I am training a pytorch model for sign language classification. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). 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. (github repo: GitHub - skorch-dev/skorch: A scikit-learn compatible neural network library that wraps PyTorch). What is the limit to my entering an unlocked home of a stranger to render aid without explicit permission, Water leaving the house when water cut off. Problem is that my loss is doesnt I have tried the following with no success: U mentioned 'pre-trained model', do y mean the pre-trained bone network model (such as the mobilenetv2) or both bone model and detection model? Also i have verified my network on other tasks and works fine, so i believe it will get better result on detection&&segmentation task too. I have completely removed gap calculation and im doing a dummy mean to get the G, which i pass to the loss function now. One thing that strikes me is odd is in the decoder. TRAINABLE_SCOPE: 'norm,extras,transforms,pyramids,loc,conf' FEATURE_LAYER: [[[22, 34, 'S'], [512, 1024, 512]], im detaching x but im also adding requires_grad=True for the loss. To learn more, see our tips on writing great answers. If provided, the optional argument weight should The nms in the test procedure seems very slow. [auto] Update onnx to c7055f7 - update defs for reduce, https://colab.research.google.com/drive/170Peseik03CFYpWPNyD8B8mxUGxTQx67. Make a wide rectangle out of T-Pipes without loops, Flipping the labels in a binary classification gives different model and results. The loc and cls loss as well the learning rate seem not change so much. Stack Overflow - Where Developers Learn, Share, & Build Careers Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Epoch 900 loss: 2891.381019592285 Also, remember to clear the gradient cache of your parameters (via optimizer.zero_grad()) otherwise your gradients will acculumate from all epochs! I have another issue about the train precision and loss curve. Training loss not changing at all while training LSTM (PyTorch) Training loss not changing at all while training LSTM (PyTorch) Apart from the comment I made, I reduced the dropout and I tried playing around with learning rates, .01, .001, .0001. however my model loss and val loss are not decreasing. How do I simplify/combine these two methods for finding the smallest and largest int in an array? In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. thanks, let me try this out. MAX_EPOCHS: 500 Sign in I just tried training the model without the "Variational" parts. I've tried all types of batch sizes (4, 16, 32, 64) and learning rates (100, 10, 1, 0.1, 0.01, 0.001, 0.0001) as well as decaying the learning rate. Hello, I am new to deep learning and pytorch, I try to use DNN method to predict the output value, but the loss is saturated when training. Epoch 1500 loss: 2884.085250854492 2) Increasing the latent vector size from 292 to 350. Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? LOG_DIR: './experiments/models/fssd_vgg16_coco' In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a The loss function is MSELoss and the optimizer is Adam. After only reload the 'base' and retrain other parameters, I successfully recover the precision. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. TEST_SCOPE: [90, 100], MATCHER: Epoch 800 loss: 2877.9163970947266 Just as a suggestion from my experience: You first might to get it working without the "Variational", i.e. rev2022.11.4.43006. Epoch 300 loss: 3010.6801147460938 I try to apply Standard Scaler by following steps: Powered by Discourse, best viewed with JavaScript enabled, Adding following code after train_test_split stage, And applying Standard Scaler to test dataset before test. Why does the sentence uses a question form, but it is put a period in the end? I am new to pytorch and seeking your help with the lstm implementation. Pytorch: Training loss not decreasing in VAE - Stack Use MathJax to format equations. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? training from scratch without any pre-trained model. [Question] Loss is not decreasing - PyTorch Forums Epoch 0 loss: 82637.44604492188 The training output shows saturated loss which is not decreasing: Try training your network by removing last relu Would you mind sharing how calculate_gap is done? SIZES: [[30, 30], [60, 60], [111, 111], [162, 162], [213, 213], [264, 264], [315, 315]] SCORE_THRESHOLD: 0.01 However, it is skillful to give a good initialization of the network. You signed in with another tab or window. WARM_UP_EPOCHS: 150, TEST: I am writing a program that make use of the build in LSTM in the Pytorch, however the loss is always around some numbers and does not decrease significantly. My model look like this: Asking for help, clarification, or responding to other answers. My current training seems working. Can you maybe try running the code as well? Well occasionally send you account related emails. SSDS: fssd I read that paper the day it is published. TEST_SETS: [['2017', 'val']] When the loss decreases but accuracy stays the Epoch 400 loss: 2929.7017517089844 My own designed network outperform(imagenet/cifar) several networks, however, the