Kinda hacky way:
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients([
(grad, var)
for (grad, var) in zip(gradients, model.trainable_variables)
if grad is not None
])
I have a model that, based on certain conditions, has some unconnected gradients, and this is exactly what I want. But Tensorflow is printing out a Warning every time it encounters the unconnected gradient.
WARNING:tensorflow:Gradients do not exist for variables
Is there any way to only suppress this specific warning? I don't want to blindly suppress all warnings since there might be unexpected (and potentially useful) warnings in the future as I'm still working on my model.
Kinda hacky way:
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients([
(grad, var)
for (grad, var) in zip(gradients, model.trainable_variables)
if grad is not None
])