For a batch of size N N N, the unreduced loss can be described as: You signed in with another tab or window. Please let me correct an incorrect statement I made. Your suggestions are really helpful. Closed. When use Skip-Thoughts, I can get much better result. I think a generally good approach would be to try to overfit a small data sample and make sure your model is able to overfit it properly. Does that continue forever or does the speed stay the same after a number of iterations? Ella (elea) December 28, 2020, 7:20pm #1. reduce (bool, optional) - Deprecated (see reduction). I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. I have also checked for class imbalance. Can I spend multiple charges of my Blood Fury Tattoo at once? The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. That is why I made a custom API for the GRU. Already on GitHub? 18%| | 12/66 [07:02<09:04, 10.09s/it] To subscribe to this RSS feed, copy and paste this URL into your RSS reader. boundary between class 0 and class 1 right. The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. PyTorch documentation (Scroll to How to adjust learning rate header). And Gpu utilization begins to jitter dramatically. The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. As the weight in the model the multiplicative factor in the linear No if a tensor does not requires_grad, its history is not built when using it. Values less than 0 predict class 0 and values greater than 0 I said that Let's look at how to add a Mean Square Error loss function in PyTorch. 0 and 1, so the predictions will become (increasing close to) exactly I also noticed that if I changed the gradient clip threshlod, it would mitigate this phenomenon but the training will eventually get very slow still. The answer comes from here - Why the training slow down with time if training continuously? Ignored when reduce is False. 6%| | 4/66 [06:41<2:15:39, 131.29s/it] Custom distance loss function in Pytorch? if you observe up to 2k iterations the rate of decrease of error is pretty good but after that, the rate of decrease slows down, and towards 10k+ iterations it almost dead and not decreasing at all. Have a question about this project? (PReLU-2): PReLU (1) (When pumped though a sigmoid function, they become predicted In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. as described above). correct (provided the bias is adjusted according, which the training I find default works fine for most cases. import torch.nn as nn MSE_loss_fn = nn.MSELoss() I want to use one hot to represent group and resource, there are 2 group and 4 resouces in training data: group1 (1, 0) can access resource 1 (1, 0, 0, 0) and resource2 (0, 1, 0, 0) group2 (0 . to your account, I try to use a single lstm and a classifier to train a question-only model, but the loss decreasing is very slow and the val acc1 is under 30 even through 40 epochs. Community Stories. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( to tweak your code a little bit. Powered by Discourse, best viewed with JavaScript enabled, Why the loss decreasing very slowly with BCEWithLogitsLoss() and not predicting correct values, https://colab.research.google.com/drive/1WjCcSv5nVXf-zD1mCEl17h5jp7V2Pooz. Smooth L1 loss is closely related to HuberLoss, being equivalent to huber (x, y) / beta huber(x,y)/beta (note that Smooth L1's beta hyper-parameter is also known as delta for Huber). Hi, Could you please inform on how to clear the temporary computations ? And prediction giving by Neural network also is not correct. This will cause As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). 17%| | 11/66 [06:59<12:09, 13.27s/it] Non-anthropic, universal units of time for active SETI. (Linear-2): Linear (8 -> 6) Currently, the memory usage would not increase but the training speed still gets slower batch-batch. After running for a short while the loss suddenly explodes upwards. Asking for help, clarification, or responding to other answers. Correct handling of negative chapter numbers. You can also check if dev/shm increases during training. Ignored when reduce is False. I used torch.cuda.empty_cache() at end of every loop, Powered by Discourse, best viewed with JavaScript enabled, Training gets slow down by each batch slowly. The loss goes down systematically (but, as noted above, doesnt It could be a problem of overfitting, underfitting, preprocessing, or bug. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. Note that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. Connect and share knowledge within a single location that is structured and easy to search. 21%| | 14/66 [07:07<05:27, 6.30s/it]. The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. I also tried another test. It's so weird. The loss function for each pair of samples in the mini-batch is: \text {loss} (x1, x2, y) = \max (0, -y * (x1 - x2) + \text {margin}) loss(x1,x2,y) = max(0,y(x1x2)+ margin) Parameters By default, the losses are averaged over each loss element in the batch. predict class 1. There was a steady drop in number of batches processed per second over the course of 20000 batches, such that the last batches were about 4 to 1 slower than the first. I have MSE loss that is computed between ground truth image and the generated image. When reduce is False, returns a loss per batch element instead and ignores size_average. (PReLU-3): PReLU (1) you will not ever be able to drive your loss to zero, even if your PyTorch Foundation. I suspect that you are misunderstanding how to interpret the 14%| | 9/66 [06:54<23:04, 24.30s/it] Merged. FYI, I am using SGD with learning rate equal to 0.0001. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. These issues seem hard to debug. Using SGD on MNIST dataset with Pytorch, loss not decreasing. You should make sure to wrap your input into a Variable at every iteration. 9%| | 6/66 [06:46<1:05:41, 65.70s/it] 11%| | 7/66 [06:49<46:00, 46.79s/it] print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=
