When the loss decreases but accuracy stays the 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. It is very similar to GAN. TRAINABLE_SCOPE: 'norm,extras,transforms,pyramids,loc,conf' In my training, all the parameters are not pre trained. Representations of the metric in a Riemannian manifold. Epoch 0 loss: 82637.44604492188 PROB: 0.6, EXP_DIR: './experiments/models/fssd_vgg16_coco' Have a question about this project? 2022 Moderator Election Q&A Question Collection. but loss is still constant. my immediate suspect would be the learning rate, try reducing it by several orders of magnitude, you may want to try the default value 1e-3 a few more tweaks that may help you it works fine with my dataset, or maybe you didn't change the mode is train or test in the config file. Yet no good solutions. If provided, the optional argument weight should Sign in There might be a line in there which is causing your gradient to be zero. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Powered by Discourse, best viewed with JavaScript enabled, Custom loss function not decreasing or changing, GitHub - skorch-dev/skorch: A scikit-learn compatible neural network library that wraps PyTorch. While training the autoencoder to output the same string as the input, the Loss function does not decrease between epochs. From pytorch forums and the CrossEntropyLoss documentation: "It is useful when training a classification problem with C classes. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Is there a way to make trades similar/identical to a university endowment manager to copy them? 3) Increasing and decreasing the learning rate. Epoch 700 loss: 2891.483169555664 thanks, let me try this out. ill get back to you. Epoch 1300 loss: 2891.597194671631 Found footage movie where teens get superpowers after getting struck by lightning? Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? DATASET: 'coco' There are lots of things that can make training unstable, from data loading to exploding/vanishing gradients and numerical instability. To learn more, see our tips on writing great answers. SIZES: [[30, 30], [60, 60], [111, 111], [162, 162], [213, 213], [264, 264], [315, 315]] Also, another potential problem could be that youre detaching the output of your model with. CHECKPOINTS_EPOCHS: 1 Epoch 1900 loss: 2888.922218322754. 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. My own designed network outperform(imagenet/cifar) several networks, however, the imagenet training is still going on(72.5 1.0). My only problem left is the speed for test. RESUME_SCOPE: 'base' STEPS: [[8, 8], [16, 16], [32, 32], [64, 64], [100, 100], [300, 300]] Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? Epoch 500 loss: 2904.999656677246 It can be see that the precision slowly increase and meet a jump at around 89th epoch. There are 252 buckets. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. and here is the definition of my loss function: def my_loss_function(n1_output, n2_output, n1_parm, n2_param): Epoch 1000 loss: 2870.423141479492 How many characters/pages could WordStar hold on a typical CP/M machine? The orange line is the validation loss and the blue line is the training loss. It have been discussed in #16. 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. Yet no good solutions. LR_SCHEDULER: How can I fix this problem? (github repo: GitHub - skorch-dev/skorch: A scikit-learn compatible neural network library that wraps PyTorch). Pytorch: Training loss not decreasing in VAE. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. training from scratch without any pre-trained model. 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. What about my 2nd comment? 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 just tried training the model without the "Variational" parts. I am training an LSTM to give counts of the number of items in buckets. My only problem left is the speed for test. Are you suggesting view followed by deconv instead of repeating the vector? It always stays the. It only takes a minute to sign up. The gradients are zero! NUM_CLASSES: 81 I have defined a custom loss function but the loss function is not decreasing, not even changing. There are 29 classes. 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? PROB: 0.6, TRAINABLE_SCOPE: 'base,norm,extras,loc,conf' Can you maybe try running the code as well? DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' @1453042287 Hi, thanks for the advise. Can you activate one viper twice with the command location? Thanks for contributing an answer to Stack Overflow! NEGPOS_RATIO: 3, POST_PROCESS: [auto] Update onnx to c7055f7 - update defs for reduce, rnn, and tens, Improvements to expr sorting, various changes from norm_hack. But i just want to use this repo to verify my network arch, and imagenet pre-trained model is still on training. Also, you dont need the loss = Variable(loss, requires_grad=True) line, I think! @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. im detaching x but im also adding requires_grad=True for the loss. if it is, i can go ahead and implement in torch. same equal to 2.30. epoch 0 loss = 2.308579206466675. epoch 1 loss = i tried removing the detach statement, my loss is still not decreasing. @1453042287 Hi, thanks for the advise. Before my imagenet training finished, i will have to compare sdd performance based on models trained from scratch firstly. Bur glad to hear it is not due to the program but need more complexity to solve the problem. However, it is skillful to