, 5, 9, 2, 6], ... row_lengths=[4, 0, 3, 1, 0])) <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []])>
from_row_limits
@classmethod
from_row_limits( values, row_limits, name=None, validate=True )
Creates a RaggedTensor
with rows partitioned by row_limits
.
Equivalent to: from_row_splits(values, concat([0, row_limits]))
.
Args | |
---|---|
values
|
A potentially ragged tensor with shape [nvals, ...] .
|
row_limits
|
A 1-D integer tensor with shape [nrows] . Must be sorted in
ascending order. If nrows>0 , then row_limits[-1] must be nvals .
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor . result.rank = values.rank + 1 .
result.ragged_rank = values.ragged_rank + 1 .
|
Example:
>>> print(tf.RaggedTensor.from_row_limits( ... values=[3, 1, 4, 1, 5, 9, 2, 6], ... row_limits=[4, 4, 7, 8, 8])) <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
from_row_splits
@classmethod
from_row_splits( values, row_splits, name=None, validate=True )
Creates a RaggedTensor
with rows partitioned by row_splits
.
The returned RaggedTensor
corresponds with the python list defined by:
result = [values[row_splits[i]:row_splits[i + 1]]
for i in range(len(row_splits) - 1)]
Args | |
---|---|
values
|
A potentially ragged tensor with shape [nvals, ...] .
|
row_splits
|
A 1-D integer tensor with shape [nrows+1] . Must not be
empty, and must be sorted in ascending order. row_splits[0] must be
zero and row_splits[-1] must be nvals .
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor . result.rank = values.rank + 1 .
result.ragged_rank = values.ragged_rank + 1 .
|
Raises | |
---|---|
ValueError
|
If row_splits is an empty list.
|
Example:
>>> print(tf.RaggedTensor.from_row_splits( ... values=[3, 1, 4, 1, 5, 9, 2, 6], ... row_splits=[0, 4, 4, 7, 8, 8])) <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
from_row_starts
@classmethod
from_row_starts( values, row_starts, name=None, validate=True )
Creates a RaggedTensor
with rows partitioned by row_starts
.
Equivalent to: from_row_splits(values, concat([row_starts, nvals]))
.
Args | |
---|---|
values
|
A potentially ragged tensor with shape [nvals, ...] .
|
row_starts
|
A 1-D integer tensor with shape [nrows] . Must be
nonnegative and sorted in ascending order. If nrows>0 , then
row_starts[0] must be zero.
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor . result.rank = values.rank + 1 .
result.ragged_rank = values.ragged_rank + 1 .
|
Example:
>>> print(tf.RaggedTensor.from_row_starts( ... values=[3, 1, 4, 1, 5, 9, 2, 6], ... row_starts=[0, 4, 4, 7, 8])) <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
from_sparse
@classmethod
from_sparse( st_input, name=None, row_splits_dtype=tf.dtypes.int64 )
Converts a 2D tf.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
it is not ragged-right, then an error will be generated.
Example:
st = SparseTensor(indices=[[0, 1], [0, 2], [0, 3], [1, 0], [3, 0]],
values=[1, 2, 3, 4, 5],
dense_shape=[4, 3])
rt.RaggedTensor.from_sparse(st).eval().tolist()
[[1, 2, 3], [4], [], [5]]
Currently, only two-dimensional SparseTensors
are supported.
Args | |
---|---|
st_input
|
The sparse tensor to convert. Must have rank 2. |
name
|
A name prefix for the returned tensors (optional). |
row_splits_dtype
|
dtype for the returned RaggedTensor 's row_splits
tensor. One of tf.int32 or tf.int64 .
|
Returns | |
---|---|
A RaggedTensor with the same values as st_input .
output.ragged_rank = rank(st_input) - 1 .
output.shape = [st_input.dense_shape[0], None] .
|
Raises | |
---|---|
ValueError
|
If the number of dimensions in st_input is not known
statically, or is not two.
|
from_tensor
@classmethod
from_tensor( tensor, lengths=None, padding=None, ragged_rank=1, name=None, row_splits_dtype=tf.dtypes.int64 )
Converts a tf.Tensor
into a RaggedTensor
.
