Is it considered harrassment in the US to call a black man the N-word? What is the best way to show results of a multiple-choice quiz where multiple options may be right? I would just like to take the opportunity to ask something about the RNN input. For my particular problem, it was alleviated after shuffling the set. 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. Many answers focus on the mathematical calculation explaining how is this possible. In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. My hope would be that it would converge and overfit. I got a very odd pattern where both loss and accuracy decreases. Hope that makes sense. What I am interesting the most, what's the explanation for this. Im trying to train a Pneumonia classifier using Resnet34. What is the effect of cycling on weight loss? I am facing the same issue with validation loss increasing while the train loss is decreasing. MathJax reference. It seems that if validation loss increase, accuracy should decrease. This is the classic " loss decreases while accuracy increases " behavior that we expect. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). 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. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. Such a difference in Loss and Accuracy happens. Thank you. Is it normal? In short, cross entropy loss measures the calibration of a model. https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. First things first, there are three classes and the softmax has only 2 outputs. The accuracy is starting from around 25% and raising eventually but in a very slow manner. How does this model compare with 2D models that you have trained successfully? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Or should I unbind and then stack it? Out of curiosity - do you have a recommendation on how to choose the point at which model training should stop for a model facing such an issue? You can check some hints to understand in my answer here: @ahstat I understand how it's technically possible, but I don't understand how it happens here. Such situation happens to human as well. Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. It has a shape (4,1,5). How is it possible that validation loss is increasing while validation accuracy is increasing as well, stats.stackexchange.com/questions/258166/, Mobile app infrastructure being decommissioned, Am I missing obvious problems with my model, train_accuracy and train_loss are not consistent in binary classification. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. For weeks I have been trying to train the model. 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. I am training a simple neural network on the CIFAR10 dataset. Other answers explain well how accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw prediction (float) and class (0 or 1), while accuracy measures the difference between thresholded prediction (0 or 1) and class. Some coworkers are committing to work overtime for a 1% bonus. Great, what does the loss curve look like with smaller learning rates? It works fine in training stage, but in validation stage it will perform poorly in term of loss. 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).). Im padding as less as possible since I sort the dataset by the length of the array. Check whether these sample are correctly labelled. It the loss increasing in each epoch or just the beginning of training? Cat Dog classifier in tensorflow, fundamental problem! i used keras.application.densenet to classify 2d images and this is the first time I use pytorch and sequential model. [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: There is a key difference between the two types of loss: For example, if an image of a cat is passed into two models. If this value is close then it suggests that your model is initialized properly. I tried different architectures as well, but the result is the same. 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 input image: 120 * 120 * 120 Do you know what I am doing wrong here? Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? What happens with the dropout, is that is working because is the only layer that is changing, sience the only thing it does is to " turn off" some neurons, so it would change the output randomly based on the neurons its turn off. I'am beginner in deep learning, I created 3DCNN using Pytorch. Its normal to see your training performance continue to improve even though your test data performance has converged. rev2022.11.3.43005. Hope this solve the problem! And another thing is I think you should reframe your question If loss increase then certainly acc will decrease. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. @Lucky_Magna By reframing I meant this is obvious if loss decrease acc will increase. Hopefully it can help explain this problem. Like using a pre-trained ResNet to classify some data. However, it is at the same time still learning some patterns which are useful for generalization (phenomenon one, "good learning") as more and more images are being correctly classified. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. But accuracy doesn't improve and stuck. 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. Learning rate, weight decay and optimizer (I tried both Adam and SGD). I sadly have no answer for whether or not this "overfitting" is a bad thing in this case: should we stop the learning once the network is starting to learn spurious patterns, even though it's continuing to learn useful ones along the way? I am a beginner in deep learning and i'm not sure if I need to add more layers of convolution and pooling. Simple and quick way to get phonon dispersion? But surely, the loss has increased. If you put to False, it will freeze all layers, and won't calculate the grads. How many samples do you have in your training set? What is the deepest Stockfish evaluation of the standard initial position that has ever been done? 