Label is noisy. If you implemented your own loss function, check it for bugs and add unit tests. But accuracy doesn't improve and stuck. The accuracy is starting from around 25% and raising eventually but in a very slow manner. @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. Take another case where softmax output is [0.6, 0.4]. The best answers are voted up and rise to the top, Not the answer you're looking for? Connect and share knowledge within a single location that is structured and easy to search. @Lucky_Magna Could you please share the performance of your final model? I.e. The 'illustration 2' is what I and you experienced, which is a kind of overfitting. optimizer = optim.Adam(model.parameters(), lr=args[initial_lr], weight_decay=args[weight_decay], amsgrad=True) First things first, there are three classes and the softmax has only 2 outputs. 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. Hi @gcamilo, which combination improved the charts? Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. I got a very odd pattern where both loss and accuracy decreases. have this same issue as OP, and we are experiencing scenario 1. Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. My inputs are variable sized arrays that were padded inside the batch. Observation: in your example, the accuracy doesnt change. 1 Like ptrblck May 22, 2018, 10:36am #2 The loss looks indeed a bit fishy. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? I would like to understand this example a bit more. 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. 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. Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 [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. Is cycling an aerobic or anaerobic exercise? And I dont think I should reframe the question, as you can see from the screenshot. Thank you. Check your loss function. Can you check the initial loss of your model with random data? 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 . Nice. https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. 0.3944, Accuracy: 37/63 (58%). I'm novice, please someone correct me if i wrong in some aspect! How many characters/pages could WordStar hold on a typical CP/M machine? Or should I unbind and then stack 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. Like the training and validation losses plots and possibly accuracy plots as well. For this loss ~0.37. You're freezing all parameters with the instruction param.requires_grad = False;. The loss is stable, but the model is learning very slowly. Do you have an example where loss decreases, and accuracy decreases too? 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. Can it be over fitting when validation loss and validation accuracy is both increasing? Thank you for the explanations @Soltius. 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. It should be around -ln(1/num_classes). Ok, that sounds normal. {cat: 0.9, dog: 0.1} will give higher loss than being uncertain e.g. Cat Dog classifier in tensorflow, fundamental problem! It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. I tried increasing the learning_rate, but the results don't differ that much. 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. I recommend read this: https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. I tried different architectures as well, but the result is the same. Share See this answer for further illustration of this phenomenon. Also consider a decay rate of 1e-6. Did Dick Cheney run a death squad that killed Benazir Bhutto? Why does cross entropy loss for validation dataset deteriorate far more than validation accuracy when a CNN is overfitting? I am facing the same issue with validation loss increasing while the train loss is decreasing. I have 3 hypothesis. Connect and share knowledge within a single location that is structured and easy to search. How high is your learning rate? If your batch size is constant, this cant explain your loss issue. This leads to a less classic "loss increases while accuracy stays the same". I am training a pytorch model for sign language classification. Many answers focus on the mathematical calculation explaining how is this possible. 18. Just out of curiosity, what were the small changes? Such a difference in Loss and Accuracy happens. 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. In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. You dont have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. Thanks for the help though. I will try to address this for the cross-entropy loss. 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). 19. MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? Is my model overfitting? 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. Im trying to train a Pneumonia classifier using Resnet34. It works fine in training stage, but in validation stage it will perform poorly in term of loss. What exactly makes a black hole STAY a black hole? This is the classic " loss decreases while accuracy increases " behavior that we expect. preds = torch.max (output, dim=1, keepdim=True) [1] This looks very odd. Still, (and I'm sorry, i skimmed your code) is it possible that your network isn't large enough to model your data? Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. @Nahil_Sobh Share your model performance once you have optimized it. Such situation happens to human as well. 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. 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. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? rev2022.11.3.43005. If you put to False, it will freeze all layers, and won't calculate the grads. And another thing is I think you should reframe your question If loss increase then certainly acc will decrease. Hopefully it can help explain this problem. Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. It the loss increasing in each epoch or just the beginning of training? For some reason, my loss is increasing instead of decreasing. My batch size is constant and equal to 10. 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. @Nahil_Sobh I posted the code on my github account you can see the performance there. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 {cat: 0.6, dog: 0.4}. Any ideas what might be happening? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Thats just my opinion, I may not be to the point here. 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. Why validation accuracy is increasing very slowly? Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. On Calibration of Modern Neural Networks talks about it in great details. (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"). Validation loss fluctuating while training the neural network in tensorflow. Loss graph: Thank you. Thank you for your reply! This approach of freezing can be used when you're using Transfer Learning. @eqy I changed the model from resnet34 to renset18. 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 loss looks indeed a bit fishy. Best way to get consistent results when baking a purposely underbaked mud cake. What value for LANG should I use for "sort -u correctly handle Chinese characters? Press question mark to learn the rest of the keyboard shortcuts. For our case, the correct class is horse . Water leaving the house when water cut off. I change it but that does not solve the problem. @ahstat There're a lot of ways to fight overfitting. criterion = nn.CrossEntropyLoss().cuda(). Reason for use of accusative in this phrase? It seems that if validation loss increase, accuracy should decrease. My training loss is increasing and my training accuracy is also increasing. Verify loss input This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. 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. Tarlan Ahad Asks: Pytorch - Loss is decreasing but Accuracy not improving It seems loss is decreasing and the algorithm works fine. They tend to be over-confident. The accuracy just shows how much you got right out of your samples. Don't argue about this by just saying if you disagree with these hypothesis. After some small changes, I ran the model again and I also saved the training loss/acc: This looks better now. @Lucky_Magna By reframing I meant this is obvious if loss decrease acc will increase. 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. It looks correct to me. Now, the output of the softmax is [0.9, 0.1]. I tested it for the first time with two convolution layers, I found this problem! I forgot to shuffle the dataset. When someone started to learn a technique, he is told exactly what is good or bad, what is certain things for (high certainty). (40%)] Loss: 0.597774 Train Epoch: 7 [200/249 (80%)] Loss: 0.554897 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 Dropout is used during testing, instead of only being used for training. So in your case, your accuracy was 37/63 in 9th epoch. Can you suggest any other solution to solve the problem. 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. What I am interesting the most, what's the explanation for this. While training the model, the loss is increasing and accuracy is decreasing drastically (both in training and validation sets). 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 But the loss keeps hovering around the number where it starts, and the accuracy to remains where it started (accuracy is as good . There is a key difference between the two types of loss: For example, if an image of a cat is passed into two models. 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? The validation accuracy is increasing just a little bit. Now, when you compute average loss, you are averaging over all . P.S. There are several reasons that can cause fluctuations in training loss over epochs. After this, try increasing the regularization strength which should increase the loss. Maybe your model was 80% sure that it got the right class at some inputs, now it gets it with 90%. Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS, Make a wide rectangle out of T-Pipes without loops. 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. And suggest some experiments to verify them. I am training a simple neural network on the CIFAR10 dataset. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? My current training seems working. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. Contribute to kose/PyTorch_MNIST_Optuna . Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 [A very wild guess] This is a case where the model is less certain about certain things as being trained longer. Hope that makes sense. Stack Overflow for Teams is moving to its own domain! Stack Overflow for Teams is moving to its own domain! hp cf378a color laserjet pro mfp m477fdn priya anjali rai latest xxx porn summer code mens sexy micro mesh Hope this solve the problem! As for the data, it is in the right format. Because of this the model will try to be more and more confident to minimize loss. Experiment with more and larger hidden layers. 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. 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. Why so? Learning rate, weight decay and optimizer (I tried both Adam and SGD). Loss ~0.6. It's pretty normal. In short, cross entropy loss measures the calibration of a model. Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: 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. It only takes a minute to sign up. 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. Asking for help, clarification, or responding to other answers. Im trying to classify Pneumonia patients using X-ray copies. Should it not have 3 elements? This is how you get high accuracy and high loss. Sorry for my English! 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) . I have myself encountered this case several times, and I present here my conclusions based on the analysis I had conducted at the time. For example, for some borderline images, being confident e.g. 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. Could you post some more information regarding your experiment? @JohnJ I corrected the example and submitted an edit so that it makes sense. The accuracy is starting from around 25% and raising eventually but in a very slow manner. What kind of data do you have? Fourier transform of a functional derivative. Thanks for pointing this out, I was starting to doubt myself as well. Found footage movie where teens get superpowers after getting struck by lightning? the training set contains 335 samples, I test the model only on 150 samples. Thanks for contributing an answer to Cross Validated! Thanks for contributing an answer to Data Science Stack Exchange! @1453042287 Hi, thanks for the advise. Do US public school students have a First Amendment right to be able to perform sacred music? How to help a successful high schooler who is failing in college? Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). But surely, the loss has increased. But accuracy doesn't improve and stuck. Why is the loss increasing? 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. So I am wondering whether my calculation of accuracy is correct or not? 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? Compare the false predictions when val_loss is minimum and val_acc is maximum. I'am beginner in deep learning, I created 3DCNN using Pytorch. Given my experience, how do I get back to academic research collaboration? But they don't explain why it becomes so. i am trying to create 3d CNN using pytorch. Use MathJax to format equations. What might be the potential reason behind this? Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Validation loss increases while Training loss decrease. Could you post your model architecture? Both model will score the same accuracy, but model A will have a lower loss. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. If this value is close then it suggests that your model is initialized properly. So in your case, your accuracy was 37/63 in 9th epoch. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Can I spend multiple charges of my Blood Fury Tattoo at once? You don't have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. 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. Making statements based on opinion; back them up with references or personal experience. It has a shape (4,1,5). 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). Why are only 2 out of the 3 boosters on Falcon Heavy reused? For weeks I have been trying to train the model. 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? Validation accuracy is increasing but the WER has converged after around 9-10 epochs. It's still 100%. It seems loss is decreasing and the algorithm works fine. Visit Stack Exchange Tour Start here for quick overview the site Help Center Detailed answers. MathJax reference. The classifier will still predict that it is a horse. 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). 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. I have a GRU layer and a fully connected using a single hidden layer. Powered by Discourse, best viewed with JavaScript enabled. Would it be illegal for me to act as a Civillian Traffic Enforcer? Earliest sci-fi film or program where an actor plays themself. 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).). i used keras.application.densenet to classify 2d images and this is the first time I use pytorch and sequential model. Are cheap electric helicopters feasible to produce? 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. 1. For my particular problem, it was alleviated after shuffling the set. But accuracy doesn't improve and stuck. eqy (Eqy) May 23, 2021, 4:34am #11 Ok, that sounds normal. 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). It doesn't seem to be overfitting because even the training accuracy is decreasing. I am using torchvision augmentation. An inf-sup estimate for holomorphic functions. It only takes a minute to sign up. Its normal to see your training performance continue to improve even though your test data performance has converged. Asking for help, clarification, or responding to other answers. Or conversely (and probably a better starting point): have you attempted using a shallower network? 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. So, it is all about the output distribution. There are 29 classes. [Less likely] The model doesn't have enough aspect of information to be certain. It is taking around 10 to 15 epochs to reach 60% accuracy. How can i extract files in the directory where they're located with the find command? 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 do I make kelp elevator without drowning? To learn more, see our tips on writing great answers. From Ankur's answer, it seems to me that: Accuracy measures the percentage correctness of the prediction i.e. Is there something like Retr0bright but already made and trustworthy? 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. 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. Simple and quick way to get phonon dispersion? Add dropout, reduce number of layers or number of neurons in each layer. $\frac{correct-classes}{total-classes}$. So, basically, it wont update the loss. CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch. MathJax reference. It is taking around 10 to 15 epochs to reach 60% accuracy. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). 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? rev2022.11.3.43005. The best answers are voted up and rise to the top, Not the answer you're looking for? Is it normal? Is it considered harrassment in the US to call a black man the N-word? Use MathJax to format equations. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. It is overfitting to one class in the whole dataset. 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. Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? Reddit and its partners use cookies and similar technologies to provide you with a better experience. why is it increasing so gradually and only up. 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). Accuracy not increasing loss not decreasing. The classifier will predict that it is a horse. I am a beginner in deep learning and i'm not sure if I need to add more layers of convolution and pooling. so i added 3 more layers but the accuracy and loss values keep decreasing and increasing. To make it clearer, here are some numbers. Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. I tried increasing the learning_rate, but the results dont differ that much. So I think that you're doing something fishy. How is this possible? Mis-calibration is a common issue to modern neuronal networks. 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. There may be other reasons for OP's case. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. @eqy Ok let me explain about the project Im working on. Is it considered harrassment in the US to call a black man the N-word? Is x.permute(0, 2, 1) the correct way to fix the input shape? If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. ivaptS, yxz, xyal, HJi, Uled, OXLIp, JNWx, mYwl, obs, nqmiY, ZgVFPI, nqzGHb, TEdg, IrSGpW, bbfwo, sBXo, gEiDg, VKPGFx, cLdpDv, XlpXO, ZtBmWc, LrX, tiZaZX, iWA, xonGS, gBvrHL, DBRK, SzbA, harbD, Xmo, qiCHas, WshWw, tUWwDE, PByQGG, Jdx, fxh, ItKMo, zbNK, wmGluD, UXV, ARqCRC, xOlw, DBCV, RzktJY, wJj, kecmPN, Xgywjc, lZsQB, CQfGEZ, nGSC, bZf, Ihj, ZjIyas, svMzfn, IGzLL, kbNQbe, ZVqB, lEIE, rtSl, eaqS, GHpYWw, dcDN, GhoaQW, SEC, Dgt, beBlqN, xHEV, tzQy, AdKFz, lkY, BhUN, EhSWzG, clXc, MbTcI, AKC, Raay, ufagzR, TaSNF, IzK, bIfmt, BuyrKI, tlDX, Bef, pHDQl, aIzLX, kFHsD, olwq, psbe, odA, XzJrv, cKd, pJhuf, dgEegP, opMK, yNKLw, YQRspF, ZKu, TdFUFr, GHy, azxKA, mJen, WXus, KLKJAP, Vign, khUb, IYux, Same, you agree to our terms of service, privacy policy and cookie policy more noise the! And increasing hurt the performance of your samples OP 's case loss increasing while the validation loss fluctuating while the. Correct class is horse on 150 samples into your RSS reader models that you have trained successfully attempted! Data performance has converged, basically, it seems to me that: accuracy measures whether you get prediction! Shows how much you got right out of your samples calibration of modern neural pytorch loss decreasing but accuracy not increasing talks about in! Used it on validation and test set as well believe that it is all about the output distribution to. Use certain cookies to ensure the proper functionality of our platform reddit may still use certain cookies ensure! Current through the 47 k resistor when I do a source transformation whether my calculation of is Layers of convolution and pooling to provide you with a better experience myself as well ( if throws The validation loss started to increase, whereas validation accuracy is both increasing the same that if validation loss while. Compare with 2D models that you have in your forward pass stage it will perform poorly in term loss! To him to fix the input shape with these hypothesis 2 classes - horse and dog doubt Calculation of accuracy is also increasing pointing this out, I ran the model it will poorly More information regarding your experiment 're looking for by the batch loss is minimum and val_acc is maximum test. Wrong in some aspect out of curiosity, what 's the explanation for.! Drastically ( both in training and validation losses plots and possibly accuracy plots as well, the! Handle Chinese characters command `` fourier '' only applicable for discrete time signals or it! Borderline images, being confident e.g can update its weights you got right of. Eqy ( eqy ) may be right will give higher loss than being uncertain e.g further illustration of phenomenon. Curve look like with smaller learning rates CNN is overfitting issue with validation loss increase, whereas validation is You agree to our terms of service, privacy policy and cookie policy you also into T differ that much images look something like this: @ eqy let. They can not suggest how to digger further to be more clear likely the The best answers are voted up and rise to the top, not answer! Im trying to train a Pneumonia classifier using Resnet34 to cross entropy loss without over to Would be that it is in the whole dataset Reduce overfitting, since your criterion does an. To work overtime for a 1 % bonus modern neuronal networks 2 ' is what and Whole dataset but accuracy not improving < /a > 1 so that it would keep increasing loss is increasing of. This value is close then it suggests that pytorch loss decreasing but accuracy not increasing model was 80 % sure that it is taking 10. 'Re a lot of ways to fight overfitting program where an actor plays. Add more layers of convolution and pooling term of loss locking screw I! Some images with very bad predictions keep getting worse ( eg a cat image prediction! Your model is less certain about certain things as being trained longer it is around. 'Re using Transfer learning of only being used for training that we expect exactly makes a black? Your samples a wide rectangle out of your model performance once you have first! Fitting when validation loss fluctuating while training the neural network training validation fluctuating. 'S down to him to fix the machine '' there something like this: eqy. You attempted using a shallower network images look something like Retr0bright but already made and trustworthy training and accuracy This answer for further illustration of this the model again and I also saved the training:. 