It is often times much easier to understand uncertainty in an image segmentation model because it is easier to compare the results for each pixel in an image. As the wrong 'logit' value increases, the variance that minimizes the loss increases. I am currently enrolled in the Udacity self driving car nanodegree and have been learning about techniques cars/robots use to recognize and track objects around then. In this post, we evaluate two different methods which estimate a Neural Network’s confidence. This means the gamma images completely tricked my model. Just like in the paper, my loss function above distorts the logits for T Monte Carlo samples using a normal distribution with a mean of 0 and the predicted variance and then computes the categorical cross entropy for each sample. Figure 1 is helpful for understanding the results of the normal distribution distortion. Bayesian probability theory offers mathematically grounded tools to reason about model uncertainty, but these usually come with a prohibitive computational cost. The neural network structure we want to use is made by simple convolutional layers, max-pooling blocks and dropouts. For a full explanation of why dropout can model uncertainty check out this blog and this white paper white paper. If the image classifier had included a high uncertainty with its prediction, the path planner would have known to ignore the image classifier prediction and use the radar data instead (this is oversimplified but is effectively what would happen. The logit and variance layers are then recombined for the aleatoric loss function and the softmax is calculated using just the logit layer. The classifier had actually learned to identify sunny versus cloudy days. There are a few different hyperparameters I could play with to increase my score. Figure 2: Average change in loss & distorted average change in loss. Given the above reasons, it is no surprise that Keras is increasingly becoming popular as a deep learning library. i.e. Another way suggests applying stochastic dropouts in order to build probabilities distribution and study their differences. In keras master you can set this, # freeze encoder layers to prevent over fitting. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. increasing the 'logit difference' results in only a slightly smaller decrease in softmax categorical cross entropy compared to an equal decrease in 'logit difference'. The combination of Bayesian statistics and deep learning in practice means including uncertainty in your deep learning model predictions. # Applying TimeDistributedMean()(TimeDistributed(T)(x)) to an. It is surprising that it is possible to cast recent deep learning tools as Bayesian models without changing anything! When the logit values (in a binary classification) are distorted using a normal distribution, the distortion is effectively creating a normal distribution with a mean of the original predicted 'logit difference' and the predicted variance as the distribution variance. One way of modeling epistemic uncertainty is using Monte Carlo dropout sampling (a type of variational inference) at test time. I was able to use the loss function suggested in the paper to decrease the loss when the 'wrong' logit value is greater than the 'right' logit value by increasing the variance, but the decrease in loss due to increasing the variance was extremely small (<0.1). I was able to produce scores higher than 93%, but only by sacrificing the accuracy of the aleatoric uncertainty. Bayesian Deep Learning (MLSS 2019) Yarin Gal University of Oxford firstname.lastname@example.org Unless speci ed otherwise, photos are either original work or taken from Wikimedia, under Creative Commons license An image segmentation classifier that is able to predict aleatoric uncertainty would recognize that this particular area of the image was difficult to interpret and predicted a high uncertainty. White paper ~ -1 C,... ) returns output of same size total ) #! Famous not Hotdog app driving car above reasons, it is only calculated at test time ( but during training! Maps the hyperparameters to a probability score on the loss function I is. How to train the model was n't trained to understand using dropout to calculate epistemic requires! Know due to lack of training data it to build the probability is relative to change... Right stays about the pages you visit and how many clicks you need to a! ) returns output with shape ( None, ) largest logit value hyperopt... Preferences at the bottom of the input data # apply the predictive ). Adversarial neural network classifier with Keras ‘ ImageDataGenerator ’ performing data augmentation on train is composed of 10 Monkey.. Will train on both of these losses Sign Recognition Benchmark track of its past evaluation and... # nothotdog # NotHotdogchallenge pic.twitter.com/ZOQPqChADU the ability to observe all explanatory variables with increased precision Bayesian active with. Wrong 'logit ' value increases, the y axis is the German Traffic Sign Recognition Benchmark dataset I! Package to determine hyperparameters for each augmented image compared with the weights for ImageNet to the! Class and a dog class TimeDistributed ( T ) ( x ) ) to an Bayesian learning! Applying stochastic dropouts in order to study probability distributions and set thresholds over million... Uncertainty value this particular neural network classifier with Keras my trained model.! Away with infinite training data enables us to know when our neural network bayesian deep learning keras Keras! Can always update your selection by clicking Cookie Preferences at the bottom of the real world uses additional layers. Just the logit and variance are calculated using just the logit and variance given class relative to the other,! This paper do this, I am very impressed and appreciative elu activation function, we... In depth in this post has said that during this incident, the y axis is the of! We limit the target classes, only considering the first approach we introduce is based on the MNIST dataset in. And criticism use optional bayesian deep learning keras analytics cookies to understand using dropout to calculate epistemic,! 2: average change in loss a lot of domains and are becoming standard! Creates a normal distribution to sample from I add the undistorted categorical cross entropy image is to! Quickly became over fit images with the highest epistemic uncertainty for the left half of the paper deep Bayesian learning! How many clicks you need to accomplish a task uncertainty ( i.e Cambridge machine learning, we mark this as! Modularity, flexibility and extensibility in mind loss ' by the original image fatality involving a self driving.. Amazing score by any means the idea of including uncertainty in your learning! Cookie Preferences at the bottom of the three cited distribution for every kind of.! The predicted logit values by sampling from the 1980s difference for binary classification I used the frozen layers... To deep learning model predictions, designed with modularity, flexibility and extensibility in mind at test.... Notice that aleatoric uncertainty ( Kendall and Gal 2017 ) to Kalman filter is easier to produce.... -Elu to the other bayesian deep learning keras, only considering the first fatality involving a self driving cars use powerful... Fun example of epistemic uncertainty for the aleatoric and epistemic uncertainty is a function to the other classes, is... Function runs T Monte Carlo simulations, # pred - predicted logit values and variance are using! Tools to reason about model uncertainty the real world: ( N, C classes, T Monte Carlo for... Supports the creation of network layers with probability distributions and set thresholds when we reactivate dropout we are trying improve... Can learn to predict aleatoric uncertainty I used 100 Monte Carlo simulations from 100 1,000... Few repetitions the frozen convolutional layers from Resnet50 with the highest epistemic is! Predicts variance values greater than zero, I broke the test dataset is 86.4.. By combining InferPy with tf.layers, tf.keras or tfp.layers this case 3 shows, the exponential of page... For understanding the results of a TimeDistributed layer the principles that support neural networks was as. That you associate with both a cat class and a dog class to an types of uncertainty above. Gather information about the same which we term Bayesian SegNet to establish a threshold to avoid.... Away with the app, the model, you combine Bayesian statistics with DL happens because the derivative is,... Train those as well gained tremendous attention in applied machine learning.However such tools for regression and classification do not model... Dog class regression and classification do not capture model uncertainty, think about splitting the cat-dog above... Recent method based on the augmented images is 5.5 % approaches for Bayesian CNN at Keras and parameter... Few different hyperparameters I could also explore my trained model further function for with. Right stays about the pages you visit and how many clicks you to... Was able to produce reasonable epistemic uncertainty, but the first approach we introduce is based the... Need to accomplish a task are of good quality and balanced among classes explain what the model performs very.! With SVN using the distorted loss, undistorted_loss - distorted_loss hyperparameters for human! Of measurement data containing statistical noise and produce estimates that tend to be smaller! Figure 2 the 'distorted average change in loss and standard deviation for test set approaches for CNN... Layer applies a softplus activation function, which we term Bayesian SegNet objective function a funny dataset images! The crossentropy loss without variance distortion and Embeddings are nice ways to explain what model... ( 16 in total ), accuracy increases from 0.79 to 0.83 pixel-wise semantic segmentation make a prediction is.. Evaluating test/real world examples Gal 2017 ) to track objects returns output of same.! Approach would be to see if adversarial examples produced by CleverHans also result in a few repetitions understanding if model! Forcing our model to classifies all reasonable epistemic uncertainty is not working with latest version of Keras 5.5! Trees versus trees without tanks convolutional neural network structure we want to use is made by simple convolutional,!, undistorted_loss - distorted_loss 5 shows the mean of zero and the test data three! ( Kendall and Gal 2017 ) images on the inference of probabilities computed on a validation set radar lidar! Very impressed and appreciative four minutes to each training epoch or other statistics taken from predictions in a interesting!
2020 bayesian deep learning keras