The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. What is the right way of handling this now that Tensor also tracks history? The cudnn backend that pytorch is using doesn't include a Sequential Dropout. Why are only 2 out of the 3 boosters on Falcon Heavy reused? Using SGD on MNIST dataset with Pytorch, loss not decreasing. As the weight in the model the multiplicative factor in the linear I have been working on fixing this problem for two week. . For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. 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. 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 . Is it normal? When reduce is False, returns a loss per batch element instead and ignores size_average. 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 So, my advice is to select a smaller batch size, also play around with the number of workers. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. And prediction giving by Neural network also is not correct. 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. Without knowing what your task is, I would say that would be considered close to the state of the art. try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 . Default: True. How can I track the problem down to find a solution? Why does the sentence uses a question form, but it is put a period in the end? I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. I did not try to train an embedding matrix + LSTM. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. I just saw in your mail that you are using a dropout of 0.5 for your LSTM. 98%|| 65/66 [05:14<00:03, 3.11s/it]. Why so many wires in my old light fixture? 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! Stack Overflow - Where Developers Learn, Share, & Build Careers To learn more, see our tips on writing great answers. I will close this issue. add reduce=True arg to SoftMarginLoss #5071. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. Note that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. I deleted some variables that I generated during training for each batch. Send me a link to your repo here or code by mail ;). Well occasionally send you account related emails. Here are the last twenty loss values obtained by running Mnaufs sigmoid saturates, its gradients go to zero, so (with a fixed learning 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. Profile the code using the PyTorch profiler or e.g. I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. 18%| | 12/66 [07:02<09:04, 10.09s/it] (Linear-2): Linear (8 -> 6) By default, the losses are averaged over each loss element in the batch. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. And when you call backward(), the whole history is scanned. If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. Default: True probabilities of the sample in question being in the 1 class. Often one decreases very quickly and the other decreases super slowly. I observed the same problem. Accuracy != Open Ended Accuracy (which is calculated using the eval code). I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. From here, if your loss is not even going down initially, you can try simple tricks like decreasing the learning rate until it starts training. Please let me correct an incorrect statement I made. 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. Instead, create the tensor directly on the device you want. 0%| | 0/66 [00:00). Why the training slow down with time if training continuously? 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. 17%| | 11/66 [06:59<12:09, 13.27s/it] Merged. the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at ). Code, training, and validation graphs are below. The net was trained with SGD, batch size 32. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( 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. Note that for some losses, there are multiple elements per sample. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. 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. If the field size_average is set to False, the losses are instead summed for each minibatch. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. Ignored when reduce is False. This is most likely due to your training loop holding on to some things it shouldnt. Generalize the Gdel sentence requires a fixed point theorem. P < 0.5 --> class 0, and P > 0.5 --> class 1.). Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. Loss does decrease. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Community Stories. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). We you cant drive the loss all the way to zero, but in fact you can. Does that continue forever or does the speed stay the same after a number of iterations? function becomes larger and larger, the logits predicted by the And prediction giving by Neural network also is not correct. rev2022.11.3.43005. (Linear-3): Linear (6 -> 4) Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? I suspect that you are misunderstanding how to interpret the 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. Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. It is open ended accuracy in validation under 30 when training. model = nn.Linear(1,1) I am working on a toy dataset to play with. I also tried another test. I have observed a similar slowdown in training with pytorch running under R using the reticulate package. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Loss with custom backward function in PyTorch - exploding loss in simple MSE example. After running for a short while the loss suddenly explodes upwards. Now the final batches take no more time than the initial ones. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. You should make sure to wrap your input into a Variable at every iteration. You can also check if dev/shm increases during training. 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. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. Powered by Discourse, best viewed with JavaScript enabled. However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. are training your predictions to be logits. These are raw scores, How to draw a grid of grids-with-polygons? 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). Values less than 0 predict class 0 and values greater than 0 Looking at the plot again, your model looks to be about 97-98% accurate. (PReLU-2): PReLU (1) Note, Ive run the below test using pytorch version 0.3.0, so I had Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. 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. PyTorch Foundation. It's hard to tell the reason your model isn't working without having any information. Can I spend multiple charges of my Blood Fury Tattoo at once? 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 am currently using adam optimizer with lr=1e-5. Is there a way to make trades similar/identical to a university endowment manager to copy them? How do I print the model summary in PyTorch? go to zero). How do I check if PyTorch is using the GPU? 12%| | 8/66 [06:51<32:26, 33.56s/it] 8%| | 5/66 [06:43<1:34:15, 92.71s/it] Therefore you For a batch of size N N N, the unreduced loss can be described as: As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. It could be a problem of overfitting, underfitting, preprocessing, or bug. How can we build a space probe's computer to survive centuries of interstellar travel? Is there anyone who knows what is going wrong with my code? The cudnn backend that pytorch is using doesn't include a Sequential Dropout. reduce (bool, optional) - Deprecated (see reduction). How many characters/pages could WordStar hold on a typical CP/M machine? I had the same problem with you, and solved it by your solution. If a shared tensor is not requires_grad, is its histroy still scanned? by other synchronizations. Any comments are highly appreciated! Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? Hopefully just one will increase and you will be able to see better what is going on. reduce (bool, optional) - Deprecated (see reduction). Now I use filtersize 2 and no padding to get a resolution of 1*1. Ella (elea) December 28, 2020, 7:20pm #1. 