A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

Last update: Jan 08, 2023

Overview

Blitz - Bayesian Layers in Torch Zoo

BLiTZ is a simple and extensible library to create Bayesian Neural Network Layers (based on whats proposed in Weight Uncertainty in Neural Networks paper) on PyTorch. By using BLiTZ layers and utils, you can add uncertanity and gather the complexity cost of your model in a simple way that does not affect the interaction between your layers, as if you were using standard PyTorch.

By using our core weight sampler classes, you can extend and improve this library to add uncertanity to a bigger scope of layers as you will in a well-integrated to PyTorch way. Also pull requests are welcome.

Our objective is empower people to apply Bayesian Deep Learning by focusing rather on their idea, and not the hard-coding part.

Roadmap:

Enable reparametrization for different posterior distributions than Normal.

Index

Install
Documentation
A simple example for regression
Bayesian Deep Learning in a Nutshell
Citing
References

Install

To install BLiTZ you can use pip command:

pip install blitz-bayesian-pytorch

You can also git-clone it and pip-install it locally:

conda create -n blitz python=3.6
conda activate blitz
git clone https://github.com/piEsposito/blitz-bayesian-deep-learning.git
cd blitz-bayesian-deep-learning
pip install .

Documentation

Documentation for our layers, weight (and prior distribution) sampler and utils:

A simple example for regression

(You can see it for your self by running this example on your machine).

We will now see how can Bayesian Deep Learning be used for regression in order to gather confidence interval over our datapoint rather than a pontual continuous value prediction. Gathering a confidence interval for your prediction may be even a more useful information than a low-error estimation.

I sustain my argumentation on the fact that, with good/high prob a confidence interval, you can make a more reliable decision than with a very proximal estimation on some contexts: if you are trying to get profit from a trading operation, for example, having a good confidence interval may lead you to know if, at least, the value on which the operation wil procees will be lower (or higher) than some determinate X.

Knowing if a value will be, surely (or with good probability) on a determinate interval can help people on sensible decision more than a very proximal estimation that, if lower or higher than some limit value, may cause loss on a transaction. The point is that, sometimes, knowing if there will be profit may be more useful than measuring it.

In order to demonstrate that, we will create a Bayesian Neural Network Regressor for the Boston-house-data toy dataset, trying to create confidence interval (CI) for the houses of which the price we are trying to predict. We will perform some scaling and the CI will be about 75%. It will be interesting to see that about 90% of the CIs predicted are lower than the high limit OR (inclusive) higher than the lower one.

Importing the necessary modules

Despite from the known modules, we will bring from BLiTZ athe variational_estimatordecorator, which helps us to handle the BayesianLinear layers on the module keeping it fully integrated with the rest of Torch, and, of course, BayesianLinear, which is our layer that features weight uncertanity.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np

from blitz.modules import BayesianLinear
from blitz.utils import variational_estimator

from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

Loading and scaling data

Nothing new under the sun here, we are importing and standard-scaling the data to help with the training.

X, y = load_boston(return_X_y=True)
X = StandardScaler().fit_transform(X)
y = StandardScaler().fit_transform(np.expand_dims(y, -1))

X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=.25,
                                                    random_state=42)


X_train, y_train = torch.tensor(X_train).float(), torch.tensor(y_train).float()
X_test, y_test = torch.tensor(X_test).float(), torch.tensor(y_test).float()

Creating our variational regressor class

We can create our class with inhreiting from nn.Module, as we would do with any Torch network. Our decorator introduces the methods to handle the bayesian features, as calculating the complexity cost of the Bayesian Layers and doing many feedforwards (sampling different weights on each one) in order to sample our loss.

