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A tensor is a multidimensional array of elements represented by a
tf.Tensor( op, value_index, dtype )
tf.Tensor
object. All elements are of a single known data type.
When writing a TensorFlow program, the main object that is manipulated and passed around is the tf.Tensor
.
A tf.Tensor
has the following properties:
TensorFlow supports eager execution and graph execution. In eager execution, operations are evaluated immediately. In graph execution, a computational graph is constructed for later evaluation.
TensorFlow defaults to eager execution. In the example below, the matrix multiplication 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.]]
In TensorFlow, tf.function
s are a common way to define graph execution.
A Tensor's shape (that is, the rank of the Tensor and the size of each dimension) may not always be fully known. In tf.function
definitions, the shape may only be 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.
A number of specialized tensors are available: see tf.Variable
, tf.constant
, tf.placeholder
, tf.sparse.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 a tf.TensorShape that represents the shape of this tensor. t = tf.constant([1,2,3,4,5]) t.shape TensorShape([5])
In a A See |
value_index | The index of this tensor in the outputs of its Operation . |
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
.
Note: If you are not usingcompat.v1
libraries, you should not need this, (orfeed_dict
orSession
). In eager execution (or withintf.function
) you do not need to calleval
.
Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.
Note: Before invokingTensor.eval()
, its graph must have been launched in a session, and either a default session must be available, orsession
must be specified explicitly.
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()
Returns a tf.TensorShape
that represents the shape of this tensor.
In eager execution the shape is always fully-known.
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) print(a.shape) (2, 3)
tf.Tensor.get_shape()
is equivalent to tf.Tensor.shape
.
When executing in a tf.function
or building a model using tf.keras.Input
, Tensor.shape
may return a partial shape (including None
for unknown dimensions). See tf.TensorShape
for more details.
inputs = tf.keras.Input(shape = [10]) # Unknown batch size print(inputs.shape) (None, 10)
The shape is computed using shape inference functions that are registered for each tf.Operation
.
The returned tf.TensorShape
is determined at build time, without executing the underlying kernel. It is not a tf.Tensor
. If you need a shape tensor, either convert the tf.TensorShape
to a tf.constant
, or use the tf.shape(tensor)
function, which returns the tensor's shape at execution time.
This is useful for debugging and providing early errors. For example, when tracing a tf.function
, no ops are being executed, shapes may be unknown (See the Concrete Functions Guide for details).
@tf.function def my_matmul(a, b): result = [email protected] # the `print` executes during tracing. print("Result shape: ", result.shape) return result
The shape inference functions propagate shapes to the extent possible:
f = my_matmul.get_concrete_function( tf.TensorSpec([None,3]), tf.TensorSpec([3,5])) Result shape: (None, 5)
Tracing may fail if a shape missmatch can be detected:
cf = my_matmul.get_concrete_function( tf.TensorSpec([None,3]), tf.TensorSpec([4,5])) Traceback (most recent call last): ValueError: Dimensions must be equal, but are 3 and 4 for 'matmul' (op: 'MatMul') with input shapes: [?,3], [4,5].
In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, tf.ensure_shape
or Tensor.set_shape()
can be used to augment the inferred shape.
@tf.function def my_fun(a): a = tf.ensure_shape(a, [5, 5]) # the `print` executes during tracing. print("Result shape: ", a.shape) return a
cf = my_fun.get_concrete_function( tf.TensorSpec([None, None])) Result shape: (5, 5)
Returns | |
---|---|
A tf.TensorShape representing the shape of this tensor. |
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.
Note: It is recommended to usetf.ensure_shape
instead ofTensor.set_shape
, becausetf.ensure_shape
provides better checking for programming errors and can create guarantees for compiler optimization.
With eager execution this operates as a shape assertion. Here the shapes match:
t = tf.constant([[1,2,3]]) t.set_shape([1, 3])
Passing a None
in the new shape allows any value for that axis:
t.set_shape([1,None])
An error is raised if an incompatible shape is passed.
t.set_shape([1,5]) Traceback (most recent call last): ValueError: Tensor's shape (1, 3) is not compatible with supplied shape [1, 5]
When executing in a tf.function
, or building a model using tf.keras.Input
, Tensor.set_shape
will merge the given shape
with the current shape of this tensor, and set the tensor's shape to the merged value (see tf.TensorShape.merge_with
for details):
t = tf.keras.Input(shape=[None, None, 3]) print(t.shape) (None, None, None, 3)
Dimensions set to None
are not updated:
t.set_shape([None, 224, 224, None]) print(t.shape) (None, 224, 224, 3)
The main use case for this is to provide additional shape information that cannot be inferred from the graph alone.
