⬆️ Bump keras from 2.2.4 to 2.4.3

[dependabot-preview]

Bumps keras from 2.2.4 to 2.4.3.

Release notes

Sourced from keras's releases.

Keras 2.4.0

As previously announced, we have discontinued multi-backend Keras to refocus exclusively on the TensorFlow implementation of Keras.

In the future, we will develop the TensorFlow implementation of Keras in the present repo, at keras-team/keras. For the time being, it is being developed in tensorflow/tensorflow and distributed as tensorflow.keras. In this future, the keras package on PyPI will be the same as tf.keras.

This release (2.4.0) simply redirects all APIs in the standalone keras package to point to tf.keras. This helps address user confusion regarding differences and incompatibilities between tf.keras and the standalone keras package. There is now only one Keras: tf.keras.

• Note that this release may be breaking for some workflows when going from Keras 2.3.1 to 2.4.0. Test before upgrading.
• Note that we still recommend that you import Keras as from tensorflow import keras, rather than import keras, for the time being.

Keras 2.3.1 is a minor bug-fix release. In particular, it fixes an issue with using Keras models across multiple threads.

Changes

• Bug fixes
• Documentation fixes
• No API changes
• No breaking changes

Keras 2.3.0 is the first release of multi-backend Keras that supports TensorFlow 2.0. It maintains compatibility with TensorFlow 1.14, 1.13, as well as Theano and CNTK.

This release brings the API in sync with the tf.keras API as of TensorFlow 2.0. However note that it does not support most TensorFlow 2.0 features, in particular eager execution. If you need these features, use tf.keras.

This is also the last major release of multi-backend Keras. Going forward, we recommend that users consider switching their Keras code to tf.keras in TensorFlow 2.0. It implements the same Keras 2.3.0 API (so switching should be as easy as changing the Keras import statements), but it has many advantages for TensorFlow users, such as support for eager execution, distribution, TPU training, and generally far better integration between low-level TensorFlow and high-level concepts like Layer and Model. It is also better maintained.

Development will focus on tf.keras going forward. We will keep maintaining multi-backend Keras over the next 6 months, but we will only be merging bug fixes. API changes will not be ported.

API changes

• Add size(x) to backend API.
• add_metric method added to Layer / Model (used in a similar way as add_loss, but for metrics), as well as the metrics property.
• Variables set as attributes of a Layer are now tracked in layer.weights (including layer.trainable_weights or layer.non_trainable_weights as appropriate).
• Layers set as attributes of a Layer are now tracked (so the weights/metrics/losses/etc of a sublayer are tracked by parent layers). This behavior already existed for Model specifically and is now extended to all Layer subclasses.
• Introduce class-based losses (inheriting from Loss base class). This enables losses to be parameterized via constructor arguments. Loss classes added:
  • MeanSquaredError
  • MeanAbsoluteError
  • MeanAbsolutePercentageError
  • MeanSquaredLogarithmicError
  • BinaryCrossentropy
  • CategoricalCrossentropy
  • SparseCategoricalCrossentropy
  • Hinge
  • SquaredHinge
  • CategoricalHinge
  • Poisson
  • LogCosh
  • KLDivergence
  • Huber
• Introduce class-based metrics (inheriting from Metric base class). This enables metrics to be stateful (e.g. required for supported AUC) and to be parameterized via constructor arguments. Metric classes added:

Commits

• See full diff in compare view

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[coveralls]

Pull Request Test Coverage Report for Build 536

• 0 of 0 changed or added relevant lines in 0 files are covered.
• No unchanged relevant lines lost coverage.
• Overall coverage remained the same at 0.0%

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Totals
Change from base Build 524:	0%
Covered Lines:	0
Relevant Lines:	0

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