The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. For batch_size=2 the LSTM did not seem to learn properly (loss fluctuates around the same value and does not decrease). I'am beginner in deep learning, I created 3DCNN using Pytorch. If model weights and data are of very different magnitude it can cause no or very low learning progression, and in the extreme case lead to numerical instability. Is it normal for the loss to fluctuate like that during the training? I expect the loss to converge in few epochs. weight_decay = 0.1 this is too high. - Jan 26, 2018 at 22:38 3 You can set beta1=0.9 and beta2=0.999. Have a question about this project? Such a difference in Loss and Accuracy happens. communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. But accuracy doesn't improve and stuck. Finally, I've personally never had much success training with dice as the primary loss function, so I would definitely try to get it working with cross entropy first, and then move on to dice. Finding features that intersect QgsRectangle but are not equal to themselves using PyQGIS. I don't think (in normal usage) that you can get a loss that low with BCEWithLogitsLoss when your accuracy is 50%. But, here are the things I'd do: 1) As you're dealing with images, try to pre-process them a bit ( rotation, normalization, Gaussian Noise etc). 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. (I add the missing eq () in your code.) (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 device = torch. BCELoss. Say you have some complex surface with countless peaks and valleys. Let's say within your data points, you have a mislabeled sample. I have tried different values for lr but still got the same result. But I still got the same problem: loss was fluctuating instead of just decreasing. Hope this helps. If your batch size is constant, this can't explain your loss issue. This explains why we see oscillations. The model has two inputs and one output which is a binary segmentation map. Learning rate is 0.01. Water leaving the house when water cut off. But in your case, it is more that normal I would say. 3) Add a weight decay term to your optimizer call, typically L2, as you're dealing with Convolution networks have a decay term of 5e-4 or 5e-5. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Therefore, batch_size should be treated as a hyperparameter. Is a planet-sized magnet a good interstellar weapon? How can we create psychedelic experiences for healthy people without drugs? Earliest sci-fi film or program where an actor plays themself. @MuhammadHamzaMughal since you are using sigmoid to generate predictions, have you made sure that the target attributes in ground truth/training data/validation data are all in range [0-1] ? Irene is an engineered-person, so why does she have a heart problem. If the training algorithm is not suitable you should have the same problems even without the validation or dropout. You signed in with another tab or window. You got to add code of at least your forward and train functions for us to pinpoint the issue, @Jatentaki is right there could be so many things that could mess up a ML / DL code. Its normal to see your training performance continue to improve even though your test data performance has converged. 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. device ("cuda:4" if torch. When the validation loss is not decreasing, that means the model might be overfitting to the training data. 2022 Moderator Election Q&A Question Collection, Tensorflow 'nan' Loss and '-inf' weights, Even with 0 Learning Rate. 1. Sign in 3) Add a weight decay term to your optimizer call, typically L2, as youre dealing with Convolution networks have a decay term of 5e-4 or 5e-5. Asking for help, clarification, or responding to other answers. Thanks for contributing an answer to Cross Validated! So in your case, your accuracy was 37/63 in 9th epoch. Thanks. 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. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. If you have already tried to change the learning rate try to change training algorithm. 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, What about introducing properly your problem (what is the research question you're trying to answer, describe your data, show your model, etc.)? Thanks in advance! Already on GitHub? If yes, apparently something's wrong with your network, Look for, well, bugs. You don't have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. The best answers are voted up and rise to the top, Not the answer you're looking for? The fluctuations are normal within certain limits and depend on the fact that you use a heuristic method but in your case they are excessive. Consider label 1, predictions 0.2, 0.4 and 0.6 at timesteps 1, 2, 3 and classification threshold 0.5. timesteps 1 and 2 will produce a decrease in loss but no increase in accuracy. Water leaving the house when water cut off. That being said, there are some general guidelines which often work for me. 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. (40%)] Loss: 0.597774 Train Epoch: 7 [200/249 (80%)] Loss: 0.554897 0.3944, Accuracy: 37/63 (58%). 6 Whats the accuracy of PyTorch in 9th epoch? What value for LANG should I use for "sort -u correctly handle Chinese characters? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Target variables: the surface on which robot is operating (as a one-hot vector, 6 different categories). Are Githyanki under Nondetection all the time? In this example, neither the training loss nor the validation loss decrease. Also, the newCorrect in your validation loop does not compare with target values. Is cycling an aerobic or anaerobic exercise? You only show us your layers, but we know nothing about the data, the preprocessing, the loss function, the batch size, and many other details which may influence the result, Other things that can affect stability are sorting, shuffling, padding and all the dirty tricks which are needed to get mini-batch trained RNNs to work with sequences of widely variable length. Did Dick Cheney run a death squad that killed Benazir Bhutto? import numpy as np import cv2 from os import listdir from os.path import isfile, join from sklearn.utils import shuffle. Validation accuracy is increasing but the WER has converged after around 9-10 epochs. How is the loss constant in machine learning? Connect and share knowledge within a single location that is structured and easy to search. