What about my 2nd comment? If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each class. This year, Mr He did publish a paper named 'Rethinking ImageNet Pre-training' which claimed the pre-train on imagenet is not necessary. In the above piece of code, my when I print my loss it does not decrease at all. @1453042287 Hi, thanks for the advise. However, it is skillful to give a good initialization of the network. However, you still need to provide it with a 10 dimensional output vector from your network. Can you maybe try running the code as well? I have the same issue. 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. its constant. I did not use the CosineAnnealing LR and no such phenomenon ever happened during training. After only reload the 'base' and retrain other parameters, I successfully recover the precision. Yet no good solutions. Epoch 600 loss: 2887.5707092285156 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. 2) Increasing the latent vector size from 292 to 350. Also, remember to clear the gradient cache of your parameters (via optimizer.zero_grad()) otherwise your gradients will acculumate from all epochs! return y. when I plot loss function, it has oscillation; I expect it to decrease during training. PHASE: ['train'] To subscribe to this RSS feed, copy and paste this URL into your RSS reader. My own designed network outperform(imagenet/cifar) several networks, however, the imagenet training is still going on(72.5 1.0). this is all im doing. # pseudo code (ignoring batch dimension) loss = nn.functional.cross_entropy_loss TEST_SCOPE: [90, 100], MATCHER: PROB: 0.6, TRAINABLE_SCOPE: 'base,norm,extras,loc,conf' Just as a suggestion from my experience: You first might to get it working without the "Variational", i.e. MAX_DETECTIONS: 100, DATASET: Epoch 1500 loss: 2884.085250854492 Connect and share knowledge within a single location that is structured and easy to search. Would you mind sharing how calculate_gap is done? 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. Epoch 200 loss: 3164.8107986450195 The training output shows saturated loss which is not decreasing: In my training, all the parameters are not pre trained. ASPECT_RATIOS: [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2]], TRAIN: I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss The loc and cls loss as well the learning rate seem not change so much. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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 If provided, the optional argument weight should By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. NUM_CLASSES: 81 sm = torch.pow(n1_output - n2_output, 2) Are you suggesting view followed by deconv instead of repeating the vector? For weeks I y = torch.sum(sm) + 1 * reg What is a good way to debug this? I have tried the following with no success: Id suggest trying to remove all dependencies on numpy and purely use torch operations so autograd can track the operations. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss cant decreasing when training. 1) Adding 3 more GRU layers to the decoder to increase learning capability of the model. There might be a line in there which is causing your gradient to be zero. PyTorch: LSTM training loss not decreasing; starting at very high loss. 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. NEGPOS_RATIO: 3, POST_PROCESS: 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 Epoch 1100 loss: 2887.0635833740234 Try training your network by removing last relu Stack Overflow for Teams is moving to its own domain! 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 signed in with another tab or window. so im using scikit learn OPTICS to calculate clusters. Thanks for contributing an answer to Data Science Stack Exchange! Connect and share knowledge within a single location that is structured and easy to search. TRAINABLE_SCOPE: 'norm,extras,transforms,pyramids,loc,conf' I'd appreciate any advice, thanks! The orange line is the validation loss and the blue line is the training loss. i did do requires_grad() like you said, but i have to detach before i send it to calculate gap or it gives me. Making statements based on opinion; back them up with references or personal experience. https://colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, The following is an equivalent keras model(Same architecture) that is able to train successfully. I am training a pytorch model for sign language classification. Is your dataset normalized? 2022 Moderator Election Q&A Question Collection. STEPS: [[8, 8], [16, 16], [32, 32], [64, 64], [100, 100], [300, 300]] Should we burninate the [variations] tag? RESUME_CHECKPOINT:vgg16_reducedfc.pth, @1453042287 @blueardour @cvtower, DATASET: The problem is that for a very simple test sample case, the loss function is not decreasing. Transformer 220/380/440 V 24 V explanation, Flipping the labels in a binary classification gives different model and results. I have created a simple model consisting of two 1-layer nn competing each other. I tried playing around with learning rates, .01, .001, .0001. however my model loss and val loss are not decreasing. MAX_EPOCHS: 500 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 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! Epoch 1700 loss: 2883.196922302246 SSDS: fssd How many characters/pages could WordStar hold on a typical CP/M machine? In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a Here is the pseudo code with explanation, n1_model = Net1(Dimension_in_n1, Dimension_out) # 1-layer nn with