lengjia/kfac_pytorch

kfac_pytorch

Requirements: MKL, TensorFlow (for fetching MNIST), CUDA, PyTorch

Usage:

python kfac_pytorch.py

Using MKL
Step   0 loss 97.542419434
Step   1 loss 62.339828491
Step   2 loss 44.860393524
Step   3 loss 79.031013489
Step   4 loss 56.055324554
Step   5 loss 48.206447601
Step   6 loss 43.934066772
Step   7 loss 40.302700043
Step   8 loss 38.371196747
Step   9 loss 38.781795502
Times: min: 388.66, median: 400.81, mean: 2198.33

• Write-up: Optimizing deeper networks with KFAC in PyTorch.
• Experiments: deep_autoencoder.ipynb