Coremltools

Neural Networks
* Half precision weights
* New to .mlmodel specification version 2
* Supported by macOS 10.13.2, iOS 11.2, watchOS 4.2, tvOS 11.2
* WeightParams can now be specified in half precision (float16)
* New float16 conversion utility function can convert existing models with neural networks to half precision by calling coremltools.utils.convert_neural_network_spec_weights_to_fp16
* Can also pass in a flag in keras or caffe converter functions during model conversion time to convert models to half precision
* See: https://developer.apple.com/documentation/coreml/reducing_the_size_of_your_core_ml_app
* Custom Layers
* New to .mlmodel specification version 2
* Supported by macOS 10.13.2, iOS 11.2, watchOS 4.2, tvOS 11.2
* Added CustomLayerParams message to possible layers
* NeuralNetworkBuilder has new add_custom method
* Keras converter has options for using custom layers. See add_custom_layers and custom_conversion_functions arguments
* See: https://developer.apple.com/documentation/coreml/core_ml_api/creating_a_custom_layer

Visualization
* Visualize model specification with: coremltools.utils.visualize_spec

Python 3
* Conversion for most model types work in Python 3.
* No predictions: https://github.com/apple/coremltools/issues/37
* Converting Caffe models does not work: https://github.com/apple/coremltools/issues/79
* To use in Python 3, you must build from source.

Misc
* Support grayscale image outputs in python predictions
* Bug fixes

Neural Network Builder

Added support for layers in the NeuralNetworkBuilder that were present in the neural network protobuf but missing from the builder:
* Local response normalization (LRN) layer
* Split layer
* Unary function layer
* Bias, scale layers
* Load constant layer
* L2 normalization layer
* Mean variance normalization (MVN) layer
* Elementwise min layer
* Depthwise and separable convolutions

Added support for some of the missing parameters in NeuralNetworkBuilder:
* Padding options in convolution, pooling and padding layers
* Scale and shift options for linear activation

Other bug fixes & enhancements

* Bug-fix in the caffe converter that was preventing the elementwise max layer from converting.
* Support for converting DepthwiseConv2D and SeparableConv2D from Keras