The set of absent/default values may be specified using a vector of lengths
or a padding value (but not both). If lengths
is specified, then the
output tensor will satisfy output[row] = tensor[row][:lengths[row]]
. If
'lengths' is a list of lists or tuple of lists, those lists will be used
as nested row lengths. If padding
is specified, then any row suffix
consisting entirely of padding
will be excluded from the returned
RaggedTensor
. If neither lengths
nor padding
is specified, then the
returned RaggedTensor
will have no absent/default values.
Examples:
dt = tf.constant([[5, 7, 0], [0, 3, 0], [6, 0, 0]])
tf.RaggedTensor.from_tensor(dt)
<tf.RaggedTensor [[5, 7, 0], [0, 3, 0], [6, 0, 0]]>
tf.RaggedTensor.from_tensor(dt, lengths=[1, 0, 3])
<tf.RaggedTensor [[5], [], [6, 0, 0]]>
tf.RaggedTensor.from_tensor(dt, padding=0)
<pre class="devsite-click-to-copy prettyprint lang-py">
<code class="devsite-terminal" data-terminal-prefix=">>>">dt = tf.constant([[[5, 0], [7, 0], [0, 0]],</code>
<code class="no-select nocode"> [[0, 0], [3, 0], [0, 0]],</code>
<code class="no-select nocode"> [[6, 0], [0, 0], [0, 0]]])</code>
<code class="devsite-terminal" data-terminal-prefix=">>>">tf.RaggedTensor.from_tensor(dt, lengths=([2, 0, 3], [1, 1, 2, 0, 1]))</code>
<code class="no-select nocode"><tf.RaggedTensor [[[5], [7]], [], [[6, 0], [], [0]]]></code>
</pre>
Args | |
---|---|
tensor
|
The Tensor to convert. Must have rank ragged_rank + 1 or
higher.
|
lengths
|
An optional set of row lengths, specified using a 1-D integer
Tensor whose length is equal to tensor.shape[0] (the number of rows
in tensor ). If specified, then output[row] will contain
tensor[row][:lengths[row]] . Negative lengths are treated as zero. You
may optionally pass a list or tuple of lengths to this argument, which
will be used as nested row lengths to construct a ragged tensor with
multiple ragged dimensions.
|
padding
|
An optional padding value. If specified, then any row suffix
consisting entirely of padding will be excluded from the returned
RaggedTensor. padding is a Tensor with the same dtype as tensor
and with shape=tensor.shape[ragged_rank + 1:] .
|
ragged_rank
|
Integer specifying the ragged rank for the returned
RaggedTensor . Must be greater than zero.
|
name
|
A name prefix for the returned tensors (optional). |
row_splits_dtype
|
dtype for the returned RaggedTensor 's row_splits
tensor. One of tf.int32 or tf.int64 .
|
Returns | |
---|---|
A RaggedTensor with the specified ragged_rank . The shape of the
returned ragged tensor is compatible with the shape of tensor .
|
Raises | |
---|---|
ValueError
|
If both lengths and padding are specified.
|
from_value_rowids
@classmethod
from_value_rowids( values, value_rowids, nrows=None, name=None, validate=True )
Creates a RaggedTensor
with rows partitioned by value_rowids
.
The returned RaggedTensor
corresponds with the python list defined by:
result = [[values[i] for i in range(len(values)) if value_rowids[i] == row]
for row in range(nrows)]
Args | |
---|---|
values
|
A potentially ragged tensor with shape [nvals, ...] .
|
value_rowids
|
A 1-D integer tensor with shape [nvals] , which corresponds
one-to-one with values , and specifies each value's row index. Must be
nonnegative, and must be sorted in ascending order.
|
nrows
|
An integer scalar specifying the number of rows. This should be
specified if the RaggedTensor may containing empty training rows. Must
be greater than value_rowids[-1] (or zero if value_rowids is empty).
Defaults to value_rowids[-1] (or zero if value_rowids is empty).
|
name
|
A name prefix for the RaggedTensor (optional). |
validate
|
If true, then use assertions to check that the arguments form
a valid RaggedTensor .
|
Returns | |
---|---|
A RaggedTensor . result.rank = values.rank + 1 .
result.ragged_rank = values.ragged_rank + 1 .
|
Raises | |
---|---|
ValueError
|
If nrows is incompatible with value_rowids .
|
Example:
>>> print(tf.RaggedTensor.from_value_rowids( ... values=[3, 1, 4, 1, 5, 9, 2, 6], ... value_rowids=[0, 0, 0, 0, 2, 2, 2, 3], ... nrows=5)) <tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2], [6], []]>
nested_row_lengths
nested_row_lengths(
name=None
)
Returns a tuple containing the row_lengths for all ragged dimensions.
rt.nested_row_lengths()
is a tuple containing the row_lengths
tensors
for all ragged dimensions in rt
, ordered from outermost to innermost.