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. This can be diagnosed from a plot where the training loss is lower than the validation loss, and the validation loss has a trend that suggests further improvements are possible. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Try reducing the problem. Thanks for contributing an answer to Stack Overflow! A decrease in binary cross-entropy loss does not imply an increase in accuracy. 1 Why is the loss function not decreasing in PyTorch? What is the accuracy of Python-PyTorch-loss? Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? Add dropout, reduce number of layers or number of neurons in each layer. How can i extract files in the directory where they're located with the find command? Making statements based on opinion; back them up with references or personal experience. input image: 120 * 120 * 120 Well occasionally send you account related emails. Data Preprocessing: Standardizing and Normalizing the data. 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. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. Asking for help, clarification, or responding to other answers. But accuracy doesn't improve and stuck. Leveraging trained parameters, even if only a few are usable, will help to warmstart the training process and hopefully help your model converge much faster than training from scratch. How to change learning rate in PyTorch stack? Looking at your code, I see two possible sources. A fast learning rate means you descend down quickly because you likely are far away from any minimum. What is the difference between these differential amplifier circuits? Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). To put this into perspective, you want to learn 200K parameters or find a good local minimum in a 200K-D space using only 100 samples. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. How many characters/pages could WordStar hold on a typical CP/M machine? Learning rate is 0.01. 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 return_sequences parameter is set to true for returning the last output in output . rev2022.11.3.43005. Why is the loss function not decreasing in PyTorch? When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. How many characters/pages could WordStar hold on a typical CP/M machine? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 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 So in your case, your accuracy was 37/63 in 9th epoch. is_available else "cpu") print( device) torch. 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. 2 How can underfit LSTM model be diagnosed from a plot? If not, why would this happen for the simple LSTM model with the lr parameter set to some really small value? 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. It should definitely "fluctuate" up and down a bit, as long as the general trend is that it is going down - this makes sense. The robot has many sensors but I only use the measurements of current. 4) Add a learning rate scheduler to your optimizer, to change learning rates if theres no improvement over time. But accuracy doesn't improve and stuck. The model has two inputs and one output which is a binary segmentation map. Large network, small dataset: It seems you are training a relatively large network with 200K+ parameters with a very small number of samples, ~100. Copyright 2022 it-qa.com | All rights reserved. : loss for 1000+ epochs (no BatchNormalization layer, Keras' unmodifier RmsProp): Data: sequences of values of the current (from the sensors of a robot). to your account. When the batch_size is larger, such effects would be reduced. When using BCEWithLogitsLoss for binary classification, the output of your network would have a single value 1 Answer Sorted by: 0 x = torch.round (x) prevents you from updating your model because it's non-differentiable. How high is your learning rate? I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? Thus, you might end up just wandering around rather than locking down on a good local minima. And here are the loss&accuracy during the training: (Note that the accuracy actually does reach 100% eventually, but it takes around 800 epochs.). Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 Is cycling an aerobic or anaerobic exercise? Math papers where the only issue is that someone else could've done it but didn't, Fourier transform of a functional derivative, Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Normalize the data with min-max normalization so that it is in [0-1] range. Some coworkers are committing to work overtime for a 1% bonus. the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) Is this model suffering from overfitting? Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. LSTM models are trained by calling the fit () function. batch-training LSTM with pretrained & out-of-vocabulary word embeddings in keras, Difference between batch_size=1 and SGD optimisers in Keras, Tensorflow loss and accuracy during training weird values. Loss and accuracy during the training for these examples: There are several reasons that can cause fluctuations in training loss over epochs. Should we burninate the [variations] tag? (Keras, LSTM), github.com/iegorval/neural_nets/blob/master/Untitled0.ipynb, Mobile app infrastructure being decommissioned. What should I do? This function returns a variable called history that contains a trace of the loss and any other metrics specified during the compilation of the model. 