The NanGuardMode aims to prevent the model from outputting NaNs or Infs. It has a number of self-checks, which can help to find out which Apply node is generating those incorrect outputs. It provides automatic detection of three types of abnormal values: NaNs, Infs, and abnormally big values.

NanGuardMode can be used as follows:

import numpy as np
import pytensor
import pytensor.tensor as pt
from pytensor.compile.nanguardmode import NanGuardMode

x = pt.matrix()
w = pytensor.shared(np.random.standard_normal((5, 7)).astype(pytensor.config.floatX))
y =, w)
fun = pytensor.function(
    [x], y,
    mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True)

While using the PyTensor function fun, it will monitor the values of each input and output variable of each node. When abnormal values are detected, it raises an error to indicate which node yields the NaNs. For example, if we pass the following values to fun:

infa = np.tile((np.asarray(100.) ** 1000000).astype(pytensor.config.floatX), (3, 5))

It will raise an AssertionError indicating that Inf value is detected while executing the function.

You can also set the three parameters in NanGuardMode to indicate which kind of abnormal values to monitor. nan_is_error and inf_is_error has no default values, so they need to be set explicitly, but big_is_error is set to be True by default.


NanGuardMode significantly slows down computations; only enable as needed.


class pytensor.compile.nanguardmode.NanGuardMode(nan_is_error=None, inf_is_error=None, big_is_error=None, optimizer='default', linker=None, db=None)[source]#

A PyTensor compilation Mode that makes the compiled function automatically detect NaNs and Infs and detect an error if they occur.

  • nan_is_error (bool) – If True, raise an error anytime a NaN is encountered.

  • inf_is_error (bool) – If True, raise an error anytime an Inf is encountered. Note that some pylearn2 modules currently use np.inf as a default value (e.g. mlp.max_pool) and these will cause an error if inf_is_error is True.

  • big_is_error (bool) – If True, raise an error when a value greater than 1e10 is encountered.


We ignore the linker parameter