@variational_estimator
class BayesianRegressor(nn.Module):
    def __init__(self, input_dim, output_dim):
        super().__init__()
        #self.linear = nn.Linear(input_dim, output_dim)
        self.blinear1 = BayesianLinear(input_dim, 512)
        self.blinear2 = BayesianLinear(512, output_dim)
        
    def forward(self, x):
        x_ = self.blinear1(x)
        x_ = F.relu(x_)
        return self.blinear2(x_)

Defining a confidence interval evaluating function

This function does create a confidence interval for each prediction on the batch on which we are trying to sample the label value. We then can measure the accuracy of our predictions by seeking how much of the prediciton distributions did actually include the correct label for the datapoint.

def evaluate_regression(regressor,
                        X,
                        y,
                        samples = 100,
                        std_multiplier = 2):
    preds = [regressor(X) for i in range(samples)]
    preds = torch.stack(preds)
    means = preds.mean(axis=0)
    stds = preds.std(axis=0)
    ci_upper = means + (std_multiplier * stds)
    ci_lower = means - (std_multiplier * stds)
    ic_acc = (ci_lower <= y) * (ci_upper >= y)
    ic_acc = ic_acc.float().mean()
    return ic_acc, (ci_upper >= y).float().mean(), (ci_lower <= y).float().mean()

Creating our regressor and loading data

Notice here that we create our BayesianRegressor as we would do with other neural networks.

regressor = BayesianRegressor(13, 1)
optimizer = optim.Adam(regressor.parameters(), lr=0.01)
criterion = torch.nn.MSELoss()

ds_train = torch.utils.data.TensorDataset(X_train, y_train)
dataloader_train = torch.utils.data.DataLoader(ds_train, batch_size=16, shuffle=True)

ds_test = torch.utils.data.TensorDataset(X_test, y_test)
dataloader_test = torch.utils.data.DataLoader(ds_test, batch_size=16, shuffle=True)

Our main training and evaluating loop

We do a training loop that only differs from a common torch training by having its loss sampled by its sample_elbo method. All the other stuff can be done normally, as our purpose with BLiTZ is to ease your life on iterating on your data with different Bayesian NNs without trouble.

Here is our very simple training loop:

iteration = 0
for epoch in range(100):
    for i, (datapoints, labels) in enumerate(dataloader_train):
        optimizer.zero_grad()
        
        loss = regressor.sample_elbo(inputs=datapoints,
                           labels=labels,
                           criterion=criterion,
                           sample_nbr=3)
        loss.backward()
        optimizer.step()
        
        iteration += 1
        if iteration%100==0:
            ic_acc, under_ci_upper, over_ci_lower = evaluate_regression(regressor,
                                                                        X_test,
                                                                        y_test,
                                                                        samples=25,
                                                                        std_multiplier=3)
            
            print("CI acc: {:.2f}, CI upper acc: {:.2f}, CI lower acc: {:.2f}".format(ic_acc, under_ci_upper, over_ci_lower))
            print("Loss: {:.4f}".format(loss))

Bayesian Deep Learning in a Nutshell

A very fast explanation of how is uncertainity introduced in Bayesian Neural Networks and how we model its loss in order to objectively improve the confidence over its prediction and reduce the variance without dropout.

First of all, a deterministic NN layer linear transformation

As we know, on deterministic (non bayesian) neural network layers, the trainable parameters correspond directly to the weights used on its linear transformation of the previous one (or the input, if it is the case). It corresponds to the following equation:

(Z correspond to the activated-output of the layer i)

The purpose of Bayesian Layers

Bayesian layers seek to introduce uncertainity on its weights by sampling them from a distribution parametrized by trainable variables on each feedforward operation.

This allows we not just to optimize the performance metrics of the model, but also gather the uncertainity of the network predictions over a specific datapoint (by sampling it much times and measuring the dispersion) and aimingly reduce as much as possible the variance of the network over the prediction, making possible to know how much of incertainity we still have over the label if we try to model it in function of our specific datapoint.

Weight sampling on Bayesian Layers

To do so, on each feedforward operation we sample the parameters of the linear transformation with the following equations (where ρ parametrizes the standard deviation and μ parametrizes the mean for the samples linear transformation parameters) :

For the weights:

Where the sampled W corresponds to the weights used on the linear transformation for the ith layer on the nth sample.