imagenet training is still going on(72.5 1.0). SOLUTIONS: Check if you pass the softmax into the CrossEntropy loss. If you do, correct it. For more information, check @rasbt s answer above. Use a smaller learning rate in the optimizer, or add a learning rate scheduler which will decrease the learning rate automatically during training. There are 252 buckets. Stack Overflow - Where Developers Learn, Share, & Build Careers It have been discussed in #16. There are lots of things that can make training unstable, from data loading to exploding/vanishing gradients and numerical instability. Stack Overflow for Teams is moving to its own domain! It will helps you a lot. Well occasionally send you account related emails. Book where a girl living with an older relative discovers she's a robot. My only problem left is the speed for test. ill get back to you. Connect and share knowledge within a single location that is structured and easy to search. Also, you dont need the loss = Variable(loss, requires_grad=True) line, I think! There are 252 buckets. PHASE: ['train'] Youll want to have something like this within your code! The text was updated successfully, but these errors were encountered: Maybe the model is underfitting or there's something wrong with the training procedure. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Youve missed the return statement within your loss function. RESUME_CHECKPOINT:vgg16_reducedfc.pth, @1453042287 @blueardour @cvtower, DATASET: 6) Increasing and decreasing the batch size. [['', 'S', 'S', 'S', '', ''], [512, 512, 256, 256, 256, 256]]] What about my 2nd comment? Yet no good solutions. [Sloved] Why my loss not decreasing - PyTorch Forums LR_SCHEDULER: Repeating the vector is suggested here for sequence-to-sequence autoencoders. Is there a way to make trades similar/identical to a university endowment manager to copy them? 2022 Moderator Election Q&A Question Collection. BATCH_SIZE: 64 The problem is that for a very simple test sample case, the loss function is not decreasing. It can be see that the precision slowly increase and meet a jump at around 89th epoch. And to get it back you need to find and fight the damn boss again. to your account. Can you help me out with this? https://colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, The following is an equivalent keras model(Same architecture) that is able to train successfully. Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? Epoch 700 loss: 2891.483169555664 There are 29 classes. Have a question about this project? All the The best answers are voted up and rise to the top, 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: LSTM training loss not decreasing; starting at very high loss, 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, Understanding LSTM behaviour: Validation loss smaller than training loss throughout training for regression problem, LSTM training/prediction with no starting sequence, Using SMAPE as a loss function for an LSTM, Multivariate LSTM RMSE value is getting very high. Are Githyanki under Nondetection all the time? The following is the result from tensorboardX. Does activating the pump in a vacuum chamber produce movement of the air inside? I don't why the precision changes so dramatically at this point. After having a brief look through, it seems youre swapping between torch and numpy, when moving back and forth between the library would break the gradient of any intermediate computations, no? Train/validation loss not decreasing - vision - PyTorch Model loss not decreasing via transfer learning - PyTorch Forums Shall i only reload the 'base' paras here? Found footage movie where teens get superpowers after getting struck by lightning? i tried removing the detach statement, my loss is still not decreasing. I did not use the CosineAnnealing LR and no such phenomenon ever happened during training. pre-train weightweight @1453042287, fssd_vgg16_train_coco.yml,coco2017conf_loss5loc_loss2 NEGPOS_RATIO: 3, POST_PROCESS: nlp - Pytorch LSTM model's loss not decreasing - Stack TRAIN_SETS: [['2017', 'train']] It is very similar to GAN. im doing this now. My current training seems working. DATASET: 'coco' But i just want to use this repo to verify my network arch, and imagenet pre-trained model is still on training. this is a toy code: The loss is not even changing, my model isnt learning anything. I am training an LSTM to give counts of the number of items in buckets. ASPECT_RATIOS: [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2]], TRAIN: Epoch 500 loss: 2904.999656677246 same equal to 2.30. epoch 0 loss = 2.308579206466675. epoch 1 loss = How to apply layer-wise learning rate in Pytorch? return y. when I plot loss function, it has oscillation; I expect it to decrease during training. Epoch 600 loss: 2887.5707092285156 Its a PyTorch version of scikit-learn that wraps around it. We're using the GitHub issues only for bug reports and feature requests not for general help. PROB: 0.6, TRAINABLE_SCOPE: 'base,norm,extras,loc,conf' Thanks for contributing an answer to Data Science Stack Exchange! but loss is still constant. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss cant decreasing when training. OPTIMIZER: sgd @SiNML You can use Standard Scaler from scikit learn and normalize training data and use same mean and variance of train data to normalize test data as well. PROB: 0.6, EXP_DIR: './experiments/models/fssd_vgg16_coco' What exactly makes a black hole STAY a black hole? Also, another potential problem could be that youre detaching the output of your model with. My current training seems working. Do you observe a similar phenomenon or do you have any explanation on it? CHECKPOINTS_EPOCHS: 1 Any comment will be very helpful. You lose it. okseems like training from scratch might not be well supported. 1) Adding 3 more GRU layers to the decoder to increase learning capability of the model. Epoch 1400 loss: 2881.264518737793 MOMENTUM: 0.9 I have the same issue. its constant. 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. After only reload the 'base' and retrain other parameters, I successfully recover the precision. Loss does not decrease for pytorch LSTM - Stack Overflow Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Should we burninate the [variations] tag? Epoch 1900 loss: 2888.922218322754. (. privacy statement. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? For now I am using non-stochastic optimizer to eliminate randomness. Epoch 100 loss: 3913.1080932617188 Yes, set all parameter to re-trainable seems hard to converge. Representations of the metric in a Riemannian manifold. Which is why its not decreasing! By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. if it is, i can go ahead and implement in torch. I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). Epoch 1300 loss: 2891.597194671631 Stack Overflow for Teams is moving to its own domain! By clicking Sign up for GitHub, you agree to our terms of service and MODEL: I had a second look at your code, but it's not obvious what might be wrong. Yet no good solutions. Thanks for contributing an answer to Stack Overflow! and here is the definition of my loss function: def my_loss_function(n1_output, n2_output, n1_parm, n2_param): Fourier transform of a functional derivative. n2_optimizer = torch.optim.LBFGS(n2_model.parameters(), lr=0.01, max_iter = 50), for t in range(iter): Hi, This was a typo in this code, i am returning the loss. Epoch 1100 loss: 2887.0635833740234 Epoch 1800 loss: 2891.262664794922 MATCHED_THRESHOLD: 0.5 NETS: vgg16 Accuracy not increasing loss not decreasing - PyTorch Forums For weeks I Any comments DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? Pytorch - Loss is decreasing but Accuracy not improving If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each class. @1453042287 Hi, thanks for the advise. While training the autoencoder to output the same string as the input, the Loss function does not decrease between epochs. Thanks for the suggestion. In the above piece of code, my when I print my loss it does not decrease at all. NUM_CLASSES: 81 OPTIMIZER: The main issue is that the outputs of your model are being detached, so they have no connection to your model weights, and therefore as your loss is dependent on output and x (both of which are detached), your loss will have no gradient with respect to your model parameters! From pytorch forums and the CrossEntropyLoss documentation: "It is useful when training a classification problem with C classes. It helps to have your features normalized, you can use Standard Scaler from scikit learn and normalize training data and use same mean and variance of train data to normalize test data as well, maybe also try introducing bit of complexity in your model, add drop-out layer, batch norm, use regularisation, add learning rate decay. I have a single layer LSTM followed by a fully connected layer So I found out the added the new mode to shindo life, I am wondering if you lose a tailed beast after you use the mode, or you can just keep activating the op mode over and over again like normal. Connect and share knowledge within a single location that is structured and easy to search. I'd appreciate any advice, thanks! my loss function aims to minimize the inverse of gap statistic which is used to evaluate the cluster formed from my embeddings. It have been discussed in #16. Have a question about this project? Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? IOU_THRESHOLD: 0.6 4) Changing the optimizer from Adam to SGD. 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 year, Mr He did publish a paper named 'Rethinking ImageNet Pre-training' which claimed the pre-train on imagenet is not necessary. WEIGHT_DECAY: 0.0001 Pytorch tutorial loss is not decreasing as expected I have trained ssd with mobilenetv2 on VOC but after almost 500 epochs, the loss is still like this: It's doesn't change and loss is very hight What's the problem with implementation? Pytorch: Training loss not decreasing in VAE. apaszke closed this as completed on Feb 25, 2017. onnxbot added a commit that referenced this issue on May 2, 2018. Is your dataset normalized? Does squeezing out liquid from shredded potatoes significantly reduce cook time? y = torch.sum(sm) + 1 * reg n2_model =Net2(Dimension_in_n2, Dimension_out) # 1-layer nn with sigmoid, n1_optimizer = torch.optim.LBFGS(n1_model.parameters(), lr=0.01,max_iter = 50) Bur glad to hear it is not due to the program but need more complexity to solve the problem. To learn more, see our tips on writing great answers. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' TRAIN_SETS: [['2017', 'train']] The text was updated successfully, but these errors were encountered: did you load the pre-train weight? The main issue is that the outputs of your model are being detached, so they have no connection to your model weights, and therefore as your loss is dependent on output and x Making statements based on opinion; back them up with references or personal experience. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Using the detach function will kill any gradients in your network which is most likely the explanation as to why its not learning. Epoch 1700 loss: 2883.196922302246 Also could you indent your code by wrapping it in three backticks ``` , it makes it easier for people to read/copy! it works fine with my dataset, or maybe you didn't change the mode is train or test in the config file. PyTorch: LSTM training loss not decreasing; starting at very high loss. zjmtlab (zhang jian) April 4, 2018, 8:45am #1. thanks. Transformer 220/380/440 V 24 V explanation, Flipping the labels in a binary classification gives different model and results. However, you still need to provide it with a 10 dimensional output vector from your network. that requires the input x to be in numpy. I was worry about the problem comes from the program itself. sm = torch.pow(n1_output - n2_output, 2) Sign in Hello, I am new to deep learning and pytorch, I try to use DNN method to predict the output value, but the loss is saturated when training. Training loss not changing at all while training LSTM (PyTorch) x_n1 = Variable(torch.from_numpy()) #load input of nn1 in batch size TEST_SETS: [['2017', 'val']] I have created a simple model consisting of two 1-layer nn competing each other. The loss is still not changing between epochs. Youll need to calculate your loss value without using the detach() method at all. How does taking the difference between commitments verifies that the messages are correct? just checked skorch out, they dont have clustering algorithms implemented, i willl try and create a dummy function using torch to see if my loss is decreasing. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. So, the problem is probably with the encoder and decoder parts itself or might be arising from my training loop. @jinfagang Have you solved the problem? Can you activate one viper twice with the command location? Found footage movie where teens get superpowers after getting struck by lightning? Cross Entropy loss is not decreasing - autograd - PyTorch i want to know if that really is what is causing the issue. Epoch 1600 loss: 2883.3774032592773 Personally, i greatly agree with views from "Detnet" and "rethinking imagenet pre-training", however, seems like that much more computation cost and specific tuning skills are needed. You can add x.requires_grad_() before your loop. Code, training, and validation graphs are below. loss/val_loss are decreasing but accuracies are the same in LSTM! all my variables are requires_grad True. 5) Trained the model on upto 50 epochs. DATASET: 'coco' Epoch 200 loss: 3164.8107986450195 What percentage of page does/should a text occupy inkwise. I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss Already on GitHub? The gradients are zero! I'm really not sure. Asking for help, clarification, or responding to other answers. I am using Densenet from Pytorch models, and have copied Having issues with neural network training. Loss not Including page number for each page in QGIS Print Layout. [auto] Update onnx to c7055f7 - update defs for reduce, rnn, and tens, Improvements to expr sorting, various changes from norm_hack. RESUME_SCOPE: 'base,norm,extras,loc,conf' 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. UNMATCHED_THRESHOLD: 0.5 By clicking Sign up for GitHub, you agree to our terms of service and reg = torch.norm(n1_parm,2) + torch.norm(n2_param,2) the loss is not decreasing Issue #847 pytorch/pytorch In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. shindo life tailed beast In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. MAX_DETECTIONS: 100, DATASET: This means you won't be getting GPU acceleration. 5. torchvision is designed with all the standard transforms and datasets and is built to be used with PyTorch. I recommend using it. Hi, I am taking the output from my final convolutional transpose layer into a softmax layer and then trying to measure the mse loss with my target. The network does overfit on a very small dataset of 4 samples (giving training loss < 0.01) but on larger data set, the loss seems to plateau around a very large loss. 3) Increasing and decreasing the learning rate. How can we create psychedelic experiences for healthy people without drugs? So, I have my own loss function based on those nn outputs. This will break the gradients within the model and probably explains why your model isnt learning! , loss4base~, TRAINABLE_SCOPERESUME_SCOPEconf()-------- -------- Damon2019
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