Tensor
Defined in tensorflow/python/framework/ops.py
.
See the guide: Building Graphs > Core graph data structures
Represents one of the outputs of an Operation
.
A Tensor
is a symbolic handle to one of the outputs of an Operation
. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow tf.Session
.
This class has two primary purposes:
A Tensor
can be passed as an input to another Operation
. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire Graph
that represents a large, multi-step computation.
After the graph has been launched in a session, the value of the Tensor
can be computed by passing it to tf.Session.run
. t.eval()
is a shortcut for calling tf.get_default_session().run(t)
.
In the following example, c
, d
, and e
are symbolic Tensor
objects, whereas result
is a numpy array that stores a concrete value:
# Build a dataflow graph. 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) # Construct a `Session` to execute the graph. sess = tf.Session() # Execute the graph and store the value that `e` represents in `result`. result = sess.run(e)
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 Operation
. See tf.TensorShape
for more details of what a shape represents.
The inferred shape of a tensor is used to provide shape information without having to launch the graph in a session. This can be used for debugging, and providing early error messages. For example:
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) print(c.shape) ==> TensorShape([Dimension(2), Dimension(3)]) d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]]) print(d.shape) ==> TensorShape([Dimension(4), Dimension(2)]) # Raises a ValueError, because `c` and `d` do not have compatible # inner dimensions. e = tf.matmul(c, d) f = tf.matmul(c, d, transpose_a=True, transpose_b=True) print(f.shape) ==> TensorShape([Dimension(3), Dimension(4)])
In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, Tensor.set_shape()
can be used to augment the inferred shape.
A TensorShape
representing the shape of this tensor.
value_index
The index of this tensor in the outputs of its Operation
.
__init__
__init__( op, value_index, dtype )
Creates a new Tensor
.
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.TypeError
: If the op is not an Operation
.__abs__
__abs__( x, name=None )
Computes the absolute value of a tensor.
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]
x
: A Tensor
or SparseTensor
of type float32
, float64
, int32
, int64
, complex64
or complex128
.name
: A name for the operation (optional).A Tensor
or SparseTensor
the same size and type as x
with absolute values. Note, for complex64
or complex128
input, the returned Tensor
will be of type float32
or float64
, respectively.
__add__
__add__( x, y )
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, half
, float32
, float64
, uint8
, int8
, int16
, int32
, int64
, complex64
, complex128
, string
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
__and__
__and__( x, y )
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).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 (e.g. in an if
statement). For example:
if tf.constant(True): # Will raise. # ... if tf.constant(5) < tf.constant(7): # Will raise. # ...
This disallows ambiguities between testing the Python value vs testing the dynamic condition of the Tensor
.
TypeError
.
__div__
__div__( x, y )
Divide two values using Python 2 semantics. Used for Tensor.div.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
returns the quotient of x and y.
__eq__
__eq__(other)
Return self==value.
__floordiv__
__floordiv__( x, y )
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.div(x,y)
for integers, but uses tf.floor(tf.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
.
Note that for efficiency, floordiv
uses C semantics for negative numbers (unlike Python and Numpy).
x
and y
must have the same type, and the result will have the same type as well.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
TypeError
: If the inputs are complex.__ge__
__ge__( x, y, name=None )
Returns the truth value of (x >= y) element-wise.
NOTE: GreaterEqual
supports broadcasting. More about broadcasting here
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).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 row and reverse every column 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]]]
Notes: - tf.newaxis
is None
as in NumPy. - An implicit ellipsis is placed at the end of the slice_spec
- NumPy advanced indexing is currently not supported.
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).The appropriate slice of "tensor", based on "slice_spec".
ValueError
: If a slice range is negative size.TypeError
: If the slice indices aren't int, slice, or Ellipsis.__gt__
__gt__( x, y, name=None )
Returns the truth value of (x > y) element-wise.
NOTE: Greater
supports broadcasting. More about broadcasting here
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).A Tensor
of type bool
.
__invert__
__invert__( x, name=None )
Returns the truth value of NOT x element-wise.
x
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__iter__
__iter__()
__le__
__le__( x, y, name=None )
Returns the truth value of (x <= y) element-wise.
NOTE: LessEqual
supports broadcasting. More about broadcasting here
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).A Tensor
of type bool
.
__lt__
__lt__( x, y, name=None )
Returns the truth value of (x < y) element-wise.
NOTE: Less
supports broadcasting. More about broadcasting here
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).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 arguments, and any further outer dimensions match.
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
.