Args | |
---|---|
name
|
A name prefix for the returned tensors (optional). |
Returns | |
---|---|
A tuple of 1-D integer Tensors . The length of the tuple is equal to
self.ragged_rank .
|
nested_value_rowids
nested_value_rowids(
name=None
)
Returns a tuple containing the value_rowids for all ragged dimensions.
rt.nested_value_rowids
is a tuple containing the value_rowids
tensors
for
all ragged dimensions in rt
, ordered from outermost to innermost. In
particular, rt.nested_value_rowids = (rt.value_rowids(),) + value_ids
where:
* `value_ids = ()` if `rt.values` is a `Tensor`.
* `value_ids = rt.values.nested_value_rowids` otherwise.
Args | |
---|---|
name
|
A name prefix for the returned tensors (optional). |
Returns | |
---|---|
A tuple of 1-D integer Tensor s.
|
Example:
rt = ragged.constant([[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]])
for i, ids in enumerate(rt.nested_value_rowids()):
print('row ids for dimension %d: %s' % (i+1, ids))
row ids for dimension 1: [0]
row ids for dimension 2: [0, 0, 0, 2, 2]
row ids for dimension 3: [0, 0, 0, 0, 2, 2, 2, 3]
nrows
nrows(
out_type=None, name=None
)
Returns the number of rows in this ragged tensor.
I.e., the size of the outermost dimension of the tensor.
Args | |
---|---|
out_type
|
dtype for the returned tensor. Defaults to
self.row_splits.dtype .
|
name
|
A name prefix for the returned tensor (optional). |
Returns | |
---|---|
A scalar Tensor with dtype out_type .
|
Example:
>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) >>> rt.nrows() # rt has 5 rows. 5
row_lengths
row_lengths(
axis=1, name=None
)
Returns the lengths of the rows in this ragged tensor.
rt.row_lengths()[i]
indicates the number of values in the
i
th row of rt
.
Args | |
---|---|
axis
|
An integer constant indicating the axis whose row lengths should be returned. |
name
|
A name prefix for the returned tensor (optional). |
Returns | |
---|---|
A potentially ragged integer Tensor with shape self.shape[:axis] .
|
Raises | |
---|---|
ValueError
|
If axis is out of bounds.
|
Example:
>>> rt = ragged.constant([[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []]) >>> rt.row_lengths(rt) # lengths of rows in rt tf.Tensor([2, 0, 2, 1, 0]) >>> rt.row_lengths(axis=2) # lengths of axis=2 rows. <tf.RaggedTensor [[3, 1], [], [2, 1], [1], []]>
row_limits
row_limits(
name=None
)
Returns the limit indices for rows in this ragged tensor.
These indices specify where the values for each row end in
self.values
. rt.row_limits(self)
is equal to rt.row_splits[:-1]
.
Args | |
---|---|
name
|
A name prefix for the returned tensor (optional). |
Returns | |
---|---|
A 1-D integer Tensor with shape [nrows] .
The returned tensor is nonnegative, and is sorted in ascending order.
|
Example:
>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) >>> rt.values tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) >>> rt.row_limits() # indices of row limits in rt.values tf.Tensor([4, 4, 7, 8, 8])
row_starts
row_starts(
name=None
)
Returns the start indices for rows in this ragged tensor.
These indices specify where the values for each row begin in
self.values
. rt.row_starts()
is equal to rt.row_splits[:-1]
.
Args | |
---|---|
name
|
A name prefix for the returned tensor (optional). |
Returns | |
---|---|
A 1-D integer Tensor with shape [nrows] .
The returned tensor is nonnegative, and is sorted in ascending order.
|
Example:
>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) >>> rt.values tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) >>> rt.row_starts() # indices of row starts in rt.values tf.Tensor([0, 4, 4, 7, 8])
to_list
to_list()
Returns a nested Python list
with the values for this RaggedTensor
.
Requires that rt
was constructed in eager execution mode.
Returns | |
---|---|
A nested Python list .
|
to_sparse
to_sparse(
name=None
)
Converts this RaggedTensor
into a tf.SparseTensor
.
