Is there a way to make trades similar/identical to a university endowment manager to copy them? For a batch of size N N N, the unreduced loss can be described as: After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. Loss function: BCEWithLogitsLoss() By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. Default: True I did not try to train an embedding matrix + LSTM. FYI, I am using SGD with learning rate equal to 0.0001. At least 2-3 times slower. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Each batch contained a random selection of training records. model = nn.Linear(1,1) I am working on a toy dataset to play with. Should we burninate the [variations] tag? 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. 11%| | 7/66 [06:49<46:00, 46.79s/it] The loss is decreasing/converging but very slowlly(below image). Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? I have also tried playing with learning rate. By default, the losses are averaged over each loss element in the batch. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? 5%| | 3/66 [06:28<3:11:06, 182.02s/it] If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. 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 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. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. I suspect that you are misunderstanding how to interpret the I had the same problem with you, and solved it by your solution. (PReLU-1): PReLU (1) Looking at the plot again, your model looks to be about 97-98% accurate. 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. class classification (nn.Module): def __init__ (self): super (classification, self . the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at (PReLU-2): PReLU (1) The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. My architecture below ( from here ) (Linear-1): Linear (277 -> 8) How do I check if PyTorch is using the GPU? From your six data points that li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). Have a question about this project? At least 2-3 times slower. Therefore it cant cluster predictions together it can only get the Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? Any suggestions in terms of tweaking the optimizer? And Gpu utilization begins to jitter dramatically. 2%| | 1/66 [05:53<6:23:05, 353.62s/it] 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 also noticed that if I changed the gradient clip threshlod, it would mitigate this phenomenon but the training will eventually get very slow still. Second, your model is a simple (one-dimensional) linear function. Is it normal? The cudnn backend that pytorch is using doesn't include a Sequential Dropout. 6%| | 4/66 [06:41<2:15:39, 131.29s/it] I observed the same problem. No if a tensor does not requires_grad, its history is not built when using it. Profile the code using the PyTorch profiler or e.g. print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) Generalize the Gdel sentence requires a fixed point theorem. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. You signed in with another tab or window. Non-anthropic, universal units of time for active SETI. And Gpu utilization begins to jitter dramatically? Ignored when reduce is False. After running for a short while the loss suddenly explodes upwards. 8%| | 5/66 [06:43<1:34:15, 92.71s/it] I have also checked for class imbalance. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. Find centralized, trusted content and collaborate around the technologies you use most. That is why I made a custom API for the GRU. (Because of this, function becomes larger and larger, the logits predicted by the How to draw a grid of grids-with-polygons? sigmoid saturates, its gradients go to zero, so (with a fixed learning The l is total_loss, f is the class loss function, g is the detection loss function. For example, the average training speed for epoch 1 is 10s. Hi, Could you please inform on how to clear the temporary computations ? are training your predictions to be logits. These are raw scores, That is why I made a custom API for the GRU. I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. you will not ever be able to drive your loss to zero, even if your Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. Stack Overflow - Where Developers Learn, Share, & Build Careers 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. you cant drive the loss all the way to zero, but in fact you can. 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. The replies from @knoriy explains your situation better and is something that you should try out first. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. Default: True reduce ( bool, optional) - Deprecated (see reduction ). algorithm does), and the loss approaches zero. Now the final batches take no more time than the initial ones. Thanks for your reply! or you can use a learning rate that changes over time as discussed here. 12%| | 8/66 [06:51<32:26, 33.56s/it] 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. Correct handling of negative chapter numbers. It turned out the batch size matters. Let's look at how to add a Mean Square Error loss function in PyTorch. 97%|| 64/66 [05:11<00:06, 3.29s/it] outputs: tensor([[-0.1054, -0.2231, -0.3567]], requires_grad=True) labels: tensor([[0.9000, 0.8000, 0.7000]]) loss: tensor(0.7611, grad_fn=<BinaryCrossEntropyBackward>) When use Skip-Thoughts, I can get much better result. import torch.nn as nn MSE_loss_fn = nn.MSELoss() 17%| | 11/66 [06:59<12:09, 13.27s/it] 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! If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. Not the answer you're looking for? Loss Functions MLE Loss sequence_softmax_cross_entropy texar.torch.losses. Note that for some losses, there are multiple elements per sample. model get pushed out towards -infinity and +infinity. When reduce is False, returns a loss per batch element instead and ignores size_average. The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. System: Linux pixel 4.4.0-66-generic #87-Ubuntu SMP Fri Mar 3 15:29:05 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux I just saw in your mail that you are using a dropout of 0.5 for your LSTM. How can we build a space probe's computer to survive centuries of interstellar travel? Some reading materials. The loss goes down systematically (but, as noted above, doesnt Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? 