Stack Overflow
Stack Overflow

Questions tagged [bayesian-deep-learning]

24 questions
5
votes
1 answer

XGBoost and scikit-optimize: BayesSearchCV and XGBRegressor are incompatible - why?

I have a very large dataset (7 million rows, 54 features) that I would like to fit a regression model to using XGBoost. To train the best possible model, I want to use BayesSearchCV from scikit-optimize to run the fit repeatedly for different…
Electronic Ant
  • 91
  • 10
2
votes
1 answer

Measuring incertainty in Bayesian Neural Network

Hy everybody, I'm beginning with tensorflow probability and I have some difficulties to interpret my Bayesian neural network outputs. I'm working on a regression case, and started with the example provided by tensorflow notebook here:…
maxlamenace
  • 71
  • 1
  • 7
2
votes
0 answers

Trainable priors in tensorflow-probability

Default Conv2DFlipout layer looks like this tfp.layers.Convolution2DFlipout( filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', dilation_rate=(1, 1), activation=None, activity_regularizer=None, …
uselessrunner
  • 125
  • 6
2
votes
1 answer

How to compute the uncertainty of a Monte Carlo Dropout neural network with PyTorch?

I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, the main idea is that by applying dropout at test time and running over many forward passes, you get predictions from a variety of different models. I need to obtain the uncertainty,…
MERAH Samia
  • 43
  • 9
1
vote
0 answers

How to get the standard Deviation and mean values after training a BNN model using DenseFlipout Layers in Tensorflow?

After I trained the Bayesian Neural Network model and saved it into a .h5 file, I cannot plot its result because I don't know the standard deviation and predicted mean trained by the model. I'm using the following method to get the predictions: with…
Charlie Li
  • 11
  • 2
1
vote
0 answers

Variational Inference with Normalizing Flows in Tensorflow Probability

In the last time I've read a little bit about using normalizing flows to improve variational inference f.e. Link1 Link2. Tensorflow probability already offers RealNVP and MaskedAutoregressiveFlow in the bijector submodule as well as an…
Fermat
  • 11
  • 2
1
vote
0 answers

Likelihood function in bayes by backprop

I generate my regression data synthetically using finite element method. So inherently my synthetic data is noiseless. In this case can someone help me understanding how can I write my likelihood function? (Likelihood term of my ELBO) I have seen…
uselessrunner
  • 125
  • 6
1
vote
1 answer

How to calculate gradients on tensorflow_probability layers?

I would like to calculate the gradients on tensorflow_probability layers using tf.GradientTape(). This is rather simple using a normal, e.g., Dense layer inp = tf.random.normal((2,5)) layer = tf.keras.layers.Dense(10) with tf.GradientTape() as…
jlapin
  • 11
  • 2
0
votes
1 answer

I got an error of input type when I use Bayesopt function of Bayesian Optimization Toolbox

I am trying to use Bayesopt for a very simple problem and finding the minimum value of a function fun = @(x) (x - 3)^2 + 5; xvar = optimizableVariable('x',[-10,10]); % Modify the range: xvar.Range = [1,5]; fun = @(x)(x - 3)^2 + 5; results =…
user78333
  • 3
  • 3
0
votes
0 answers

Binned data and Bayesian blocks

I'm studying bayesian blocks. And I don't understand how to make my function bayesian_blocks() work with binned data. I have data in .txt file where 0 column - bin start (in time), 1st - bin end, 2nd - counts in bin. Like this: -1.0000 …
Ruuzaki
  • 1
  • 1
0
votes
0 answers

Get the training history of Bayesian optimized keras model?

I used BayesianOptimization to optimize hyper-parameters of a Keras model. I got best_model as a result. Is it possible to get the training history of best_model (training loss and validation loss during epochs)? I tried to get best_model.history…
DaliMidou
  • 111
  • 1
  • 3
  • 14
0
votes
0 answers

How to solve this issues "AssertionError: Must have at least 1 validation dataset for early stopping"

I am developing a propensity scorecard model in xgboost in 1.6.2 version and while running below code it gives me errors. I searched lots of relevant question on stack overflow but wasn't able to find best solution. how to change it in my code? from…
SHESADEV SHA
  • 9
  • 3
0
votes
0 answers

Bayesian neural network is not predicting accurate results

I am new to Bayesian neural network using tensorflow probability. Here is a toy example I created, but the neural network is not predicting accurate results. I am creating a linear x,y data and using sklearn.model_selection.train_test_split() x_1 =…
ewr3243
  • 397
  • 3
  • 19
0
votes
0 answers

How do I get the probability of a non-categorical variable, given a certain set of parameters?

For example, let's say I have the following conditional probability: P(competitive=1(yes) | currency=EUR, sellerRating=1000) If I run the following code, I'll get a probability table: library(e1071) nb.model <- naiveBayes(Competitive~.,…
Friendly-Hooman
  • 1
0
votes
0 answers

What is the best way to evaluate and compare Bayesian Optimisation surrogate models?

This question is not relating to hyperparameter tuning. I am trying to find the best multidimensional combination of parameters for an application based on a given metric by using bayesian optimisation to search the parameter space and efficiently…
Oludhe
  • 69
  • 1
  • 8
1
2