Fix the compatibility issues of Orthogonal and GRU
#19844
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
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #19844 +/- ##
=======================================
Coverage 78.84% 78.84%
=======================================
Files 498 498
Lines 45846 45846
Branches 8448 8448
=======================================
Hits 36149 36149
Misses 7995 7995
Partials 1702 1702
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
fchollet
approved these changes
Jun 12, 2024
google-ml-butler
bot
added
kokoro:force-run
ready to pull
google-ml-butler
bot
removed
ready to pull
james77777778
pushed a commit
to james77777778/keras
that referenced
this pull request
Jun 15, 2024
Fix `LayerNormalization.get_config` (keras-team#19807) Propagate kwargs through `keras.ops.isclose` (keras-team#19782) * propagate kwargs through isclose this allows passing atol and rtol * switch isclose **kwargs to explicit kwargs * reduce line lengths * fix ops.isclose signature * fix ops.IsClose compute_output_spec signature * implement isclose rtol atol equal_nan args for all backends * shorten line lengths again * revert using tf.experimental.numpy.isclose tensorflow version now uses code inspired from tf.experimental.numpy.isclose * fix lint * add docs for new parameters Faster in_top_k implementation for Jax backend (keras-team#19814) * Faster in_top_k implementation. * Fix bug in rank computation. Fix CI Fix TypeError in `Lambda.from_config` (keras-team#19827) fixing dmtree.is_nested() and parameterized tree test (keras-team#19822) Fix `keras.ops.repeat` cannot return an expected shape when `x` is a … (keras-team#19826) * Fix `keras.ops.repeat` cannot return an expected shape when `x` is a `KerasTensor` and the `axis` is `None` * Test dynamic is still dynamic after repetition * Improve error messages `Metric.variables` is now recursive. (keras-team#19830) This allows it to surface variables from metrics nested at any depth. Previously, metrics within metrics within metrics would not have their variables tracked in JAX, causing them to not be updated. Fix `get_file` when the HTTP response has no `Content-Length` header (keras-team#19833) Add `ops.switch` (keras-team#19834) * Add `ops.switch` * Update tests * Fix out-of-bound issue * Revert `torch.cond` Use `absl.testing.parameterized` for `tree_test.py`. (keras-team#19842) For consistency, use `absl.testing.parameterized` instead of `parameterized` for `tree_test.py` since that is used for all other tests. It's one less dependency. It also says `optree` or `dmtree` in each test name. Make batch norm mask shape error more descriptive (keras-team#19829) * Made batch norm mask shape error more descriptive * Added shape info in mask error message to help with degugging Fix code style doc: `ops.slice` (keras-team#19843) corrected the example code in unit_normalization.py (keras-team#19845) Added missing closing bracket and exact output value in example code after replicating the code. Adjust code example Add `training` argument to `Model.compute_loss()`. (keras-team#19840) This allows models to perform different computations during training and evaluation. For instance, some expensive to compute metrics can be skipped during training and only computed during evaluation. Note that backwards compatibility with overrides that do not have the `training` argument is maintained. Fix the compatibility issues of `Orthogonal` and `GRU` (keras-team#19844) * Add legacy `Orthogonal` class name * Add legacy `implementation` arg to `GRU` Fix inconsistent behavior of `losses.sparse_categorical_crossentropy`… (keras-team#19838) * Fix inconsistent behavior of `losses.sparse_categorical_crossentropy` with and without `ignore_class` * Test * chore(format) * Fix tests in `losses` Fix bugs with `Mean`, `Accuracy` and `BinaryAccuracy` metrics. (keras-team#19847) - `reduce_to_samplewise_values` would not reduce `sample_weights` correctly because the number of dimensions of `values` was checked. - `reduce_to_samplewise_values` needs to explicitely broadcast `sample_weights`. Before, it was implicitly broadcast in the multiplication with `values`. However, the explicit broadcast is needed for the computation of `num_samples` for the averaging to be correct. This causes a bug when `sample_weights` is of rank 2 or more and a broadcast happens when doing the multiplication. This logic existed in `tf_keras`: https://github.com/keras-team/tf-keras/blob/master/tf_keras/metrics/base_metric.py#L508 - `Accuracy` and `BinaryAccuracy` were doing a mean reduction too early, before multiplying by `sample_weights`. This matters when the rank of `sample_weights` is the same as `y_true` and `y_pred`. Add tests for `DTypePolicyMap` Fix test Update the logic of `default_policy` Improve serialization of `DTypePolicyMap` Improve `__repr__` and `__eq__` Add `custom_gradient` for the numpy backend (keras-team#19849) fix variable name when add in init function (keras-team#19853) Address comments
