pytorch loss decrease slow

As the weight in the model the multiplicative factor in the linear Not the answer you're looking for? Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? 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. (Linear-2): Linear (8 -> 6) Any suggestions in terms of tweaking the optimizer? This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Learn about PyTorch's features and capabilities. And prediction giving by Neural network also is not correct. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). privacy statement. Why the training slow down with time if training continuously? 15%| | 10/66 [06:57<16:37, 17.81s/it] 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. There are only four parameters that are changing in the current program. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! 12%| | 8/66 [06:51<32:26, 33.56s/it] I have observed a similar slowdown in training with pytorch running under R using the reticulate package. 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. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. This will cause I am currently using adam optimizer with lr=1e-5. Well occasionally send you account related emails. 1 Like vision. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. We (PReLU-1): PReLU (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. 20%| | 13/66 [07:05<06:56, 7.86s/it] Without knowing what your task is, I would say that would be considered close to the state of the art. Is there a way to make trades similar/identical to a university endowment manager to copy them? 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). The loss goes down systematically (but, as noted above, doesnt And Gpu utilization begins to jitter dramatically? 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. Connect and share knowledge within a single location that is structured and easy to search. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. 8%| | 5/66 [06:43<1:34:15, 92.71s/it] 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. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. How do I check if PyTorch is using the GPU? 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 You should make sure to wrap your input into a Variable at every iteration. It turned out the batch size matters. Thanks for your reply! 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. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. I observed the same problem. 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. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. 97%|| 64/66 [05:11<00:06, 3.29s/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! Stack Overflow - Where Developers Learn, Share, & Build Careers you cant drive the loss all the way to zero, but in fact you can. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. Values less than 0 predict class 0 and values greater than 0 Merged. try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 P < 0.5 --> class 0, and P > 0.5 --> class 1.). As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). 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. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) I am sure that all the pre-trained models parameters have been changed into mode autograd=false. At least 2-3 times slower. 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. Please let me correct an incorrect statement I made. Thank you very much! The l is total_loss, f is the class loss function, g is the detection loss function. R version 3.4.2 (2017-09-28) with reticulate_1.2 5%| | 3/66 [06:28<3:11:06, 182.02s/it] are training your predictions to be logits. These are raw scores, 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 . By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. How many characters/pages could WordStar hold on a typical CP/M machine? I did not try to train an embedding matrix + LSTM. Looking at the plot again, your model looks to be about 97-98% accurate. you will not ever be able to drive your loss to zero, even if your Your suggestions are really helpful. rev2022.11.3.43005. . I said that PyTorch documentation (Scroll to How to adjust learning rate header). Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. This is most likely due to your training loop holding on to some things it shouldnt. I though if there is anything related to accumulated memory which slows down the training, the restart training will help. Hi, Could you please inform on how to clear the temporary computations ? I have MSE loss that is computed between ground truth image and the generated image. . I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Find centralized, trusted content and collaborate around the technologies you use most. By default, the losses are averaged over each loss element in the batch. And Gpu utilization begins to jitter dramatically. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. model get pushed out towards -infinity and +infinity. Is that correct? or you can use a learning rate that changes over time as discussed here. I suspect that you are misunderstanding how to interpret the When reduce is False, returns a loss per batch element instead and ignores size_average. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. If the field size_average is set to False, the losses are instead summed for each minibatch. 0%| | 0/66 [00:00 1) You signed in with another tab or window. How can i extract files in the directory where they're located with the find command? Ignored when reduce is False. 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. boundary is somewhere around 5.0. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? It has to be set to False while you create the graph. Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? That is why I made a custom API for the GRU. I had the same problem with you, and solved it by your solution. Short story about skydiving while on a time dilation drug. All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. function becomes larger and larger, the logits predicted by the Instead, create the tensor directly on the device you want. The run was CPU only (no GPU). Merged. See Huber loss for more information. 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. Asking for help, clarification, or responding to other answers. boundary between class 0 and class 1 right. if you will, that are real numbers ranging from -infinity to +infinity. I find default works fine for most cases. add reduce=True arg to SoftMarginLoss #5071. 18%| | 12/66 [07:02<09:04, 10.09s/it] The answer comes from here - Why the training slow down with time if training continuously? Is it considered harrassment in the US to call a black man the N-word? By clicking Sign up for GitHub, you agree to our terms of service and Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. The replies from @knoriy explains your situation better and is something that you should try out first. 21%| | 14/66 [07:07<05:27, 6.30s/it]. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? It's hard to tell the reason your model isn't working without having any information. Closed. algorithm does), and the loss approaches zero. (When pumped though a sigmoid function, they become predicted ). By default, the losses are averaged over each loss element in the batch. Powered by Discourse, best viewed with JavaScript enabled. So, my advice is to select a smaller batch size, also play around with the number of workers. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. Can I spend multiple charges of my Blood Fury Tattoo at once? These issues seem hard to debug. Ella (elea) December 28, 2020, 7:20pm #1. It could be a problem of overfitting, underfitting, preprocessing, or bug. Default: True Correct handling of negative chapter numbers.

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