Example:
rt = ragged.constant([[1, 2, 3], [4], [], [5, 6]])
rt.to_sparse().eval()
SparseTensorValue(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [3, 0], [3, 1]],
values=[1, 2, 3, 4, 5, 6],
dense_shape=[4, 3])
Args | |
---|---|
name
|
A name prefix for the returned tensors (optional). |
Returns | |
---|---|
A SparseTensor with the same values as self .
|
to_tensor
to_tensor(
default_value=None, name=None
)
Converts this RaggedTensor
into a tf.Tensor
.
Example:
rt = ragged.constant([[9, 8, 7], [], [6, 5], [4]])
print rt.to_tensor()
[[9 8 7]
[0 0 0]
[6 5 0]
[4 0 0]]
Args | |
---|---|
default_value
|
Value to set for indices not specified in self . Defaults
to zero. default_value must be broadcastable to
self.shape[self.ragged_rank + 1:] .
|
name
|
A name prefix for the returned tensors (optional). |
Returns | |
---|---|
A Tensor with shape ragged.bounding_shape(self) and the
values specified by the non-empty values in self . Empty values are
assigned default_value .
|
value_rowids
value_rowids(
name=None
)
Returns the row indices for the values
in this ragged tensor.
rt.value_rowids()
corresponds one-to-one with the outermost dimension of
rt.values
, and specifies the row containing each value. In particular,
the row rt[row]
consists of the values rt.values[j]
where
rt.value_rowids()[j] == row
.
Args | |
---|---|
name
|
A name prefix for the returned tensor (optional). |
Returns | |
---|---|
A 1-D integer Tensor with shape self.values.shape[:1] .
The returned tensor is nonnegative, and is sorted in ascending order.
|
Example:
>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []]) >>> rt.values tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6]) >>> rt.value_rowids() tf.Tensor([0, 0, 0, 0, 2, 2, 2, 3]) # corresponds 1:1 with rt.values
with_flat_values
with_flat_values(
new_values
)
Returns a copy of self
with flat_values
replaced by new_value
.
Preserves cached row-partitioning tensors such as self.cached_nrows
and
self.cached_value_rowids
if they have values.
Args | |
---|---|
new_values
|
Potentially ragged tensor that should replace
self.flat_values . Must have rank > 0 , and must have the same
number of rows as self.flat_values .
|
Returns | |
---|---|
A RaggedTensor .
result.rank = self.ragged_rank + new_values.rank .
result.ragged_rank = self.ragged_rank + new_values.ragged_rank .
|
with_row_splits_dtype
with_row_splits_dtype(
dtype
)
Returns a copy of this RaggedTensor with the given row_splits
dtype.
For RaggedTensors with multiple ragged dimensions, the row_splits
for all
nested RaggedTensor
objects are cast to the given dtype.
Args | |
---|---|
dtype
|
The dtype for row_splits . One of tf.int32 or tf.int64 .
|
Returns | |
---|---|
A copy of this RaggedTensor, with the row_splits cast to the given
type.
|
with_values
with_values(
new_values
)
Returns a copy of self
with values
replaced by new_value
.
Preserves cached row-partitioning tensors such as self.cached_nrows
and
self.cached_value_rowids
if they have values.
Args | |
---|---|
new_values
|
Potentially ragged tensor to use as the values for the
returned RaggedTensor . Must have rank > 0 , and must have the same
number of rows as self.values .
|
Returns | |
---|---|
A RaggedTensor . result.rank = 1 + new_values.rank .
result.ragged_rank = 1 + new_values.ragged_rank
|
__abs__
__abs__(
x, name=None
)
Computes the absolute value of a tensor.
Given a tensor of integer or floating-point values, this operation returns a tensor of the same type, where each element contains the absolute value of the corresponding element in the input.
Given a tensor x
of complex numbers, this operation returns a tensor of type
float32
or float64
that is the absolute value of each element in x
. All
elements in x
must be complex numbers of the form \(a + bj\). The
absolute value is computed as \( \sqrt{a^2 + b^2}\). For example:
x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x) # [5.25594902, 6.60492229]
Args | |
---|---|
x
|
A Tensor or SparseTensor of type float16 , float32 , float64 ,
int32 , int64 , complex64 or complex128 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor or SparseTensor the same size, type, and sparsity as x with
absolute values.
Note, for complex64 or complex128 input, the returned Tensor will be
of type float32 or float64 , respectively.