james77777778
pushed a commit
to james77777778/keras
that referenced
this pull request
Jun 15, 2024
Introduce `DTypePolicyMap` Fix `LayerNormalization.get_config` (keras-team#19807) Propagate kwargs through `keras.ops.isclose` (keras-team#19782) * propagate kwargs through isclose this allows passing atol and rtol * switch isclose **kwargs to explicit kwargs * reduce line lengths * fix ops.isclose signature * fix ops.IsClose compute_output_spec signature * implement isclose rtol atol equal_nan args for all backends * shorten line lengths again * revert using tf.experimental.numpy.isclose tensorflow version now uses code inspired from tf.experimental.numpy.isclose * fix lint * add docs for new parameters Faster in_top_k implementation for Jax backend (keras-team#19814) * Faster in_top_k implementation. * Fix bug in rank computation. Fix CI Fix TypeError in `Lambda.from_config` (keras-team#19827) fixing dmtree.is_nested() and parameterized tree test (keras-team#19822) Fix `keras.ops.repeat` cannot return an expected shape when `x` is a … (keras-team#19826) * Fix `keras.ops.repeat` cannot return an expected shape when `x` is a `KerasTensor` and the `axis` is `None` * Test dynamic is still dynamic after repetition * Improve error messages `Metric.variables` is now recursive. (keras-team#19830) This allows it to surface variables from metrics nested at any depth. Previously, metrics within metrics within metrics would not have their variables tracked in JAX, causing them to not be updated. Fix `get_file` when the HTTP response has no `Content-Length` header (keras-team#19833) Add `ops.switch` (keras-team#19834) * Add `ops.switch` * Update tests * Fix out-of-bound issue * Revert `torch.cond` Use `absl.testing.parameterized` for `tree_test.py`. (keras-team#19842) For consistency, use `absl.testing.parameterized` instead of `parameterized` for `tree_test.py` since that is used for all other tests. It's one less dependency. It also says `optree` or `dmtree` in each test name. Make batch norm mask shape error more descriptive (keras-team#19829) * Made batch norm mask shape error more descriptive * Added shape info in mask error message to help with degugging Fix code style doc: `ops.slice` (keras-team#19843) corrected the example code in unit_normalization.py (keras-team#19845) Added missing closing bracket and exact output value in example code after replicating the code. Adjust code example Add `training` argument to `Model.compute_loss()`. (keras-team#19840) This allows models to perform different computations during training and evaluation. For instance, some expensive to compute metrics can be skipped during training and only computed during evaluation. Note that backwards compatibility with overrides that do not have the `training` argument is maintained. Fix the compatibility issues of `Orthogonal` and `GRU` (keras-team#19844) * Add legacy `Orthogonal` class name * Add legacy `implementation` arg to `GRU` Fix inconsistent behavior of `losses.sparse_categorical_crossentropy`… (keras-team#19838) * Fix inconsistent behavior of `losses.sparse_categorical_crossentropy` with and without `ignore_class` * Test * chore(format) * Fix tests in `losses` Fix bugs with `Mean`, `Accuracy` and `BinaryAccuracy` metrics. (keras-team#19847) - `reduce_to_samplewise_values` would not reduce `sample_weights` correctly because the number of dimensions of `values` was checked. - `reduce_to_samplewise_values` needs to explicitely broadcast `sample_weights`. Before, it was implicitly broadcast in the multiplication with `values`. However, the explicit broadcast is needed for the computation of `num_samples` for the averaging to be correct. This causes a bug when `sample_weights` is of rank 2 or more and a broadcast happens when doing the multiplication. This logic existed in `tf_keras`: https://github.com/keras-team/tf-keras/blob/master/tf_keras/metrics/base_metric.py#L508 - `Accuracy` and `BinaryAccuracy` were doing a mean reduction too early, before multiplying by `sample_weights`. This matters when the rank of `sample_weights` is the same as `y_true` and `y_pred`. Add tests for `DTypePolicyMap` Fix test Update the logic of `default_policy` Improve serialization of `DTypePolicyMap` Improve `__repr__` and `__eq__` Add `custom_gradient` for the numpy backend (keras-team#19849) fix variable name when add in init function (keras-team#19853) Address comments Update docstrings
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
4 participants
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.
Fix #19828
Just two simple fixes. Tests have been added.