tf.Tensor

TensorFlow 2 version View source on GitHub

Class Tensor

Represents one of the outputs of an Operation.

Aliases:

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.compat.v1.Session.

This class has two primary purposes:

  1. 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.

  2. 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.compat.v1.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.compat.v1.Session()

# Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e)

__init__

View source

__init__(
    op,
    value_index,
    dtype
)

Creates a new Tensor.

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.

Properties

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.

Returns:

A TensorShape representing the shape of this tensor.

value_index

The index of this tensor in the outputs of its Operation.

Methods

__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]

Args:

  • x: A Tensor or SparseTensor of type float16, float32, float64, int32, int64, complex64 or complex128.
  • name: A name for the operation (optional).

Returns:

A Tensor or SparseTensor the same size, type, and sparsity as x with absolute values. Note, for complex64 or complex128 input, the returned Tensor will be of type float32 or float64, respectively.

If x is a SparseTensor, returns SparseTensor(x.indices, tf.math.abs(x.values, ...), x.dense_shape)

__add__

View source

__add__(
    x,
    y
)

Dispatches to add for strings and add_v2 for all other types.

__and__

View source

__and__(
    x,
    y
)

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

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

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__

View source

__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__

View source

__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__

View source

__eq__(other)

Compares two tensors element-wise for equality.

__floordiv__

View source

__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__

Defined in generated file: python/ops/gen_math_ops.py

__ge__(
    x,
    y,
    name=None
)

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

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

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__

View source

__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 is None 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__

Defined in generated file: python/ops/gen_math_ops.py

__gt__(
    x,
    y,
    name=None
)

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

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

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__

Defined in generated file: python/ops/gen_math_ops.py

__invert__(
    x,
    name=None
)

Returns the truth value of NOT x element-wise.

Args:

  • x: A Tensor of type bool.
  • name: A name for the operation (optional).

Returns:

A Tensor of type bool.

__iter__

View source

__iter__()

__le__

Defined in generated file: python/ops/gen_math_ops.py

__le__(
    x,
    y,
    name=None
)

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

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

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__

View source

__len__()

__lt__

Defined in generated file: python/ops/gen_math_ops.py

__lt__(
    x,
    y,
    name=None
)

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

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

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__

View source

__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.]])

Args:

  • 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 b