Explore different restrictions to neural networks.
Not yet prepared for public use. Use at your own risk!
Use {mnist, cifar10, imagenet, wlm}.py --help to see usage information.
Control how individual layers are structured, usually by changing how each input influences each output.
These restrictions apply to the convolution layers. Linear/fully connected layers have been mapped to convolution layers for a selection of networks.
Options:
- Butterfly
- Deep roots
- Depthwise-separable
- Also depthwise-butterfly and depthwise-shuffle
- Shift
- Shuffle
Control which values can be used in computations. Restrictions can be applied separately to weights/activations/gradients, and to isolated parts of the network.
Options:
- Set min/max
- Add random noise
- Set precision (all fixed-point values are a multiple of the precision)
- Apply an arbitrary function (not exposed through the command line interface)