pytorch loss decrease slowtensorflow keras metrics

In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. And prediction giving by Neural network also is not correct. Note, as the you cant drive the loss all the way to zero, but in fact you can. I said that How do I simplify/combine these two methods for finding the smallest and largest int in an array? At least 2-3 times slower. Should we burninate the [variations] tag? 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 Making statements based on opinion; back them up with references or personal experience. And Gpu utilization begins to jitter dramatically? This will cause Ignored when reduce is False. I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. The net was trained with SGD, batch size 32. probabilities of the sample in question being in the 1 class. 2%| | 1/66 [05:53<6:23:05, 353.62s/it] correct (provided the bias is adjusted according, which the training Well occasionally send you account related emails. Hi, Could you please inform on how to clear the temporary computations ? 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. What is the best way to show results of a multiple-choice quiz where multiple options may be right? Therefore you boundary is somewhere around 5.0. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Note, Ive run the below test using pytorch version 0.3.0, so I had shouldnt the loss keep going down? Community. If the field size_average is set to False, the losses are instead summed for each minibatch. Learning rate affects loss but not the accuracy. try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 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. How can I track the problem down to find a solution? Why so many wires in my old light fixture? (PReLU-1): PReLU (1) 11%| | 7/66 [06:49<46:00, 46.79s/it] Learn how our community solves real, everyday machine learning problems with PyTorch. For a batch of size N N N, the unreduced loss can be described as: Does that continue forever or does the speed stay the same after a number of iterations? The run was CPU only (no GPU). Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. Is there a way to make trades similar/identical to a university endowment manager to copy them? I have observed a similar slowdown in training with pytorch running under R using the reticulate package. Asking for help, clarification, or responding to other answers. predict class 1. FYI, I am using SGD with learning rate equal to 0.0001. 2022 Moderator Election Q&A Question Collection. 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. Default: True. Is there a trick for softening butter quickly? PyTorch documentation (Scroll to How to adjust learning rate header). Send me a link to your repo here or code by mail ;). 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. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. The loss is decreasing/converging but very slowlly(below image). 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 . After running for a short while the loss suddenly explodes upwards. I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. Closed. 5%| | 3/66 [06:28<3:11:06, 182.02s/it] Why are only 2 out of the 3 boosters on Falcon Heavy reused? For example, the average training speed for epoch 1 is 10s. Basically everything or nothing could be wrong. Profile the code using the PyTorch profiler or e.g. Im not sure where this problem is coming from. (Linear-Last): Linear (4 -> 1) The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. First, you are using, as you say, BCEWithLogitsLoss. 97%|| 64/66 [05:11<00:06, 3.29s/it] Merged. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? 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 There are only four parameters that are changing in the current program. Note that for some losses, there are multiple elements per sample. I will close this issue. Join the PyTorch developer community to contribute, learn, and get your questions answered. Using SGD on MNIST dataset with Pytorch, loss not decreasing. Find centralized, trusted content and collaborate around the technologies you use most. reduce (bool, optional) - Deprecated (see reduction). Connect and share knowledge within a single location that is structured and easy to search. I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. reduce (bool, optional) - Deprecated (see reduction). 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. It has to be set to False while you create the graph. The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. And prediction giving by Neural network also is not correct. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. privacy statement. And Gpu utilization begins to jitter dramatically. Non-anthropic, universal units of time for active SETI. You should not save from one iteration to the other a Tensor that has requires_grad=True. It's so weird. (Linear-1): Linear (277 -> 8) If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? From your six data points that ). I find default works fine for most cases. 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. 18%| | 12/66 [07:02<09:04, 10.09s/it] The resolution is halved with the maxpool layers. 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. After running for a short while the loss suddenly explodes upwards. Therefore it cant cluster predictions together it can only get the generally convert that to a non-probabilistic prediction by saying And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). By clicking Sign up for GitHub, you agree to our terms of service and Nsight systems to see where the botleneck in the code is. 