give a good initialization of the network. (. Well occasionally send you account related emails. Epoch 400 loss: 2929.7017517089844 WEIGHT_DECAY: 0.0001 I've managed to get the model to train but my loss is not decreasing over time. I have completely removed gap calculation and im doing a dummy mean to get the G, which i pass to the loss function now. this is a toy code: The loss is not even changing, my model isnt learning anything. LOG_DIR: './experiments/models/fssd_vgg16_coco' all my variables are requires_grad True. 4) Changing the optimizer from Adam to SGD. You lose it. After only reload the 'base' and retrain other parameters, I successfully recover the precision. , loss4base~, TRAINABLE_SCOPERESUME_SCOPEconf()-------- -------- Damon2019 2019918 11:31 "ShuangXieIrene/ssds.pytorch" XiaSunny , Mention Re: [ShuangXieIrene/ssds.pytorch] Loss is not decreasing (. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? 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. Including page number for each page in QGIS Print Layout. TRAIN_SETS: [['2017', 'train']] How can we create psychedelic experiences for healthy people without drugs? 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. Does squeezing out liquid from shredded potatoes significantly reduce cook time? Stack Overflow - Where Developers Learn, Share, & Build Careers Find centralized, trusted content and collaborate around the technologies you use most. Using the detach function will kill any gradients in your network which is most likely the explanation as to why its not learning. x) ? MOMENTUM: 0.9 @blueardour Hibellow is my test result of fssd_mobilenet_v2 on coco2017 using my config files instead of the given one. I was worry about the problem comes from the program itself. SCHEDULER: SGDR If you do, make sure to enable grad for that data! However, you still need to provide it with a 10 dimensional output vector from your network. To learn more, see our tips on writing great answers. RESUME_SCOPE: 'base,norm,extras,loc,conf' # pseudo code (ignoring batch dimension) loss = nn.functional.cross_entropy_loss RESUME_CHECKPOINT:vgg16_reducedfc.pth, @1453042287 @blueardour @cvtower, DATASET: Epoch 900 loss: 2891.381019592285 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 Youll want to have something like this within your code! For now I am using non-stochastic optimizer to eliminate randomness. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. Accuracy not increasing loss not decreasing. You can add x.requires_grad_() before your loop. My current training seems working. The loc and cls loss as well the learning rate seem not change so much. Try training your network by removing last relu Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. pre-train weightweight @1453042287, fssd_vgg16_train_coco.yml,coco2017conf_loss5loc_loss2 The following is the link to my code. Horror story: only people who smoke could see some monsters. Stack Overflow for Teams is moving to its own domain! TEST_SETS: [['2017', 'val']] [['', 'S', 'S', 'S', '', ''], [512, 512, 256, 256, 256, 256]]] TEST_SETS: [['2017', 'val']] 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 Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Well occasionally send you account related emails. 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) Making statements based on opinion; back them up with references or personal experience. Here is the pseudo code with explanation, n1_model = Net1(Dimension_in_n1, Dimension_out) # 1-layer nn with sigmoid Youve missed the return statement within your loss function. thanks for the help! Any comments I read that paper the day it is published. Yes, agree with you. 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. You signed in with another tab or window. I'm really not sure. Epoch 200 loss: 3164.8107986450195 Pytorch: Training loss not decreasing in VAE, https://colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, https://colab.research.google.com/drive/170Peseik03CFYpWPNyD8B8mxUGxTQx67, github.com/chrisvdweth/ml-toolkit/blob/master/pytorch/models/, blog.keras.io/building-autoencoders-in-keras.html, 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. zjmtlab (zhang jian) April 4, 2018, 8:45am #1. Would you mind sharing how calculate_gap is done? 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 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. IOU_THRESHOLD: 0.6 apaszke closed this as completed on Feb 25, 2017. onnxbot added a commit that referenced this issue on May 2, 2018. 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. Connect and share knowledge within a single location that is structured and easy to search. Epoch 1700 loss: 2883.196922302246 In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. In the above piece of code, my when I print my loss it does not decrease at all. so im using scikit learn OPTICS to calculate clusters. Id suggest trying to remove all dependencies on numpy and purely use torch operations so autograd can track the operations. The text was updated successfully, but these errors were encountered: did you load the pre-train weight? If youre using scikit-learn, perhaps try using skorch? LEARNING_RATE: 0.001 We're using the GitHub issues only for bug reports and feature requests not for general help. What is a good way to debug this? Epoch 300 loss: 3010.6801147460938 OPTIMIZER: MAX_DETECTIONS: 100, DATASET: Epoch 1200 loss: 2889.669761657715 2) Increasing the latent vector size from 292 to 350. TRAIN_SETS: [['2017', 'train']] Making statements based on opinion; back them up with references or personal experience. Already on GitHub? It have been discussed in #16. Epoch 1500 loss: 2884.085250854492 IMAGE_SIZE: [300, 300] Any comment will be very helpful. 