This example illustrates how to use BackwardInterface with a simple
multi-layer perceptron. Code is mostly copied from the official PyTorch
MNIST example:
https://github.com/pytorch/examples/blob/master/mnist/main.py
Classification model is replaced by a multi-layer perceptron with two hidden layers, 256 neurons in each, batch normalization after every layer and ReLU activation function. DNI is inserted between the last hidden layer and the output layer (before activation).
Synthesizer used is an MLP with one hidden layer with 256 neurons and ReLU activation function.
To install requirements:
$ pip install -r requirements.txt
To train with regular backpropagation:
$ python main.py
To train with DNI (no label conditioning):
$ python main.py --dni
To train with cDNI (label conditioning):
$ python main.py --dni --context