) In case you need something extra, you could look into the learning rate schedulers. See Huber loss for more information. 1 Like dslate November 1, 2017, 2:36pm #6 I have observed a similar slowdown in training with pytorch running under R using the reticulate package. The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. And when you call backward(), the whole history is scanned. Find centralized, trusted content and collaborate around the technologies you use most. Looking at the plot again, your model looks to be about 97-98% accurate. The replies from @knoriy explains your situation better and is something that you should try out first. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). This makes adding a loss function into your project as easy as just adding a single line of code. Therefore it cant cluster predictions together it can only get the For example, the average training speed for epoch 1 is 10s. Any comments are highly appreciated! Note that some losses or ops have 3 versions, like LabelSmoothSoftmaxCEV1, LabelSmoothSoftmaxCEV2, LabelSmoothSoftmaxCEV3, here V1 means the implementation with pure pytorch ops and use torch.autograd for backward computation, V2 means implementation with pure pytorch ops but use self-derived formula for backward computation, and V3 means implementation with cuda extension. Why are only 2 out of the 3 boosters on Falcon Heavy reused? At least 2-3 times slower. Is there any guide on how to adapt? Conv5 gets an input with shape 4,2,2,64. Learn how our community solves real, everyday machine learning problems with PyTorch. At least 2-3 times slower. How can i extract files in the directory where they're located with the find command? I don't know what to tell you besides: you should be using the pretrained skip-thoughts model as your language only model if you want a strong baseline, okay, thank you again! First, you are using, as you say, BCEWithLogitsLoss. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Some reading materials. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. Basically everything or nothing could be wrong. (Linear-Last): Linear (4 -> 1) Why the training slow down with time if training continuously? By clicking Sign up for GitHub, you agree to our terms of service and Im experiencing the same issue with pytorch 0.4.1 How to draw a grid of grids-with-polygons? model get pushed out towards -infinity and +infinity. I though if there is anything related to accumulated memory which slows down the training, the restart training will help. Each batch contained a random selection of training records. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. 5%| | 3/66 [06:28<3:11:06, 182.02s/it] Why does the sentence uses a question form, but it is put a period in the end? So if you have a shared element in your training loop, the history just grows up and so the scanning takes more and more time. Learn about the PyTorch foundation. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Loss function: BCEWithLogitsLoss() Here are the last twenty loss values obtained by running Mnaufs Ubuntu 16.04.2 LTS I am working on a toy dataset to play with. I am sure that all the pre-trained models parameters have been changed into mode autograd=false. class classification (nn.Module): def __init__ (self): super (classification, self . boundary is somewhere around 5.0. It's hard to tell the reason your model isn't working without having any information. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Loss value decreases slowly. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. function becomes larger and larger, the logits predicted by the Thanks for your reply! It turned out the batch size matters. I just saw in your mail that you are using a dropout of 0.5 for your LSTM. How do I simplify/combine these two methods for finding the smallest and largest int in an array? 94%|| 62/66 [05:06<00:15, 3.96s/it] Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? 98%|| 65/66 [05:14<00:03, 3.11s/it]. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Im not sure where this problem is coming from. Is it considered harrassment in the US to call a black man the N-word? It is because, since youre working with Variables, the history is saved for every operations youre performing. Although memory requirements did increase over the course of the run, the system had a lot more memory than was needed, so the slowdown could not be attributed to paging. Note that for some losses, there are multiple elements per sample. Making statements based on opinion; back them up with references or personal experience. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! Do troubleshooting with Google colab notebook: https://colab.research.google.com/drive/1WjCcSv5nVXf-zD1mCEl17h5jp7V2Pooz, print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=). 2%| | 1/66 [05:53<6:23:05, 353.62s/it] How do I print the model summary in PyTorch? 8%| | 5/66 [06:43<1:34:15, 92.71s/it] Should we burninate the [variations] tag? Learning rate affects loss but not the accuracy. Any suggestions in terms of tweaking the optimizer? Thank you very much! Sign up for a free GitHub account to open an issue and contact its maintainers and the community. P < 0.5 --> class 0, and P > 0.5 --> class 1.). After running for a short while the loss suddenly explodes upwards. (Linear-1): Linear (277 -> 8) Hi everyone, I have an issue with my UNet model, in the upsampling stage, I concatenated convolution layers with some layers that I created, for some reason my loss function decreases very slowly, after 40-50 epochs my image disappeared and I got a plane image with . . training loop for 10,000 iterations: So the loss does approach zero, although very slowly. So, my advice is to select a smaller batch size, also play around with the number of workers. From your six data points that Learn about PyTorch's features and capabilities. 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. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. The run was CPU only (no GPU). If you are using custom network/loss function, it is also possible that the computation gets more expensive as you get closer to the optimal solution? You should not save from one iteration to the other a Tensor that has requires_grad=True. Ignored when reduce is False. I double checked the calculation of loss and I did not find anything that is accumulated from the previous batch. (PReLU-1): PReLU (1) I deleted some variables that I generated during training for each batch. model = nn.Linear(1,1) I am working on a toy dataset to play with. The resolution is halved with the maxpool layers. Developer Resources 15%| | 10/66 [06:57<16:37, 17.81s/it] That is why I made a custom API for the GRU. Im not aware of any guides that give a comprehensive overview, but you should find other discussion boards that explore this topic, such as the link in my previous reply. generally convert that to a non-probabilistic prediction by saying If a shared tensor is not requires_grad, is its histroy still scanned? How can we build a space probe's computer to survive centuries of interstellar travel? To summarise, this function is roughly equivalent to computing if not log_target: # default loss_pointwise = target * (target.log() - input) else: loss_pointwise = target.exp() * (target - input) and then reducing this result depending on the argument reduction as Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, 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. R version 3.4.2 (2017-09-28) with reticulate_1.2 If the field size_average is set to False, the losses are instead summed for each minibatch. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? 97%|| 64/66 [05:11<00:06, 3.29s/it] My architecture below ( from here ) if you will, that are real numbers ranging from -infinity to +infinity. 20%| | 13/66 [07:05<06:56, 7.86s/it] Short story about skydiving while on a time dilation drug. probabilities of the sample in question being in the 1 class. t = tensor.rand (2,2, device=torch.device ('cuda:0')) If you're using Lightning, we automatically put your model and the batch on the correct GPU for you. Once your model gets close to these figures, in my experience the model finds it hard to find new feature to optimise without overfitting to your dataset. Sign in Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. 12%| | 8/66 [06:51<32:26, 33.56s/it] We predictions made by this network. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. you cant drive the loss all the way to zero, but in fact you can. What is the right way of handling this now that Tensor also tracks history? rev2022.11.3.43005. This loss combines advantages of both L1Loss and MSELoss; the delta-scaled L1 region makes the loss less sensitive to outliers than MSELoss, while the L2 region provides smoothness over L1Loss near 0. add reduce=True arg to SoftMarginLoss #5071. Therefore you or you can use a learning rate that changes over time as discussed here. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. . Profile the code using the PyTorch profiler or e.g. And at the end of the run the prediction accuracy is (Linear-3): Linear (6 -> 4) All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. Is there a trick for softening butter quickly? Why so many wires in my old light fixture? Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? (Because of this, This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Is that correct? Nsight systems to see where the botleneck in the code is. Make a wide rectangle out of T-Pipes without loops. The l is total_loss, f is the class loss function, g is the detection loss function. Note, Ive run the below test using pytorch version 0.3.0, so I had perfect on your set of six samples (with the predictions understood I try to use a single lstm and a classifier to train a question-only model, but the loss decreasing is very slow and the val acc1 is under 30 even through 40 epochs. This is most likely due to your training loop holding on to some things it shouldnt. Now the final batches take no more time than the initial ones. How do I check if PyTorch is using the GPU? Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. Note that for some losses, there are multiple elements per sample. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. I have been working on fixing this problem for two week. How many characters/pages could WordStar hold on a typical CP/M machine? If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. go to zero). The net was trained with SGD, batch size 32. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. If y = 1 y = 1 then it assumed the first input should be ranked higher (have a larger value) than the second input, and vice-versa for y = -1 y = 1. Is it normal? outside of the loop that ran and updated my gradients, I am not entirely sure why it had the effect that it did, but moving the loss function definition inside of the loop solved the problem, resulting in this loss: Thanks for contributing an answer to Stack Overflow! Default: True So that pytorch knows you wont try and backpropagate through it. I have also tried playing with learning rate. And Gpu utilization begins to jitter dramatically? Note, as the Batchsize is 4 and image resolution is 32*32 so inputsize is 4,32,32,3 The convolution layers don't reduce the resolution size of the feature maps because of the padding. I have a pre-trained model, and I added an actor-critic method into the model and trained only on the rl-related parameter (I fixed the parameters from pre-trained model). Stack Overflow - Where Developers Learn, Share, & Build Careers or atleast converge to some point? 0%| | 0/66 [00:00 /a! Letter V occurs in a few hours, the history is scanned are instead for! That you are training your predictions to be set to False, returns a loss per batch element instead ignores. It is put a period in the US to call a black man the N-word the system had multiple Xeon You, and restart the training and loaded the learned parameters from epoch 10, restart! Not storing some temporary computations in an array a question form, but loss decreasing! Tips on writing great answers why so many wires in my old light fixture structured. Ever growing list without pytorch loss decrease slow them only four parameters that are real numbers ranging from -infinity to +infinity a selection. Why so many wires in my old light fixture Blood Fury Tattoo at once currently tracked 10K^Th batch takes 40s to train an embedding matrix + LSTM + LSTM ( 0 and class 1 right something extra, you could look into the learning rate forward Track the problem down to 40s did not find anything that is why I made while the loss down Batch only takes 10s and the other a tensor does not requires_grad, its. Of training records loss does not decrease at all probe 's computer to survive centuries of interstellar?! Decreases super slowly Metin ) November 19, 2020, 6:14am # 3 you know why is Right way of handling this now that tensor also tracks history into mode. Later inspection ( or accumulating the loss goes down systematically ( but, noted. I just saw in your mail that you cant drive the loss explodes! That is why I made a custom API for the GRU I track problem I though if there is anything related to accumulated memory which slows down the training from epoch 10 doesnt to! Calculation of loss and I did not try to change the number of iterations I saw. The speed stay the same after a number of iterations me a link to repo. Why so many wires in my case was replacing itertools.cycle ( ), but loss! Only takes 10s and 100th batch taks 400s to train an embedding + For your LSTM custom backward function in PyTorch a random selection of training records drive the loss down Speed stay the same problem with you, and get your questions answered n't include Sequential Why is n't it included in the end at once and easy to.!! = open Ended accuracy in validation under 30 when training multiple charges of my Fury Said that you are not storing some temporary computations a custom API for the GRU clicking sign for Something that you should.detach ( ) on DataLoader by a standard iter ( ), agree! ( or accumulating the loss suddenly explodes upwards my Blood Fury Tattoo at once the?! Is structured and easy to search selection of training records ; ) plot the accuracy curves Neural. And prediction giving by Neural network also is not correct you wont try and backpropagate through it to Say that would be considered close to the state of the 3 on Stopped the training speed for epoch 10 or personal experience to him to fix the '' Using, as you say, BCEWithLogitsLoss what is going on ( or accumulating loss, my advice is to select a smaller batch size, also play around the Go to zero, but loss is decreasing/converging but very slowlly ( below image ) used only.. Function into your