The classifier will still predict that it is a horse. 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. Dropout is used during testing, instead of only being used for training. After applying the transforms the images look something like this: @eqy Solved it! 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. Hopefully it can help explain this problem. 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). What might be the potential reason behind this? They tend to be over-confident. 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. In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. Ok, that sounds normal. Loss graph: Thank you. This leads to a less classic "loss increases while accuracy stays the same". 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. 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. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. 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. @JohnJ I corrected the example and submitted an edit so that it makes sense. After some time, validation loss started to increase, whereas validation accuracy is also increasing. Is it considered harrassment in the US to call a black man the N-word? Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Why does cross entropy loss for validation dataset deteriorate far more than validation accuracy when a CNN is overfitting? Earliest sci-fi film or program where an actor plays themself. I'am beginner in deep learning, I created 3DCNN using Pytorch. 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. Making statements based on opinion; back them up with references or personal experience. Such situation happens to human as well. There are several similar questions, but nobody explained what was happening there. why is it increasing so gradually and only up. Reddit and its partners use cookies and similar technologies to provide you with a better experience. Is my model overfitting? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. The validation accuracy is increasing just a little bit. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Try to change the requires_grads to True for all parameters, so the model can update its weights. Accuracy not increasing loss not decreasing. I tested it for the first time with two convolution layers, I found this problem! 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). I would just like to take the opportunity to ask something about the RNN input. 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. Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 You don't have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. the training set contains 335 samples, I test the model only on 150 samples. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. And another thing is I think you should reframe your question If loss increase then certainly acc will decrease. 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. Below mentioned are the transforms Im currently using. @ahstat There're a lot of ways to fight overfitting. 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". Given my experience, how do I get back to academic research collaboration? [Less likely] The model doesn't have enough aspect of information to be certain. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? Cat Dog classifier in tensorflow, fundamental problem! If your batch size is constant, this can't explain your loss issue. Thank you for your reply! 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). It doesn't seem to be overfitting because even the training accuracy is decreasing. And suggest some experiments to verify them. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). Hi @gcamilo, which combination improved the charts? I change it to True but the problem is not solved. This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. The 'illustration 2' is what I and you experienced, which is a kind of overfitting. Thanks for the help though. Are cheap electric helicopters feasible to produce? 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 . 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. Did Dick Cheney run a death squad that killed Benazir Bhutto? Experiment with more and larger hidden layers. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. Correct handling of negative chapter numbers. Why is the loss increasing? What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? Hope this solve the problem! 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. 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. Is it normal? Such a difference in Loss and Accuracy happens. There are several reasons that can cause fluctuations in training loss over epochs. 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. Why so? 18. A model can overfit to cross entropy loss without over overfitting to accuracy. Like using a pre-trained ResNet to classify some data. Maybe your model was 80% sure that it got the right class at some inputs, now it gets it with 90%. How high is your learning rate? Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. Stack Overflow for Teams is moving to its own domain! The loss is stable, but the model is learning very slowly. 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. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. Still, (and I'm sorry, i skimmed your code) is it possible that your network isn't large enough to model your data? Validation accuracy is increasing but the WER has converged after around 9-10 epochs. Many answers focus on the mathematical calculation explaining how is this possible. My inputs are variable sized arrays that were padded inside the batch. Found footage movie where teens get superpowers after getting struck by lightning? It should be around -ln(1/num_classes). The best answers are voted up and rise to the top, Not the answer you're looking for? Is there something like Retr0bright but already made and trustworthy? In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. MathJax reference. How can I best opt out of this? I am training a simple neural network on the CIFAR10 dataset. In short, cross entropy loss measures the calibration of a model. How many samples do you have in your training set? MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? It will be more meaningful to discuss with experiments to verify them, no matter the results prove them right, or prove them wrong. Can it be over fitting when validation loss and validation accuracy is both increasing? https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. Math papers where the only issue is that someone else could've done it but didn't. Great, what does the loss curve look like with smaller learning rates? An inf-sup estimate for holomorphic functions. i used keras.application.densenet to classify 2d images and this is the first time I use pytorch and sequential model. 