For the biases:

Where the sampled b corresponds to the biases used on the linear transformation for the ith layer on the nth sample.

It is possible to optimize our trainable weights

Even tough we have a random multiplier for our weights and biases, it is possible to optimize them by, given some differentiable function of the weights sampled and trainable parameters (in our case, the loss), summing the derivative of the function relative to both of them:

Let
Let
Let
Let be differentiable relative to its variables

Therefore:

and

It is also true that there is complexity cost function differentiable along its variables

It is known that the crossentropy loss (and MSE) are differentiable. Therefore if we prove that there is a complexity-cost function that is differentiable, we can leave it to our framework take the derivatives and compute the gradients on the optimization step.

The complexity cost is calculated, on the feedforward operation, by each of the Bayesian Layers, (with the layers pre-defined-simpler apriori distribution and its empirical distribution). The sum of the complexity cost of each layer is summed to the loss.

As proposed in Weight Uncertainty in Neural Networks paper, we can gather the complexity cost of a distribution by taking the Kullback-Leibler Divergence from it to a much simpler distribution, and by making some approximation, we will can differentiate this function relative to its variables (the distributions):

Let be a low-entropy distribution pdf set by hand, which will be assumed as an "a priori" distribution for the weights
Let be the a posteriori empirical distribution pdf for our sampled weights, given its parameters.

Therefore, for each scalar on the W sampled matrix:

By assuming a very large n, we could approximate:

and therefore:

As the expected (mean) of the Q distribution ends up by just scaling the values, we can take it out of the equation (as there will be no framework-tracing). Have a complexity cost of the nth sample as:

Which is differentiable relative to all of its parameters.

To get the whole cost function at the nth sample:

Let a performance (fit to data) function be:

Therefore the whole cost function on the nth sample of weights will be:

We can estimate the true full Cost function by Monte Carlo sampling it (feedforwarding the netwok X times and taking the mean over full loss) and then backpropagate using our estimated value. It works for a low number of experiments per backprop and even for unitary experiments.

Some notes and wrap up

We came to the and of a Bayesian Deep Learning in a Nutshell tutorial. By knowing what is being done here, you can implement your bnn model as you wish.

Maybe you can optimize by doing one optimize step per sample, or by using this Monte-Carlo-ish method to gather the loss some times, take its mean and then optimizer. Your move.

FYI: Our Bayesian Layers and utils help to calculate the complexity cost along the layers on each feedforward operation, so don't mind it to much.

References:

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424, 2015.

Citing

If you use BLiTZ in your research, you can cite it as follows:

@misc{esposito2020blitzbdl,
    author = {Piero Esposito},
    title = {BLiTZ - Bayesian Layers in Torch Zoo (a Bayesian Deep Learing library for Torch)},
    year = {2020},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/piEsposito/blitz-bayesian-deep-learning/}},
}

Special thanks to Intel Student Ambassador program

Made by Pi Esposito

Comments

Huge Memory Demand

Hi, when I try to train a simple model using Blizt I get huge memory demand > 12 GB for a very small dataset.

@variational_estimator class BayesianLstm(Module): def init(self, output_size=1, input_size=24, hidden_size=32, seq_length=30, hidden_neurons=8, batch_size=512): super(BayesianLstm, self).init()

      self.input_size = input_size
      self.hidden_size = hidden_size
      self.seq_length = seq_length
      self.hidden_layer_size = hidden_size

      # First lstm cell
      self.lstm1 = BayesianLSTM(input_size, hidden_size)
      # second lstm cell
      self.lstm2 = BayesianLSTM(hidden_size, hidden_size*2)
      # first fully connected layer
      self.fc1 = BayesianLinear(hidden_size * 2, hidden_neurons)
      self.act1 = nn.ReLU()
      # self.bat1 = nn.BatchNorm1d(num_features=hidden_neurons)
      self.drop = nn.Dropout(inplace=True, p=0.5)

      # second fully connected layer
      self.fc2 = BayesianLinear(hidden_neurons, hidden_neurons)
      self.act2 = nn.ReLU()
      # self.bat2 = nn.BatchNorm1d(num_features=hidden_neurons)

