keras 2.2.3 on Python PyPI

Areas of improvement

Keras models can now be safely pickled.
Consolidate the functionality of the activation layers ThresholdedReLU and LeakyReLU into the ReLU layer.
As a result, the ReLU layer now takes new arguments negative_slope and threshold, and the relu function in the backend takes a new threshold argument.
Add update_freq argument in TensorBoard callback, controlling how often to write TensorBoard logs.
Add the exponential function to keras.activations.
Add data_format argument in all 4 Pooling1D layers.
Add interpolation argument in UpSampling2D layer and in resize_images backend function, supporting modes "nearest" (previous behavior, and new default) and "bilinear" (new).
Add dilation_rate argument in Conv2DTranspose layer and in conv2d_transpose backend function.
The LearningRateScheduler now receives the lr key as part of the logs argument in on_epoch_end (current value of the learning rate).
Make GlobalAveragePooling1D layer support masking.
The the filepath argument save_model and model.save() can now be a h5py.Group instance.
Add argument restore_best_weights to EarlyStopping callback (optionally reverts to the weights that obtained the highest monitored score value).
Add dtype argument to keras.utils.to_categorical.
Support run_options and run_metadata as optional session arguments in model.compile() for the TensorFlow backend.

Modify the return value of Sequential.get_config(). Previously, the return value was a list of the config dictionaries of the layers of the model. Now, the return value is a dictionary with keys layers, name, and an optional key build_input_shape. The old config is equivalent to new_config['layers']. This makes the output of get_config consistent across all model classes.

Thanks to our 38 contributors whose commits are featured in this release: