Fixed points of nonnegative neural networks

Official PyTorch repository for an experimental section of the paper Fixed points of nonnegative neural networks

Requirements

• pip install -r requirements

Training

To train autoencoders used in paper, use the following command

python train.py -net <net_name> -lr <lr_rate> -epochs <epochs> -wd <weight_decay> -b <batch> 

where:

• <net_name> - the name of the pcDEQ model, one from the following list:
  • nn_sigmoid - autoencoder from Section 6.1
  • nn_tanh - autoencoder from section 6.2
  • pn_tanh - autoencoder from section 6.3
  • nn_relu - autoencoder from section 6.4
  • pn_relu - autoencoder from section 6.5
  • nn_tanh_swish - autoencoder from section 6.6
  • nr_relu_sigmoid - autoencoder from section 6.7
  • rr_relu_sigmoid - autoencoder from section 6.8
• <lr_rate> - learning rate
• <epochs> - number of epochs
• <wd> - weight decay
• <b> - batch size

The following command shows the example of training autoencoder from section 6.1

python train.py -net nn_sigmoid -lr 5e-3 -epochs 30 -wd 0 -b 64