We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of samples. Our method is described in the paper (UAI2021): “Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks”.
In addition, we provide an implementation framework to make your deterministic network Bayesian in
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Bayesify your Neural Network
There are 3 main files which help you to
Bayesify your deterministic network:
bayes_layers.py– file contains a bayesian implementation of convolution(1d, 2d, 3d, transpose) and linear layers, according to approx posterior from
Location-Scalefamily, i.e. which has 2 parameters mu and sigma. This file contains general definition, independent of specific distribution, as long as distribution contains 2 parameters mu and sigma. It uses forward method defined in
vi_posteriors.pyfile. One of the main arguments for redefined classes is
approx_post, which defined which posterior class to use from
vi_posteriors.py. Please, specify this name same way as defined class in
vi_posteriors.py. For example, if
vi_posteriors.pycontains class Gaus, then
vi_posteriors.py– file describes forward method, including kl term, for different approximate posterior distributions. Current implementation contains following disutributions:
If you would like to implement your own class of distrubtions, in
vi_posteriors.py copy one of defined classes and redefine following functions:
forward(obj, x, fun=""),
get_kl(obj, n_mc_iter, device).
It also contains usefull Utils class which provides
- definition of loss functions:
- different beta coefficients:
get_betafor KL term and
- allows to turn on/off computing the KL term, with function
set_compute_kl. this is useful, when you perform testing/evaluation, and kl term is not required to be computed. In that case it accelerates computations.
Below is an example to bayesify your own network. Note the forward method, which handles situations if a layer is not of a Bayesian type, and thus, does not return kl term, e.g. ReLU(x).
import bayes_layers as bl # important for defining bayesian layers class YourBayesNet(nn.Module): def __init__(self, num_classes, in_channels, **bayes_args): super(YourBayesNet, self).__init__() self.conv1 = bl.Conv2d(in_channels, 64, kernel_size=11, stride=4, padding=5, **bayes_args) self.classifier = bl.Linear(1*1*128, num_classes, **bayes_args) self.layers = [self.conv1, nn.ReLU(), self.classifier] def forward(self, x): kl = 0 for layer in self.layers: tmp = layer(x) if isinstance(tmp, tuple): x, kl_ = tmp kl += kl_ else: x = tmp x = x.view(x.size(0), -1) logits, _kl = self.classifier.forward(x) kl += _kl return logits, kl
Then later in the main file during training, you can either use one of the loss functions, defined in utils as following:
output, kl = model(inputs) kl = kl.mean() # if several gpus are used to split minibatch loss, _ = vi.Utils.get_loss_categorical(kl, output, targets, beta=beta) #loss, _ = vi.Utils.get_loss_normal(kl, output, targets, beta=beta) loss.backward()
or design your own, e.g.
loss = kl_coef*kl - loglikelihood loss.backward()
uncertainty_estimate.py– file describes set of functions to perform uncertainty estimation, e.g.
- get_prediction_class – function which return the most common class in iterations
- summary_class – function creates a summary file with statistics
Current implementation of networks for different problems
Script bayesian_dnn_class/main.py is the main executable code and all standard DNN models are located in bayesian_dnn_class/models, and are:
- Fully Connected