Any ideas what might be happening? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. (40%)] Loss: 0.597774 Train Epoch: 7 [200/249 (80%)] Loss: 0.554897 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. How can I best opt out of this? The validation accuracy is increasing just a little bit. I am a beginner in deep learning and i'm not sure if I need to add more layers of convolution and pooling. 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}. Use MathJax to format equations. Simple and quick way to get phonon dispersion? MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? Thank you for your reply! Do US public school students have a First Amendment right to be able to perform sacred music? Hi @gcamilo, which combination improved the charts? The accuracy just shows how much you got right out of your samples. Take another case where softmax output is [0.6, 0.4]. What kind of data do you have? A model can overfit to cross entropy loss without over overfitting to accuracy. This is how you get high accuracy and high loss. Pytorch weight decay value - progd.reunionideas.info 37 Reasons why your Neural Network is not working Observation: in your example, the accuracy doesnt change. As for the data, it is in the right format. It should be around -ln(1/num_classes). hp cf378a color laserjet pro mfp m477fdn priya anjali rai latest xxx porn summer code mens sexy micro mesh Contribute to kose/PyTorch_MNIST_Optuna . When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. If you put to False, it will freeze all layers, and won't calculate the grads. Such situation happens to human as well. After this, try increasing the regularization strength which should increase the loss. CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch. 0.564388 Train Epoch: 8 [200/249 (80%)] Loss: 0.517878 Test set: Average loss: 0.4522, Accuracy: 37/63 (58%) Train Epoch: 9 [0/249 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 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". They tend to be over-confident. 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. Hope that makes sense. Validation loss increasing while WER decreases #78 - GitHub See this answer for further illustration of this phenomenon. @JohnJ I corrected the example and submitted an edit so that it makes sense. $\frac{correct-classes}{total-classes}$. I got a very odd pattern where both loss and accuracy decreases. The 'illustration 2' is what I and you experienced, which is a kind of overfitting. criterion = nn.CrossEntropyLoss().cuda(). Mis-calibration is a common issue to modern neuronal networks. Hello there! 0.3944, Accuracy: 37/63 (58%). 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. @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. The best answers are voted up and rise to the top, Not the answer you're looking for? Label is noisy. It the loss increasing in each epoch or just the beginning of training? Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 why is it increasing so gradually and only up. If your batch size is constant, this cant explain your loss issue. It doesn't seem to be overfitting because even the training accuracy is decreasing. Connect and share knowledge within a single location that is structured and easy to search. It's still 100%. For my particular problem, it was alleviated after shuffling the set. Don't argue about this by just saying if you disagree with these hypothesis. What I am interesting the most, what's the explanation for this. Like the training and validation losses plots and possibly accuracy plots as well. Im trying to train a Pneumonia classifier using Resnet34. Loss decreasing but accuracy is still the same - PyTorch Forums Check your loss function. https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. Pytorch lstm last output - ztlhab.goldhunter.shop Can I spend multiple charges of my Blood Fury Tattoo at once? {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. There may be other reasons for OP's case. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? The loss looks indeed a bit fishy. 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. Add dropout, reduce number of layers or number of neurons in each layer. But the loss keeps hovering around the number where it starts, and the accuracy to remains where it started (accuracy is as good . Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. To learn more, see our tips on writing great answers. Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. So I think that when both accuracy and loss are increasing, the network is starting to overfit, and both phenomena are happening at the same time. This is the classic " loss decreases while accuracy increases " behavior that we expect. Is cycling an aerobic or anaerobic exercise? Is it considered harrassment in the US to call a black man the N-word? But accuracy doesn't improve and stuck. In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. I'am beginner in deep learning, I created 3DCNN using Pytorch. 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). Like using a pre-trained ResNet to classify some data. Why is the loss increasing? I change it to True but the problem is not solved. Hope this solve the problem! First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. @eqy I changed the model from resnet34 to renset18. I.e. @eqy Ok let me explain about the project Im working on. 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. My learning rate starts at 1e-3 and Im using decay: The architecture that Im trying is pretty much Convolutional Layers followed by Max Pool layers (the last one is an Adaptive Max Pool), using ReLU and batch normalization. Some coworkers are committing to work overtime for a 1% bonus. 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. How does this model compare with 2D models that you have trained successfully? I recommend read this: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. It is taking around 10 to 15 epochs to reach 60% accuracy. In short, cross entropy loss measures the calibration of a model. Loss increasing instead of decreasing - PyTorch Forums How many characters/pages could WordStar hold on a typical CP/M machine? 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? Press question mark to learn the rest of the keyboard shortcuts. Thanks for contributing an answer to Cross Validated! For this loss ~0.37. The classifier will still predict that it is a horse. I change it but that does not solve the problem. So I am wondering whether my calculation of accuracy is correct or not? Im trying to classify Pneumonia patients using X-ray copies. Thanks for contributing an answer to Data Science Stack Exchange! Below mentioned are the transforms Im currently using. optimizer = optim.Adam(model.parameters(), lr=args[initial_lr], weight_decay=args[weight_decay], amsgrad=True) If you're training the model from zero, with no pre-trained weights, you can't do this (not for all parameters). 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? Is it considered harrassment in the US to call a black man the N-word? So I think that you're doing something fishy. I am training a pytorch model for sign language classification. 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? Is x.permute(0, 2, 1) the correct way to fix the input shape? So, I used it on validation and test set as well (If it is a bad idea the correct me). Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. Now, when you compute average loss, you are averaging over all . So in your case, your accuracy was 37/63 in 9th epoch. But they don't explain why it becomes so. What is the best way to show results of a multiple-choice quiz where multiple options may be right? 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. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. Are cheap electric helicopters feasible to produce? Or conversely (and probably a better starting point): have you attempted using a shallower network? For weeks I have been trying to train the model. Best way to get consistent results when baking a purposely underbaked mud cake. Learning rate, weight decay and optimizer (I tried both Adam and SGD). And I dont think I should reframe the question, as you can see from the screenshot. Before you may ask why am I using Invert transform on the validation set, I think this transform is able to capture the pneumonia parts in the x-ray copies. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. Thank you. [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. So in your case, your accuracy was 37/63 in 9th epoch. 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. After some time, validation loss started to increase, whereas validation accuracy is also increasing. 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). Found footage movie where teens get superpowers after getting struck by lightning? @Nahil_Sobh Share your model performance once you have optimized it. 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. Just out of curiosity, what were the small changes? CNN: accuracy and loss are increasing and decreasing : pytorch To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. Also consider a decay rate of 1e-6. Correct handling of negative chapter numbers. Water leaving the house when water cut off. 19. Asking for help, clarification, or responding to other answers. Could you post your model architecture? Loss functions are not measured on the correct scale (for example, cross-entropy loss can be expressed in terms of probability or logits) The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). 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. How can i extract files in the directory where they're located with the find command? Such a difference in Loss and Accuracy happens. Accuracy and loss values are stable #47170 - GitHub When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Sanity Check : Validation loss not increasing - PyTorch Forums Maybe your model was 80% sure that it got the right class at some inputs, now it gets it with 90%. 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. The loss is stable, but the model is learning very slowly. Hopefully it can help explain this problem. Try to change the requires_grads to True for all parameters, so the model can update its weights. Verify loss input Thank you for the explanations @Soltius. Nice. 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. 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 learn more, see our tips on writing great answers. My training loss is increasing and my training accuracy is also increasing. I'm novice, please someone correct me if i wrong in some aspect! 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. After some small changes, I ran the model again and I also saved the training loss/acc: This looks better now. I have 3 hypothesis. Validation accuracy is increasing but the WER has converged after around 9-10 epochs. Loss ~0.6. I am facing the same issue with validation loss increasing while the train loss is decreasing. You don't have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. This is the classic "loss decreases while accuracy increases" behavior that we expect. It works fine in training stage, but in validation stage it will perform poorly in term of loss. Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Thanks for pointing this out, I was starting to doubt myself as well. 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. And suggest some experiments to verify them. I tried increasing the learning_rate, but the results don't differ that much. It is overfitting to one class in the whole dataset. rev2022.11.3.43005. I am using torchvision augmentation. 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. have this same issue as OP, and we are experiencing scenario 1. Im training only for a small number of epochs since the error is weird, but I believe that it would keep increasing. Loss is increasing and accuracy is decreasing - PyTorch Forums Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS, Make a wide rectangle out of T-Pipes without loops. Can you check the initial loss of your model with random data? It only takes a minute to sign up. I tried increasing the learning_rate, but the results dont differ that much. import numpy as np import cv2 from os import listdir from os.path import isfile, join from sklearn.utils import shuffle. How is this possible? Cat Dog classifier in tensorflow, fundamental problem! the training set contains 335 samples, I test the model only on 150 samples. recurrent neural network - Why does the loss/accuracy fluctuate during How can I find a lens locking screw if I have lost the original one? What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? There are several reasons that can cause fluctuations in training loss over epochs. My current training seems working. If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. The best answers are voted up and rise to the top, Not the answer you're looking for? 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. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. How high is your learning rate? Pytorch - Loss is decreasing but Accuracy not improving In the docs, it says that that the tensor should be (Batch, Sequence, Features) when using batch_first=True, however my input is (Batch, Features, Sequence). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Connect and share knowledge within a single location that is structured and easy to search. [Solved] Pytorch - Loss is decreasing but Accuracy not improving It will be more meaningful to discuss with experiments to verify them, no matter the results prove them right, or prove them wrong. Validation loss fluctuating while training the neural network in tensorflow. 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. It's pretty normal. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Thresholding of predictions can be done as below: def thresholded_output_transform(output): y_pred, y = output y_pred = torch.round(y_pred) return y_pred, y metric = Accuracy(output_transform=thresholded_output_transform) metric.attach(default_evaluator . Because of this the model will try to be more and more confident to minimize loss. Decreasing loss does not mean improving accuracy always. Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 From Ankur's answer, it seems to me that: Accuracy measures the percentage correctness of the prediction i.e. Experiment with more and larger hidden layers. Is it normal? Sorry for my English! preds = torch.max (output, dim=1, keepdim=True) [1] This looks very odd. Or should I unbind and then stack it? So, basically, it wont update the loss. Why so? Earliest sci-fi film or program where an actor plays themself. Accuracy not increasing loss not decreasing - PyTorch Forums 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. And they cannot suggest how to digger further to be more clear. Ok, that sounds normal. I believe that in this case, two phenomenons are happening at the same time. This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. python - Pytorch - Loss is decreasing but Accuracy not improving - Data It looks correct to me. Share To subscribe to this RSS feed, copy and paste this URL into your RSS reader. I will try to address this for the cross-entropy loss. Suppose there are 2 classes - horse and dog. But surely, the loss has increased. Why does loss decrease but accuracy decreases too (Pytorch, LSTM)? It is taking around 10 to 15 epochs to reach 60% accuracy. 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. Can it be over fitting when validation loss and validation accuracy is both increasing? Im padding as less as possible since I sort the dataset by the length of the array. It has a shape (4,1,5). From here, if your loss is not even going down initially, you can try simple tricks like decreasing the learning rate until it starts training. Accuracy PyTorch-Ignite v0.4.10 Documentation For a cat image, the loss is $log(1-prediction)$, so even if many cat images are correctly predicted (low loss), a single misclassified cat image will have a high loss, hence "blowing up" your mean loss. I would like to understand this example a bit more. Stack Overflow for Teams is moving to its own domain! This approach of freezing can be used when you're using Transfer Learning. Why are only 2 out of the 3 boosters on Falcon Heavy reused? Is my model overfitting? A high Loss score indicates that, even when the model is making good predictions, it is $less$ sure of the predictions it is makingand vice-versa. On Calibration of Modern Neural Networks talks about it in great details.