What value for LANG should I use for "sort -u correctly handle Chinese characters? 1. have this same issue as OP, and we are experiencing scenario 1. 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. 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. Thats just my opinion, I may not be to the point here. My hope would be that it would converge and overfit. I forgot to shuffle the dataset. When the loss decreases but accuracy stays the same, you probably better predict the images you already predicted. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Dropout is used during testing, instead of only being used for training. 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. If I have a training set with 20,000 samples, maybe I just select 200 or even 50, and let it train on that. Hi @gcamilo, which combination improved the charts? criterion = nn.CrossEntropyLoss().cuda(). Try to change the requires_grads to True for all parameters, so the model can update its weights. It looks correct to me. Such a difference in Loss and Accuracy happens. Stack Overflow for Teams is moving to its own domain! Thanks for contributing an answer to Cross Validated! The loss looks indeed a bit fishy. Great, what does the loss curve look like with smaller learning rates? In short, cross entropy loss measures the calibration of a model. How does this model compare with 2D models that you have trained successfully? Thanks for contributing an answer to Data Science Stack Exchange! The accuracy just shows how much you got right out of your samples. 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? Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. If this value is close then it suggests that your model is initialized properly. What kind of data do you have? My current training seems working. 0.3944, Accuracy: 37/63 (58%). Take another case where softmax output is [0.6, 0.4]. Can you suggest any other solution to solve the problem. Learning rate, weight decay and optimizer (I tried both Adam and SGD). I am using torchvision augmentation. So in this case, I suggest experiment with adding more noise to the training data (not label) may be helpful. Is my model overfitting? Just out of curiosity, what were the small changes? This is the classic "loss decreases while accuracy increases" behavior that we expect. Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Maybe your model was 80% sure that it got the right class at some inputs, now it gets it with 90%. In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. They tend to be over-confident. Im padding as less as possible since I sort the dataset by the length of the array. It seems that if validation loss increase, accuracy should decrease. Also consider a decay rate of 1e-6. Is cycling an aerobic or anaerobic exercise? It's still 100%. [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: It seems loss is decreasing and the algorithm works fine. And they cannot suggest how to digger further to be more clear. rev2022.11.3.43005. Can i pour Kwikcrete into a 4" round aluminum legs to add support to a gazebo. Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 For example, I might use dropout. Reddit and its partners use cookies and similar technologies to provide you with a better experience. 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). Thank you for your reply! If you implemented your own loss function, check it for bugs and add unit tests. In binary and multilabel cases, the elements of y and y_pred should have 0 or 1 values. Fourier transform of a functional derivative. 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. rev2022.11.3.43005. How high is your learning rate? It works fine in training stage, but in validation stage it will perform poorly in term of loss. 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. Simple and quick way to get phonon dispersion? Sorry for my English! Found footage movie where teens get superpowers after getting struck by lightning? Thanks in advance! Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 For weeks I have been trying to train the model. And suggest some experiments to verify them. 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 doesn't seem to be overfitting because even the training accuracy is decreasing. The validation accuracy is increasing just a little bit. So, it is all about the output distribution. 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. Im trying to classify Pneumonia patients using X-ray copies. 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. 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. Let's say a label is horse and a prediction is: So, your model is predicting correct, but it's less sure about it. After some time, validation loss started to increase, whereas validation accuracy is also increasing. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. Or should I unbind and then stack it? If you put to False, it will freeze all layers, and won't calculate the grads. Validation loss increases while Training loss decrease. 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. Can I spend multiple charges of my Blood Fury Tattoo at once? 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. MathJax reference. 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.). Compare the false predictions when val_loss is minimum and val_acc is maximum. Water leaving the house when water cut off. On Calibration of Modern Neural Networks talks about it in great details. But the loss keeps hovering around the number where it starts, and the accuracy to remains where it started (accuracy is as good . 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. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? After some small changes, I ran the model again and I also saved the training loss/acc: This looks better now. I am training a simple neural network on the CIFAR10 dataset. 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. It is overfitting to one class in the whole dataset. optimizer = optim.Adam(model.parameters(), lr=args[initial_lr], weight_decay=args[weight_decay], amsgrad=True) Many answers focus on the mathematical calculation explaining how is this possible. Decreasing loss does not mean improving accuracy always. How can I get a huge Saturn-like ringed moon in the sky? It should be around -ln(1/num_classes). (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 As for the data, it is in the right format. Hello there! Nice. After applying the transforms the images look something like this: @eqy Solved it! Or conversely (and probably a better starting point): have you attempted using a shallower network? So I think that you're doing something fishy. Such situation happens to human as well. 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. So I am wondering whether my calculation of accuracy is correct or not? The loss is stable, but the model is learning very slowly. Math papers where the only issue is that someone else could've done it but didn't. If you're training the model from zero, with no pre-trained weights, you can't do this (not for all parameters). By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Thank you. Loss ~0.6. 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. 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. Both model will score the same accuracy, but model A will have a lower loss. 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. 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 First of all i'm a beniggner at machine learning, but I think you have a problem when doing backward. How many characters/pages could WordStar hold on a typical CP/M machine? @JohnJ I corrected the example and submitted an edit so that it makes sense. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? Why validation accuracy is increasing very slowly? 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. Are cheap electric helicopters feasible to produce? Add dropout, reduce number of layers or number of neurons in each layer. How do I make kelp elevator without drowning? So in your case, your accuracy was 37/63 in 9th epoch. I tried increasing the learning_rate, but the results dont differ that much. 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. When he goes through more cases and examples, he realizes sometimes certain border can be blur (less certain, higher loss), even though he can make better decisions (more accuracy). Like using a pre-trained ResNet to classify some data. After this, try increasing the regularization strength which should increase the loss. Connect and share knowledge within a single location that is structured and easy to search. 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. 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). MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. CNN: accuracy and loss are increasing and decreasing Hello, i am trying to create 3d CNN using pytorch. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? There is a key difference between the two types of loss: For example, if an image of a cat is passed into two models. @Lucky_Magna By reframing I meant this is obvious if loss decrease acc will increase. For some reason, my loss is increasing instead of decreasing. But accuracy doesn't improve and stuck. Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. But accuracy doesn't improve and stuck. It is taking around 10 to 15 epochs to reach 60% 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. Should it not have 3 elements? @eqy Loss of the model with random data is very close to -ln(1/num_classes), as you mentioned. Earliest sci-fi film or program where an actor plays themself. Some coworkers are committing to work overtime for a 1% bonus. When someone started to learn a technique, he is told exactly what is good or bad, what is certain things for (high certainty). Often, my loss would be slightly incorrect and hurt the performance of the network in a subtle way. First things first, there are three classes and the softmax has only 2 outputs. This leads to a less classic "loss increases while accuracy stays the same". 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). Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. This is how you get high accuracy and high loss. The main one though is the fact that almost all neural nets are trained with different forms of stochastic gradient descent. hp cf378a color laserjet pro mfp m477fdn priya anjali rai latest xxx porn summer code mens sexy micro mesh 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.
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