from_logits=True and from_logits=False get different training result for tf.losses.CategoricalCrossentropy for UNet

Pushing the "softmax" activation into the cross-entropy loss layer significantly simplifies the loss computation and makes it more numerically stable.
It might be the case that in your example the numerical issues are significant enough to render the training process ineffective for the from_logits=False option.

You can find a derivation of the cross entropy loss (a special case of "info gain" loss) in this post. This derivation illustrates the numerical issues that are averted when combining softmax with cross entropy loss.