TensorFlow 1 version | View source on GitHub |
A tensor represents a rectangular array of data.
tf.Tensor(
op, value_index, dtype
)
When writing a TensorFlow program, the main object you manipulate and pass
around is the tf.Tensor
. A tf.Tensor
object represents a rectangular array
of arbitrary dimension, filled with data of a specific data type.
A tf.Tensor
has the following properties:
- a data type (float32, int32, or string, for example)
- a shape
Each element in the Tensor has the same data type, and the data type is always known.
In eager execution, which is the default mode in TensorFlow, results are calculated immediately.
# Compute some values using a Tensor
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)
print(e)
tf.Tensor(
[[1. 3.]
[3. 7.]], shape=(2, 2), dtype=float32)
Note that during eager execution, you may discover your Tensors
are actually
of type EagerTensor
. This is an internal detail, but it does give you
access to a useful function, numpy
:
type(e)
<class '...ops.EagerTensor'>
print(e.numpy())
[[1. 3.]
[3. 7.]]
TensorFlow can define computations without immediately executing them, most
commonly inside tf.function
s, as well as in (legacy) Graph mode. In those
cases, the shape (that is, the rank of the Tensor and the size of
each dimension) might be only partially known.
Most operations produce tensors of fully-known shapes if the shapes of their inputs are also fully known, but in some cases it's only possible to find the 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
.
For more on Tensors, see the guide.
Args | |
---|---|
op
|
An Operation . Operation that computes this tensor.
|
value_index
|
An int . Index of the operation's endpoint that produces
this tensor.
|
dtype
|
A DType . Type of elements stored in this tensor.
|
Raises | |
---|---|
TypeError
|
If the op is not an Operation .
|
Attributes | |
---|---|
device
|
The name of the device on which this tensor will be produced, or None. |
dtype
|
The DType of elements in this tensor.
|
graph
|
The Graph that contains this tensor.
|
name
|
The string name of this tensor. |
op
|
The Operation that produces this tensor as an output.
|
shape
|
Returns the TensorShape that represents the shape of this tensor.
The shape is computed using shape inference functions that are
registered in the Op for each The inferred shape of a tensor is used to provide shape information without having to execute the underlying kernel. This can be used for debugging and providing early error messages. For example:
In some cases, the inferred shape may have unknown dimensions. If
the caller has additional information about the values of these
dimensions, |
value_index
|
The index of this tensor in the outputs of its Operation .
|
Methods
consumers
consumers()
Returns a list of Operation
s that consume this tensor.
Returns | |
---|---|
A list of Operation s.
|
eval
eval(
feed_dict=None, session=None
)
Evaluates this tensor in a Session
.
Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.
Args | |
---|---|
feed_dict
|
A dictionary that maps Tensor objects to feed values. See
tf.Session.run for a description of the valid feed values.
|
session
|
(Optional.) The Session to be used to evaluate this tensor. If
none, the default session will be used.
|
Returns | |
---|---|
A numpy array corresponding to the value of this tensor. |
experimental_ref
experimental_ref()
DEPRECATED FUNCTION
get_shape
get_shape()
Alias of tf.Tensor.shape
.
ref
ref()
Returns a hashable reference object to this Tensor.
The primary use case for this API is to put tensors in a set/dictionary.
We can't put tensors in a set/dictionary as tensor.__hash__()
is no longer
available starting Tensorflow 2.0.
The following will raise an exception starting 2.0
x = tf.constant(5)
y = tf.constant(10)
z = tf.constant(10)
tensor_set = {x, y, z}
Traceback (most recent call last):
TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key.
tensor_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
TypeError: Tensor is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use tensor.ref()
.
tensor_set = {x.ref(), y.ref(), z.ref()}
x.ref() in tensor_set
True
tensor_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
tensor_dict[y.ref()]
'ten'
Also, the reference object provides .deref()
function that returns the
original Tensor.
x = tf.constant(5)
x.ref().deref()
<tf.Tensor: shape=(), dtype=int32, numpy=5>
set_shape
set_shape(
shape
)
Updates the shape of this tensor.
This method can be called multiple times, and will merge the given
shape
with the current shape of this tensor. It can be used to
provide additional information about the shape of this tensor that
cannot be inferred from the graph alone. For example, this can be used
to provide additional information about the shapes of images:
_, image_data = tf.compat.v1.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)
# The height and width dimensions of `image` are data dependent, and
# cannot be computed without executing the op.
print(image.shape)
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])
# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.shape)
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
Args | |
---|---|
shape
|
A TensorShape representing the shape of this tensor, a
TensorShapeProto , a list, a tuple, or None.
|
Raises | |
---|---|
ValueError
|
If shape is not compatible with the current shape of
this tensor.
|
__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
. For
a complex number \(a + bj\), its 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)
<tf.Tensor: shape=(2, 1), dtype=float64, numpy=
array([[5.25594901],
[6.60492241]])>
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 of 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
)
Dispatches to add for strings and add_v2 for all other types.
__and__
__and__(
x, y
)
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 tensor from being used as a Python bool
.
