Keras 1.2.2 with MXNet Backend  

Highlights

1. Adding Apache MXNet backend for Keras 1.2.2.
2. Easy to use multi-gputraining with MXNet backend.
3. High-performance model training in Keras with MXNet backend.

Getting Started Resources

1. Installation - https://github.com/dmlc/keras/wiki/Installation
2. How to use Multi-GPU for training in Keras with MXNet backend - https://github.com/dmlc/keras/wiki/Using-Keras-with-MXNet-in-Multi-GPU-mode
3. For more examples explore keras/examples directory.
4. Source Repo - https://github.com/dmlc/keras

For more details on unsupported functionalities, known issues and resources refer to release notes - https://github.com/dmlc/keras/releases

Apple CoreML Converter

You can now convert your MXNet models into Apple CoreML format so that they can run on Apple devices which means that you can build your next iPhone app using your own MXNet model!

List of layers that can be converted:

1. Activation
2. Batchnorm
3. Concat
4. Convolution
5. Deconvolution
6. Dense
7. Elementwise
8. Flatten
9. Pooling
10. Reshape
11. Softmax
12. Transpose

1. MacOS - High Sierra 10.13
2. Xcode 9
3. Coremltools 0.5.0 or greater (pip install coremltools)
4. Mxnet 0.10.0 or greater (Installation Instructions)
5. Yaml (pip install pyyaml)
6. Python 2.7

Example:

In order to convert, say a squeezenet model (which can be downloaded from here), you can execute the following command: (assuming you are in the directory where mxnet_coreml_converter.py resides):

python mxnet_coreml_converter.py --model-prefix='squeezenet_v1.1' --epoch=0 --input-shape='{"data":"3,227,227"}' --mode=classifier --pre-processing-arguments='{"image_input_names":"data"}' --class-labels classLabels.txt --output-file="squeezenetv11.mlmodel"

You can find explanations for each parameter along with more examples here.

In order to use the generated CoreML model file into your project, refer to Apple's tutorial here.