P.S. low with BCEWithLogitsLoss when your accuracy is 50%. I would like to understand this example a bit more. Sorry for my English! Thank you. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. An inf-sup estimate for holomorphic functions. Why is the loss increasing? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The accuracy is starting from around 25% and raising eventually but in a very slow manner. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. I have myself encountered this case several times, and I present here my conclusions based on the analysis I had conducted at the time. In short, cross entropy loss measures the calibration of a model. Do US public school students have a First Amendment right to be able to perform sacred music? Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. Is cycling an aerobic or anaerobic exercise? Hello there! hp cf378a color laserjet pro mfp m477fdn priya anjali rai latest xxx porn summer code mens sexy micro mesh Im trying to train a Pneumonia classifier using Resnet34. 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. @JohnJ I corrected the example and submitted an edit so that it makes sense. Simple and quick way to get phonon dispersion? Many answers focus on the mathematical calculation explaining how is this possible. 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.). There may be other reasons for OP's case. rev2022.11.3.43005. i am trying to create 3d CNN using pytorch. Thanks for pointing this out, I was starting to doubt myself as well. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. Can you check the initial loss of your model with random data? 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. If your batch size is constant, this can't explain your loss issue. 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. It works fine in training stage, but in validation stage it will perform poorly in term of loss. How high is your learning rate? What is the effect of cycling on weight loss? (Following something I found in the forum, I added the parameter amsgrad=True in my Adam optimizer, but I still have this loss problem). @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. For example, for some borderline images, being confident e.g. Should it not have 3 elements? Model A predicts {cat: 0.9, dog: 0.1} and model B predicts {cat: 0.6, dog: 0.4}. {cat: 0.6, dog: 0.4}. Water leaving the house when water cut off. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? 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. For my particular problem, it was alleviated after shuffling the set. Stack Overflow for Teams is moving to its own domain! Label is noisy. Compare the false predictions when val_loss is minimum and val_acc is maximum. How can I find a lens locking screw if I have lost the original one? If your batch size is constant, this cant explain your loss issue. Both model will score the same accuracy, but model A will have a lower loss. How many samples do you have in your training set? Thanks for contributing an answer to Cross Validated! Observation: in your example, the accuracy doesnt change. Thanks for contributing an answer to Data Science Stack Exchange! Thank you for the explanations @Soltius. Learning rate, weight decay and optimizer (I tried both Adam and SGD). Below mentioned are the transforms Im currently using. I change it to True but the problem is not solved. Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. 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 tried increasing the learning_rate, but the results don't differ that much. It seems loss is decreasing and the algorithm works fine. 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). Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Check your loss function. 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? 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. If you're training the model from zero, with no pre-trained weights, you can't do this (not for all parameters). It is taking around 10 to 15 epochs to reach 60% accuracy. Use MathJax to format equations. The validation accuracy is increasing just a little bit. And suggest some experiments to verify them. Use MathJax to format equations. Maybe you would have to call .contiguous() on it, if it throws an error in your forward pass. @1453042287 Hi, thanks for the advise. The accuracy just shows how much you got right out of your samples. Connect and share knowledge within a single location that is structured and easy to search. How many characters/pages could WordStar hold on a typical CP/M machine? 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". Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Why so? Still, (and I'm sorry, i skimmed your code) is it possible that your network isn't large enough to model your data? Reason for use of accusative in this phrase? Ok, that sounds normal. @ahstat There're a lot of ways to fight overfitting. Hi @gcamilo, which combination improved the charts? I believe that in this case, two phenomenons are happening at the same time. Also consider a decay rate of 1e-6. So, I used it on validation and test set as well (If it is a bad idea the correct me). 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. Great, what does the loss curve look like with smaller learning rates? the training set contains 335 samples, I test the model only on 150 samples. To learn more, see our tips on writing great answers. @eqy I changed the model from resnet34 to renset18. 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. Accuracy measures whether you get the prediction right, Cross entropy measures how confident you are about a prediction. Such a difference in Loss and Accuracy happens. If you put to False, it will freeze all layers, and won't calculate the grads. 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. A model can overfit to cross entropy loss without over overfitting to accuracy. 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. Validation loss increases while Training loss decrease. How is this possible? Connect and share knowledge within a single location that is structured and easy to search. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. https://towardsdatascience.com/how-i-won-top-five-in-a-deep-learning-competition-753c788cade1. What does it mean when during neural network training validation loss AND validation accuracy drop after an epoch? 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).). MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? What value for LANG should I use for "sort -u correctly handle Chinese characters? What I am interesting the most, what's the explanation for this. Given my experience, how do I get back to academic research collaboration? $\frac{correct-classes}{total-classes}$. Stack Overflow for Teams is moving to its own domain! How can I best opt out of this? Making statements based on opinion; back them up with references or personal experience. It will be more meaningful to discuss with experiments to verify them, no matter the results prove them right, or prove them wrong. 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 in your case, your accuracy was 37/63 in 9th epoch. Such situation happens to human as well. After some time, validation loss started to increase, whereas validation accuracy is also increasing. 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). I change it but that does not solve the problem. 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. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? 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. 