keras 2.0.9 on Python PyPI

Areas of improvement

RNN improvements:
- Refactor RNN layers to rely on atomic RNN cells. This makes the creation of custom RNN very simple and user-friendly, via the RNN base class.
- Add ability to create new RNN cells by stacking a list of cells, allowing for efficient stacked RNNs.
- Add CuDNNLSTM and CuDNNGRU layers, backend by NVIDIA's cuDNN library for fast GPU training & inference.
- Add RNN Sequence-to-sequence example script.
- Add constants argument in RNN's call method, making RNN attention easier to implement.
Easier multi-GPU data parallelism via keras.utils.multi_gpu_model.
Bug fixes & performance improvements (in particular, native support for NCHW data layout in TensorFlow).
Documentation improvements and examples improvements.

Add "fashion mnist" dataset as keras.datasets.fashion_mnist.load_data()
Add Minimum merge layer as keras.layers.Minimum (class) and keras.layers.minimum(inputs) (function)
Add InceptionResNetV2 to keras.applications.
Support bool variables in TensorFlow backend.
Add dilation to SeparableConv2D.
Add support for dynamic noise_shape in Dropout
Add keras.layers.RNN() base class for batch-level RNNs (used to implement custom RNN layers from a cell class).
Add keras.layers.StackedRNNCells() layer wrapper, used to stack a list of RNN cells into a single cell.
Add CuDNNLSTM and CuDNNGRU layers.
Deprecate implementation=0 for RNN layers.
The Keras progbar now reports time taken for each past epoch, and average time per step.
Add option to specific resampling method in keras.preprocessing.image.load_img().
Add keras.utils.multi_gpu_model for easy multi-GPU data parallelism.
Add constants argument in RNN's call method, used to pass a list of constant tensors to the underlying RNN cell.

Implementation change in keras.losses.cosine_proximity results in a different (correct) scaling behavior.
Implementation change for samplewise normalization in ImageDataGenerator results in a different normalization behavior.

Thanks to our 59 contributors whose commits are featured in this release!