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. 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. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. Simple and quick way to get phonon dispersion? 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 {cat: 0.6, dog: 0.4}. Thanks in advance! It's pretty normal. It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. [A very wild guess] This is a case where the model is less certain about certain things as being trained longer. While training the model, the loss is increasing and accuracy is decreasing drastically (both in training and validation sets). My hope would be that it would converge and overfit. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. After some small changes, I ran the model again and I also saved the training loss/acc: This looks better now. Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS, Make a wide rectangle out of T-Pipes without loops. 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. 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. 1. I am using torchvision augmentation. @Lucky_Magna By reframing I meant this is obvious if loss decrease acc will increase. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. 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. How can i extract files in the directory where they're located with the find command? What is the deepest Stockfish evaluation of the standard initial position that has ever been done? But they don't explain why it becomes so. 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. 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? Take another case where softmax output is [0.6, 0.4]. Im trying to classify Pneumonia patients using X-ray copies. Im trying to train a Pneumonia classifier using Resnet34. Can I spend multiple charges of my Blood Fury Tattoo at once? How is this possible? In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. 19. I am training a pytorch model for sign language classification. My training loss is increasing and my training accuracy is also increasing. Connect and share knowledge within a single location that is structured and easy to search. This is the classic " loss decreases while accuracy increases " behavior that we expect. I have 3 hypothesis. Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Verify loss input I would like to understand this example a bit more. 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 For weeks I have been trying to train the model. See this answer for further illustration of this phenomenon. Thanks for contributing an answer to Data Science Stack Exchange! [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: Do US public school students have a First Amendment right to be able to perform sacred music? @eqy Ok let me explain about the project Im working on. I have a GRU layer and a fully connected using a single hidden layer. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. So, it is all about the output distribution. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. As for the data, it is in the right format. Do you have an example where loss decreases, and accuracy decreases too? Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. There is a key difference between the two types of loss: For example, if an image of a cat is passed into two models. Press question mark to learn the rest of the keyboard shortcuts. Its normal to see your training performance continue to improve even though your test data performance has converged. Im padding as less as possible since I sort the dataset by the length of the array. There are 29 classes. Is x.permute(0, 2, 1) the correct way to fix the input shape? This is the classic "loss decreases while accuracy increases" behavior that we expect. How to help a successful high schooler who is failing in college? CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch. It has a shape (4,1,5). @Nahil_Sobh Share your model performance once you have optimized it. Does it need to be deeper? So in your case, your accuracy was 37/63 in 9th epoch. Would it be illegal for me to act as a Civillian Traffic Enforcer? I will try to address this for the cross-entropy loss. Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. Should it not have 3 elements? The accuracy is starting from around 25% and raising eventually but in a very slow manner. It only takes a minute to sign up. 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. There may be other reasons for OP's case. It seems loss is decreasing and the algorithm works fine. To make it clearer, here are some numbers. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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).). @eqy I changed the model from resnet34 to renset18. My current training seems working. Best way to get consistent results when baking a purposely underbaked mud cake. Making statements based on opinion; back them up with references or personal experience. {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. Share Nice. 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). MathJax reference. What is the best way to show results of a multiple-choice quiz where multiple options may be right? Because of this the model will try to be more and more confident to minimize loss. But accuracy doesn't improve and stuck. 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. Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. It only takes a minute to sign up. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? So I think that you're doing something fishy. 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. Asking for help, clarification, or responding to other answers. so i added 3 more layers but the accuracy and loss values keep decreasing and increasing. (Following something I found in the forum, I added the parameter amsgrad=True in my Adam optimizer, but I still have this loss problem). To learn more, see our tips on writing great answers. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. I recommend read this: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. But surely, the loss has increased. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 Check your loss function. What kind of data do you have? But accuracy doesn't improve and stuck. Add dropout, reduce number of layers or number of neurons in each layer. 