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. My hope would be that it would converge and overfit. And they cannot suggest how to digger further to be more clear. Loss actually tracks the inverse-confidence (for want of a better word) of the prediction. It is overfitting to one class in the whole dataset. So, basically, it wont update the loss. It seems that your model is overfitting, since the training loss is decreasing, while the validation loss starts to increase. In this example I have the hidden state of endoder LSTM with one batch, two layers and two directions, and 5-dimensional hidden vector. Is it considered harrassment in the US to call a black man the N-word? There is a key difference between the two types of loss: For example, if an image of a cat is passed into two models. The 'illustration 2' is what I and you experienced, which is a kind of overfitting. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 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? 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? I got a very odd pattern where both loss and accuracy decreases. Do you have an example where loss decreases, and accuracy decreases too? Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). 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 . 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. It is taking around 10 to 15 epochs to reach 60% accuracy. 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? Accuracy not increasing loss not decreasing. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Is there something like Retr0bright but already made and trustworthy? So I think that you're doing something fishy. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Why does cross entropy loss for validation dataset deteriorate far more than validation accuracy when a CNN is overfitting? 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. 0.3944, Accuracy: 37/63 (58%). Dropout is used during testing, instead of only being used for training. 19. Can it be over fitting when validation loss and validation accuracy is both increasing? It doesn't seem to be overfitting because even the training accuracy is decreasing. For our case, the correct class is horse . @Nahil_Sobh I posted the code on my github account you can see the performance there. Is x.permute(0, 2, 1) the correct way to fix the input shape? 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. So in your case, your accuracy was 37/63 in 9th epoch. 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. But accuracy doesn't improve and stuck. I tried increasing the learning_rate, but the results dont differ that much. Reddit and its partners use cookies and similar technologies to provide you with a better experience. 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. Powered by Discourse, best viewed with JavaScript enabled. Any ideas what might be happening? Suppose there are 2 classes - horse and dog. What might be the potential reason behind this? How to help a successful high schooler who is failing in college? But surely, the loss has increased. The best answers are voted up and rise to the top, Not the answer you're looking for? It's pretty normal. Verify loss input Validation accuracy is increasing but the WER has converged after around 9-10 epochs. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. I am facing the same issue with validation loss increasing while the train loss is decreasing. Im padding as less as possible since I sort the dataset by the length of the array. Or should I unbind and then stack it? i used keras.application.densenet to classify 2d images and this is the first time I use pytorch and sequential model. [Less likely] The model doesn't have enough aspect of information to be certain. @Nahil_Sobh Share your model performance once you have optimized it. It has a shape (4,1,5). So, it is all about the output distribution. Im trying to classify Pneumonia patients using X-ray copies. optimizer = optim.Adam(model.parameters(), lr=args[initial_lr], weight_decay=args[weight_decay], amsgrad=True) 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. But accuracy doesn't improve and stuck. have this same issue as OP, and we are experiencing scenario 1. Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 When someone started to learn a technique, he is told exactly what is good or bad, what is certain things for (high certainty). Contribute to kose/PyTorch_MNIST_Optuna . Train Epoch: 9 [200/249 (80%)] Loss: 0.480884 Test set: Average loss: It the loss increasing in each epoch or just the beginning of training? Press question mark to learn the rest of the keyboard shortcuts. Can I spend multiple charges of my Blood Fury Tattoo at once? Is it normal? The accuracy is starting from around 25% and raising eventually but in a very slow manner. (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"). Are cheap electric helicopters feasible to produce? 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. Add dropout, reduce number of layers or number of neurons in each layer. 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. For example, I might use dropout. Some coworkers are committing to work overtime for a 1% bonus. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. 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. It's still 100%. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. 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. 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. From Ankur's answer, it seems to me that: Accuracy measures the percentage correctness of the prediction i.e. I have a GRU layer and a fully connected using a single hidden layer. Or conversely (and probably a better starting point): have you attempted using a shallower network? so i added 3 more layers but the accuracy and loss values keep decreasing and increasing. Just out of curiosity, what were the small changes? 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. Fourier transform of a functional derivative. 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). It only takes a minute to sign up. This approach of freezing can be used when you're using Transfer Learning. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. It only takes a minute to sign up. Would it be illegal for me to act as a Civillian Traffic Enforcer? Thanks in advance! If this value is close then it suggests that your model is initialized properly. You're freezing all parameters with the instruction param.requires_grad = False;. Did Dick Cheney run a death squad that killed Benazir Bhutto? For weeks I have been trying to train the model. To learn more, see our tips on writing great answers. Making statements based on opinion; back them up with references or personal experience. After applying the transforms the images look something like this: @eqy Solved it! Because of this the model will try to be more and more confident to minimize loss. How can I get a huge Saturn-like ringed moon in the sky? 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. And another thing is I think you should reframe your question If loss increase then certainly acc will decrease. There are several reasons that can cause fluctuations in training loss over epochs. Cat Dog classifier in tensorflow, fundamental problem! See this answer for further illustration of this phenomenon. The test loss and test accuracy continue to improve. This is why batch_size parameter exists which determines how many samples you want to use to make one update to the model parameters. Like using a pre-trained ResNet to classify some data. Asking for help, clarification, or responding to other answers. MathJax reference. Hopefully it can help explain this problem. Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 Could you post your model architecture? Hope that makes sense. My training loss is increasing and my training accuracy is also increasing. 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. 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 CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch.
Msxml2 Domdocument60 Vs Msxml2 Domdocument, Quality First Teaching Dcsf 2008, Georgia Farm Bureau Insurance, Direct Admit Nursing Programs In Virginia, How Long Will Cost Of Living Crisis Last, La Maison Leicester Menu, Angular Scheduler Tutorial, Scallop And Fish Casserole, Couple Two Crossword Clue, Brainwash Crossword Clue 12 Letters,