If |
__add__
__add__(
x, y, name=None
)
Returns x + y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , int16 , int32 , int64 , complex64 , complex128 , string .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__and__
__and__(
x, y, name=None
)
Returns the truth value of x AND y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__bool__
__bool__(
_
)
Dummy method to prevent a RaggedTensor from being used as a Python bool.
__div__
__div__(
x, y, name=None
)
Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)
This function divides x
and y
, forcing Python 2 semantics. That is, if x
and y
are both integers then the result will be an integer. This is in
contrast to Python 3, where division with /
is always a float while division
with //
is always an integer.
Args | |
---|---|
x
|
Tensor numerator of real numeric type.
|
y
|
Tensor denominator of real numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y returns the quotient of x and y.
|
__floordiv__
__floordiv__(
x, y, name=None
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.compat.v1.div(x,y)
for integers, but uses
tf.floor(tf.compat.v1.div(x,y))
for
floating point arguments so that the result is always an integer (though
possibly an integer represented as floating point). This op is generated by
x // y
floor division in Python 3 and in Python 2.7 with
from __future__ import division
.
x
and y
must have the same type, and the result will have the same type
as well.
Args | |
---|---|
x
|
Tensor numerator of real numeric type.
|
y
|
Tensor denominator of real numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y rounded down.
|
Raises | |
---|---|
TypeError
|
If the inputs are complex. |
__ge__
__ge__(
x, y, name=None
)
Returns the truth value of (x >= y) element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__getitem__
__getitem__(
key
)
Returns the specified piece of this RaggedTensor.
Supports multidimensional indexing and slicing, with one restriction: indexing into a ragged inner dimension is not allowed. This case is problematic because the indicated value may exist in some rows but not others. In such cases, it's not obvious whether we should (1) report an IndexError; (2) use a default value; or (3) skip that value and return a tensor with fewer rows than we started with. Following the guiding principles of Python ("In the face of ambiguity, refuse the temptation to guess"), we simply disallow this operation.
Any dimensions added by array_ops.newaxis
will be ragged if the following
dimension is ragged.
Args | |
---|---|
self
|
The RaggedTensor to slice. |
key
|
Indicates which piece of the RaggedTensor to return, using standard
Python semantics (e.g., negative values index from the end). key
may have any of the following types:
|
Returns | |
---|---|
A Tensor or RaggedTensor object. Values that include at least one
ragged dimension are returned as RaggedTensor . Values that include no
ragged dimensions are returned as Tensor . See above for examples of
expressions that return Tensor s vs RaggedTensor s.
|
Raises | |
---|---|
ValueError
|
If key is out of bounds.
|
ValueError
|
If key is not supported.
|
TypeError
|
If the indices in key have an unsupported type.
|
Examples:
# A 2-D ragged tensor with 1 ragged dimension.
rt = ragged.constant([['a', 'b', 'c'], ['d', 'e'], ['f'], ['g']])
rt[0].eval().tolist() # First row (1-D `Tensor`)
['a', 'b', 'c']
rt[:3].eval().tolist() # First three rows (2-D RaggedTensor)
[['a', 'b', 'c'], ['d', 'e'], '[f'], [g']]
rt[3, 0].eval().tolist() # 1st element of 4th row (scalar)
'g'
A 3-D ragged tensor with 2 ragged dimensions.
rt = ragged.constant([[[1, 2, 3], [4]], ... [[5], [], [6]], ... [[7]], ... [[8, 9], [10]]]) rt[1].eval().tolist() # Second row (2-D RaggedTensor) [[5], [], [6]] rt[3, 0].eval().tolist() # First element of fourth row (1-D Tensor) [8, 9] rt[:, 1:3].eval().tolist() # Items 1-3 of each row (3-D RaggedTensor) [[[4]], [[], [6]], [], [[10]]] rt[:, -1:].eval().tolist() # Last item of each row (3-D RaggedTensor) [[[4]], [[6]], [[7]], [[10]]]
__gt__
__gt__(
x, y, name=None
)
Returns the truth value of (x > y) element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__invert__
__invert__(
x, name=None
)
Returns the truth value of NOT x element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__le__
__le__(
x, y, name=None
)
Returns the truth value of (x <= y) element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__lt__
__lt__(
x, y, name=None
)
Returns the truth value of (x < y) element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__mod__
__mod__(
x, y, name=None
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent
with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: int32 , int64 , bfloat16 , half , float32 , float64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__mul__
__mul__(
x, y, name=None
)
Returns x * y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__neg__
__neg__(
x, name=None
)
Computes numerical negative value element-wise.