This overload raises a TypeError
when the user inadvertently
treats a Tensor
as a boolean (most commonly in an if
or while
statement), in code that was not converted by AutoGraph. For example:
if tf.constant(True): # Will raise.
# ...
if tf.constant(5) < tf.constant(7): # Will raise.
# ...
Raises | |
---|---|
TypeError .
|
__div__
__div__(
x, y
)
Divide two values using Python 2 semantics.
Used for Tensor.div.
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.
|
__eq__
__eq__(
other
)
Compares two tensors element-wise for equality.
__floordiv__
__floordiv__(
x, y
)
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.
Example:
x = tf.constant([5, 4, 6, 7])
y = tf.constant([5, 2, 5, 10])
tf.math.greater_equal(x, y) ==> [True, True, True, False]
x = tf.constant([5, 4, 6, 7])
y = tf.constant([5])
tf.math.greater_equal(x, y) ==> [True, False, True, True]
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__(
tensor, slice_spec, var=None
)
Overload for Tensor.getitem.
This operation extracts the specified region from the tensor. The notation is similar to NumPy with the restriction that currently only support basic indexing. That means that using a non-scalar tensor as input is not currently allowed.
Some useful examples:
# Strip leading and trailing 2 elements
foo = tf.constant([1,2,3,4,5,6])
print(foo[2:-2].eval()) # => [3,4]
# Skip every other row and reverse the order of the columns
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[::2,::-1].eval()) # => [[3,2,1], [9,8,7]]
# Use scalar tensors as indices on both dimensions
print(foo[tf.constant(0), tf.constant(2)].eval()) # => 3
# Insert another dimension
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]],
[[7],[8],[9]]]
# Ellipses (3 equivalent operations)
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis, ...].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
print(foo[tf.newaxis].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
# Masks
foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
print(foo[foo > 2].eval()) # => [3, 4, 5, 6, 7, 8, 9]
Notes:
tf.newaxis
isNone
as in NumPy.- An implicit ellipsis is placed at the end of the
slice_spec
- NumPy advanced indexing is currently not supported.
Args | |
---|---|
tensor
|
An ops.Tensor object. |
slice_spec
|
The arguments to Tensor.getitem. |
var
|
In the case of variable slice assignment, the Variable object to slice (i.e. tensor is the read-only view of this variable). |
Returns | |
---|---|
The appropriate slice of "tensor", based on "slice_spec". |
Raises | |
---|---|
ValueError
|
If a slice range is negative size. |
TypeError
|
If the slice indices aren't int, slice, ellipsis, tf.newaxis or scalar int32/int64 tensors. |
__gt__
__gt__(
x, y, name=None
)
Returns the truth value of (x > y) element-wise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5, 2, 5])
tf.math.greater(x, y) ==> [False, True, True]
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.greater(x, y) ==> [False, False, True]
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.
Example:
tf.math.logical_not(tf.constant([True, False]))
<tf.Tensor: shape=(2,), dtype=bool, numpy=array([False, True])>
Args | |
---|---|
x
|
A Tensor of type bool . A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__iter__
__iter__()
__le__
__le__(
x, y, name=None
)
Returns the truth value of (x <= y) element-wise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less_equal(x, y) ==> [True, True, False]
x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 6])
tf.math.less_equal(x, y) ==> [True, True, True]
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 .
|
__len__
__len__()
__lt__
__lt__(
x, y, name=None
)
Returns the truth value of (x < y) element-wise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less(x, y) ==> [False, True, False]
x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 7])
tf.math.less(x, y) ==> [False, True, True]
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 .
|
__matmul__
__matmul__(
x, y
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication dimensions, and any further outer dimensions specify matching batch size.
Both matrices must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
a_is_sparse
or b_is_sparse
flag to True
. These are False
by default.
This optimization is only available for plain matrices (rank-2 tensors) with
datatypes bfloat16
or float32
.
A simple 2-D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2-D tensor
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2-D tensor
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 7, 8],
[ 9, 10],
[11, 12]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 58, 64],
[139, 154]], dtype=int32)>
A batch matrix multiplication with batch shape [2]:
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3-D tensor
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]], dtype=int32)>
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3-D tensor
<tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
array([[[13, 14],
[15, 16],
[17, 18]],
[[19, 20],
[21, 22],
[23, 24]]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
array([[[ 94, 100],
[229, 244]],
[[508, 532],
[697, 730]]], dtype=int32)>
Since python >= 3.5 the @ operator is supported
(see PEP 465). In TensorFlow,
it simply calls the tf.matmul()
function, so the following lines are
equivalent:
d = a @ b @ [[10], [11]]
d = tf.matmul(tf.matmul(a, b), [[10], [11]])
Args | |
---|---|
a
|
tf.Tensor of type float16 , float32 , float64 , int32 ,
complex64 , complex128 and rank > 1.
|
b
|
tf.Tensor with same type and rank as a .
|
transpose_a
|
If True , a is transposed before multiplication.
|
transpose_b
|
If True , b is transposed before multiplication.
|
adjoint_a
|
If True , a is conjugated and transposed before
multiplication.
|
adjoint_b
|
If True , b is conjugated and transposed before
multiplication.
|
a_is_sparse
|
If True , a 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.
|
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.
|
name
|
Name for the operation (optional). |
Returns | |
---|---|
A tf.Tensor of the same type as a and b where each inner-most matrix
is the product of the corresponding matrices in a and b , e.g. if all
transpose or adjoint attributes are False :
|
|
Note
|
This is matrix product, not element-wise product. |
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a , or transpose_b and
adjoint_b are both set to True .
|
__mod__
__mod__(
x, y
)
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
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ne__
__ne__(
other
)
Compares two tensors element-wise for equality.
