Release Keras 2.0.7 · keras-team/keras

Areas of improvement

Bug fixes.
Performance improvements.
Documentation improvements.
Better support for training models from data tensors in TensorFlow (e.g. Datasets, TFRecords). Add a related example script.
Improve TensorBoard UX with better grouping of ops into name scopes.
Improve test coverage.

Add clone_model method, enabling to construct a new model, given an existing model to use as a template. Works even in a TensorFlow graph different from that of the original model.
Add target_tensors argument in compile, enabling to use custom tensors or placeholders as model targets.
Add steps_per_epoch argument in fit, enabling to train a model from data tensors in a way that is consistent with training from Numpy arrays.
Similarly, add steps argument in predict and evaluate.
Add Subtract merge layer, and associated layer function subtract.
Add weighted_metrics argument in compile to specify metric functions meant to take into account sample_weight or class_weight.
Make the stop_gradients backend function consistent across backends.
Allow dynamic shapes in repeat_elements backend function.
Enable stateful RNNs with CNTK.

The backend methods categorical_crossentropy, sparse_categorical_crossentropy, binary_crossentropy had the order of their positional arguments (y_true, y_pred) inverted. This change does not affect the losses API. This change was done to achieve API consistency between the losses API and the backend API.
Move constraint management to be based on variable attributes. Remove the now-unused constraints attribute on layers and models (not expected to affect any user).

Thanks to our 47 contributors whose commits are featured in this release!