This pacakge implements PyTorch ResNet & AlexNet models that support two asymmetric feedback learning algorithms:
- sign-symmetry (Liao, Leibo, & Poggio, 2016);
- feedback alignment (Lillicrap et al., 2016).
To use different weights in forward and backward passes, we needed to modify
the backward() routine.
Since the interface to backward() in torch.nn.Conv2d is not exposed
to Python, it is necessary to implement a custom convolution autograd.Function.
This is achieved by borrowing routines from torch.legacy.nn.SpatialConvolution
that expose separate forward/backward interfaces but still use C libraries
to carry out the computations efficiently.
Then, a custom Conv2d module is defined with the custom Conv2d Function and
used to construct ResNet models by customizing torchvision/models/resnet.py
to replace standard Conv2d layers with the custom version.
In this implementation, batch-norm and pooling layers continue to use
backprop, but in our experience this does not affect performance.
In addition, a training script for ImageNet is included modified from
this one.
The command line arguments --algo and --lalgo control the algorithm used
for convolutional layers and the last layer, respectively (only
implemented for ResNet and AlexNet).
By default, sign-symmetry is used for the convlutional layers and
backpropagation is used for the last layer.
Another option is added to use batch-manhattan SGD
(Liao, Leibo, & Poggio, 2016) in place of standard SGD
because it was previously found to improve training with sign-symmetry.
It is implemented by extending torch.optim.SGD.
It should be relatively straightforward to extend this package to support other network architectures.