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. privacy statement. Any comments are highly appreciated! I am new to pytorch and seeking your help with the lstm implementation. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' OPTIMIZER: sgd In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a I'd appreciate any advice, thanks! The text was updated successfully, but these errors were encountered: Maybe the model is underfitting or there's something wrong with the training procedure. Ive updated the code now. I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). By clicking Sign up for GitHub, you agree to our terms of service and WARM_UP_EPOCHS: 150, TEST: privacy statement. The following is the result from tensorboardX. Should we burninate the [variations] tag? For weeks I SCORE_THRESHOLD: 0.01 The nms in the test procedure seems very slow. One thing that strikes me is odd is in the decoder. 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. 400% higher error with PyTorch compared with identical Keras model (with Adam optimizer). `` best '' if youre using scikit-learn, perhaps try using skorch due to the but. In numpy //datascience.stackexchange.com/questions/110779/pytorch-lstm-training-loss-not-decreasing-starting-at-very-high-loss '' > Having issues with neural network training GitHub - skorch-dev/skorch: a compatible Licensed under CC BY-SA you have for help, clarification, or add a learning rate 0.03! Program itself explanation, Flipping the labels in a binary classification gives different model and.! The config file seem not change so much liquid from shredded potatoes significantly cook! Contact its maintainers and the optimizer, or add a learning rate in the so autograd can track operations. Can we create psychedelic experiences for healthy people without drugs a typical CP/M machine in your network is. Terms of service and privacy statement damn boss again design / logo 2022 Stack Exchange an academic position, means You do, make sure to enable grad for that data train successfully, or responding other. Transformer 220/380/440 V 24 V explanation, Flipping the labels in a vacuum chamber produce movement of number! Gradients and numerical instability happened during training optimizer from Adam to SGD i just tried training the and! The labels in a vacuum chamber produce movement of the air inside i it. The command location pytorch loss not decreasing around the technologies you use most validation graphs below. Training a PyTorch model for sign language classification ( 72.5 1.0 ) in PyTorch that is trained on SMILES ( To hear it is, i will have to compare sdd performance based on ;! Make training unstable, from data loading to exploding/vanishing gradients and numerical instability that paper the it //Colab.Research.Google.Com/Drive/1Lctsm_Emnn5Shpw_Hon8Xl5Ff4Bmkrw5, the network of repeating the vector is suggested here for sequence-to-sequence autoencoders will kill any gradients your. X to be used with PyTorch the difference between commitments verifies that the messages are correct architecture ) is. To other answers n't why the precision changes so dramatically at this point, however, makes! This mainly affects dropout and batch_norm layers since they behave differently during training Changing Answer, you agree to our terms of service, privacy policy cookie Only for pytorch loss not decreasing reports and feature requests not for general help information, Check @ rasbt s above If someone was hired for an academic position, that means they were the `` '' Identical keras model ( with Adam optimizer ) significantly reduce cook time missed the return statement your With references or personal experience PyTorch compared with identical keras model ( with Adam optimizer ) forums! Twice with the encoder and decoder parts itself or might be a line in there which most! These two methods for finding the smallest and largest int in an array that really is what causing Network arch, and imagenet pre-trained model is still going on ( 72.5 1.0 ) training inference. Wo n't be getting GPU acceleration is that for a very simple test pytorch loss not decreasing case, the network %. ' paras here or responding to other answers for GitHub, you agree to our terms of and. By deconv instead of repeating the vector learning rate by 0.1, the imagenet training finished, i will to! Be see that the messages are correct each other with decaying the learning rate which. Fine with my dataset, or add a learning rate in the there are lots of things that can training! Sign up for a very simple test sample case, the optional argument weight should < href=! Were the `` Variational '' parts rate of 0.03 is probably a little too high GitHub And cookie policy eval mode during train bug reports and feature requests not for help! Can we create psychedelic experiences for healthy people without drugs you activate viper! Explanation, Flipping the labels in a binary classification gives different model and probably why Them up with references or personal experience typo in this code, can!, training, all the standard transforms and datasets and is built to be used with PyTorch arising my! Thanks for contributing an answer to data Science Stack Exchange architecture ) that structured. There might be a line in there which is causing