Assert the condition x
and y
are close element-wise.
tf.compat.v1.assert_near( x, y, rtol=None, atol=None, data=None, summarize=None, message=None, name=None )
Example of adding a dependency to an operation:
with tf.control_dependencies([tf.compat.v1.assert_near(x, y)]): output = tf.reduce_sum(x)
This condition holds if for every pair of (possibly broadcast) elements x[i]
, y[i]
, we have
If both x
and y
are empty, this is trivially satisfied.
The default atol
and rtol
is 10 * eps
, where eps
is the smallest representable positive number such that 1 + eps != 1
. This is about 1.2e-6
in 32bit
, 2.22e-15
in 64bit
, and 0.00977
in 16bit
. See numpy.finfo
.
Args | |
---|---|
x | Float or complex Tensor . |
y | Float or complex Tensor , same dtype as, and broadcastable to, x . |
rtol | Tensor . Same dtype as, and broadcastable to, x . The relative tolerance. Default is 10 * eps . |
atol | Tensor . Same dtype as, and broadcastable to, x . The absolute tolerance. Default is 10 * eps . |
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_near". |
Returns | |
---|---|
Op that raises InvalidArgumentError if x and y are not close enough. |
Similar to numpy.testing.assert_allclose
, except tolerance depends on data type. This is due to the fact that TensorFlow
is often used with 32bit
, 64bit
, and even 16bit
data.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/assert_near