import torchbearer
from torchbearer.callbacks import Callback
import math
[docs]class TerminateOnNaN(Callback):
"""Callback which montiors the given metric and halts training if its value is nan or
inf.
:param monitor: The metric name to monitor
:type monitor: str
"""
def __init__(self, monitor='running_loss'):
super(TerminateOnNaN, self).__init__()
self._monitor = monitor
def _check(self, state):
if self._monitor in state[torchbearer.METRICS]:
value = state[torchbearer.METRICS][self._monitor]
if value is not None:
if math.isnan(value) or math.isinf(value):
print('Invalid ' + self._monitor + ', terminating')
state[torchbearer.STOP_TRAINING] = True
[docs] def on_step_training(self, state):
self._check(state)
[docs] def on_end_epoch(self, state):
self._check(state)
[docs] def on_step_validation(self, state):
self._check(state)