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Keras 1.2.2 with MXNet Backend
Highlights
- Adding Apache MXNet backend for Keras 1.2.2.
- Easy to use multi-gputraining with MXNet backend.
- High-performance model training in Keras with MXNet backend.
Getting Started Resources
- Installation - https://github.com/dmlc/keras/wiki/Installation
- 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
- For more examples explore keras/examples directory.
- 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 be run on Apple devices which means that you can build your next iPhone app using your own MXNet model!
This tool currently supports conversion of models that are similar to:
- Inception
- Network-In-Network
- Squeezenet
- Resnet
- Vgg
List of layers that can be currently converted: Activation, Batchnorm, Concat, Convolution, Deconvolution, Dense, Elementwise, Flatten, Pooling, Reshape, Softmax, Transpose.
For more details on how to convert the models, refer to https://github.com/apache/incubator-mxnet/tree/master/tools/coreml
Requires:
- MacOS - High Sierra 10.13
- Xcode 9
- Mxnet 0.10.0 or greater
- Python 2.7
- Coremltools 0.5.0 or greater
- Yaml (Pyyaml)