removed numpy.vectorize() wrappers #20
Merged
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
directly, instead of wrapping them numpy's vectorize() function
|
Thanks for making the changes. You look to have been quite thorough in modifying both network and network2. With that said, I'll hold off on merging the pull request until I've got time to return to my work on the book - I'd rather the code in the book and on the repo don't get out of sync. In the meantime, I'll keep this pull request open. |
mnielsen
added a commit
that referenced
this pull request
Aug 6, 2015
removed numpy.vectorize() wrappers
|
Looking through the code, I will also change spv to sp throughout. I'll make that commit separately. |
|
I've now made the spv -> sp change. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
As they slow down the sigmoid functions considerably and unnecessarily. The network's SGD method runs several times faster with this simple modification.