However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. By clicking Sign up for GitHub, you agree to our terms of service and Profile the code using the PyTorch profiler or e.g. The net was trained with SGD, batch size 32. Community. My architecture below ( from here ) Loss value decreases slowly. I had the same problem with you, and solved it by your solution. Ubuntu 16.04.2 LTS Sign up for a free GitHub account to open an issue and contact its maintainers and the community. In case you need something extra, you could look into the learning rate schedulers. To learn more, see our tips on writing great answers. Now I use filtersize 2 and no padding to get a resolution of 1*1. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. Asking for help, clarification, or responding to other answers. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! import torch.nn as nn MSE_loss_fn = nn.MSELoss() utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. boundary between class 0 and class 1 right. Making statements based on opinion; back them up with references or personal experience. Loss function: BCEWithLogitsLoss() 94%|| 62/66 [05:06<00:15, 3.96s/it] And prediction giving by Neural network also is not correct. For example, the average training speed for epoch 1 is 10s. Learn how our community solves real, everyday machine learning problems with PyTorch. P < 0.5 --> class 0, and P > 0.5 --> class 1.). 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=). First, you are using, as you say, BCEWithLogitsLoss. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? Im not sure where this problem is coming from. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. 2%| | 1/66 [05:53<6:23:05, 353.62s/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>) Each batch contained a random selection of training records. Connect and share knowledge within a single location that is structured and easy to search. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? 21%| | 14/66 [07:07<05:27, 6.30s/it]. PyTorch Foundation. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. Merged. Loss does decrease. I just saw in your mail that you are using a dropout of 0.5 for your LSTM. 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 . See Huber loss for more information. Powered by Discourse, best viewed with JavaScript enabled. How can we build a space probe's computer to survive centuries of interstellar travel? Why does the sentence uses a question form, but it is put a period in the end? if you will, that are real numbers ranging from -infinity to +infinity. From your six data points that 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. Generalize the Gdel sentence requires a fixed point theorem. Find centralized, trusted content and collaborate around the technologies you use most. 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 checked my model, loss function and read documentation but couldn't figure out what I've done wrong. At least 2-3 times slower. generally convert that to a non-probabilistic prediction by saying There are only four parameters that are changing in the current program. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. 15%| | 10/66 [06:57<16:37, 17.81s/it] ). correct (provided the bias is adjusted according, which the training I have been working on fixing this problem for two week. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. You should not save from one iteration to the other a Tensor that has requires_grad=True. saypal: Also in my case, the time is not too different from just doing loss.item () every time. Ignored when reduce is False. How can I track the problem down to find a solution? 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. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Code, training, and validation graphs are below. 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. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. as described above). Not the answer you're looking for? I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. (PReLU-1): PReLU (1) 0 and 1, so the predictions will become (increasing close to) exactly Values less than 0 predict class 0 and values greater than 0 It's hard to tell the reason your model isn't working without having any information. I suspect that you are misunderstanding how to interpret the Ella (elea) December 28, 2020, 7:20pm #1. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? I am currently using adam optimizer with lr=1e-5. try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 14%| | 9/66 [06:54<23:04, 24.30s/it] 12%| | 8/66 [06:51<32:26, 33.56s/it] 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. 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. 5%| | 3/66 [06:28<3:11:06, 182.02s/it] After running for a short while the loss suddenly explodes upwards. algorithm does), and the loss approaches zero. or you can use a learning rate that changes over time as discussed here. rev2022.11.3.43005. Already on GitHub? Sign in Looking at the plot again, your model looks to be about 97-98% accurate. This could mean that your code is already bottlenecks e.g. Note, Ive run the below test using pytorch version 0.3.0, so I had It is because, since youre working with Variables, the history is saved for every operations youre performing. I observed the same problem. I am sure that all the pre-trained models parameters have been changed into mode autograd=false. sigmoid saturates, its gradients go to zero, so (with a fixed learning By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 2022 Moderator Election Q&A Question Collection. Developer Resources Conv5 gets an input with shape 4,2,2,64. 98%|| 65/66 [05:14<00:03, 3.11s/it]. These issues seem hard to debug. class classification (nn.Module): def __init__ (self): super (classification, self . Thanks for your reply! Learn about PyTorch's features and capabilities. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. 17%| | 11/66 [06:59<12:09, 13.27s/it] Some reading materials. By default, the losses are averaged over each loss element in the batch. boundary is somewhere around 5.0. How can i extract files in the directory where they're located with the find command? Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. prediction accuracy is perfect.) training loop for 10,000 iterations: So the loss does approach zero, although very slowly. model get pushed out towards -infinity and +infinity. 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. add reduce=True arg to SoftMarginLoss #5071. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. are training your predictions to be logits. These are raw scores, Note that for some losses, there are multiple elements per sample. It turned out the batch size matters. Non-anthropic, universal units of time for active SETI. I have also tried playing with learning rate. R version 3.4.2 (2017-09-28) with reticulate_1.2 I said that At least 2-3 times slower. So, my advice is to select a smaller batch size, also play around with the number of workers. Therefore it cant cluster predictions together it can only get the Default: True. I find default works fine for most cases. The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. As the weight in the model the multiplicative factor in the linear Is it normal? 0%| | 0/66 [00:00

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