      # output
      self.output = BayesianLinear(hidden_neurons, output_size)

My data is of shape [batchsize, sequence_length, number_features] I tried this for batchsize = 512, sequence_length= 30, number_features=24.

opened by serop96 8

Bayesian MLP not learning for regression tasks

Hi (and thanks a bunch for this framework!),

I'm testing out a Bayesian neural net for a simple regression task. However, after a lot of training, when I test the output, I just get an (almost) constant output. I follow the same workflow as in the Boston Housing example, except I use a function to generate my dataset.

Here's my code, if you're interested:

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.tensor as Tensor
import numpy as np
import matplotlib.pyplot as plt

from blitz.modules import BayesianLinear
from blitz.utils import variational_estimator

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

X = np.expand_dims(np.random.uniform(0,10,1000), -1)    # these two lines are the only thing I changed as far
y = np.sin(X)                                                                            # as data preprocessing is concerned

X = StandardScaler().fit_transform(X)
y = StandardScaler().fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=.25,
                                                    random_state=42)
X_train, y_train = torch.tensor(X_train).float(), torch.tensor(y_train).float()
X_test, y_test = torch.tensor(X_test).float(), torch.tensor(y_test).float()

ds_train = torch.utils.data.TensorDataset(X_train, y_train)
dataloader_train = torch.utils.data.DataLoader(ds_train, batch_size=16, shuffle=True)

ds_test = torch.utils.data.TensorDataset(X_test, y_test)
dataloader_test = torch.utils.data.DataLoader(ds_test, batch_size=16, shuffle=True)

@variational_estimator
class BayesianRegressor(nn.Module):
    def __init__(self):
        super().__init__()
        #self.linear = nn.Linear(input_dim, output_dim)
        self.blinear1 = BayesianLinear(1, 100)
        self.blinear2 = BayesianLinear(100, 100)
        self.blinear3 = BayesianLinear(100, 1)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x_):
        x = self.sigmoid(self.blinear1(x_))
        x = self.sigmoid(self.blinear2(x))
        x = self.blinear3(x)
        return x

regressor = BayesianRegressor().to(device)
criterion = torch.nn.MSELoss()
optimizer = optim.Adam(regressor.parameters(), lr=0.005)

iteration = 0
hist = []
for epoch in range(1000):
    totalloss = 0
    u = 0
    for i, (datapoints, labels) in enumerate(dataloader_train):
        u += 1
        optimizer.zero_grad()
        
        loss = regressor.sample_elbo(inputs=datapoints.to(device),
                           labels=labels.to(device),
                           criterion=criterion,
                           sample_nbr=3,
                           complexity_cost_weight=1/X_train.shape[0])
        totalloss += loss.item()
        loss.backward()
        optimizer.step()
    hist.append(totalloss/u)
    print(f"[Epoch {epoch}] "+"Loss: {:.4f}".format(totalloss/u))

Then I generate the outputs:

plt.scatter(X,y)
plt.scatter(X,regressor(Tensor(X).float().to(device)).detach().cpu(),s=5)
plt.show()

which gives me: this

Is there something I'm doing wrong here?

opened by tiwalayo 8

Do you have cuda support?

Hi! If I run model on GPU, it's cause RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_addmm

Do you have plans to add cuda support?

opened by Archelunch 8
Extra -0.5 in log_posterior()?
In https://github.com/piEsposito/blitz-bayesian-deep-learning/blob/master/blitz/modules/weight_sampler.py#L50,

log_posteriors = -log_sqrt2pi - torch.log(self.sigma) - (((w - self.mu) ** 2)/(2 * self.sigma ** 2)) - 0.5

why is there a -0.5 at the end of the line? The log-likelihood of a Gaussian does not have that -0.5.
opened by isaac-cfwong 7
Variance of predictions is to small

Hi,

I am trying to use Blitz for a regression problem. Unfortunately, my nets don´t seem to learn the variance of the data correctly. The variance in the predictions is so low, that the true value is never within a reasonable confidence interval around the mean of the predictions. However, the means of the predictions are acceptable and clearly show that the nets are learning something. For better understanding i attached a plot. What I have tried so far: • Different architectures with and without convolutional layers • Changing prior_sigma_1, prior_sigma_2 and prior_pi • Changing complexity_cost_weight in the sample_elbo method

Best regards Lukas

opened by LukasWFurtner 6
-inf in log_prior and nan in loss breaks training
Hello, first of all amazing work, and thank you for this project! I'm trying to train simple 3-layered NN and I encountered some problems I wanted to ask about. Here is my model:

BayesianRegressor( (blinear1): BayesianLinear( (weight_sampler): GaussianVariational() (bias_sampler): GaussianVariational() (weight_prior_dist): ScaleMixturePrior() (bias_prior_dist): ScaleMixturePrior() ) (relu): ReLU() (blinear2): BayesianLinear( (weight_sampler): GaussianVariational() (bias_sampler): GaussianVariational() (weight_prior_dist): ScaleMixturePrior() (bias_prior_dist): ScaleMixturePrior() ) (relu2): ReLU() (blinear3): BayesianLinear( (weight_sampler): GaussianVariational() (bias_sampler): GaussianVariational() (weight_prior_dist): ScaleMixturePrior() (bias_prior_dist): ScaleMixturePrior() ) )

I'm training it on dataset with prices of flats/houses I recently scraped, and I've encountered problem I cannot seem to fully understand: after a few epochs, loss returned by the model.sample_elbo method is sometimes equal to nan, which when backpropagated breaks the whole training, as some of the weights are 'optimized' to nans:

model_copy.sample_elbo(inputs=datapoints.to(device), labels=labels.to(device), criterion=criterion, sample_nbr=3, complexity_cost_weight=1/X_train.shape[0])

I managed to track down where the incorrect values appears first, before backpropagation of these nans, and it turned out that value of log_prior in first bayesian layer is sometimes equal to -inf

first_layer = list(model_copy.modules())[0].blinear1 first_layer .log_prior # returns -inf

Going further I checked that the problem is in weight_prior_dist, which sometimes, like one in 5 times returns -inf:

w =first_layer.weight_sampler.sample() #sampled weigths prior_dist = first_layer.weight_prior_dist print(prior_dist.log_prior(w)) #sometimes returns -inf

Going deeper I realised, that the problem is in prior_pdf of first prior distribution in weight_prior_dist of first layer. Some of logarithms of probabilities for the sampled values of weights (prior_dist.dist1.log_prob(w)) are very small, equal to ~-100, and when passed through torch.exp such small values are approximated to 0. When these 0-weights go through torch.log in prior_dist.log_prior(w) they are equal to -inf, and the whole mean approaches then -inf, which corrupts further calculations of loss:

prob_n1 = torch.exp(prior_dist.dist1.log_prob(w)) # minimal value of this tensor is equal to 0 if prior_dist.dist2 is not None: prob_n2 = torch.exp(prior_dist.dist2.log_prob(w)) prior_pdf = (prior_dist.pi * prob_n1 + (1 - prior_dist.pi) * prob_n2) # minimal value of this tensor is equal to 0 (torch.log(prior_pdf)).mean() #formula for calculating log_prior of weight_prior_dist, returns -inf

If I understand correctly, it means that the probabilities of such sampled weights for prior distribution are very very small, approaching zero, but could you suggest me the way of tackling this problem somehow, so they remain very small, and not zero? Or maybe the problem is different? I'm still learning details of Bayesian DL, so I hope there aren't so many silly mistakes, and thank you for any kind of help! best regards Rafał
opened by rafaljanwojcik 6
Specification of Additional Priors and Posterior MCMC

Hey there! Just want to say that I am really impressed by your repo. Good work!

Will there be options to expand the set of priors that may be accessible and posterior sampling methods such as that featured in https://arxiv.org/abs/1902.03932 ?

Thanks!

opened by jlevy44 6
ScaleMixturePrior::log_prior()

In the code below the log_prior is calculated by taking the mean of the log probabilities, however the paper describes taking the product of these weights.

https://github.com/piEsposito/blitz-bayesian-deep-learning/blob/566eaa495f28fe9a8c2b076a7388548aa4f792f0/blitz/modules/weight_sampler.py#L61-L72

Would it perhaps be preferable to use something like:

reduce(lambda a, b: a * b, log(prior_pdf))

opened by danielkelshaw 6
Interface features both parts of the ELBO-loss (likelihood part and performance part)

For my recent work about Bayesian Neural Networks for Time Series Forecasting in Hydrology I needed a slight modification of the BLiTZ-library where the function sample_elbo in variational_estimator.py does not only output the total loss, but the likelihood part and performance part individually. In addition, my version outputs the model predictions.

As I think that this could be sensible addition for public use of the library, I would like to apply for a change here.

Best regards, Jonas Fill

opened by filljonas 5
Providing a minimal working example
I really like the idea of this library. However, it is really hard to get started with it. For example, the minimal working example in the read me doesn't work. No matter what I try, I cannot get it to improve the accuracy. The loss decrease towards zero, but the accuracy doesn't change. The problem is that it is totally unclear why this is the case is it because:

there is no non-linear activation function in the model?

Is the model too big for BNNs?

or is something else the problem?

Any help would be appreciated.
opened by j0rd1smit 5
GPU compatible if bias is set to zero conv2d fixed

Bug for conv2d if bias is set to False cus bias then torch.zeros were generated at cpu while other parameters were at cuda.... Now fixed only for conv2d...

opened by Hannan4252 5

Error in BayesianRNN class

In the code of BayesianRNN, there has two self.bias = None, the first one should be self.bias_mu = None I suppose. And this class also lack the definition of self.bias_prior_dist, which used in def sharpen_posterior(self, loss, input_shape)

class BayesianRNN(BayesianModule):
    """
    implements base class for B-RNN to enable posterior sharpening
    """
    def __init__(self,
                 sharpen=False):
        super().__init__()
        
        self.weight_ih_mu = None
        self.weight_hh_mu = None
        self.bias = None
        
        self.weight_ih_sampler = None
        self.weight_hh_sampler = None
        self.bias_sampler = None

        self.weight_ih = None
        self.weight_hh = None
        self.bias = None
        
        self.sharpen = sharpen
        
        self.weight_ih_eta = None
        self.weight_hh_eta = None
        self.bias_eta = None
        self.ff_parameters = None
        self.loss_to_sharpen = None
        
    
    def sample_weights(self):
        pass
    
    def init_sharpen_parameters(self):
        if self.sharpen:
            self.weight_ih_eta = nn.Parameter(torch.Tensor(self.weight_ih_mu.size()))
            self.weight_hh_eta = nn.Parameter(torch.Tensor(self.weight_hh_mu.size()))
            self.bias_eta = nn.Parameter(torch.Tensor(self.bias_mu.size()))
            
            self.ff_parameters = []

            self.init_eta()
    
    def init_eta(self):
        stdv = 1.0 / math.sqrt(self.weight_hh_eta.shape[0]) #correspond to hidden_units parameter
        self.weight_ih_eta.data.uniform_(-stdv, stdv)
        self.weight_hh_eta.data.uniform_(-stdv, stdv)
        self.bias_eta.data.uniform_(-stdv, stdv)
    