__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 tensor from being used as a Python bool
.
This is the Python 2.x counterpart to __bool__()
above.
Raises | |
---|---|
TypeError .
|
__or__
__or__(
x, y
)
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
)
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__(
y, x
)
Dispatches to add for strings and add_v2 for all other types.
__rand__
__rand__(
y, x
)
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__(
y, x
)
Divide two values using Python 2 semantics.
Used for Tensor.div.
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__(
y, x
)
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. |
__rmatmul__
__rmatmul__(
y, x
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication dimensions, and any further outer dimensions specify matching batch size.
Both matrices must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
a_is_sparse
or b_is_sparse
flag to True
. These are False
by default.
This optimization is only available for plain matrices (rank-2 tensors) with
datatypes bfloat16
or float32
.
A simple 2-D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2-D tensor
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2-D tensor
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 7, 8],
[ 9, 10],
[11, 12]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 58, 64],
[139, 154]], dtype=int32)>
A batch matrix multiplication with batch shape [2]:
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3-D tensor
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]], dtype=int32)>
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3-D tensor
<tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
array([[[13, 14],
[15, 16],
[17, 18]],
[[19, 20],
[21, 22],
[23, 24]]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
array([[[ 94, 100],
[229, 244]],
[[508, 532],
[697, 730]]], dtype=int32)>
Since python >= 3.5 the @ operator is supported
(see PEP 465). In TensorFlow,
it simply calls the tf.matmul()
function, so the following lines are
equivalent:
d = a @ b @ [[10], [11]]
d = tf.matmul(tf.matmul(a, b), [[10], [11]])
Args | |
---|---|
a
|
tf.Tensor of type float16 , float32 , float64 , int32 ,
complex64 , complex128 and rank > 1.
|
b
|
tf.Tensor with same type and rank as a .
|
transpose_a
|
If True , a is transposed before multiplication.
|
transpose_b
|
If True , b is transposed before multiplication.
|
adjoint_a
|
If True , a is conjugated and transposed before
multiplication.
|
adjoint_b
|
If True , b is conjugated and transposed before
multiplication.
|
a_is_sparse
|
If True , a 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.
|
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.
|
name
|
Name for the operation (optional). |
Returns | |
---|---|
A tf.Tensor of the same type as a and b where each inner-most matrix
is the product of the corresponding matrices in a and b , e.g. if all
transpose or adjoint attributes are False :
|
|
Note
|
This is matrix product, not element-wise product. |
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a , or transpose_b and
adjoint_b are both set to True .
|
__rmod__
__rmod__(
y, x
)
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__(
y, x
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ror__
__ror__(
y, x
)
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__(
y, x
)
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__(
y, x
)
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__(
y, x
)
__rxor__
__rxor__(
y, x
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
The operation works for the following input types:
- Two single elements of type
bool
- One
tf.Tensor
of typebool
and one singlebool
, where the result will be calculated by applying logical XOR with the single element to each element in the larger Tensor. - Two
tf.Tensor
objects of typebool
of the same shape. In this case, the result will be the element-wise logical XOR of the two input tensors.
Usage:
a = tf.constant([True])
b = tf.constant([False])
tf.math.logical_xor(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
c = tf.constant([True])
x = tf.constant([False, True, True, False])
tf.math.logical_xor(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([ True, False, False, True])>
y = tf.constant([False, False, True, True])
z = tf.constant([False, True, False, True])
tf.math.logical_xor(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
Args | |
---|---|
x
|
A tf.Tensor type bool.
|
y
|
A tf.Tensor of type bool.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A tf.Tensor of type bool with the same size as that of x or y.
|
__sub__
__sub__(
x, y
)
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
)
__xor__
__xor__(
x, y
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
The operation works for the following input types:
- Two single elements of type
bool
- One
tf.Tensor
of typebool
and one singlebool
, where the result will be calculated by applying logical XOR with the single element to each element in the larger Tensor. - Two
tf.Tensor
objects of typebool
of the same shape. In this case, the result will be the element-wise logical XOR of the two input tensors.
Usage:
a = tf.constant([True])
b = tf.constant([False])
tf.math.logical_xor(a, b)
<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>
c = tf.constant([True])
x = tf.constant([False, True, True, False])
tf.math.logical_xor(c, x)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([ True, False, False, True])>
y = tf.constant([False, False, True, True])
z = tf.constant([False, True, False, True])
tf.math.logical_xor(y, z)
<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, False])>
Args | |
---|---|
x
|
A tf.Tensor type bool.
|
y
|
A tf.Tensor of type bool.
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A tf.Tensor of type bool with the same size as that of x or y.
|