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. 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. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. We 94%|| 62/66 [05:06<00:15, 3.96s/it] Is there any guide on how to adapt? Loss does decrease. 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. The net was trained with SGD, batch size 32. Problem confirmed. These issues seem hard to debug. Send me a link to your repo here or code by mail ;). sequence_softmax_cross_entropy (labels, logits, sequence_length, average_across_batch = True, average_across_timesteps = False, sum_over_batch = False, sum_over_timesteps = True, time_major = False, stop_gradient_to_label = False) [source] Computes softmax cross entropy for each time step of sequence predictions. My model is giving logits as outputs and I want it to give me probabilities but if I add an activation function at the end, BCEWithLogitsLoss() would mess up because it expects logits as inputs. Why so many wires in my old light fixture? It is open ended accuracy in validation under 30 when training. 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 If the field size_average is set to False, the losses are instead summed for each minibatch. Is that correct? PyTorch Foundation. prediction accuracy is perfect.) Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. Closed. 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. PyTorch documentation (Scroll to How to adjust learning rate header). By clicking Sign up for GitHub, you agree to our terms of service and or atleast converge to some point? Why are only 2 out of the 3 boosters on Falcon Heavy reused? How many characters/pages could WordStar hold on a typical CP/M machine? Code, training, and validation graphs are below. . I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. Is it considered harrassment in the US to call a black man the N-word? This could mean that your code is already bottlenecks e.g. I have observed a similar slowdown in training with pytorch running under R using the reticulate package. How can I track the problem down to find a solution? 9%| | 6/66 [06:46<1:05:41, 65.70s/it] So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. 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). reduce (bool, optional) - Deprecated (see reduction). Why the training slow down with time if training continuously? Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. Instead, create the tensor directly on the device you want. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). The answer comes from here - Why the training slow down with time if training continuously? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). rate) the training slows way down. 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? The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. Do you know why moving the declaration inside the loop can solve it ? Here are the last twenty loss values obtained by running Mnaufs 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. Nsight systems to see where the botleneck in the code is. Hopefully just one will increase and you will be able to see better what is going on. As the weight in the model the multiplicative factor in the linear predictions made by this network. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. Ignored when reduce is False. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. What is the best way to show results of a multiple-choice quiz where multiple options may be right? boundary between class 0 and class 1 right. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] 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 . 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). Yeah, I will try adapting the learning rate. Values less than 0 predict class 0 and values greater than 0 Therefore you I though if there is anything related to accumulated memory which slows down the training, the restart training will help. Learn about PyTorch's features and capabilities. I will close this issue. Stack Overflow for Teams is moving to its own domain! Asking for help, clarification, or responding to other answers. 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 am trying to train a latent space model in pytorch. boundary is somewhere around 5.0. Any comments are highly appreciated! as described above). Powered by Discourse, best viewed with JavaScript enabled. shouldnt the loss keep going down? Why does the sentence uses a question form, but it is put a period in the end? I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. If the field size_average is set to False, the losses are instead summed for each minibatch. Do you know why it is still getting slower? However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. It is because, since youre working with Variables, the history is saved for every operations youre performing. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. Could you tell me what wrong with embedding matrix + LSTM? Default: True. 21%| | 14/66 [07:07<05:27, 6.30s/it]. reduce (bool, optional) - Deprecated (see reduction). Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. I find default works fine for most cases. (PReLU-3): PReLU (1) P < 0.5 --> class 0, and P > 0.5 --> class 1.). In case you need something extra, you could look into the learning rate schedulers. Please let me correct an incorrect statement I made. Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. Note, as the 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. 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. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( Im not sure where this problem is coming from. 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=). try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 Merged. 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. I am currently using adam optimizer with lr=1e-5. All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. rev2022.11.3.43005. 98%|| 65/66 [05:14<00:03, 3.11s/it]. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. You may also want to learn about non-global minimum traps. And at the end of the run the prediction accuracy is Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? vision. The resolution is halved with the maxpool layers. This makes adding a loss function into your project as easy as just adding a single line of code. So that pytorch knows you wont try and backpropagate through it. And prediction giving by Neural network also is not correct. It's so weird. 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 . The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. Can I spend multiple charges of my Blood Fury Tattoo at once? 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. Im experiencing the same issue with pytorch 0.4.1 privacy statement. I said that 0 and 1, so the predictions will become (increasing close to) exactly Turns out I had declared the Variable tensors holding a batch of features and labels outside the loop over the 20000 batches, then filled them up for each batch. Ubuntu 16.04.2 LTS I find default works fine for most cases. What is the right way of handling this now that Tensor also tracks history? I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. Well occasionally send you account related emails. (Linear-3): Linear (6 -> 4) 18%| | 12/66 [07:02<09:04, 10.09s/it] This will cause Join the PyTorch developer community to contribute, learn, and get your questions answered. Developer Resources 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. Often one decreases very quickly and the other decreases super slowly. 2022 Moderator Election Q&A Question Collection. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. Short story about skydiving while on a time dilation drug. Thank you very much! You should make sure to wrap your input into a Variable at every iteration. So, my advice is to select a smaller batch size, also play around with the number of workers. correct (provided the bias is adjusted according, which the training 1 Like By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. if 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. 2 Likes. I have been working on fixing this problem for two week. Is there a trick for softening butter quickly? Your suggestions are really helpful. Connect and share knowledge within a single location that is structured and easy to search. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? 95%|| 63/66 [05:09<00:10, 3.56s/it] Is there anyone who knows what is going wrong with my code? Default: True. Loss value decreases slowly. Learn how our community solves real, everyday machine learning problems with PyTorch. (Linear-2): Linear (8 -> 6) Using SGD on MNIST dataset with Pytorch, loss not decreasing. probabilities of the sample in question being in the 1 class. This is most likely due to your training loop holding on to some things it shouldnt. Community Stories. Note that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. 0%| | 0/66 [00:00 1) add reduce=True arg to SoftMarginLoss #5071. Conv5 gets an input with shape 4,2,2,64. 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. Without knowing what your task is, I would say that would be considered close to the state of the art. Custom distance loss function in Pytorch? If a shared tensor is not requires_grad, is its histroy still scanned? Learning rate affects loss but not the accuracy. Make a wide rectangle out of T-Pipes without loops. Basically everything or nothing could be wrong. . I am working on a toy dataset to play with. by other synchronizations. Ignored when reduce is False. ). go to zero). 15%| | 10/66 [06:57<16:37, 17.81s/it] Sign in You can also check if dev/shm increases during training. training loop for 10,000 iterations: So the loss does approach zero, although very slowly. I am sure that all the pre-trained models parameters have been changed into mode autograd=false. 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. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! How do I print the model summary in PyTorch? import numpy as np import scipy.sparse.csgraph as csg import torch from torch.autograd import Variable import torch.autograd as autograd import matplotlib.pyplot as plt %matplotlib inline def cmdscale (D): # Number of points n = len (D) # Centering matrix H = np.eye (n) - np . 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! And when you call backward(), the whole history is scanned. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. (When pumped though a sigmoid function, they become predicted To learn more, see our tips on writing great answers. Ella (elea) December 28, 2020, 7:20pm #1. You should not save from one iteration to the other a Tensor that has requires_grad=True. How do I simplify/combine these two methods for finding the smallest and largest int in an array? R version 3.4.2 (2017-09-28) with reticulate_1.2 First, you are using, as you say, BCEWithLogitsLoss. predict class 1. Already on GitHub? It's hard to tell the reason your model isn't working without having any information. Learn about the PyTorch foundation. if you will, that are real numbers ranging from -infinity to +infinity.
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pytorch loss decrease slow
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