18. To learn more, see our tips on writing great answers. For example, for some borderline images, being confident e.g. I will usually (when I'm trying to built a model that I haven't vetted or proven yet to be correct for the data) test the model with only a couple samples. Suppose there are 2 classes - horse and dog. There are 29 classes. Hopefully it can help explain this problem. If I can demonstrate that the model is overfitting on a couple samples, then I would expect the model to learn something when I trained it on all the samples. In short, cross entropy loss measures the calibration of a model. Thank you for the explanations @Soltius. I change it to True but the problem is not solved. I tried increasing the learning_rate, but the results don't differ that much. $\frac{correct-classes}{total-classes}$. From Ankur's answer, it seems to me that: Accuracy measures the percentage correctness of the prediction i.e. My hope would be that it would converge and overfit. Im training only for a small number of epochs since the error is weird, but I believe that it would keep increasing. Im padding as less as possible since I sort the dataset by the length of the array. The test loss and test accuracy continue to improve. MathJax reference. have this same issue as OP, and we are experiencing scenario 1. It only takes a minute to sign up. Ok, that sounds normal. High Validation Accuracy + High Loss Score vs High Training Accuracy + Low Loss Score suggest that the model may be over-fitting on the training data. Mis-calibration is a common issue to modern neuronal networks. Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. I believe that in this case, two phenomenons are happening at the same time. I am using torchvision augmentation. I recommend read this: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. And another thing is I think you should reframe your question If loss increase then certainly acc will decrease. If you take away these layer, the model it will be freeze completely and won't update the weights. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? See this answer for further illustration of this phenomenon. It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. At this point I would see if there are any data augmentations that you can apply that make sense for you dataset, as well as other model architectures, etc. Many answers focus on the mathematical calculation explaining how is this possible. Loss actually tracks the inverse-confidence (for want of a better word) of the prediction. Is it normal? Thanks for pointing this out, I was starting to doubt myself as well. Fourier transform of a functional derivative. For weeks I have been trying to train the model. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. How many samples do you have in your training set? The best answers are voted up and rise to the top, Not the answer you're looking for? First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. What I am interesting the most, what's the explanation for this. {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. Note that when one uses cross-entropy loss for classification as it is usually done, bad predictions are penalized much more strongly than good predictions are rewarded. Accuracy not increasing loss not decreasing. I am facing the same issue with validation loss increasing while the train loss is decreasing. Water leaving the house when water cut off. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. Like the training and validation losses plots and possibly accuracy plots as well. How can I find a lens locking screw if I have lost the original one? Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 I would like to understand this example a bit more. I'am beginner in deep learning, I created 3DCNN using Pytorch. Use MathJax to format equations. Take another case where softmax output is [0.6, 0.4]. The network is starting to learn patterns only relevant for the training set and not great for generalization, leading to phenomenon 2, some images from the validation set get predicted really wrong, with an effect amplified by the "loss asymmetry". Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. the training set contains 335 samples, I test the model only on 150 samples. Such situation happens to human as well. @Nahil_Sobh Share your model performance once you have optimized it. Add dropout, reduce number of layers or number of neurons in each layer. Im trying to train a Pneumonia classifier using Resnet34. By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. A model can overfit to cross entropy loss without over overfitting to accuracy. Because of this the model will try to be more and more confident to minimize loss. Stack Overflow for Teams is moving to its own domain! It has a shape (4,1,5). I change it but that does not solve the problem. Well, the obvious answer is, nothing wrong here, if the model is not suited for your data distribution then, it simply wont work for desirable results. Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. This is the classic " loss decreases while accuracy increases " behavior that we expect. It seems that if validation loss increase, accuracy should decrease. I.e. Why so? Is it considered harrassment in the US to call a black man the N-word? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. I need to reshape it into an initial hidden state of decoder LSTM, which should has one batch, a single direction and two layers, and 10-dimensional hidden vector, final shape is (2,1,10).). Press question mark to learn the rest of the keyboard shortcuts. P.S. On Calibration of Modern Neural Networks talks about it in great details. What might be the potential reason behind this? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Below mentioned are the transforms Im currently using. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Making statements based on opinion; back them up with references or personal experience. What exactly makes a black hole STAY a black hole? Check your loss function. Compare the false predictions when val_loss is minimum and val_acc is maximum. 19. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. So I am wondering whether my calculation of accuracy is correct or not? It is overfitting to one class in the whole dataset. the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) NOTE: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs Do you know what I am doing wrong here? But they don't explain why it becomes so. Loss graph: Thank you. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). Or should I unbind and then stack it? Finally, I think this effect can be further obscured in the case of multi-class classification, where the network at a given epoch might be severely overfit on some classes but still learning on others. Loss ~0.6. Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. How to train multiple PyTorch models in parallel on a is it possible to use several different pytorch models on Press J to jump to the feed. Stack Overflow for Teams is moving to its own domain! Validation loss increases while Training loss decrease. Try to change the requires_grads to True for all parameters, so the model can update its weights. I have a GRU layer and a fully connected using a single hidden layer. My batch size is constant and equal to 10. Is cycling an aerobic or anaerobic exercise? I will try to address this for the cross-entropy loss. Check whether these sample are correctly labelled. Correct handling of negative chapter numbers. Found footage movie where teens get superpowers after getting struck by lightning? This suggests that the initial suspicion that the dataset was too small might be true because both times I ran the network with the complete librispeech dataset, the WER converged while validation accuracy started to increase which suggests overfitting. Thanks for contributing an answer to Cross Validated! i am trying to create 3d CNN using pytorch. How can I best opt out of this? There may be other reasons for OP's case. Do you have an example where loss decreases, and accuracy decreases too? the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%), NOTE: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs. Is my model overfitting? The validation accuracy is increasing just a little bit. For some reason, my loss is increasing instead of decreasing. To learn more, see our tips on writing great answers. {cat: 0.6, dog: 0.4}. How many characters/pages could WordStar hold on a typical CP/M machine? Why validation accuracy is increasing very slowly? @eqy I changed the model from resnet34 to renset18. But accuracy doesn't improve and stuck. MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? Contribute to kose/PyTorch_MNIST_Optuna . Some images with borderline predictions get predicted better and so their output class changes (eg a cat image whose prediction was 0.4 becomes 0.6). When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. However, accuracy and loss intuitively seem to be somewhat (inversely) correlated, as better predictions should lead to lower loss and higher accuracy, and the case of higher loss and higher accuracy shown by OP is surprising. To make it clearer, here are some numbers. Thanks for contributing an answer to Data Science Stack Exchange!
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