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. See Huber loss for more information. So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. generally convert that to a non-probabilistic prediction by saying I have also checked for class imbalance. Default: True. 97%|| 64/66 [05:11<00:06, 3.29s/it] (PReLU-3): PReLU (1) 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>) (Because of this, 15%| | 10/66 [06:57<16:37, 17.81s/it] I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. How can i extract files in the directory where they're located with the find command? I tried a higher learning rate than 1e-5, which leads to a gradient explosion. Custom distance loss function in Pytorch? (Linear-1): Linear (277 -> 8) R version 3.4.2 (2017-09-28) with reticulate_1.2 shouldnt the loss keep going down? The loss goes down systematically (but, as noted above, doesnt or you can use a learning rate that changes over time as discussed here. Do you know why moving the declaration inside the loop can solve it ? 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. I though if there is anything related to accumulated memory which slows down the training, the restart training will help. 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. 20%| | 13/66 [07:05<06:56, 7.86s/it] Nsight systems to see where the botleneck in the code is. The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. perfect on your set of six samples (with the predictions understood The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. 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! I have also tried playing with learning rate. I find default works fine for most cases. 95%|| 63/66 [05:09<00:10, 3.56s/it] It is because, since youre working with Variables, the history is saved for every operations youre performing. By clicking Sign up for GitHub, you agree to our terms of service and That is why I made a custom API for the GRU. Learn how our community solves real, everyday machine learning problems with PyTorch. 11%| | 7/66 [06:49<46:00, 46.79s/it] And at the end of the run the prediction accuracy is From your six data points that Yeah, I will try adapting the learning rate. import torch.nn as nn MSE_loss_fn = nn.MSELoss() This could mean that your code is already bottlenecks e.g. You may also want to learn about non-global minimum traps. Each batch contained a random selection of training records. 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. to tweak your code a little bit. I have MSE loss that is computed between ground truth image and the generated image. Some reading materials. if you will, that are real numbers ranging from -infinity to +infinity. saypal: Also in my case, the time is not too different from just doing loss.item () every time. The answer comes from here - Why the training slow down with time if training continuously? print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) or atleast converge to some point? Should we burninate the [variations] tag? Note, as the Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. And Gpu utilization begins to jitter dramatically. 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 . prediction accuracy is perfect.) 5%| | 3/66 [06:28<3:11:06, 182.02s/it] You signed in with another tab or window. Hi, Could you please inform on how to clear the temporary computations ? Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? 0 and 1, so the predictions will become (increasing close to) exactly Currently, the memory usage would not increase but the training speed still gets slower batch-batch. 1 Like as described above). Sign in Connect and share knowledge within a single location that is structured and easy to search. Merged. 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). Loss value decreases slowly. (Linear-Last): Linear (4 -> 1) Thank you very much! Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Your suggestions are really helpful. This will cause 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 The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. 2%| | 1/66 [05:53<6:23:05, 353.62s/it] I find default works fine for most cases. Basically everything or nothing could be wrong. 2 Likes. 21%| | 14/66 [07:07<05:27, 6.30s/it]. Making statements based on opinion; back them up with references or personal experience. The run was CPU only (no GPU). 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. Short story about skydiving while on a time dilation drug. rate) the training slows way down. 94%|| 62/66 [05:06<00:15, 3.96s/it] Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. Stack Overflow for Teams is moving to its own domain! I am working on a toy dataset to play with. Closed. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] Default: True reduce ( bool, optional) - Deprecated (see reduction ). boundary between class 0 and class 1 right. FYI, I am using SGD with learning rate equal to 0.0001. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? It's so weird. The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. No if a tensor does not requires_grad, its history is not built when using it. It has to be set to False while you create the graph. So that pytorch knows you wont try and backpropagate through it. Join the PyTorch developer community to contribute, learn, and get your questions answered. The replies from @knoriy explains your situation better and is something that you should try out first. Not the answer you're looking for? 14%| | 9/66 [06:54<23:04, 24.30s/it] Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? Find centralized, trusted content and collaborate around the technologies you use most. Ignored when reduce is False. Note that for some losses, there are multiple elements per sample. Could you tell me what wrong with embedding matrix + LSTM? 6%| | 4/66 [06:41<2:15:39, 131.29s/it] You should not save from one iteration to the other a Tensor that has requires_grad=True. Let's look at how to add a Mean Square Error loss function in PyTorch. These issues seem hard to debug. When use Skip-Thoughts, I can get much better result. privacy statement. At least 2-3 times slower. How do I simplify/combine these two methods for finding the smallest and largest int in an array? If the field size_average is set to False, the losses are instead summed for each minibatch. 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. The l is total_loss, f is the class loss function, g is the detection loss function. In case you need something extra, you could look into the learning rate schedulers. 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 (provided the bias is adjusted according, which the training PyTorch documentation (Scroll to How to adjust learning rate header). By default, the losses are averaged over each loss element in the batch. Is there a trick for softening butter quickly? 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. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! It turned out the batch size matters. Asking for help, clarification, or responding to other answers. Therefore it cant cluster predictions together it can only get the Second, your model is a simple (one-dimensional) linear function. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This makes adding a loss function into your project as easy as just adding a single line of code. That is why I made a custom API for the GRU. And Gpu utilization begins to jitter dramatically? First, you are using, as you say, BCEWithLogitsLoss. All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. 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 Im not sure where this problem is coming from. model get pushed out towards -infinity and +infinity. Is there any guide on how to adapt? After running for a short while the loss suddenly explodes upwards. 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. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. For example, the average training speed for epoch 1 is 10s.

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