Bump keras from 2.2.4 to 2.3.0

[dependabot-preview]

Bumps keras from 2.2.4 to 2.3.0.

Release notes

Sourced from keras's releases.

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:
  • Accuracy
  • MeanSquaredError
  • Hinge
  • CategoricalHinge
  • SquaredHinge
  • FalsePositives
  • TruePositives
  • FalseNegatives
  • TrueNegatives
  • BinaryAccuracy
  • CategoricalAccuracy
  • TopKCategoricalAccuracy
  • LogCoshError
  • Poisson
  • KLDivergence
  • CosineSimilarity
  • MeanAbsoluteError
  • MeanAbsolutePercentageError
  • MeanSquaredError

... (truncated)

Commits

• a0335a3 Update README
• 9080613 Fix deprecation warnings related to TF v1
• cf9595a Fix sequence timeout deadlock (#13322)
• 7869134 Added messages about the future of multi-backend Keras. (#13315)
• d3512f7 #13239 Improved documentation for EarlyStopping/ReduceLROnPlateau, take valid...
• 7183813 Merge branch 'master' of github.com:keras-team/keras
• 033983d Fix Travis SSL issue.
• 93b0f1c Complete the docs by adding data to multi-input/output example (#12775)
• cb96315 Added batch_normalization in the numpy backend. (#11556)
• 1eac861 fix in "Layer.compute_output_shape" description (#13210)
• Additional commits viewable in compare view

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