Class Tensor

Represents one of the outputs of an Operation.

Aliases:

Class tf.compat.v1.Tensor
Class tf.compat.v2.Tensor tf.compat.v1.SessionA 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 .

This class has two primary purposes: In the following example, c, d, and e are symbolic Tensor obje``cts, 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. tf.TensorShapeThe shape is computed using shape inference functions that are registered in the Op for each Operation. See 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)])

Tensor.set_shape()In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, 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 . The absolute value is computed as . 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. math.logical_andNOTE: 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. tf.compat.v1.div(x,y)The same as for integers, but uses tf.floor() 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. math.greater_equalNOTE: 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. math.greaterNOTE: 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. math.less_equalNOTE: 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. math.lessNOTE: 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 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).

Returns:

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.

Raises:

ValueError: If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True.

mod

View source

 __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. math.floormodNOTE: supports broadcasting. More about broadcasting here

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

View source

 __mul__(
    x,
    y
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

ne

View source

 __ne__(other)

Compares two tensors element-wise for equality.

neg

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

 __neg__(
    x,
    name=None
)

Computes numerical negative value element-wise. I.e., .

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

nonzero

View source

 __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

View source

 __or__(
    x,
    y
)

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

pow

View source

 __pow__(
    x,
    y
)

Computes the power of one value to another. Given a tensor x and a tensor y, this operation computes 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

View source

 __radd__(
    y,
    x
)

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

rand

View source

 __rand__(
    y,
    x
)

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

rdiv

View source

 __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

View source

 __rfloordiv__(
    y,
    x
)

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

View source

 __rmatmul__(
    y,
    x
)

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 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).

Returns:

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

View source

 __rmod__(
    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

View source

 __rmul__(
    y,
    x
)

Dispatches cwise mul for "DenseDense" and "DenseSparse".

ror

View source

 __ror__(
    y,
    x
)

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

rpow

View source

 __rpow__(
    y,
    x
)

Computes the power of one value to another. Given a tensor x and a tensor y, this operation computes 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

View source

 __rsub__(
    y,
    x
)

Returns x - y element-wise. hereNOTE: Subtract supports broadcasting. More about broadcasting

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

View source

 __rtruediv__(
    y,
    x
)

rxor

View source

 __rxor__(
    y,
    x
)

Logical XOR function. x ^ y = (x | y) & ~(x & y) Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.

Usage:

 x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
#  here z = [False  True  True False]

Args:

x: A Tensor type bool.
y: A Tensor of type bool.

Returns:

A Tensor of type bool with the same size as that of x or y.

sub

View source

 __sub__(
    x,
    y
)

Returns x - y element-wise. hereNOTE: Subtract supports broadcasting. More about broadcasting

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

View source

 __truediv__(
    x,
    y
)

xor

View source

 __xor__(
    x,
    y
)

Logical XOR function. x ^ y = (x | y) & ~(x & y) Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.

Usage:

 x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
#  here z = [False  True  True False]

Args:

x: A Tensor type bool.
y: A Tensor of type bool.

Returns:

A Tensor of type bool with the same size as that of x or y.

consumers

View source

 consumers()

Returns a list of Operations that consume this tensor.

Returns:

A list of Operations.

eval

View source

 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. Tensor.eval()N.B. Before invoking , its graph must have been launched in a session, and either a default session must be available, or session 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

View source

 experimental_ref()

Returns a hashable reference object to this Tensor. The primary usecase 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.

 import tensorflow as tf

x = tf.constant(5)
y = tf.constant(10)
z = tf.constant(10)

# The followings will raise an exception starting 2.0
# TypeError: Tensor is unhashable if Tensor equality is enabled.
tensor_set = {x, y, z}
tensor_dict = {x: 'five', y: 'ten', z: 'ten'}

Instead, we can use tensor.experimental_ref().

 tensor_set = {x.experimental_ref(),
              y.experimental_ref(),
              z.experimental_ref()}

print(x.experimental_ref() in tensor_set)
==> True

tensor_dict = {x.experimental_ref(): 'five',
               y.experimental_ref(): 'ten',
               z.experimental_ref(): 'ten'}

print(tensor_dict[y.experimental_ref()])
==> ten

Also, the reference object provides .deref() function that returns the original Tensor.

 x = tf.constant(5)
print(x.experimental_ref().deref())
==> tf.Tensor(5, shape=(), dtype=int32)

get_shape

View source

 get_shape()

Alias of Tensor.shape.

set_shape

View source

 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)])

tf.ensure_shapeNOTE: This shape is not enforced at runtime. Setting incorrect shapes can result in inconsistencies between the statically-known graph and the runtime value of tensors. For runtime validation of the shape, use instead.

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.

Class Members

OVERLOADABLE_OPERATORS

# Class Tensor

# Aliases:

# init

# Args:

# Raises:

# Properties

# device

# dtype

# graph

# name

# op

# shape

# Returns:

# value_index

# Methods

# abs

# Args:

# Returns:

# add

# and

# Args:

# Returns:

# bool

# Raises:

# div

# Args:

# Returns:

# eq

# floordiv

# Args:

# Returns:

# Raises:

# ge

# Args:

# Returns:

# getitem

# Some useful examples:

# Notes:

# Args:

# Returns:

# Raises:

# gt

# Args:

# Returns:

# invert

# Args:

# Returns:

# iter

# le

# Args:

# Returns:

# len

# lt

# Args:

# Returns:

# matmul

# For example:

# Args:

# Returns:

# Raises:

# mod

# Args:

# Returns:

# mul

# ne

# neg

# Args:

# Returns:

# nonzero

# Raises:

# or

# Args:

# Returns:

# pow

# Args:

# Returns:

# radd

# rand

# Args:

# Returns:

Class Tensor

Aliases:

init

Args:

Raises:

Properties

device

dtype

graph

name

op

shape

Returns:

value_index

Methods

abs

Args:

Returns:

add

and

Args:

Returns:

bool

Raises:

div

Args:

Returns:

eq

floordiv

Args:

Returns:

Raises:

ge

Args:

Returns:

getitem

Some useful examples:

Notes:

Args:

Returns:

Raises:

gt

Args:

Returns:

invert

Args:

Returns:

iter

le

Args:

Returns:

len

lt

Args:

Returns:

matmul

For example:

Args:

Returns:

Raises:

mod

Args:

Returns:

mul

ne

neg

Args:

Returns:

nonzero

Raises:

or

Args:

Returns:

pow

Args:

Returns:

radd

rand

Args:

Returns: