tf.debugging.assert_none_equal | TensorFlow v2.16.1 (original) (raw)
tf.debugging.assert_none_equal
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Assert the condition x != y holds element-wise.
tf.debugging.assert_none_equal(
x, y, summarize=None, message=None, name=None
)
This Op checks that x[i] != y[i] holds for every pair of (possibly broadcast) elements of x and y. If both x and y are empty, this is trivially satisfied.
If x != y does not hold, message, as well as the first summarizeentries of x and y are printed, and InvalidArgumentError is raised.
When using inside tf.function, this API takes effects during execution. It's recommended to use this API with tf.control_dependencies to ensure the correct execution order.
In the following example, without tf.control_dependencies, errors may not be raised at all. Check tf.control_dependencies for more details.
def check_size(x):
with tf.control_dependencies([
tf.debugging.assert_none_equal(tf.size(x), 6,
message='Bad tensor size')]):
return x
check_size(tf.ones([2, 3], tf.float32))
Traceback (most recent call last):
``
InvalidArgumentError: ...
| Args | |
|---|---|
| x | Numeric Tensor. |
| y | Numeric Tensor, same dtype as and broadcastable to x. |
| message | A string to prefix to the default message. (optional) |
| summarize | Print this many entries of each tensor. (optional) |
| name | A name for this operation (optional). Defaults to "assert_none_equal". |
| Returns |
|---|
| Op that raises InvalidArgumentError if x != y is False. This can be used with tf.control_dependencies inside of tf.functions to block followup computation until the check has executed. |
| Raises | |
|---|---|
| InvalidArgumentError | if the check can be performed immediately andx == y is False. The check can be performed immediately during eager execution or if x and y are statically known. |
eager compatibility
returns None