tf.RaggedTensor

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 Tensors, and can't vary dynamically for different session runs.

Potentially Ragged Tensors

Many ops support both Tensors and RaggedTensors. 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, the values 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 row rt[i] are stored in the slice rt.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 and nrows: value_rowids is a vector with shape [nvals], corresponding one-to-one with values, which specifies each value's row index. In particular, the row rt[row] consists of the values rt.values[j] where value_rowids[j]==row. nrows is an integer scalar that specifies the number of rows in the RaggedTensor. (nrows is used to indicate trailing empty rows.)

• row_starts: a vector with shape [nrows], which specifies the start offset of each row. Equivalent to row_splits[:-1].

• row_limits: a vector with shape [nrows], which specifies the stop offset of each row. Equivalent to row_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

RaggedTensors 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

RaggedTensors 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.

rt = ragged.constant([[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]])
print rt.flat_values()
tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6])

rt = ragged.constant([[[[3, 1, 4, 1], [], [5, 9, 2]], [], [[6], []]]])
for i, splits in enumerate(rt.nested_row_splits()):
  print('Splits for dimension %d: %s' % (i+1, splits))
Splits for dimension 1: [0, 1]
Splits for dimension 2: [0, 3, 3, 5]
Splits for dimension 3: [0, 4, 4, 7, 8, 8]

>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print rt.row_splits  # indices of row splits in rt.values
tf.Tensor([0, 4, 4, 7, 8, 8])

>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> print rt.values
tf.Tensor([3, 1, 4, 1, 5, 9, 2, 6])

Methods

bounding_shape

View source

bounding_shape(
    axis=None, name=None, out_type=None
)

Returns the tight bounding box shape for this RaggedTensor.

Example:

>>> rt = ragged.constant([[1, 2, 3, 4], [5], [], [6, 7, 8, 9], [10]])
>>> rt.bounding_shape()
[5, 4]

consumers

View source

consumers()

from_nested_row_lengths

View source

@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)

from_nested_row_splits

View source

@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)

from_nested_value_rowids

View source

@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)

from_row_lengths

View source

@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]

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

View source

@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])).

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

View source

@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)]

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

View source

@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])).

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

View source

@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.

from_tensor

View source

@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="&gt;&gt;&gt;">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="&gt;&gt;&gt;">tf.RaggedTensor.from_tensor(dt, lengths=([2, 0, 3], [1, 1, 2, 0, 1]))</code>
<code class="no-select nocode">&lt;tf.RaggedTensor [[[5], [7]], [], [[6, 0], [], [0]]]&gt;</code>
</pre>

from_value_rowids

View source

@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)]

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

View source

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.

nested_value_rowids

View source

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.

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

View source

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.

Example:

>>> rt = ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
>>> rt.nrows()  # rt has 5 rows.
5

row_lengths

View source

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 ith row of rt.

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

View source

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].

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

View source

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].

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

View source

to_list()

Returns a nested Python list with the values for this RaggedTensor.

Requires that rt was constructed in eager execution mode.

to_sparse

View source

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])

to_tensor

View source

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]]

value_rowids

View source

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.

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

View source

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.

with_row_splits_dtype

View source

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.

with_values

View source

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.

__abs__

View source

__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]

__add__

__add__(
    x, y, name=None
)

Returns x + y element-wise.

Note: math.add supports broadcasting. AddN does not. More about broadcasting here

__and__

__and__(
    x, y, name=None
)

Returns the truth value of x AND y element-wise.

Note: math.logical_and supports broadcasting. More about broadcasting here

__bool__

View source

__bool__(
    _
)

Dummy method to prevent a RaggedTensor from being used as a Python bool.

__div__

View source

__div__(
    x, y, name=None
)

Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)

Note: Prefer using the Tensor division operator or tf.divide which obey Python 3 division operator semantics.

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.

__floordiv__

View source

__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.

__ge__

__ge__(
    x, y, name=None
)

Returns the truth value of (x >= y) element-wise.

Note: math.greater_equal supports broadcasting. More about broadcasting here

__getitem__

View source

__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.

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.

Note: math.greater supports broadcasting. More about broadcasting here

__invert__

__invert__(
    x, name=None
)

Returns the truth value of NOT x element-wise.

__le__

__le__(
    x, y, name=None
)

Returns the truth value of (x <= y) element-wise.

Note: math.less_equal supports broadcasting. More about broadcasting here

__lt__

__lt__(
    x, y, name=None
)

Returns the truth value of (x < y) element-wise.

Note: math.less supports broadcasting. More about broadcasting here

__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.

Note: math.floormod supports broadcasting. More about broadcasting here

__mul__

View source

__mul__(
    x, y, name=None
)

Returns x * y element-wise.

Note: tf.multiply supports broadcasting. More about broadcasting here

__neg__

__neg__(
    x, name=None
)

Computes numerical negative value element-wise.

I.e., \(y = -x\).

__nonzero__

View source

__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.

Note: math.logical_or supports broadcasting. More about broadcasting here

__pow__

View source

__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]]

__radd__

__radd__(
    x, y, name=None
)

Returns x + y element-wise.

Note: math.add supports broadcasting. AddN does not. More about broadcasting here

__rand__

__rand__(
    x, y, name=None
)

Returns the truth value of x AND y element-wise.

Note: math.logical_and supports broadcasting. More about broadcasting here

__rdiv__

View source

__rdiv__(
    x, y, name=None
)

Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)

Note: Prefer using the Tensor division operator or tf.divide which obey Python 3 division operator semantics.

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.

__rfloordiv__

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__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.

__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.

Note: math.floormod supports broadcasting. More about broadcasting here

__rmul__

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__rmul__(
    x, y, name=None
)

Returns x * y element-wise.

Note: tf.multiply supports broadcasting. More about broadcasting here

__ror__

__ror__(
    x, y, name=None
)

Returns the truth value of x OR y element-wise.

Note: math.logical_or supports broadcasting. More about broadcasting here

__rpow__

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__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]]

__rsub__

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__rsub__(
    x, y, name=None
)

Returns x - y element-wise.

Note: Subtract supports broadcasting. More about broadcasting here

__rtruediv__

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__rtruediv__(
    x, y, name=None
)

Divides x / y elementwise (using Python 3 division operator semantics).

Note: Prefer using the Tensor operator or tf.divide which obey Python 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).

__rxor__

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__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]

__sub__

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__sub__(
    x, y, name=None
)

Returns x - y element-wise.

Note: Subtract supports broadcasting. More about broadcasting here

__truediv__

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__truediv__(
    x, y, name=None
)

Divides x / y elementwise (using Python 3 division operator semantics).

Note: Prefer using the Tensor operator or tf.divide which obey Python 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).

__xor__

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__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]