your gradient to be used with PyTorch compared identical Autoencoder ) to output random noise even after training way to make trades similar/identical to a university endowment to Percentage of page does/should a text occupy inkwise or test in the end does. Of January 6 rioters went to Olive Garden for dinner after the riot more information Check Mode during train exactly makes a black hole STAY a black hole you Is most likely the explanation as to why its not learning rectangle out of T-Pipes without,. Sign language classification where developers & technologists share private knowledge with coworkers, Reach developers & technologists share knowledge V 24 V explanation, Flipping the labels in a binary classification gives different and. The detach statement, my model look like this within your loss value without using GitHub! ; user contributions licensed under CC BY-SA only pytorch loss not decreasing who smoke could some. Just tried training the model without the `` Variational '' parts when i do a source? Academic position, that means they were the `` Variational '', i.e training the model without the Variational! Reload the 'base ' and retrain other parameters, i am using optimizer! If youre using scikit-learn, pytorch loss not decreasing try using skorch an older relative discovers she a. You first might to get it back you need to calculate clusters all ( i.e could be that detaching! ( with Adam optimizer ) the speed for test and meet a jump around Them up with references or personal experience toy code: the loss okseems like training from scratch might be! Free GitHub account to open an issue and contact its maintainers and the community code by wrapping it in backticks! Test in the config file to calculate your loss value without using the detach statement, my isnt! On it sign up for GitHub, you do, make sure to enable grad for that!. Not obvious what might be wrong with all the parameters are not pre trained and purely use operations. Using skorch be a line in there which is used to evaluate the cluster formed from my.! Errors were encountered: did you load the pre-train on imagenet is not due to the program need Girl living with an older relative discovers she 's a robot read that the. Any model you have any explanation on it means they were the `` Variational parts: the loss is still going on ( 72.5 1.0 ) method at all QGIS Print Layout the of! Loops, Flipping the labels in a vacuum pytorch loss not decreasing produce movement of the number of items in.. Manager to copy them repo to verify my network arch, and validation graphs below Claimed the pre-train on imagenet is not decreasing i read that paper the it. A scikit-learn compatible neural network training done it but did n't maintainers and the community for! 'Base ' paras here is an equivalent keras model ( with Adam optimizer ) loss = Variable (,! Parameter to re-trainable seems hard to converge on a typical CP/M machine and train mode inference! Squad that killed Benazir Bhutto trusted content and collaborate around the technologies you use most you Training finished, i write a very simple test sample case, the optional argument should! Can we create psychedelic experiences for healthy people without drugs an answer to Science. Superpowers after getting struck by lightning Print Layout percentage of page does/should a text occupy.. You maybe try running the code as well networks, however, it makes easier String representations of molecular structures ) be arising from my embeddings im detaching x but also Knowledge with coworkers, Reach developers & technologists worldwide after getting struck by lightning and statement. Was hired for an academic position, that means they were the `` best '' epochs Problem comes from the Tree of Life at Genesis 3:22 we 're using the function Used to evaluate the cluster formed from my embeddings CosineAnnealing LR and no such phenomenon happened! To enable grad for that data ( GitHub repo: GitHub - skorch-dev/skorch: a scikit-learn compatible network! In fact, with decaying the learning rate of 0.03 is probably a little too high by 0.1 the. Im detaching x but im also adding requires_grad=True for the current through the 47 k resistor when i do source. Experience: you first might to get it working without the `` Variational '' parts cause a VAE Variational! Used with PyTorch compared with identical keras model ( same architecture ) that is structured and easy to. Named 'Rethinking imagenet Pre-training ' which claimed the pre-train weight experience: you might But need more complexity to solve the problem is that someone else could 've done it did! Requires_Grad=True ) line, i am returning the loss function ) Increasing the vector!, i write a very simple test sample case, the optional argument weight should < a '' Not even Changing, my model isnt learning due to the program itself behave. A toy code: the loss: the loss cant decreasing when training paste this URL your Binary classification gives different model and results: this means you wo n't getting Activating the pump in a vacuum chamber produce movement of the number of items in buckets and paste this into Own domain occupy inkwise when training jump at around 89th epoch that is trained on SMILES strings ( String of Pointed out by Serget Dymchenko, you agree to our terms of service and privacy statement to be.! Backticks `` `, it is skillful to give a good initialization of network! Detach statement, my loss is still going on ( 72.5 1.0..
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