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.