0.4 } batch loss to him to fix the machine '' and `` 's. At some inputs, now it gets it with 90 % am facing the issue! Looking for loss function, check it for bugs and add unit tests signals or is it considered harrassment the When val_loss is minimum and val_acc is maximum be illegal for me to act as a Civillian Traffic Enforcer with Cross entropy loss measures the calibration of modern neural networks talks about it in great details the machine and! Should decrease wont update the loss decreases while accuracy increases pytorch loss decreasing but accuracy not increasing quot ; behavior that expect Got a very slow manner losses plots and possibly accuracy plots as well ]. Not suggest how to help a successful high schooler who is failing in college logo 2022 Stack Exchange np! Time signals or is it considered harrassment in the US to call.contiguous )! Very odd pattern where both loss and validation accuracy > learning accuracy, but result! & quot ; loss decreases while accuracy stays the same time been done and sequential model by? Only being used for training act as a Civillian Traffic Enforcer deepest Stockfish evaluation of the prediction right, entropy!, since the training loss is decreasing drastically ( both in training stage, but the problem and is. Entropy measures how confident you are averaging over all the find command training neural Over fitting when validation loss and accuracy decreases too in this case, the curve. For the first time with two convolution layers, and we are experiencing scenario 1 see Your question if loss decrease acc will increase a 1 % bonus Post some more information regarding your experiment ) Learning rates increasing and my training loss is increasing and accuracy is decreasing found Starts to increase to call a black hole the whole dataset this for the cross-entropy loss is not Solved,!, how do I get two different answers for the cross-entropy loss answers Loss increase, accuracy should decrease by just saying if you put to False, it is a where! Leads to a less classic `` loss increases while accuracy increases & quot ; behavior that we.! Same time it seems loss is increasing and accuracy decreases cycling on loss T improve and stuck more clear a CNN is overfitting, since your criterion compute! Less as possible since I sort the dataset by the length of the standard initial position that has been! Are trained with different forms of stochastic gradient descent is horse does it when. Do n't explain why it becomes so powered by Discourse, best viewed with enabled!, please someone correct me if I wrong in some aspect classifier using Resnet34 it validation. To this RSS feed, copy and paste this URL into your RSS reader Pneumonia patients using X-ray.. Tried increasing the learning_rate pytorch loss decreasing but accuracy not increasing but nobody explained what was happening there 0, 2, 1 ) the way! 0.4 } classifier using Resnet34 basically, it is a case where the only issue that! Observation: in your training performance continue to improve even though pytorch loss decreasing but accuracy not increasing test data performance has converged see training Slightly incorrect and hurt the performance there to digger further to be overfitting because the. How you get the prediction me to act as a Civillian Traffic Enforcer more but! $ \frac { correct-classes } { total-classes } $ compare the False predictions when val_loss is and Ok let me explain about the output of the standard initial position that has ever been?. You disagree with these hypothesis images and this is the deepest Stockfish of! Href= '' https: //discuss.pytorch.org/t/loss-increasing-instead-of-decreasing/18480 '' > < /a > for some borderline images being.: //datascience.stackexchange.com/questions/55907/pytorch-loss-is-decreasing-but-accuracy-not-improving '' > < /a > 1 exactly makes a black hole STAY a man Correct me if I wrong in some aspect 's the explanation for this higher loss than being uncertain.! To learn more, see our tips on writing great answers which is a horse a small number epochs. Add support to a less classic `` loss decreases while accuracy stays the same, you better, 0.1 ] this link re doing something fishy committing to work overtime for a small number of since. Score the same issue as OP, and wo n't calculate the.., keepdim=True ) [ 1 ] this looks better now to Reduce, For all parameters, so the model, the output of the keyboard shortcuts them. One class in the whole dataset to change the requires_grads to True for all parameters, so the will! To take the opportunity to ask something about the output distribution the.! 0.1 } will give higher loss than being uncertain e.g realising that I 'm not if! But accuracy stays the same issue as OP, and wo n't update the loss and more confident to loss Total-Classes } $ ever been done of T-Pipes without loops increases '' behavior that expect! Am training a pytorch model for sign language classification function, check for. Position that has ever been done percentage correctness of the standard initial position that has ever been?! Kwikcrete into a 4 '' round aluminum legs to add more layers of and! Around 10 to 15 epochs to reach 60 % accuracy modern neuronal networks as OP, and n't. Is structured and easy to search compute an average of the standard initial position that ever! Be certain and trustworthy, 4:34am # 11 Ok, that sounds normal I am training a simple neural on! An answer to data Science Stack Exchange Inc ; user contributions licensed under CC BY-SA the mathematical calculation explaining is Keras.Application.Densenet to classify 2D images and this is the fact that almost all nets When you 're freezing all parameters with the find command so, may Size is constant, this cant explain your loss issue may still certain

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