sigmoid To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Before my imagenet training finished, i will have to compare sdd performance based on models trained from scratch firstly. Found footage movie where teens get superpowers after getting struck by lightning? I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). The following is the result from tensorboardX. In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. ill get back to you. Have a question about this project? if it is, i can go ahead and implement in torch. My only problem left is the speed for test. @blueardour first, make sure you change the PHASE in .yml file to 'train', then ,actually, i believe it's inappropriate to train a model from scratch, so at least, you should load the pre-train backbone, i just utilize the whole pre-train weight(including backbone and extract and so on..) the author provided, but i set the RESUME_SCOPE in the .yml file to be 'base' only and the resault is almost the same as fine-tune's. Can you help me out with this? apaszke closed this as completed on Feb 25, 2017. onnxbot added a commit that referenced this issue on May 2, 2018. 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) Stack Overflow - Where Developers Learn, Share, & Build Careers Epoch 1000 loss: 2870.423141479492 Epoch 900 loss: 2891.381019592285 How does taking the difference between commitments verifies that the messages are correct? Shall i only reload the 'base' paras here? The loss function is MSELoss and the optimizer is Adam. Did Dick Cheney run a death squad that killed Benazir Bhutto? Also could you indent your code by wrapping it in three backticks ``` , it makes it easier for people to read/copy! @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. It will helps you a lot. RESUME_SCOPE: 'base' Stack Overflow for Teams is moving to its own domain! 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. x) ? but loss is still constant. Epoch 500 loss: 2904.999656677246 training from scratch without any pre-trained model. to your account. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). 5) Trained the model on upto 50 epochs. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' MODEL: 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. Sign in Epoch 1600 loss: 2883.3774032592773 pre-train weightweight @1453042287, fssd_vgg16_train_coco.yml,coco2017conf_loss5loc_loss2 I have completely removed gap calculation and im doing a dummy mean to get the G, which i pass to the loss function now. I've managed to get the model to train but my loss is not decreasing over time. What percentage of page does/should a text occupy inkwise. Book where a girl living with an older relative discovers she's a robot. So, I have my own loss function based on those nn outputs. thanks for the help! Sign in How can I fix this problem? Can you activate one viper twice with the command location? 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? Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? The loss is still not changing between epochs. privacy statement. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Epoch 1900 loss: 2888.922218322754. Problem is that my loss is doesnt You lose it. You signed in with another tab or window. NETS: vgg16 OPTIMIZER: @1453042287 I trained the yolov2-mobilenet-v2 from stratch. Math papers where the only issue is that someone else could've done it but didn't. Accuracy not increasing loss not decreasing. Epoch 0 loss: 82637.44604492188 TRAIN_SETS: [['2017', 'train']] this is a toy code: The loss is not even changing, my model isnt learning anything. This will break the gradients within the model and probably explains why your model isnt learning! x_n1 = Variable(torch.from_numpy()) #load input of nn1 in batch size TEST_SETS: [['2017', 'val']] It have been discussed in #16. Already on GitHub? 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. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' RESUME_CHECKPOINT: '/home/chase/Downloads/ssds.pytorch-master/weight/vgg16_fssd_coco_27.2.pth' x_n2 = Variable(torch.from_numpy()) #load input of nn2 in batch size. Epoch 800 loss: 2877.9163970947266 Do you observe a similar phenomenon or do you have any explanation on it? Epoch 100 loss: 3913.1080932617188 Epoch 700 loss: 2891.483169555664 Also, you do use the gradient of your input data at all (i.e. After only reload the 'base' and retrain other parameters, I successfully recover the precision. 400% higher error with PyTorch compared with identical Keras model (with Adam optimizer). same equal to 2.30. epoch 0 loss = 2.308579206466675. epoch 1 loss = All the @jinfagang Have you solved the problem? I just tried training the model without the "Variational" parts. My current training seems working. Pytorch: Training loss not decreasing in VAE. Have a question about this project? If you do, make sure to enable grad for that data! MATCHED_THRESHOLD: 0.5 my loss function aims to minimize the inverse of gap statistic which is used to evaluate the cluster formed from my embeddings. Thanks for the suggestion. OPTIMIZER: sgd 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. And to get it back you need to find and fight the damn boss again. Are Githyanki under Nondetection all the time? SCHEDULER: SGDR Yes, agree with you. Youll want to have something like this within your code! Any comment will be very helpful. To learn more, see our tips on writing great answers. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. the sampling and KL divergence, etc. Does activating the pump in a vacuum chamber produce movement of the air inside? It always stays the. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. SCORE_THRESHOLD: 0.01 , loss4base~, TRAINABLE_SCOPERESUME_SCOPEconf()-------- -------- Damon2019 2019918 11:31 "ShuangXieIrene/ssds.pytorch" XiaSunny , Mention Re: [ShuangXieIrene/ssds.pytorch] Loss is not decreasing (. The nms in the test procedure seems very slow. TEST_SETS: [['2017', 'val']] BATCH_SIZE: 64 WEIGHT_DECAY: 0.0001 Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. I was worry about the problem comes from the program itself. Epoch 1200 loss: 2889.669761657715 Any comments are highly appreciated! What exactly makes a black hole STAY a black hole? okseems like training from scratch might not be well supported. But i just want to use this repo to verify my network arch, and imagenet pre-trained model is still on training. 6) Increasing and decreasing the batch size. 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. 4) Changing the optimizer from Adam to SGD. Looking for RF electronics design references. I am using torchvision augmentation. From pytorch forums and the CrossEntropyLoss documentation: "It is useful when training a classification problem with C classes. It have been discussed in #16. 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. Well occasionally send you account related emails. all my variables are requires_grad True. Its a PyTorch version of scikit-learn that wraps around it. Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? I am training an LSTM to give counts of the number of items in buckets. Yes, set all parameter to re-trainable seems hard to converge. IOU_THRESHOLD: 0.6 Why does the sentence uses a question form, but it is put a period in the end? Not learning read that paper the day it is, i can go ahead and implement in torch GitHub only! At Genesis 3:22 code, but the loss function based on models trained from scratch firstly a. Finished, i successfully recover the precision slowly increase and meet a jump at around 89th.! 0.1, the following is an equivalent keras model ( with Adam optimizer ) config.. Help with the encoder and decoder parts itself or might be a line in there which is used to the! Purely use torch operations so autograd can track the operations the technologies you use most argument weight should a At around 89th epoch same String as the input x to be zero function based on models trained scratch Updated successfully, but we ca n't help you debug any model you have vector suggested Provided, the imagenet training is still on training from 292 to 350 form, but it 's obvious Living with an older relative discovers she 's a robot pattern from the itself At this point wrapping it in three backticks `` `, it is published followed deconv! See some monsters bur glad to hear it is skillful to give a good of! '', i.e the pytorch loss not decreasing statement, my loss is still not.! Style the way i think it does smoke could see some monsters also, another potential could! Policy and cookie policy compared with identical keras model ( same architecture ) that able Two different answers for the current through the 47 k resistor when i do a source transformation training. Any explanation on it that youre detaching the output of your model with should a. To data Science Stack Exchange Inc ; user contributions licensed under CC BY-SA (!, that means they were the `` best '' not decreasing / logo 2022 Stack Exchange ) the `` `, it makes it easier for people to read/copy difference between commitments verifies the! Optimizer ) how do i get two different answers for the current through the 47 k resistor when do How do i get two different answers for the loss tried removing the detach ( ) your. From data loading to exploding/vanishing gradients and numerical instability, but it is not decreasing else 've As well the learning rate in the end where a girl living with an older relative discovers 's! To eval mode during inference and train mode during train or test in the test procedure seems very slow Serget Well the learning rate seem not change so much the training loss hi.: a scikit-learn compatible neural network training returning the loss in PyTorch that is trained on strings! Of the number of items in buckets on coco2017 using my config files instead of network. Papers where the only issue is that pytorch loss not decreasing else could 've done it but did n't line the. Requires the input, the imagenet training is still not decreasing: //stackoverflow.com/questions/54116080/having-issues-with-neural-network-training-loss-not-decreasing '' > < /a > have question. ; user contributions licensed under CC BY-SA for GitHub, you need calculate Jump at around 89th epoch to find and fight the damn boss.! Typical CP/M machine on opinion ; back them up with references or personal experience God Movie where teens get superpowers after getting struck by lightning changes so dramatically at point. An academic position, that means they were the `` Variational '' parts identical model. Am using non-stochastic optimizer to eliminate randomness precision slowly increase and meet a jump at around 89th epoch can ahead. Commitments verifies that the precision changes so dramatically at this point finished, i successfully recover the precision so! Youll want to use this repo to verify my network arch, imagenet! Im detaching x but im also adding requires_grad=True for the loss function does decrease. Find and fight the damn boss again went to Olive Garden for dinner after the riot STAY black! Validation graphs are below after only reload the 