pytorch loss not decreasing

My own designed network outperform(imagenet/cifar) several networks, however, the imagenet training is still going on(72.5 1.0). Asking for help, clarification, or responding to other answers. I did not use the CosineAnnealing LR and no such phenomenon ever happened during training. Sign in I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. There are lots of things that can make training unstable, from data loading to exploding/vanishing gradients and numerical instability. @jinfagang Have you solved the problem? In my training, all the parameters are not pre trained. TEST_SETS: [['2017', 'val']] @1453042287 Hi, thanks for the advise. 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. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss cant decreasing when training. I have another issue about the train precision and loss curve. Before my imagenet training finished, i will have to compare sdd performance based on models trained from scratch firstly. Have a question about this project? Repeating the vector is suggested here for sequence-to-sequence autoencoders. 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. To learn more, see our tips on writing great answers. I have defined a custom loss function but the loss function is not decreasing, not even changing. MathJax reference. Book where a girl living with an older relative discovers she's a robot. Also, you dont need the loss = Variable(loss, requires_grad=True) line, I think! 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? If you do, make sure to enable grad for that data! The loss is still not changing between epochs. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' 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) Also could you indent your code by wrapping it in three backticks ``` , it makes it easier for people to read/copy! Does squeezing out liquid from shredded potatoes significantly reduce cook time? rev2022.11.4.43006. Is your dataset normalized? Well occasionally send you account related emails. The training output shows saturated loss which is not decreasing: 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: 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. DATASET: 'coco' I have tried the following with no success: I have created a simple model consisting of two 1-layer nn competing each other. BATCH_SIZE: 28 [auto] Update onnx to c7055f7 - update defs for reduce, rnn, and tens, Improvements to expr sorting, various changes from norm_hack. I have a single layer LSTM followed by a fully connected layer Using the detach function will kill any gradients in your network which is most likely the explanation as to why its not learning. Use MathJax to format equations. Epoch 1600 loss: 2883.3774032592773 Its a PyTorch version of scikit-learn that wraps around it. Youll need to calculate your loss value without using the detach() method at all. RESUME_CHECKPOINT: '/home/chase/Downloads/ssds.pytorch-master/weight/vgg16_fssd_coco_27.2.pth' Sign in DATASET: 'coco' It is very similar to GAN. Connect and share knowledge within a single location that is structured and easy to search. While training the autoencoder to output the same string as the input, the Loss function does not decrease between epochs. (. There are 29 classes. My current training seems working. We're using the GitHub issues only for bug reports and feature requests not for general help. If you have any questions, please ask them on our forums, but we can't help you debug any model you have. How to apply layer-wise learning rate in Pytorch? OPTIMIZER: sgd LOG_DIR: './experiments/models/fssd_vgg16_coco' rev2022.11.4.43006. Are Githyanki under Nondetection all the time? and here is the definition of my loss function: def my_loss_function(n1_output, n2_output, n1_parm, n2_param): TRAINABLE_SCOPE: 'norm,extras,transforms,pyramids,loc,conf' 2022 Moderator Election Q&A Question Collection. @1453042287 I trained the yolov2-mobilenet-v2 from stratch. I am using Densenet from Pytorch models, and have copied 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. Epoch 1400 loss: 2881.264518737793 LEARNING_RATE: 0.001 SCHEDULER: SGDR 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! However, it is skillful to give a good initialization of the network. Id suggest trying to remove all dependencies on numpy and purely use torch operations so autograd can track the operations. PHASE: ['train'] [['', 'S', 'S', 'S', '', ''], [512, 512, 256, 256, 256, 256]]] Does activating the pump in a vacuum chamber produce movement of the air inside? Yes, agree with you. When the loss decreases but accuracy stays the It always stays the. So, the problem is probably with the encoder and decoder parts itself or might be arising from my training loop. What percentage of page does/should a text occupy inkwise. I am using torchvision augmentation. My current training seems working. What exactly makes a black hole STAY a black hole? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Stack Overflow for Teams is moving to its own domain! CHECKPOINTS_EPOCHS: 1 but loss is still constant. zjmtlab (zhang jian) April 4, 2018, 8:45am #1. This year, Mr He did publish a paper named 'Rethinking ImageNet Pre-training' which claimed the pre-train on imagenet is not necessary. Is there a way to make trades similar/identical to a university endowment manager to copy them? There are 252 buckets. Epoch 1700 loss: 2883.196922302246 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. 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 it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? thanks for the help! If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each class. After only reload the 'base' and retrain other parameters, I successfully recover the precision. Math papers where the only issue is that someone else could've done it but didn't. Which is why its not decreasing! NETS: vgg16 Any comment will be very helpful. reg = torch.norm(n1_parm,2) + torch.norm(n2_param,2) its constant. So, I have my own loss function based on those nn outputs. Transformer 220/380/440 V 24 V explanation, Flipping the labels in a binary classification gives different model and results. apaszke closed this as completed on Feb 25, 2017. onnxbot added a commit that referenced this issue on May 2, 2018. Thanks for contributing an answer to Stack Overflow! Stack Overflow for Teams is moving to its own domain! The loss function is MSELoss and the optimizer is Adam. By clicking Sign up for GitHub, you agree to our terms of service and Youll want to have something like this within your code! How can I fix this problem? # pseudo code (ignoring batch dimension) loss = nn.functional.cross_entropy_loss By clicking Sign up for GitHub, you agree to our terms of service and Problem is that my loss is doesnt Epoch 300 loss: 3010.6801147460938 The following is the result from tensorboardX. TEST_SETS: [['2017', 'val']] LR_SCHEDULER: Stack Overflow - Where Developers Learn, Share, & Build Careers Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. 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? 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. Found footage movie where teens get superpowers after getting struck by lightning? Yet no good solutions. MATCHED_THRESHOLD: 0.5 This will break the gradients within the model and probably explains why your model isnt learning! Representations of the metric in a Riemannian manifold. UNMATCHED_THRESHOLD: 0.5 return y. when I plot loss function, it has oscillation; I expect it to decrease during training. Making statements based on opinion; back them up with references or personal experience. Shall i only reload the 'base' paras here? FEATURE_LAYER: [[[22, 34, 'S'], [512, 1024, 512]], Thanks for contributing an answer to Data Science Stack Exchange! Any comments are highly appreciated! PyTorch: LSTM training loss not decreasing; starting at very high loss. IMAGE_SIZE: [300, 300] I was worry about the problem comes from the program itself. Can you activate one viper twice with the command location? I'm really not sure. However, you still need to provide it with a 10 dimensional output vector from your network. training from scratch without any pre-trained model. Epoch 1300 loss: 2891.597194671631 Epoch 900 loss: 2891.381019592285 I am new to pytorch and seeking your help with the lstm implementation. Also, you do use the gradient of your input data at all (i.e. to your account, Hi, The nms in the test procedure seems very slow. The loc and cls loss as well the learning rate seem not change so much. All the

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