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. low with BCEWithLogitsLoss when your accuracy is 50%. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: The validation accuracy is increasing just a little bit. Or conversely (and probably a better starting point): have you attempted using a shallower network? If you put to False, it will freeze all layers, and won't calculate the grads. What value for LANG should I use for "sort -u correctly handle Chinese characters? If you take away these layer, the model it will be freeze completely and won't update the weights. Any ideas what might be happening? And they cannot suggest how to digger further to be more clear. Suppose there are 2 classes - horse and dog. Learning rate, weight decay and optimizer (I tried both Adam and SGD). I tried different architectures as well, but the result is the same. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. How can I find a lens locking screw if I have lost the original one? 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. 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. i am trying to create 3d CNN using pytorch. This approach of freezing can be used when you're using Transfer Learning. Water leaving the house when water cut off. It looks correct to me. To learn more, see our tips on writing great answers. If you implemented your own loss function, check it for bugs and add unit tests. For example, I might use dropout. Label is noisy. Reading the code you post, I see that you set the model to not calculate the gradient of parameters of the mode (when you set parameters.requires_grads=False) . It works fine in training stage, but in validation stage it will perform poorly in term of loss. A PyTorch library for easily training Faster RCNN models With the introduction of torcheval, does it make sense to Visualizing word embeddings using pytorch, Human Action Recognition in Videos using PyTorch. You're freezing all parameters with the instruction param.requires_grad = False;. I am a beginner in deep learning and i'm not sure if I need to add more layers of convolution and pooling. Could you post your model architecture? What I am interesting the most, what's the explanation for this. @1453042287 Hi, thanks for the advise. Contribute to kose/PyTorch_MNIST_Optuna . How can I get a huge Saturn-like ringed moon in the sky? It seems that if validation loss increase, accuracy should decrease. Use MathJax to format equations. 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.). Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 0.3944, Accuracy: 37/63 (58%). rev2022.11.3.43005. If your batch size is constant, this cant explain your loss issue. 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. I tried increasing the learning_rate, but the results dont differ that much. @Nahil_Sobh I posted the code on my github account you can see the performance there. Can you check the initial loss of your model with random data? Validation loss fluctuating while training the neural network in tensorflow. But accuracy doesn't improve and stuck. Im training only for a small number of epochs since the error is weird, but I believe that it would keep increasing. preds = torch.max (output, dim=1, keepdim=True) [1] This looks very odd. 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? 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). 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. 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. Loss actually tracks the inverse-confidence (for want of a better word) of the prediction. I tried increasing the learning_rate, but the results don't differ that much. So I am wondering whether my calculation of accuracy is correct or not? For our case, the correct class is horse . After this, try increasing the regularization strength which should increase the loss. 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. Why validation accuracy is increasing very slowly? Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. Just out of curiosity, what were the small changes? How does this model compare with 2D models that you have trained successfully? For some reason, my loss is increasing instead of decreasing. 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, Pytorch - Loss is decreasing but Accuracy not improving, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned, Loss for CNN decreases and settles but training accuracy does not improve. 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). I'm novice, please someone correct me if i wrong in some aspect! Thank you. Like the training and validation losses plots and possibly accuracy plots as well. CNN Tanking: Six Reasons For Its Failures, CNN - 12% of job listings have salary info, CNN medical expert: Drop (almost) all COVID restrictions. Is cycling an aerobic or anaerobic exercise? 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. If this value is close then it suggests that your model is initialized properly. Use MathJax to format equations. 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 Visit Stack Exchange Tour Start here for quick overview the site Help Center Detailed answers. Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. Why are only 2 out of the 3 boosters on Falcon Heavy reused? Don't argue about this by just saying if you disagree with these hypothesis. $\frac{correct-classes}{total-classes}$. The best answers are voted up and rise to the top, Not the answer you're looking for? The accuracy just shows how much you got right out of your samples. But the loss keeps hovering around the number where it starts, and the accuracy to remains where it started (accuracy is as good . This is how you get high accuracy and high loss. 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. Thanks for pointing this out, I was starting to doubt myself as well. 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. Reason for use of accusative in this phrase? 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. 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. Hope that makes sense. Compare the false predictions when val_loss is minimum and val_acc is maximum. Let's consider the case of binary classification, where the task is to predict whether an image is a cat or a horse, and the output of the network is a sigmoid (outputting a float between 0 and 1), where we train the network to output 1 if the image is one of a cat and 0 otherwise. How many characters/pages could WordStar hold on a typical CP/M machine?
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