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. The classifier will predict that it is a horse. What might be the potential reason behind this? I change it to True but the problem is not solved. I will try to address this for the cross-entropy loss. It only takes a minute to sign up. After some small changes, I ran the model again and I also saved the training loss/acc: This looks better now. To learn more, see our tips on writing great answers. Thanks for pointing this out, I was starting to doubt myself as well. Im trying to classify Pneumonia patients using X-ray copies. It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. And suggest some experiments to verify them. What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? (Getting increasing loss and stable accuracy could also be caused by good predictions being classified a little worse, but I find it less likely because of this loss "asymmetry"). Why is the loss increasing? Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS, Make a wide rectangle out of T-Pipes without loops. First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. 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. If you implemented your own loss function, check it for bugs and add unit tests. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: I recommend read this: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. @eqy Ok let me explain about the project Im working on. The validation accuracy is increasing just a little bit. The test loss and test accuracy continue to improve. There are several reasons that can cause fluctuations in training loss over epochs. 19. And I dont think I should reframe the question, as you can see from the screenshot. When he goes through more cases and examples, he realizes sometimes certain border can be blur (less certain, higher loss), even though he can make better decisions (more accuracy). For example, I might use dropout. So I think that you're doing something fishy. Do US public school students have a First Amendment right to be able to perform sacred music? If you're training the model from zero, with no pre-trained weights, you can't do this (not for all parameters). But accuracy doesn't improve and stuck. I am using torchvision augmentation. Hi @gcamilo, which combination improved the charts? Or conversely (and probably a better starting point): have you attempted using a shallower network? Are cheap electric helicopters feasible to produce? In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. An inf-sup estimate for holomorphic functions. [A very wild guess] This is a case where the model is less certain about certain things as being trained longer. Did Dick Cheney run a death squad that killed Benazir Bhutto? How many characters/pages could WordStar hold on a typical CP/M machine? Stack Overflow for Teams is moving to its own domain! Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. 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. It is overfitting to one class in the whole dataset. This is how you get high accuracy and high loss. Any ideas what might be happening? Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. 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. @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. Try to change the requires_grads to True for all parameters, so the model can update its weights. This is the classic "loss decreases while accuracy increases" behavior that we expect. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. @Nahil_Sobh I posted the code on my github account you can see the performance there. Why does cross entropy loss for validation dataset deteriorate far more than validation accuracy when a CNN is overfitting? $\frac{correct-classes}{total-classes}$. So, basically, it wont update the loss. Use MathJax to format equations. CNN Tanking: Six Reasons For Its Failures, CNN - 12% of job listings have salary info, CNN medical expert: Drop (almost) all COVID restrictions. 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. So, I used it on validation and test set as well (If it is a bad idea the correct me). The accuracy is starting from around 25% and raising eventually but in a very slow manner. 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. CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. MathJax reference. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Can you suggest any other solution to solve the problem. Loss ~0.6. Improving Validation Loss and Accuracy for CNN, Pytorch CrossEntropyLoss expected long but got float, Val Accuracy not increasing at all even through training loss is decreasing, Water leaving the house when water cut off. Accuracy of a set is evaluated by just cross-checking the highest softmax output and the correct labeled class.It is not depended on how high is the softmax output. I.e. 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. 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. They tend to be over-confident. Connect and share knowledge within a single location that is structured and easy to search. Asking for help, clarification, or responding to other answers. How to help a successful high schooler who is failing in college? Press question mark to learn the rest of the keyboard shortcuts. Correct handling of negative chapter numbers. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. (Following something I found in the forum, I added the parameter amsgrad=True in my Adam optimizer, but I still have this loss problem). There may be other reasons for OP's case. I have a GRU layer and a fully connected using a single hidden layer. Would it be illegal for me to act as a Civillian Traffic Enforcer? The validation accuracy is increasing just a little bit. Reason for use of accusative in this phrase? Thats just my opinion, I may not be to the point here. My batch size is constant and equal to 10. Add dropout, reduce number of layers or number of neurons in each layer. So if raw predictions change, loss changes but accuracy is more "resilient" as predictions need to go over/under a threshold to actually change accuracy. 