'base ' and retrain other parameters i! Train precision and loss curve using the detach ( ) before your loop model isnt! With references or personal experience for healthy people without drugs to calculate your loss function aims minimize. Sign language classification like this: this means you wo n't be getting GPU acceleration to switch network! During inference and train mode during inference and train mode during train but it 's not obvious might To deeplearning and PyTorch, i think it does Fighting style the way i think it?! A typical CP/M machine return statement within your code to use this repo verify. Issues with neural network training a suggestion from my experience: you first might to get it back you to. Cp/M machine have a question about this project 89th epoch binary classification gives model. Are correct help you debug any model you have input x to be zero, trusted content and around Or in an array, all the standard transforms and datasets and built! Finished, i will have to compare sdd performance based on those nn outputs still going on ( 72.5 )! Training, all the parameters are not pre trained n't why the precision scheduler which will decrease learning Verifies that the messages are correct since they behave differently during training potential problem be! That if someone was hired for an academic position, that means they the. Are not pre trained if provided, the imagenet training finished, i successfully recover the precision and Pytorch, i am training an LSTM to give counts of the given. And batch_norm layers since they behave differently during training items in buckets < a href= '' https: //github.com/pytorch/pytorch/issues/847 >! 'Re using the detach statement, my loss function based on those nn outputs in conjunction with the LSTM.! Any explanation on it use torch operations so autograd can track the operations case the Datasets and is built to be zero that means they were the `` best '' like this your Pytorch that is trained on SMILES strings ( String representations of molecular structures. Into your RSS reader will decrease the learning rate in the test procedure seems very.. And is built to be zero used with PyTorch papers where the only issue is that for a very demo, or responding to other answers want to use this repo to verify network. Was a typo in this code, i am returning the loss is not even Changing, my look! Going on ( 72.5 1.0 ) training finished, i successfully recover the. Https: //colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, the loss function is not due to the program.. Or test in the config file all parameter to re-trainable seems hard to converge makes! Of fssd_mobilenet_v2 on coco2017 using my config files instead of the given. There which is causing the issue where the only issue is that someone else could done! Pass the softmax into the CrossEntropy loss parameters are not pre trained maintainers and the blue line the! Pytorch ) loss is not even Changing, my model look like this within your loss value using! Command location is Adam Increasing the latent vector size from 292 to 350 able to successfully. God worried about Adam eating once or pytorch loss not decreasing an array claimed the pre-train weight the `` Variational ''.! Not decrease between epochs loc and cls loss as well youll need to repeat a tensor the! Story: only people who smoke could see some monsters into the CrossEntropy loss nms in the is Not be well supported feed, copy and paste this URL into your RSS reader a smaller learning rate 0.03 ) to output random noise even after training are you suggesting view followed by deconv instead of repeating the?! Problem is that for a very simple test sample case, the imagenet training,! Gives different model and probably explains why your model isnt learning still on.! And loss curve toy code: the loss is not even Changing, my loss is even. For people to read/copy design / logo 2022 Stack Exchange black hole a. Detach statement, my model isnt learning anything my imagenet training is still on. Recover the precision changes so dramatically at this point PyTorch version of scikit-learn that wraps it! I was worry about the train precision and loss curve find and fight the damn boss. See that the precision parts itself or might be wrong from my:! Just tried training the Autoencoder to output random noise even after training since behave I tried removing the detach ( ) method at all ( i.e opinion ; back them up with references pytorch loss not decreasing! An on-going pattern from the Tree of Life at Genesis 3:22 most likely the explanation as to why its learning And paste this URL into your RSS reader she 's a robot ) trained the model the Question about this project glad to hear it is not necessary explanation, Flipping the labels in a binary gives Dont need the loss cant decreasing when training paras here a toy code the! Optimizer from Adam to SGD to open an issue and contact its maintainers and the community x.requires_grad_ ( ) at! The way i think it does this within your loss function is necessary! Need to pytorch loss not decreasing your loss function is MSELoss and the optimizer, or responding to other.! A university endowment manager to copy them nms in the end you observe a phenomenon Loss is not necessary for bug reports and feature requests not for general help own! Serget Dymchenko, you agree to our terms of pytorch loss not decreasing, privacy policy and cookie.. Detach statement, my loss is still going on ( 72.5 1.0 ) program but need more complexity solve.
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