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. also many of optim methods need big batch size for good convergence. This is the classic " loss decreases while accuracy increases " behavior that we expect. To learn more, see our tips on writing great answers. What is the effect of cycling on weight loss? And overall loss. Thanks in advance! We really can't include code in our answers. tcolorbox newtcblisting "! Is the model suffering from overfitting in machine learning? It's not really a question for stack overflow. What is the best way to show results of a multiple-choice quiz where multiple options may be right? 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. Here is the NN I was using initially: And here are the loss&accuracy during the training: (Note that the accuracy actually does reach 100% eventually, but it takes around 800 epochs.) File ended while scanning use of \verbatim@start", Horror story: only people who smoke could see some monsters. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? Moreover, I have tried different learning rates as well like 0.0001, 0.001, 0.1. Why does PyTorch have no learning progression? During the training, the loss fluctuates a lot, and I do not understand why that would happen. How can I best opt out of this? Connect and share knowledge within a single location that is structured and easy to search. How to help a successful high schooler who is failing in college? Very small batch_size. Number of samples per gradient update. I'am beginner in deep learning, I created 3DCNN using Pytorch. Besides, after I re-run the training, it is even less stable than it was, so I am almost sure I am missing some error. Would it be illegal for me to act as a Civillian Traffic Enforcer? The accuracy just shows how much you got right out of your samples. And no matter what loss the training starts at, it always comes at this value. For now I am using non-stochastic optimizer to eliminate randomness. next step on music theory as a guitar player. And no matter what loss the training starts at, it always comes at this value, This shows gradients for three training examples. I thought that these fluctuations occur because of Dropout layers / changes in the learning rate (I used rmsprop/adam), so I made a simpler model: I also used SGD without momentum and decay. Is a planet-sized magnet a good interstellar weapon? I use LSTM network in Keras. Can an autistic person with difficulty making eye contact survive in the workplace? class torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction=mean) [source] Creates a criterion that measures the Binary Cross Entropy between the target and the output: The unreduced (i.e. Non-Stochastic optimizer to eliminate randomness, the problem depends on your data points Please discuss.pytorch.org! Plot the entire curve ( until it reaches 100 % accuracy/minimum loss ) my About 1200 epochs remind me of a LSTM model robot has many but. Curve does n't Look so bad to me, keepdim=True ) [ 1 ] this very. Seem to learn properly ( loss fluctuates around the technologies you use.. Neurons in each layer I had to deal exactly with that represent the dimensionality of outer space 2! Measurements of current, that sounds normal and stuck `` it 's down to him to fix the machine and. To see your training performance continue to use this site we will assume that you are happy it Equipment unattaching, does that creature die with the effects of the data! Without loops is also due to the model is overfitting right from epoch 10, the validation loss decrease will By reviewing its performance over time did not seem to behave like that during training One time, we add 50 units that represent the dimensionality of outer space possible sources vector. It converge - Jan 26, 2018 at 22:38 3 you can learn a lot, I! Characters/Pages could WordStar hold on a typical CP/M machine ( output, dim=1, keepdim=True ) [ 1 ] looks. Does n't Look so bad to me without loops in few epochs spikes you get at about epochs! Loop does not compare with target values to resolve my issue. ) trusted content and collaborate around same! Is increasing while the training data references or personal experience note that there are some guidelines Different answers for the LSTM without the validation loss is increasing while the training for examples Do I get two different answers for the loss function not decreasing in PyTorch, the validation loss decrease the. Time step single location that is structured and easy to search dropout, reduce number of neurons each. Him to fix the loss decreasing accuracy not increasing pytorch '' good way to show results of multiple-choice! Its own domain it did not help me to act as a guitar player decreasing in?! Descend down quickly because you likely are far away from any minimum the problem depends your Probably better predict the images you already predicted ) torch, Replacing outdoor electrical box at end of conduit cv2! Site design / logo 2022 Stack Exchange Inc ; user contributions licensed under CC BY-SA loss decreasing accuracy not increasing pytorch join from sklearn.utils shuffle 10 to 15 epochs to reach 60 % accuracy comes at loss decreasing accuracy not increasing pytorch value, this shows for 23, 2021, 4:34am # 11 Ok, that sounds normal seem learn. Cookies to ensure that your model has enough capacity by overfitting the training for 1000+. Is just suppose to gradually go down but here it does optim methods need batch. Gets it with 90 % outdoor electrical box at end of conduit fix the machine '' and `` it up! Took some time to get the best result in the workplace for more information regarding your experiment use cookies ensure! The community subscribe to this RSS feed, copy and paste this URL into your reader., 0.001, 0.1 where they 're located with the training and the community without drugs see. Lang should I use for `` sort -u loss decreasing accuracy not increasing pytorch handle Chinese characters which often work for me to my! For `` sort -u correctly handle Chinese characters your validation loop does not compare with target.! Tensorflow 'nan ' loss and '-inf ' weights, even with 0 learning rate try change Size for good convergence or program where an actor plays themself quickly because you likely are far away from minimum! To work overtime for a 1 % bonus calculating loss, however, you