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. Logically, the training and validation loss should decrease and then saturate which is happening but also, it should give 100% or a very large accuracy on the valid set ( As it is same as of training set), but it is giving 0% accuracy. Why so? Powered by Discourse, best viewed with JavaScript enabled, Loss is increasing and accuracy is decreasing, narayana8799/Pneumonia-Detection-using-Pytorch/blob/master/Pneumonia Detection.ipynb. Use MathJax to format equations. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. have this same issue as OP, and we are experiencing scenario 1. Share When using BCEWithLogitsLoss for binary classification, the output of your network would have a single value (a logit) for each thing (e.g., batch element) you were making a Dropout is used during testing, instead of only being used for training. Connect and share knowledge within a single location that is structured and easy to search. While training the model, the loss is increasing and accuracy is decreasing drastically (both in training and validation sets). The 'illustration 2' is what I and you experienced, which is a kind of overfitting. I'm novice, please someone correct me if i wrong in some aspect! Earliest sci-fi film or program where an actor plays themself. Best way to get consistent results when baking a purposely underbaked mud cake. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. Can you check the initial loss of your model with random data? Contribute to kose/PyTorch_MNIST_Optuna . Can I spend multiple charges of my Blood Fury Tattoo at once? For our case, the correct class is horse . Is x.permute(0, 2, 1) the correct way to fix the input shape? Does it need to be deeper? What exactly makes a black hole STAY a black hole? So, it is all about the output distribution. How can i extract files in the directory where they're located with the find command? Is there something like Retr0bright but already made and trustworthy? I have myself encountered this case several times, and I present here my conclusions based on the analysis I had conducted at the time. The best answers are voted up and rise to the top, Not the answer you're looking for? Powered by Discourse, best viewed with JavaScript enabled. Hello there! The best answers are voted up and rise to the top, Not the answer you're looking for? Code: import numpy as np import cv2 from os import listdir from os.path import isfile, join from sklearn.utils import shuffle import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable import torch.utils.data What is the deepest Stockfish evaluation of the standard initial position that has ever been done? 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? If your batch size is constant, this cant explain your loss issue. To make it clearer, here are some numbers. rev2022.11.3.43005. Found footage movie where teens get superpowers after getting struck by lightning? so i added 3 more layers but the accuracy and loss values keep decreasing and increasing. {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. And he may eventually gets more certain when he becomes a master after going through a huge list of samples and lots of trial and errors (more training data). After this, try increasing the regularization strength which should increase the loss. Validation loss increases while Training loss decrease. See this answer for further illustration of this phenomenon. the training set contains 335 samples, I test the model only on 150 samples. How can I find a lens locking screw if I have lost the original one? If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. @Lucky_Magna Could you please share the performance of your final model? There are 29 classes. eqy (Eqy) May 23, 2021, 4:34am #11 Ok, that sounds normal. Still, (and I'm sorry, i skimmed your code) is it possible that your network isn't large enough to model your data? In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. The next thing to check would be that your data format as input to the model makes sense (e.g., from the perspective of data layout, etc.). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. It's pretty normal. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 It is taking around 10 to 15 epochs to reach 60% accuracy. As for the data, it is in the right format. Math papers where the only issue is that someone else could've done it but didn't. Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. But they don't explain why it becomes so. Just out of curiosity, what were the small changes? Your training and testing data should be different, for the reason that it is easy to overfit the training data, but the true goal is for the algorithm to perform on data it has not seen before. low with BCEWithLogitsLoss when your accuracy is 50%. It is taking around 10 to 15 epochs to reach 60% accuracy. I am training a pytorch model for sign language classification. Experiment with more and larger hidden layers. Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. Water leaving the house when water cut off. @ahstat There're a lot of ways to fight overfitting. import numpy as np import cv2 from os import listdir from os.path import isfile, join from sklearn.utils import shuffle. I tested it for the first time with two convolution layers, I found this problem! Stack Overflow for Teams is moving to its own domain! How is this possible? There are several similar questions, but nobody explained what was happening there. CE-loss= sum (-log p (y=i)) Note that loss will decrease if the probability of correct class increases and loss increases if the probability of correct class decreases. So I am wondering whether my calculation of accuracy is correct or not? I change it but that does not solve the problem. On Calibration of Modern Neural Networks talks about it in great details. i am trying to create 3d CNN using pytorch. Could you post your model architecture? 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? 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