Skip to content
Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
misc
README.md
cnn.py
dataset.py
dni.py
main.py support cifar10 Jun 6, 2017
mlp.py
model.py
plot.py
train.py

README.md

disclaimer: this code is modified from pytorch-tutorial

Image classification with synthetic gradient in Pytorch

I implement the Decoupled Neural Interfaces using Synthetic Gradients in pytorch. The paper uses synthetic gradient to decouple the layers among the network, which is pretty interesting since we won't suffer from update lock anymore. I test my model in mnist and almost the same performance, compared to the model updated with backpropagation.

Requirement

  • pytorch
  • python 3.5
  • torchvision
  • seaborn (optional)
  • matplotlib (optional)

TODO

  • use multi-threading on gpu to analyze the speed

What's synthetic gradients?

We ofter optimize NN by backpropogation, which is usually implemented in some well-known framework. However, is there another way for the layers in NN to communicate with other layers? Here comes the synthetic gradients! It gives us a way to allow neural networks to communicate, to learn to send messages between themselves, in a decoupled, scalable manner paving the way for multiple neural networks to communicate with each other or improving the long term temporal dependency of recurrent networks.
The neuron in each layer will automatically produces an error signal(δa_head) from synthetic-layers and do the optimzation. And how did the error signal generated? Actually, the network still does the backpropogation. While the error signal(δa) from the objective function is not used to optimize the neuron in the network, it is used to optimize the error signal(δa_head) produced by the synthetic-layer. The following is the illustration from the paper:

Result

Feed-Forward Network

Achieve accuracy=96% (compared to the original model, which with accuracy=97%)

classify loss gradient loss(log level)
cDNI classify loss cDNI gradient loss(log level)

Convolutional Neural Network

Achieve accuracy=96%, (compared to the original model, which with accuracy=98%)

classify loss gradient loss(log level)

Usage

Right now I just implement the FCN, CNN versions, which are set as the default network structure.

Run network with synthetic gradient:

python main.py --model_type mlp

or

python main.py --model_type cnn

Run network with conditioned synthetic gradient:

python main.py --model_type mlp --conditioned True

Run vanilla network, from pytorch-tutorial

python mlp.py

or

python cnn.py

Reference

You can’t perform that action at this time.