`tf.assert_negative(x, data=None, summarize=None, message=None, name=None)`

{#assert_negative}Assert the condition `x < 0`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_negative(x)], x)

Negative means, for every element `x[i]`

of `x`

, we have `x[i] < 0`

. If `x`

is empty this is trivially satisfied.

`x`

: Numeric`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_negative”.

Op raising `InvalidArgumentError`

unless `x`

is all negative.

`tf.assert_positive(x, data=None, summarize=None, message=None, name=None)`

{#assert_positive}Assert the condition `x > 0`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_positive(x)], x)

Positive means, for every element `x[i]`

of `x`

, we have `x[i] > 0`

. If `x`

is empty this is trivially satisfied.

`x`

: Numeric`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_positive”.

Op raising `InvalidArgumentError`

unless `x`

is all positive.

`tf.assert_proper_iterable(values)`

{#assert_proper_iterable}Static assert that values is a “proper” iterable.

`Ops`

that expect iterables of `Tensor`

can call this to validate input. Useful since `Tensor`

, `ndarray`

, byte/text type are all iterables themselves.

`values`

: Object to be checked.

`TypeError`

: If`values`

is not iterable or is one of`Tensor`

,`SparseTensor`

,`np.array`

,`tf.compat.bytes_or_text_types`

.

`tf.assert_non_negative(x, data=None, summarize=None, message=None, name=None)`

{#assert_non_negative}Assert the condition `x >= 0`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_non_negative(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_non_negative(x)], x)

Non-negative means, for every element `x[i]`

of `x`

, we have `x[i] >= 0`

. If `x`

is empty this is trivially satisfied.

`x`

: Numeric`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_non_negative”.

Op raising `InvalidArgumentError`

unless `x`

is all non-negative.

`tf.assert_non_positive(x, data=None, summarize=None, message=None, name=None)`

{#assert_non_positive}Assert the condition `x <= 0`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_non_positive(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_non_positive(x)], x)

Non-positive means, for every element `x[i]`

of `x`

, we have `x[i] <= 0`

. If `x`

is empty this is trivially satisfied.

`x`

: Numeric`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_non_positive”.

Op raising `InvalidArgumentError`

unless `x`

is all non-positive.

`tf.assert_equal(x, y, data=None, summarize=None, message=None, name=None)`

{#assert_equal}Assert the condition `x == y`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements `x[i]`

, `y[i]`

, we have `x[i] == y[i]`

. If both `x`

and `y`

are empty, this is trivially satisfied.

`x`

: Numeric`Tensor`

.`y`

: Numeric`Tensor`

, same dtype as and broadcastable to`x`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

,`y`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_equal”.

Op that raises `InvalidArgumentError`

if `x == y`

is False.

`tf.assert_integer(x, message=None, name=None)`

{#assert_integer}Assert that `x`

is of integer dtype.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_integer(x)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_integer(x)], x)

`x`

:`Tensor`

whose basetype is integer and is not quantized.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_integer”.

`TypeError`

: If`x.dtype`

is anything other than non-quantized integer.

A `no_op`

that does nothing. Type can be determined statically.

`tf.assert_less(x, y, data=None, summarize=None, message=None, name=None)`

{#assert_less}Assert the condition `x < y`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_less(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_less(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements `x[i]`

, `y[i]`

, we have `x[i] < y[i]`

. If both `x`

and `y`

are empty, this is trivially satisfied.

`x`

: Numeric`Tensor`

.`y`

: Numeric`Tensor`

, same dtype as and broadcastable to`x`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

,`y`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_less”.

Op that raises `InvalidArgumentError`

if `x < y`

is False.

`tf.assert_less_equal(x, y, data=None, summarize=None, message=None, name=None)`

{#assert_less_equal}Assert the condition `x <= y`

holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_less_equal(x, y)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_less_equal(x, y)], x)

This condition holds if for every pair of (possibly broadcast) elements `x[i]`

, `y[i]`

, we have `x[i] <= y[i]`

. If both `x`

and `y`

are empty, this is trivially satisfied.

`x`

: Numeric`Tensor`

.`y`

: Numeric`Tensor`

, same dtype as and broadcastable to`x`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

,`y`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_less_equal”

Op that raises `InvalidArgumentError`

if `x <= y`

is False.

`tf.assert_rank(x, rank, data=None, summarize=None, message=None, name=None)`

{#assert_rank}Assert `x`

has rank equal to `rank`

.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_rank(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_rank(x, 2)], x)

`x`

: Numeric`Tensor`

.`rank`

: Scalar integer`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_rank”.

Op raising `InvalidArgumentError`

unless `x`

has specified rank. If static checks determine `x`

has correct rank, a `no_op`

is returned.

`ValueError`

: If static checks determine`x`

has wrong rank.

`tf.assert_rank_at_least(x, rank, data=None, summarize=None, message=None, name=None)`

{#assert_rank_at_least}Assert `x`

has rank equal to `rank`

or higher.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_rank_at_least(x, 2)]): output = tf.reduce_sum(x)

Example of adding dependency to the tensor being checked:

x = tf.with_dependencies([tf.assert_rank_at_least(x, 2)], x)

`x`

: Numeric`Tensor`

.`rank`

: Scalar`Tensor`

.`data`

: The tensors to print out if the condition is False. Defaults to error message and first few entries of`x`

.`summarize`

: Print this many entries of each tensor.`message`

: A string to prefix to the default message.`name`

: A name for this operation (optional). Defaults to “assert_rank_at_least”.

Op raising `InvalidArgumentError`

unless `x`

has specified rank or higher. If static checks determine `x`

has correct rank, a `no_op`

is returned.

`ValueError`

: If static checks determine`x`

has wrong rank.

`tf.assert_type(tensor, tf_type, message=None, name=None)`

{#assert_type}Statically asserts that the given `Tensor`

is of the specified type.

`tensor`

: A tensorflow`Tensor`

.`tf_type`

: A tensorflow type (dtypes.float32, tf.int64, dtypes.bool, etc).`message`

: A string to prefix to the default message.`name`

: A name to give this`Op`

. Defaults to “assert_type”

`TypeError`

: If the tensors data type doesn't match tf_type.

A `no_op`

that does nothing. Type can be determined statically.

`tf.is_non_decreasing(x, name=None)`

{#is_non_decreasing}Returns `True`

if `x`

is non-decreasing.

Elements of `x`

are compared in row-major order. The tensor `[x[0],...]`

is non-decreasing if for every adjacent pair we have `x[i] <= x[i+1]`

. If `x`

has less than two elements, it is trivially non-decreasing.

See also: `is_strictly_increasing`

`x`

: Numeric`Tensor`

.`name`

: A name for this operation (optional). Defaults to “is_non_decreasing”

Boolean `Tensor`

, equal to `True`

iff `x`

is non-decreasing.

`TypeError`

: if`x`

is not a numeric tensor.

`tf.is_numeric_tensor(tensor)`

{#is_numeric_tensor}`tf.is_strictly_increasing(x, name=None)`

{#is_strictly_increasing}Returns `True`

if `x`

is strictly increasing.

Elements of `x`

are compared in row-major order. The tensor `[x[0],...]`

is strictly increasing if for every adjacent pair we have `x[i] < x[i+1]`

. If `x`

has less than two elements, it is trivially strictly increasing.

See also: `is_non_decreasing`

`x`

: Numeric`Tensor`

.`name`

: A name for this operation (optional). Defaults to “is_strictly_increasing”

Boolean `Tensor`

, equal to `True`

iff `x`

is strictly increasing.

`TypeError`

: if`x`

is not a numeric tensor.