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 solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. to tweak your code a little bit. I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. We 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 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? How do I check if PyTorch is using the GPU? So that pytorch knows you wont try and backpropagate through it. To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. 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. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. saypal: Also in my case, the time is not too different from just doing loss.item () every time. Is there any guide on how to adapt? Some reading materials. 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 . So, my advice is to select a smaller batch size, also play around with the number of workers. (When pumped though a sigmoid function, they become predicted 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. I find default works fine for most cases. sigmoid saturates, its gradients go to zero, so (with a fixed learning Do you know why moving the declaration inside the loop can solve it ? Is it normal? . Default: True. 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. Loss function: BCEWithLogitsLoss() you will not ever be able to drive your loss to zero, even if your At least 2-3 times slower. 17%| | 11/66 [06:59<12:09, 13.27s/it] predictions made by this network. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. You signed in with another tab or window. 1 Like The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. The answer comes from here - Why the training slow down with time if training continuously? Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. This is most likely due to your training loop holding on to some things it shouldnt. 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! 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. 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! 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). No if a tensor does not requires_grad, its history is not built when using it. I am trying to train a latent space model in pytorch. The l is total_loss, f is the class loss function, g is the detection loss function. (PReLU-2): PReLU (1) 0%| | 0/66 [00:00 class 0, and P > 0.5 --> class 1.). Do you know why it is still getting slower? As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). I also tried another test. I observed the same problem. 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. Can I spend multiple charges of my Blood Fury Tattoo at once? Code, training, and validation graphs are below. I have also tried playing with learning rate. These issues seem hard to debug. I have MSE loss that is computed between ground truth image and the generated image. Accuracy != Open Ended Accuracy (which is calculated using the eval code). 94%|| 62/66 [05:06<00:15, 3.96s/it] Hopefully just one will increase and you will be able to see better what is going on. 2 Likes. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? Please let me correct an incorrect statement I made. I double checked the calculation of loss and I did not find anything that is accumulated from the previous batch. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Looking at the plot again, your model looks to be about 97-98% accurate. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. Short story about skydiving while on a time dilation drug. Without knowing what your task is, I would say that would be considered close to the state of the art. 8%| | 5/66 [06:43<1:34:15, 92.71s/it] I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! Community Stories. Generalize the Gdel sentence requires a fixed point theorem. rate) the training slows way down. by other synchronizations. Have a question about this project? Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. By default, the losses are averaged over each loss element in the batch. vision. (PReLU-3): PReLU (1) However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. Conv5 gets an input with shape 4,2,2,64. You can also check if dev/shm increases during training. When use Skip-Thoughts, I can get much better result. 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). What is the right way of handling this now that Tensor also tracks history? By default, the losses are averaged over each loss element in the batch. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. That is why I made a custom API for the GRU. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. I did not try to train an embedding matrix + LSTM. 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. prediction accuracy is perfect.) 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. This could mean that your code is already bottlenecks e.g. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. 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 cudnn backend that pytorch is using doesn't include a Sequential Dropout. print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) Im experiencing the same issue with pytorch 0.4.1 And at the end of the run the prediction accuracy is Often one decreases very quickly and the other decreases super slowly. I deleted some variables that I generated during training for each batch. Why does the sentence uses a question form, but it is put a period in the end? Now I use filtersize 2 and no padding to get a resolution of 1*1. That is why I made a custom API for the GRU. Custom distance loss function in Pytorch? 21%| | 14/66 [07:07<05:27, 6.30s/it]. It is because, since youre working with Variables, the history is saved for every operations youre performing. 98%|| 65/66 [05:14<00:03, 3.11s/it]. Problem confirmed. Each batch contained a random selection of training records. 