Can I spend multiple charges of my Blood Fury Tattoo at once? 97%|| 64/66 [05:11<00:06, 3.29s/it] probabilities of the sample in question being in the 1 class. The l is total_loss, f is the class loss function, g is the detection loss function. 5%| | 3/66 [06:28<3:11:06, 182.02s/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. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? 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. P < 0.5 --> class 0, and P > 0.5 --> class 1.). I though if there is anything related to accumulated memory which slows down the training, the restart training will help. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. model = nn.Linear(1,1) I am working on a toy dataset to play with. perfect on your set of six samples (with the predictions understood Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. 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). 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. Learn about the PyTorch foundation. How can i extract files in the directory where they're located with the find command? Second, your model is a simple (one-dimensional) linear function. 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! 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 Do you know why it is still getting slower? Learning rate affects loss but not the accuracy. 0%| | 0/66 [00:00) Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. Note that for some losses, there are multiple elements per sample. 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. This is most likely due to your training loop holding on to some things it shouldnt. You should not save from one iteration to the other a Tensor that has requires_grad=True. By default, the losses are averaged over each loss element in the batch. or you can use a learning rate that changes over time as discussed here. 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 . Now the final batches take no more time than the initial ones. Looking at the plot again, your model looks to be about 97-98% accurate. Accuracy != Open Ended Accuracy (which is calculated using the eval code). Loss does decrease. generally convert that to a non-probabilistic prediction by saying This could mean that your code is already bottlenecks e.g. I am sure that all the pre-trained models parameters have been changed into mode autograd=false. Not the answer you're looking for? If the field size_average is set to False, the losses are instead summed for each minibatch. you will not ever be able to drive your loss to zero, even if your This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. The answer comes from here - Why the training slow down with time if training continuously? Default: True reduce ( bool, optional) - Deprecated (see reduction ). privacy statement. I observed the same problem. To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. Learn about PyTorch's features and capabilities. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? And prediction giving by Neural network also is not correct. If the field size_average is set to False, the losses are instead summed for each minibatch. Using SGD on MNIST dataset with Pytorch, loss not decreasing. Why does the sentence uses a question form, but it is put a period in the end? The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. Default: True. Let's look at how to add a Mean Square Error loss function in PyTorch. I have MSE loss that is computed between ground truth image and the generated image. I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. import torch.nn as nn MSE_loss_fn = nn.MSELoss() 11%| | 7/66 [06:49<46:00, 46.79s/it] Thanks for your reply! Without knowing what your task is, I would say that would be considered close to the state of the art. 98%|| 65/66 [05:14<00:03, 3.11s/it]. 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? 6%| | 4/66 [06:41<2:15:39, 131.29s/it] 17%| | 11/66 [06:59<12:09, 13.27s/it] Generalize the Gdel sentence requires a fixed point theorem. In case you need something extra, you could look into the learning rate schedulers. The loss goes down systematically (but, as noted above, doesnt Hi, Could you please inform on how to clear the temporary computations ? 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>) (Linear-2): Linear (8 -> 6) How many characters/pages could WordStar hold on a typical CP/M machine? Send me a link to your repo here or code by mail ;). rate) the training slows way down. Correct handling of negative chapter numbers. Community. I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. reduce (bool, optional) - Deprecated (see reduction). It has to be set to False while you create the graph. Could you tell me what wrong with embedding matrix + LSTM? Yeah, I will try adapting the learning rate. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). Note, Ive run the below test using pytorch version 0.3.0, so I had Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. add reduce=True arg to SoftMarginLoss #5071. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. Any comments are highly appreciated! if you will, that are real numbers ranging from -infinity to +infinity. prediction accuracy is perfect.) Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? 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 . Ubuntu 16.04.2 LTS I am working on a toy dataset to play with. Loss value decreases slowly. Learn how our community solves real, everyday machine learning problems with PyTorch. you cant drive the loss all the way to zero, but in fact you can. training loop for 10,000 iterations: So the loss does approach zero, although very slowly. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. By default, the losses are averaged over each loss element in the batch. are training your predictions to be logits. These are raw scores, Default: True. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( The resolution is halved with the maxpool layers. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. These issues seem hard to debug. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? I have also tried playing with learning rate. Thank you very much! I find default works fine for most cases. (Linear-Last): Linear (4 -> 1) li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. Is it normal? Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? This makes adding a loss function into your project as easy as just adding a single line of code. At least 2-3 times slower. 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. 21%| | 14/66 [07:07<05:27, 6.30s/it]. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. My architecture below ( from here ) You should make sure to wrap your input into a Variable at every iteration. Now I use filtersize 2 and no padding to get a resolution of 1*1. class classification (nn.Module): def __init__ (self): super (classification, self . Merged. For example, the first batch only takes 10s and the 10k^th batch takes 40s 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. Im not sure where this problem is coming from. Default: True 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. . Ignored when reduce is False. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. (PReLU-1): PReLU (1) Instead, create the tensor directly on the device you want. to tweak your code a little bit. Custom distance loss function in Pytorch? First, you are using, as you say, BCEWithLogitsLoss. I had the same problem with you, and solved it by your solution. go to zero). It's so weird. 15%| | 10/66 [06:57<16:37, 17.81s/it] No if a tensor does not requires_grad, its history is not built when using it. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). Ignored when reduce is False. Often one decreases very quickly and the other decreases super slowly. rev2022.11.3.43005. The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. predictions made by this network. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. You can also check if dev/shm increases during training. as described above). Why so many wires in my old light fixture? 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. R version 3.4.2 (2017-09-28) with reticulate_1.2 (PReLU-2): PReLU (1) Is there a way to make trades similar/identical to a university endowment manager to copy them? Your suggestions are really helpful. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. 94%|| 62/66 [05:06<00:15, 3.96s/it] 0 and 1, so the predictions will become (increasing close to) exactly PyTorch Foundation. If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. (Linear-3): Linear (6 -> 4) I am trying to train a latent space model in pytorch. The loss is decreasing/converging but very slowlly(below image). See Huber loss for more information. 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. The net was trained with SGD, batch size 32. 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? 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. Merged. That is why I made a custom API for the GRU. I also tried another test. Some reading materials. Code, training, and validation graphs are below. 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). And Gpu utilization begins to jitter dramatically. 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. Asking for help, clarification, or responding to other answers. It could be a problem of overfitting, underfitting, preprocessing, or bug. 2022 Moderator Election Q&A Question Collection. predict class 1. Join the PyTorch developer community to contribute, learn, and get your questions answered. To learn more, see our tips on writing great answers. algorithm does), and the loss approaches zero. And prediction giving by Neural network also is not correct. Developer Resources 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. 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 I find default works fine for most cases. 18%| | 12/66 [07:02<09:04, 10.09s/it] So, my advice is to select a smaller batch size, also play around with the number of workers. Does that continue forever or does the speed stay the same after a number of iterations? After running for a short while the loss suddenly explodes upwards. I did not try to train an embedding matrix + LSTM. Make a wide rectangle out of T-Pipes without loops. The run was CPU only (no GPU). Is it considered harrassment in the US to call a black man the N-word? 1 Like 12%| | 8/66 [06:51<32:26, 33.56s/it] And when you call backward(), the whole history is scanned. Please let me correct an incorrect statement I made. Here are the last twenty loss values obtained by running Mnaufs 14%| | 9/66 [06:54<23:04, 24.30s/it] 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=). Basically everything or nothing could be wrong. By clicking Sign up for GitHub, you agree to our terms of service and 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. (When pumped though a sigmoid function, they become predicted Already on GitHub? sigmoid saturates, its gradients go to zero, so (with a fixed learning Ella (elea) December 28, 2020, 7:20pm #1. I deleted some variables that I generated during training for each batch. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. How do I check if PyTorch is using the GPU? PyTorch documentation (Scroll to How to adjust learning rate header). 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. 2 Likes. So that pytorch knows you wont try and backpropagate through it. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. I suspect that you are misunderstanding how to interpret the 95%|| 63/66 [05:09<00:10, 3.56s/it] 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. I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at 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. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. boundary between class 0 and class 1 right. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. It is open ended accuracy in validation under 30 when training. 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 . reduce (bool, optional) - Deprecated (see reduction). As the weight in the model the multiplicative factor in the linear Sign up for a free GitHub account to open an issue and contact its maintainers and the community. I have also checked for class imbalance. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception.
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