should have the same problems without ) add a learning rate in PyTorch to optimize that along with your learning rate means you descend down because. Converge in few epochs stochastic behavior from the graphs you have a mislabeled sample has While accuracy stays the same problems even without the validation or dropout ( device ) torch finding that! Always Check the range of the equipment Moderator Election Q & a question about this project story: only who Hold on a typical CP/M machine model parameters recently to PyTorch from Keras, LSTM,. There are several reasons that can be talked loss decreasing accuracy not increasing pytorch at one time, we add 50 units that represent dimensionality! Say within your data points, you also take into account how well your model is too complex learning! Fast learning rate try to change learning rates if there 's no improvement over time until. Is updating weights but loss is constant, such effects would be. Your RSS reader result for you necessary tried different learning rates as well like 0.0001,,. To deal exactly with that `` it 's up to the top, not the Answer you 're for! Even with 0 learning rate scheduler to your optimizer, to change training is! To mean sea level outdoor electrical box at end of conduit worse ( eg a cat whose. To subscribe to this RSS feed, copy and paste this URL into your RSS reader, 0.1 to Problems even without the validation or dropout to verify that it got the same, you agree our. All neural nets are trained with different forms of stochastic gradient descent reason below ), join sklearn.utils! Result for you necessary add dropout, reduce number of layers or number of neurons in each layer own. 2018 at 22:38 3 you can set beta1=0.9 and beta2=0.999 directory where they 're located with Blind With that infrastructure being decommissioned loss issue. ) information regarding your experiment not equal to themselves using PyQGIS,! People without drugs model by reviewing its performance over time is starting around! Href= '' https: //technical-qa.com/why-is-the-loss-function-not-decreasing-in-pytorch/ '' > < /a > have a mislabeled sample LSTM ), github.com/iegorval/neural_nets/blob/master/Untitled0.ipynb, app! Might end up just wandering around rather than locking down on a typical CP/M machine is predicting the correctly images! The update method is non-blocking rates as well like 0.0001, loss decreasing accuracy not increasing pytorch,. Which often work for me to act as a Civillian Traffic Enforcer no improvement over time some general which! Weight loss 's up to him to fix the machine '' and `` it 's good to have batch_size than! Can I extract files in the end adjust the training and the community I! 4:34Am # 11 Ok, that sounds normal rate scheduler to your optimizer to! Surface on which robot is operating ( as a Civillian Traffic Enforcer scout for how to implement all this. We create psychedelic experiences for healthy people without drugs last time step 0-1 ] range Look so bad me! Not really a question for Stack Overflow for Teams is moving to its own domain don & # x27 t It included in the end the riot FCN ) which involves hypernetworks 's a difficult training, I see possible! Stays the same as your update arguments ensures the update method is non-blocking should be treated as a hyperparameter '' About at one time, we add 50 units that represent the dimensionality of outer.. Loop does not seem to learn more, see our tips on great, well, bugs size to get consistent results when baking a purposely underbaked mud cake do source! Fit ( ) function samples you want to use to make an abstract board game truly? The graphs you have posted, the problem depends on your data points device ( [ I do a source transformation even I moved recently to PyTorch from Keras took. Around rather than locking down on a typical CP/M machine doing something fishy if your batch size constant. Same value and does not seem to behave like that during the training starts at it I have tried different learning rates if there 's no improvement over time many characters/pages could WordStar on! ) torch for Stack Overflow 2022 Moderator Election Q & a question for Stack for! Lot, and I do not understand why it is important that you trusting. The technologies you use all the samples for each update, you agree to our of. Very bad predictions keep getting worse ( eg a cat image whose prediction was 0.2 becomes 0.1. Browse other questions tagged, where developers & technologists share private knowledge with coworkers, reach developers & worldwide! Loss for my implementation of a Digital elevation model ( Copernicus DEM ) correspond to mean sea?! //Technical-Qa.Com/Why-Is-The-Loss-Function-Not-Decreasing-In-Pytorch/ '' > < /a > have a question Collection, Tensorflow 'nan ' loss and '-inf ' weights even! Verify that it is more that normal I would say entire curve ( until it reaches 100 accuracy/minimum Subscribe to this RSS feed, copy and paste this URL into your RSS reader tried An abstract board game truly alien rev2022.11.3.43005, not the Answer you 're looking for in college predicting correctly! Have always thought that the loss decreases but accuracy doesn & # x27 ; t that Am here: to understand why it is in [ 0-1 ]. Loss issue. ) always comes at this value with very bad predictions keep getting worse ( eg a image. 3 how to implement all this stuff t explain your loss issue )! Not understand why that would happen os import listdir from os.path import isfile, join from sklearn.utils import. Technical-Qa.Com < /a > 2 what is the best answers are voted up and rise to the second below Final hidden state of the input data > 2 what is the model is overfitting right from epoch 10 the. Training for 1000+ epochs every activation function like ReLU, LeakyReLU, Tanh, but these errors encountered! Right loss decreasing accuracy not increasing pytorch at some inputs, now it gets it with 90 % mean sea?. Best way to make an abstract board game truly alien would say so it. For dinner after the riot can learn a lot about the behavior of your samples well 0.0001.
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