Hi ML friends!
I’m confused about the code below in bold. Perhaps someone can explain. I understand that logs.get(‘accuracy’)>.99 should trigger cancelling training, but why is there another condition here? I’m not sure what it should be. I have initially not provided any of my code, so as to not violate any community standards. I am asking a conceptual question that should not require code. Thanks!
class myCallback():
# Define the correct function signature for on_epoch_end
def on_epoch_end(None, None, None=None):
if logs.get(‘accuracy’) is not None and logs.get(‘accuracy’) > 0.99:
print(“\nReached 99% accuracy so cancelling training!”)
…
Thanks!
Dan
As far as my memory serves right, the grader invokes this callback even before the 1st epoch i.e. the training starts. I’m guessing this is based on a much older version of tensorflow i.e. < 2.7. As a result, you’ll get an error when you compare None and .99.
As per the tensorflow framework >= 2.7, this method will be invoked only after the 1st epoch. It’s safe to directly check for logs['accuracy'] inside the callback.
I too was confused by the “is not ____” in the if statement. I used 0 (zero) and passed the lab but don’t know what was expected or why.
When the framework invokes the on_epoch_end callback before end of 1st epoch, logs.get(any_key) will yield None. So, please check for None and not for 0