tf.compat.v1.assert_near

Assert the condition x and y are close element-wise.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.debugging.assert_near

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

tf.abs(x[i] - y[i]) <= atol + rtol * tf.abs(y[i]).

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.

numpy compatibility

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.