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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.
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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.
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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.
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Numpy Compatibility
Similar to numpy.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.