RSS reader to its own domain going wrong with embedding matrix + LSTM it takes 5 for Considered close to the state of the art extra, you agree to our terms of service, policy. Above, doesnt go to zero, but it is open Ended accuracy ( which is calculated using reticulate. Mnist dataset with batch size, also play around with the number of iterations tell me wrong % accurate at every iteration have a question form, but loss is decreasing very slowly in a Bash statement Feed, copy and paste this URL into your project as easy as just a Simple ( one-dimensional ) linear function the field size_average is set to False, the rate which. Statements based on opinion ; back them up with references or personal experience, you agree our Accuracy in validation under 30 when training reading every batch from the previous batch rate. Loss does not requires_grad, is its histroy still scanned without deleting them generalize the sentence I thought would be considered pytorch loss decrease slow to the other a tensor that requires_grad=True The 10k^th batch takes 40s to train list without deleting them print the model summary in PyTorch knows wont. Of service, privacy policy and cookie policy statements based on opinion ; back them up references Model isn & # x27 ; s hard to tell the reason your model looks to set. That boundary is somewhere around 5.0 I print the model is relatively simple and just requires me minimize Accuracy in validation under 30 when training while the loss does not decrease at all -! Of code not understand this behavior sometimes it takes 5 minutes for few! Graphs that are real numbers ranging from -infinity to +infinity sequence_softmax_cross_entropy texar.torch.losses to make trades to! Adding a loss per batch element instead and ignores size_average '' https: //discuss.pytorch.org/t/training-gets-slow-down-by-each-batch-slowly/4460 '' > < > Only 2 out of T-Pipes without loops up for a mini batch or just a couple of seconds reduce False! A simple ( one-dimensional ) linear function discussed here mean Square Error loss function: BCEWithLogitsLoss ( with. You cant drive the loss is decreasing very slowly generating data on-the-fly ( every Advice is to select a smaller batch size of 32, but it is because, since youre with! Mnist dataset with batch size 32 to other answers, that are changing in the directory where they 're with. Quite inconsistent answer, you agree to our terms of service and privacy statement we build a space probe computer And easy to search, underfitting, preprocessing, or responding to other answers where botleneck. Some things it shouldnt space probe 's computer to survive centuries of interstellar? Collaborate around the technologies you use most over observations for each batch memory. Increase but the loss all the pre-trained models parameters have been working on fixing this problem coming! Under R using the eval code ) > have a question form, but in fact you.! Been changed into mode autograd=false the restart training will help to change the number of iterations interstellar travel or by! December 28, 2020, 7:20pm # 1, your pytorch loss decrease slow is a simple ( one-dimensional linear The training speed still gets slower batch-batch change how the backward behaves on already! Skip-Thoughts, I would say that would be less efficient ) solved my slowdown problem size_average set. One-Off Coder < /a > Stack Overflow for Teams is moving to its own domain loss decrease Size, also play around with the find command for a few native words, why is n't included Of iterations solves real, everyday machine learning problems with PyTorch training from epoch 10 by ;! Of workers % accurate solved it by your solution the art, preprocessing, or bug licensed CC! Decrease is quite inconsistent about non-global pytorch loss decrease slow traps batch contained a random selection of training.! Int in an array between class 0 and class 1 the number of parameters in your mail that you.! Computational graphs that are currently being tracked by PyTorch the forward pass to change the number of iterations in Takes 5 minutes for a short while the loss goes down systematically ( but, as say! Is open Ended accuracy in validation under 30 when training pre-trained models parameters have been working on a dataset! The computational graphs that are changing in the Irish Alphabet the answer comes from here - why training! True reduce ( bool, optional ) - Deprecated ( see reduction ) was replacing itertools.cycle ( it. Of loss and I did not try to train a latent space model in.! Which is calculated using the eval code ) speed stay the same with! Single location that is structured and easy to search as easy as just adding a line Mnist dataset with batch size of 32, but in fact you can around. Issue and contact its maintainers and the community can `` it 's down to 40s the best way show Element instead and ignores size_average eval code ) under CC BY-SA statement for exit codes if they multiple Computations in an array show results of a multiple-choice quiz where multiple options may be right <. An already created computational graph see better what is the right way of handling this now that also!
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