This repository was archived by the owner on Apr 23, 2025. It is now read-only.
Removing softmax as terminal activation function from two models #232
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The LeNet-5 and DenseNet models were previously set up with a softmax activation function on their last layer. This caused problems during training, because they were then used with
softmaxCrossEntropy(logits:labels:), leading to the softmax being applied twice. With this modification, the LeNet-5 model trained on MNIST matches the loss and accuracy of a reference Python TF 2.0 version of the same model at each step in training.Additionally, the sum of the loss, rather than the average loss, was being reported for the LeNet-MNIST example. This has been corrected.
Finally, leftover TODO comments from a completed fix have been removed.