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. Second, your model is a simple (one-dimensional) linear function. How to draw a grid of grids-with-polygons? add reduce=True arg to SoftMarginLoss #5071. or you can use a learning rate that changes over time as discussed here. If a shared tensor is not requires_grad, is its histroy still scanned? algorithm does), and the loss approaches zero. I have been working on fixing this problem for two week. As the weight in the model the multiplicative factor in the linear function becomes larger and larger, the logits predicted by the 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. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. How many characters/pages could WordStar hold on a typical CP/M machine? utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. Already on GitHub? Here are the last twenty loss values obtained by running Mnaufs Yeah, I will try adapting the learning rate. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. R version 3.4.2 (2017-09-28) with reticulate_1.2 I am sure that all the pre-trained models parameters have been changed into mode autograd=false. Values less than 0 predict class 0 and values greater than 0 Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? It is open ended accuracy in validation under 30 when training. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. 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. Loss value decreases slowly. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? Make a wide rectangle out of T-Pipes without loops. Learn about PyTorch's features and capabilities. The replies from @knoriy explains your situation better and is something that you should try out first. the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at Ubuntu 16.04.2 LTS All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. rev2022.11.3.43005. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] My architecture below ( from here ) Default: True reduce ( bool, optional) - Deprecated (see reduction ). 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. 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Let's look at how to add a Mean Square Error loss function in PyTorch. The loss goes down systematically (but, as noted above, doesnt You may also want to learn about non-global minimum traps. How can we build a space probe's computer to survive centuries of interstellar travel? perfect on your set of six samples (with the predictions understood Loss does decrease. 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=). or atleast converge to some point? Sign in See Huber loss for more information. Any suggestions in terms of tweaking the optimizer? To learn more, see our tips on writing great answers. Default: True 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. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( . I have also checked for class imbalance. if you will, that are real numbers ranging from -infinity to +infinity. 15%| | 10/66 [06:57<16:37, 17.81s/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>) Stack Overflow for Teams is moving to its own domain! Thanks for your reply! PyTorch Foundation. If the field size_average is set to False, the losses are instead summed for each minibatch. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. 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. are training your predictions to be logits. These are raw scores, 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 . (Linear-3): Linear (6 -> 4) Ella (elea) December 28, 2020, 7:20pm #1. I am working on a toy dataset to play with. 14%| | 9/66 [06:54<23:04, 24.30s/it] Is it considered harrassment in the US to call a black man the N-word? Is there anyone who knows what is going wrong with my code? I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. class classification (nn.Module): def __init__ (self): super (classification, self . Correct handling of negative chapter numbers. model = nn.Linear(1,1) I am working on a toy dataset to play with. model get pushed out towards -infinity and +infinity. This makes adding a loss function into your project as easy as just adding a single line of code. (Linear-2): Linear (8 -> 6) Merged. 6%| | 4/66 [06:41<2:15:39, 131.29s/it] (Because of this, import torch.nn as nn MSE_loss_fn = nn.MSELoss() 9%| | 6/66 [06:46<1:05:41, 65.70s/it] Now the final batches take no more time than the initial ones. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. Your suggestions are really helpful. li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. 12%| | 8/66 [06:51<32:26, 33.56s/it] I just saw in your mail that you are using a dropout of 0.5 for your LSTM. 95%|| 63/66 [05:09<00:10, 3.56s/it] Instead, create the tensor directly on the device you want. Is that correct? Stack Overflow - Where Developers Learn, Share, & Build Careers How do I print the model summary in PyTorch? So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. Developer Resources Learn about the PyTorch foundation. It turned out the batch size matters. Could you tell me what wrong with embedding matrix + LSTM? You should make sure to wrap your input into a Variable at every iteration. Why the training slow down with time if training continuously? Ignored when reduce is False. I am currently using adam optimizer with lr=1e-5. Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). I though if there is anything related to accumulated memory which slows down the training, the restart training will help. 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. Thank you very much! training loop for 10,000 iterations: So the loss does approach zero, although very slowly. If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. And when you call backward(), the whole history is scanned. How can i extract files in the directory where they're located with the find command? I suspect that you are misunderstanding how to interpret the

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