I.e., \(y = -x\).
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , int32 , int64 , complex64 , complex128 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
If |
__nonzero__
__nonzero__(
_
)
Dummy method to prevent a RaggedTensor from being used as a Python bool.
__or__
__or__(
x, y, name=None
)
Returns the truth value of x OR y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__pow__
__pow__(
x, y, name=None
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for
corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
Args | |
---|---|
x
|
A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .
|
y
|
A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor .
|
__radd__
__radd__(
x, y, name=None
)
Returns x + y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , int16 , int32 , int64 , complex64 , complex128 , string .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__rand__
__rand__(
x, y, name=None
)
Returns the truth value of x AND y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__rdiv__
__rdiv__(
x, y, name=None
)
Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)
This function divides x
and y
, forcing Python 2 semantics. That is, if x
and y
are both integers then the result will be an integer. This is in
contrast to Python 3, where division with /
is always a float while division
with //
is always an integer.
Args | |
---|---|
x
|
Tensor numerator of real numeric type.
|
y
|
Tensor denominator of real numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y returns the quotient of x and y.
|
__rfloordiv__
__rfloordiv__(
x, y, name=None
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.compat.v1.div(x,y)
for integers, but uses
tf.floor(tf.compat.v1.div(x,y))
for
floating point arguments so that the result is always an integer (though
possibly an integer represented as floating point). This op is generated by
x // y
floor division in Python 3 and in Python 2.7 with
from __future__ import division
.
x
and y
must have the same type, and the result will have the same type
as well.
Args | |
---|---|
x
|
Tensor numerator of real numeric type.
|
y
|
Tensor denominator of real numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y rounded down.
|
Raises | |
---|---|
TypeError
|
If the inputs are complex. |
__rmod__
__rmod__(
x, y, name=None
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent
with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: int32 , int64 , bfloat16 , half , float32 , float64 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__rmul__
__rmul__(
x, y, name=None
)
Returns x * y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__ror__
__ror__(
x, y, name=None
)
Returns the truth value of x OR y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__rpow__
__rpow__(
x, y, name=None
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for
corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
Args | |
---|---|
x
|
A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .
|
y
|
A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor .
|
__rsub__
__rsub__(
x, y, name=None
)
Returns x - y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__rtruediv__
__rtruediv__(
x, y, name=None
)
Divides x / y elementwise (using Python 3 division operator semantics).
This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
x / y
division in Python 3 and in Python 2.7 with
from __future__ import division
. If you want integer division that rounds
down, use x // y
or tf.math.floordiv
.
x
and y
must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to float32
for int8
and int16
and float64
for int32
and int64
(matching the behavior of Numpy).
Args | |
---|---|
x
|
Tensor numerator of numeric type.
|
y
|
Tensor denominator of numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y evaluated in floating point.
|
Raises | |
---|---|
TypeError
|
If x and y have different dtypes.
|
__rxor__
__rxor__(
x, y, name='LogicalXor'
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.
Usage:
x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
# here z = [False True True False]
Args | |
---|---|
x
|
A Tensor type bool.
|
y
|
A Tensor of type bool.
|
Returns | |
---|---|
A Tensor of type bool with the same size as that of x or y.
|
__sub__
__sub__(
x, y, name=None
)
Returns x - y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
y
|
A Tensor . Must have the same type as x .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__truediv__
__truediv__(
x, y, name=None
)
Divides x / y elementwise (using Python 3 division operator semantics).
This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
x / y
division in Python 3 and in Python 2.7 with
from __future__ import division
. If you want integer division that rounds
down, use x // y
or tf.math.floordiv
.
x
and y
must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to float32
for int8
and int16
and float64
for int32
and int64
(matching the behavior of Numpy).
Args | |
---|---|
x
|
Tensor numerator of numeric type.
|
y
|
Tensor denominator of numeric type.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
x / y evaluated in floating point.
|
Raises | |
---|---|
TypeError
|
If x and y have different dtypes.
|
__xor__
__xor__(
x, y, name='LogicalXor'
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.
Usage:
x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
# here z = [False True True False]
Args | |
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x
|
A Tensor type bool.
|
y
|
A Tensor of type bool.
|
Returns | |
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A Tensor of type bool with the same size as that of x or y.
|