Adaptive-Regularization-Neural-Network

PyTorch demo code for paper "Learning Neural Networks with Adaptive Regularization"

Paper

Learning Neural Networks with Adaptive Regularization
Han Zhao *, Yao-Hung Hubert Tsai *, Ruslan Salakhutdinov, and Geoffrey J. Gordon
Thirty-third Conference on Neural Information Processing Systems (NeurIPS), 2019. (*equal contribution)

If you use this code for your research and find it helpful, please cite our paper:

@inproceedings{zhao2019adaptive,
  title={Learning Neural Networks with Adaptive Regularization},
  author={Zhao, Han and Tsai, Yao-Hung Hubert and Salakhutdinov, Ruslan and Gordon, Geoffrey J},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}

Summary

In this paper we propose a method, AdaReg, to perform an adaptive and data-dependent regularization method when training neural networks with small-scale datasets. The follow figure shows a schematic illustration of AdaReg during training:

To summarize, for a fully connected layer (usually the last layer) , AdaReg maintains two additional covariance matrices:

• Row covariance matrix
• Column covariance matrix

During the training phase, AdaReg updates both and in a coordinate descent way. As usual, the update of could use any off-the-shelf optimizers provided by PyTorch. To update two covariance matrices, we derive closed form algorithm to achieve the optimal solution given any fixed . Essentially, the algorithm contains two steps:

• Compute a SVD of a matrix of size or
• Truncate all the singular values into the range . That is, for all the singular values smaller than , set them to be . Similarly, for all the singular values greater than , set them to be .

The choice of hyperparameter should satisfy and . In practice and in our experiments we fix them to be and . Pseudo-code of the algorithm is shown in the following figure.

How to use AdaReg in your model

Really simple! Here we give a minimum code snippet (in PyTorch) to illustrate the main idea. For the full implementation, please see function BayesNet(args) in src/model.py for more details.

First, for the weight matrix , we need to define two covariance matrices:

# Define two covariance matrices (in sqrt):
self.sqrt_covt = nn.Parameter(torch.eye(self.num_tasks), requires_grad=False)
self.sqrt_covf = nn.Parameter(torch.eye(self.num_feats), requires_grad=False)

Since we will use own analytic algorithm to optimize them, we set the requires_grad to be False. Next, implement a 4 line thresholding function:

def _thresholding(self, sv, lower, upper):
    """
    Two-way thresholding of singular values.
    :param sv:  A list of singular values.
    :param lower:   Lower bound for soft-thresholding.
    :param upper:   Upper bound for soft-thresholding.
    :return:    Thresholded singular values.
    """
    uidx = sv > upper
    lidx = sv < lower
    sv[uidx] = upper
    sv[lidx] = lower
    return sv

The overall algorithm for updating both covariance matrices can then be implemented as:

def update_covs(self, lower, upper):
    """
    Update both the covariance matrix over row and over column, using the closed form solutions.
    :param lower:   Lower bound of the truncation.
    :param upper:   Upper bound of the truncation.
    """
    covt = torch.mm(self.sqrt_covt, self.sqrt_covt.t())
    covf = torch.mm(self.sqrt_covf, self.sqrt_covf.t())
    ctask = torch.mm(torch.mm(self.W, covf), self.W.t())
    cfeat = torch.mm(torch.mm(self.W.t(), covt), self.W)
    # Compute SVD.
    ct, st, _ = torch.svd(ctask.data)
    cf, sf, _ = torch.svd(cfeat.data)
    st = self.num_feats / st
    sf = self.num_tasks / sf
    # Truncation of both singular values.
    st = self._thresholding(st, lower, upper)
    st = torch.sqrt(st)
    sf = self._thresholding(sf, lower, upper)
    sf = torch.sqrt(sf)
    # Recompute the value.
    self.sqrt_covt.data = torch.mm(torch.mm(ct, torch.diag(st)), ct.t())
    self.sqrt_covf.data = torch.mm(torch.mm(cf, torch.diag(sf)), cf.t())

Finally, we need to use the optimized covariance matrices to regularize the learning of our weight matrix (our goal!):

def regularizer(self):
    """
    Compute the weight regularizer w.r.t. the weight matrix W.
    """
    r = torch.mm(torch.mm(self.sqrt_covt, self.W), self.sqrt_covf)
    return torch.sum(r * r)

Add this regularizer back to our favorite objective function (cross-entropy, mean-squared-error, etc) and backpropagate to update , done!

Have a try yourself on MNIST and CIFAR:

Running CIFAR-10

python demo.py --dataset CIFAR10 

Running MNIST

python demo.py --dataset MNIST

Running MNIST with 1000 Training Samples and BatchSize 128

python demo.py --dataset MNIST --trainPartial --trainSize 1000 --batch_size 128

Contact

Please email to han.zhao@cs.cmu.edu or yaohungt@cs.cmu.edu should you have any questions, comments or suggestions.