For example:
# 2-D tensor `a` # [[1, 2, 3], # [4, 5, 6]] a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) # 2-D tensor `b` # [[ 7, 8], # [ 9, 10], # [11, 12]] b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) # `a` * `b` # [[ 58, 64], # [139, 154]] c = tf.matmul(a, b) # 3-D tensor `a` # [[[ 1, 2, 3], # [ 4, 5, 6]], # [[ 7, 8, 9], # [10, 11, 12]]] a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3]) # 3-D tensor `b` # [[[13, 14], # [15, 16], # [17, 18]], # [[19, 20], # [21, 22], # [23, 24]]] b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2]) # `a` * `b` # [[[ 94, 100], # [229, 244]], # [[508, 532], # [697, 730]]] c = tf.matmul(a, b) # 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.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: 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.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A 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
:
output
[..., i, j] = sum_k (a
[..., i, k] * b
[..., k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.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: FloorMod
supports broadcasting. More about broadcasting here
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).A Tensor
. Has the same type as x
.
__mul__
__mul__( x, y )
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__neg__
__neg__( x, name=None )
Computes numerical negative value element-wise.
I.e., \(y = -x\).
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).A Tensor
. Has the same type as x
.
__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.
TypeError
.
__or__
__or__( x, y )
Returns the truth value of x OR y element-wise.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).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]]
x
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.y
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.name
: A name for the operation (optional).A Tensor
.
__radd__
__radd__( y, x )
Returns x + y element-wise.
NOTE: Add
supports broadcasting. AddN
does not. More about broadcasting here
x
: A Tensor
. Must be one of the following types: bfloat16
, half
, float32
, float64
, uint8
, int8
, int16
, int32
, int64
, complex64
, complex128
, string
.y
: A Tensor
. Must have the same type as x
.name
: A name for the operation (optional).A Tensor
. Has the same type as x
.
__rand__
__rand__( y, x )
Returns the truth value of x AND y element-wise.
NOTE: LogicalAnd
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).A Tensor
of type bool
.
__rdiv__
__rdiv__( y, x )
Divide two values using Python 2 semantics. Used for Tensor.div.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).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.div(x,y)
for integers, but uses tf.floor(tf.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
.
Note that for efficiency, floordiv
uses C semantics for negative numbers (unlike Python and Numpy).
x
and y
must have the same type, and the result will have the same type as well.
x
: Tensor
numerator of real numeric type.y
: Tensor
denominator of real numeric type.name
: A name for the operation (optional).x / y
rounded down (except possibly towards zero for negative integers).
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 arguments, and any further outer dimensions match.
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
.
For example:
# 2-D tensor `a` # [[1, 2, 3], # [4, 5, 6]] a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3]) # 2-D tensor `b` # [[ 7, 8], # [ 9, 10], # [11, 12]] b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2]) # `a` * `b` # [[ 58, 64], # [139, 154]] c = tf.matmul(a, b) # 3-D tensor `a` # [[[ 1, 2, 3], # [ 4, 5, 6]], # [[ 7, 8, 9], # [10, 11, 12]]] a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3]) # 3-D tensor `b` # [[[13, 14], # [15, 16], # [17, 18]], # [[19, 20], # [21, 22], # [23, 24]]] b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2]) # `a` * `b` # [[[ 94, 100], # [229, 244]], # [[508, 532], # [697, 730]]] c = tf.matmul(a, b) # 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.]])
a
: Tensor
of type float16
, float32
, float64
, int32
, complex64
, complex128
and rank > 1.b
: 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.b_is_sparse
: If True
, b
is treated as a sparse matrix.name
: Name for the operation (optional).A 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
:
output
[..., i, j] = sum_k (a
[..., i, k] * b
[..., k, j]), for all indices i, j.
Note
: This is matrix product, not element-wise product.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: FloorMod
supports broadcasting. More about broadcasting here
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).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.
NOTE: LogicalOr
supports broadcasting. More about broadcasting here
x
: A Tensor
of type bool
.y
: A Tensor
of type bool
.name
: A name for the operation (optional).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]]
x
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.y
: A Tensor
of type float32
, float64
, int32
, int64
, complex64
, or complex128
.name
: A name for the operation (optional).A Tensor
.
__rsub__
__rsub__( y, x )
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
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).A Tensor
. Has the same type as x
.
__rtruediv__
__rtruediv__( y, x )
__rxor__
__rxor__( y, x )
x ^ y = (x | y) & ~(x & y).
__sub__
__sub__( x, y )
Returns x - y element-wise.
NOTE: Subtract
supports broadcasting. More about broadcasting here
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).A Tensor
. Has the same type as x
.
__truediv__
__truediv__( x, y )
__xor__
__xor__( x, y )
x ^ y = (x | y) & ~(x & y).
consumers
consumers()
Returns a list of Operation
s that consume this tensor.
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.
N.B. Before invoking Tensor.eval()
, its graph must have been launched in a session, and either a default session must be available, or session
must be specified explicitly.
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.A numpy array corresponding to the value of this tensor.
get_shape
get_shape()
Alias of Tensor.shape.
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.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)])
shape
: A TensorShape
representing the shape of this tensor, a TensorShapeProto
, a list, a tuple, or None.ValueError
: If shape
is not compatible with the current shape of this tensor.OVERLOADABLE_OPERATORS
__array_priority__
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Code samples licensed under the Apache 2.0 License.
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