Add EfficientNet benchmark #394
Merged
Conversation
This file contains 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
|
Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA). View this failed invocation of the CLA check for more information. For the most up to date status, view the checks section at the bottom of the pull request. |
fchollet
reviewed
Jun 23, 2023
| @@ -61,8 +61,22 @@ def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None): | |||
| ) | |||
|
|
|||
|
|
|||
| def _get_concrete_noise_shape(inputs, noise_shape): | |||
There was a problem hiding this comment.
This is very odd, for some reason tf.stateless_drop does not support None in the batch dim, so we need to give it a concrete value. Without this change, TF backend throws an error at dropout layer.
chenmoneygithub
force-pushed
the
chen-resnet-benchmark
branch
from
f00e967
to
05e476b
Compare
added 2 commits
June 23, 2023 20:57
fchollet
approved these changes
Jun 23, 2023
|
@fchollet I will share a spreadsheet with the benchmark results, similar to layer benchmark. But briefly JAX is faster than TF backend. |
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Outdated suggestions cannot be applied.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.
This is a real model benchmark on real dataset, so it could give us a good overview keras core performance in different backends.
I am creating a new compute engine for TF testing, because the old one hits a cuda version incompatibility (as always!). Will share the metrics later, but the code is ready for review.