    def set_loss_to_sharpen(self, loss):
        self.loss_to_sharpen = loss
    
    def sharpen_posterior(self, loss, input_shape):
        """
        sharpens the posterior distribution by using the algorithm proposed in
        @article{DBLP:journals/corr/FortunatoBV17,
          author    = {Meire Fortunato and
                       Charles Blundell and
                       Oriol Vinyals},
          title     = {Bayesian Recurrent Neural Networks},
          journal   = {CoRR},
          volume    = {abs/1704.02798},
          year      = {2017},
          url       = {http://arxiv.org/abs/1704.02798},
          archivePrefix = {arXiv},
          eprint    = {1704.02798},
          timestamp = {Mon, 13 Aug 2018 16:48:21 +0200},
          biburl    = {https://dblp.org/rec/journals/corr/FortunatoBV17.bib},
          bibsource = {dblp computer science bibliography, https://dblp.org}
        }
        """
        bs, seq_len, in_size = input_shape
        gradients = torch.autograd.grad(outputs=loss,
                                        inputs=self.ff_parameters,
                                        grad_outputs=torch.ones(loss.size()).to(loss.device),
                                        create_graph=True,
                                        retain_graph=True,
                                        only_inputs=True)
        
        grad_weight_ih, grad_weight_hh, grad_bias = gradients
        
        #to generate sigmas on the weight sampler
        _ = self.sample_weights()
        
        weight_ih_sharpened = self.weight_ih_mu - self.weight_ih_eta * grad_weight_ih + self.weight_ih_sampler.sigma
        weight_hh_sharpened = self.weight_hh_mu - self.weight_hh_eta * grad_weight_hh + self.weight_hh_sampler.sigma
        bias_sharpened = self.bias_mu - self.bias_eta * grad_bias + self.bias_sampler.sigma
        
        if self.bias is not None:
            b_log_posterior = self.bias_sampler.log_posterior(w=bias_sharpened)
            b_log_prior_ = self.bias_prior_dist.log_prior(bias_sharpened)
            
        else:
            b_log_posterior = b_log_prior = 0
        
        
        self.log_variational_posterior += (self.weight_ih_sampler.log_posterior(w=weight_ih_sharpened) + b_log_posterior + self.weight_hh_sampler.log_posterior(w=weight_hh_sharpened)) / seq_len
        
        self.log_prior += self.weight_ih_prior_dist.log_prior(weight_ih_sharpened) + b_log_prior + self.weight_hh_prior_dist.log_prior(weight_hh_sharpened) / seq_len
        
        return weight_ih_sharpened, weight_hh_sharpened, bias_sharpened

opened by LINGLONGQIAN 1

Question about a parameter of model.sample_elbo

Hi, I'm grateful to your code. I have a question about "complexity_cost_weight". According your code, I think the "complexity cost weight" should be 1/batch_size. However, in your example "LeNet_MNIST.py", the "complexity cost weight" is 1/50000, and 50000 is the number of all training data. So how should I choose the parameter? Batch size or the number of all trianing data?

opened by KQL11 0
Radial BNN implementation is wrong

I do not see the radial factor "r" to which the normalized epsilon is projected. Kindly help me in this regard. Moreover, "r" is to be sampled from a Normal distribution.

opened by ishan-m 0
Questions about prior distributions

Can this library only use Gaussian distribution as prior distribution? Are there other prior distributions? Another question is that if the input data and the prior distribution are Gaussian distribution, is the output of the network a Gaussian distribution?

opened by closeyourmoise 2
result seems not good on Bosten example

Hi, I try to implement the Boston example and use a plot to see fitting result. but the result seems not good enough.

The followings are train result and validation result (mean with 3 times std)

So I print the criterion from elbo function, but the mse curve seems like a oscillation, not a expected descending trend.

I am curious why mse is not descending? looking for your replying :)

opened by hangzhang23 0

Releases(0.2.8)

0.2.8(Apr 15, 2022)

Minor fixes on bugs and GPU usage.
Source code(tar.gz)
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0.2.7(Nov 28, 2020)

Add Radial and Flipout layers
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0.2.6(Nov 22, 2020)

Support for pytorch 1.7
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0.2.5(Jul 1, 2020)

Version 0.2.5
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0.2.3(May 25, 2020)

Introduced:

Variational inference new features New examples Built-in Bayesian VGG model Usage of different priors on Bayesian Layers
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0.2.1(May 20, 2020)

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Owner

Pi Esposito

Software Engineer, MLOps @ unico. Google Developer Expert in Machine Learning

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