For example if you know all the images in a dataset have shape [28,28,3] you can set it with tf.set_shape
:
@tf.function def load_image(filename): raw = tf.io.read_file(filename) image = tf.image.decode_png(raw, channels=3) # the `print` executes during tracing. print("Initial shape: ", image.shape) image.set_shape([28, 28, 3]) print("Final shape: ", image.shape) return image
Trace the function, see the Concrete Functions Guide for details.
cf = load_image.get_concrete_function( tf.TensorSpec([], dtype=tf.string)) Initial shape: (None, None, 3) Final shape: (28, 28, 3)
Similarly the tf.io.parse_tensor
function could return a tensor with any shape, even the tf.rank
is unknown. If you know that all your serialized tensors will be 2d, set it with set_shape
:
@tf.function def my_parse(string_tensor): result = tf.io.parse_tensor(string_tensor, out_type=tf.float32) # the `print` executes during tracing. print("Initial shape: ", result.shape) result.set_shape([None, None]) print("Final shape: ", result.shape) return result
Trace the function
concrete_parse = my_parse.get_concrete_function( tf.TensorSpec([], dtype=tf.string)) Initial shape: <unknown> Final shape: (None, None)
t = tf.ones([5,3], dtype=tf.float32) serialized = tf.io.serialize_tensor(t) print(serialized.dtype) <dtype: 'string'> print(serialized.shape) () t2 = concrete_parse(serialized) print(t2.shape) (5, 3)
# Serialize a rank-3 tensor t = tf.ones([5,5,5], dtype=tf.float32) serialized = tf.io.serialize_tensor(t) # The function still runs, even though it `set_shape([None,None])` t2 = concrete_parse(serialized) print(t2.shape) (5, 5, 5)
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 \(\sqrt{a^2 + b^2}\).
# real number x = tf.constant([-2.25, 3.25]) tf.abs(x) <tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.25, 3.25], dtype=float32)>
# complex number 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 )
The operation invoked by the Tensor.add
operator.
Purpose in the API:
This method is exposed in TensorFlow's API so that library developers can register dispatching for <a href="../tf/Tensor#__add__"><code>Tensor.__add__</code></a> to allow it to handle custom composite tensors & other custom objects. The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.
Args | |
---|---|
x | The left-hand side of the + operator. |
y | The right-hand side of the + operator. |
name | an optional name for the operation. |
Returns | |
---|---|
The result of the elementwise + operation. |
__and__
__and__( x, y )
__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 )
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.
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 )
The operation invoked by the Tensor.eq
operator.
Compares two tensors element-wise for equality if they are broadcast-compatible; or returns False if they are not broadcast-compatible. (Note that this behavior differs from tf.math.equal
, which raises an exception if the two tensors are not broadcast-compatible.)
This method is exposed in TensorFlow's API so that library developers can register dispatching for Tensor.eq
to allow it to handle custom composite tensors & other custom objects.
The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.
Args | |
---|---|
self | The left-hand side of the == operator. |
other | The right-hand side of the == operator. |
Returns | |
---|---|
The result of the elementwise == operation, or False if the arguments are not broadcast-compatible. |
__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.
Note: math.greater_equal
supports broadcasting. More about broadcasting here
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.
# 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]
tf.newaxis
is None
as in NumPy.slice_spec
This method is exposed in TensorFlow's API so that library developers can register dispatching for Tensor.getitem
to allow it to handle custom composite tensors & other custom objects.
The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.
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.
Note: math.greater
supports broadcasting. More about broadcasting here
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 )
__iter__
__iter__()
__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
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.
Note: math.less
supports broadcasting. More about broadcasting here
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.sparse.SparseTensor multiplication. |
b_is_sparse | If True , b is treated as a sparse matrix. Notice, this does not support tf.sparse.SparseTensor , it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.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
.
Note: math.floormod
supports broadcasting. More about broadcasting here
Args | |
---|---|
x | A Tensor . Must be one of the following types: int32 , int64 , uint64 , 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 )
The operation invoked by the Tensor.ne
operator.
Compares two tensors element-wise for inequality if they are broadcast-compatible; or returns True if they are not broadcast-compatible. (Note that this behavior differs from tf.math.not_equal
, which raises an exception if the two tensors are not broadcast-compatible.)
This method is exposed in TensorFlow's API so that library developers can register dispatching for Tensor.ne
to allow it to handle custom composite tensors & other custom objects.
The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.
Args | |
---|---|
self | The left-hand side of the != operator. |
other | The right-hand side of the != operator. |
Returns | |
---|---|
The result of the elementwise != operation, or True if the arguments are not broadcast-compatible. |
__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 , int8 , int16 , 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 )
__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 )
The operation invoked by the Tensor.add
operator.
Purpose in the API:
This method is exposed in TensorFlow's API so that library developers can register dispatching for <a href="../tf/Tensor#__add__"><code>Tensor.__add__</code></a> to allow it to handle custom composite tensors & other custom objects. The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.
Args | |
---|---|
x | The left-hand side of the + operator. |
y | The right-hand side of the + operator. |
name | an optional name for the operation. |
Returns | |
---|---|
The result of the elementwise + operation. |
__rand__
__rand__( y, x )
__rdiv__
__rdiv__( y, x )
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.
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.sparse.SparseTensor multiplication. |
b_is_sparse | If True , b is treated as a sparse matrix. Notice, this does not support tf.sparse.SparseTensor , it just makes optimizations that assume most values in a are zero. See tf.sparse.sparse_dense_matmul for some support for tf.sparse.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
.
Note: math.floormod
supports broadcasting. More about broadcasting here
Args | |
---|---|
x | A Tensor . Must be one of the following types: int32 , int64 , uint64 , 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 )
__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.
Note: Subtract
supports broadcasting. More about broadcasting here
Args | |
---|---|
x | A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 , uint32 . |
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 )
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).
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__( y, x )
__sub__
__sub__( x, y )
Returns x - y element-wise.
Note: Subtract
supports broadcasting. More about broadcasting here
Args | |
---|---|
x | A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 , uint32 . |
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 )
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).
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 )
Class Variables | |
---|---|
OVERLOADABLE_OPERATORS |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/Tensor