pytorch loss decrease slow

Merged. How many characters/pages could WordStar hold on a typical CP/M machine? as described above). The l is total_loss, f is the class loss function, g is the detection loss function. (PReLU-3): PReLU (1) You should not save from one iteration to the other a Tensor that has requires_grad=True. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. Thank you very much! At least 2-3 times slower. 15%| | 10/66 [06:57<16:37, 17.81s/it] 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. When reduce is False, returns a loss per batch element instead and ignores size_average. I said that I am trying to train a latent space model in pytorch. It's so weird. This could mean that your code is already bottlenecks e.g. I have observed a similar slowdown in training with pytorch running under R using the reticulate package. rate) the training slows way down. perfect on your set of six samples (with the predictions understood 8%| | 5/66 [06:43<1:34:15, 92.71s/it] The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. 18%| | 12/66 [07:02<09:04, 10.09s/it] Generalize the Gdel sentence requires a fixed point theorem. 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. Loss function: BCEWithLogitsLoss() ). As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). Some reading materials. Learning rate affects loss but not the accuracy. Custom distance loss function in Pytorch? Join the PyTorch developer community to contribute, learn, and get your questions answered. I am currently using adam optimizer with lr=1e-5. 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. Without knowing what your task is, I would say that would be considered close to the state of the art. (PReLU-2): PReLU (1) reduce (bool, optional) - Deprecated (see reduction). What is the best way to show results of a multiple-choice quiz where multiple options may be right? Now the final batches take no more time than the initial ones. I double checked the calculation of loss and I did not find anything that is accumulated from the previous batch. What is the right way of handling this now that Tensor also tracks history? So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. Is there a trick for softening butter quickly? Can I spend multiple charges of my Blood Fury Tattoo at once? Let's look at how to add a Mean Square Error loss function in PyTorch. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). 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. Stack Overflow - Where Developers Learn, Share, & Build Careers Therefore it cant cluster predictions together it can only get the Learn about PyTorch's features and capabilities. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. Loss does decrease. Note, as the Ignored when reduce is False. Already on GitHub? That is why I made a custom API for the GRU. Have a question about this project? 9%| | 6/66 [06:46<1:05:41, 65.70s/it] I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Note that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. vision. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. Why does the sentence uses a question form, but it is put a period in the end? The cudnn backend that pytorch is using doesn't include a Sequential Dropout. Values less than 0 predict class 0 and values greater than 0 21%| | 14/66 [07:07<05:27, 6.30s/it]. When use Skip-Thoughts, I can get much better result. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. (Because of this, Community Stories. To learn more, see our tips on writing great answers. Code, training, and validation graphs are below. 2%| | 1/66 [05:53<6:23:05, 353.62s/it] For example, the average training speed for epoch 1 is 10s. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. I observed the same problem. Looking at the plot again, your model looks to be about 97-98% accurate. Profile the code using the PyTorch profiler or e.g. correct (provided the bias is adjusted according, which the training I tried a higher learning rate than 1e-5, which leads to a gradient explosion. 12%| | 8/66 [06:51<32:26, 33.56s/it] It turned out the batch size matters. Ella (elea) December 28, 2020, 7:20pm #1. 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 The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. 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? Does that continue forever or does the speed stay the same after a number of iterations? I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. 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 How can i extract files in the directory where they're located with the find command? I did not try to train an embedding matrix + LSTM. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. 0 and 1, so the predictions will become (increasing close to) exactly All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. And at the end of the run the prediction accuracy is Yeah, I will try adapting the learning rate. It has to be set to False while you create the graph. 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). (Linear-2): Linear (8 -> 6) I suspect that you are misunderstanding how to interpret the to tweak your code a little bit. Is that correct? predictions made by this network. 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). Asking for help, clarification, or responding to other answers. I am working on a toy dataset to play with. Here are the last twenty loss values obtained by running Mnaufs 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. 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 there anyone who knows what is going wrong with my code? algorithm does), and the loss approaches zero. I also tried another test. probabilities of the sample in question being in the 1 class. Closed. Please let me correct an incorrect statement I made. See Huber loss for more information. Send me a link to your repo here or code by mail ;). And when you call backward(), the whole history is scanned. I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Why so many wires in my old light fixture? try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 Note that for some losses, there are multiple elements per sample. Now I use filtersize 2 and no padding to get a resolution of 1*1. Ignored when reduce is False. Currently, the memory usage would not increase but the training speed still gets slower batch-batch. 5%| | 3/66 [06:28<3:11:06, 182.02s/it] Is it normal? 11%| | 7/66 [06:49<46:00, 46.79s/it] No if a tensor does not requires_grad, its history is not built when using it. 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 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. boundary is somewhere around 5.0. sigmoid saturates, its gradients go to zero, so (with a fixed learning My architecture below ( from here ) Why are only 2 out of the 3 boosters on Falcon Heavy reused? I have also checked for class imbalance. If the field size_average is set to False, the losses are instead summed for each minibatch. or atleast converge to some point? Stack Overflow for Teams is moving to its own domain! Could you tell me what wrong with embedding matrix + LSTM? privacy statement. The answer comes from here - Why the training slow down with time if training continuously? I had the same problem with you, and solved it by your solution. 14%| | 9/66 [06:54<23:04, 24.30s/it] (When pumped though a sigmoid function, they become predicted 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. 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. It could be a problem of overfitting, underfitting, preprocessing, or bug. How to draw a grid of grids-with-polygons? Each batch contained a random selection of training records. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. if you will, that are real numbers ranging from -infinity to +infinity. FYI, I am using SGD with learning rate equal to 0.0001. Basically everything or nothing could be wrong. are training your predictions to be logits. These are raw scores, reduce (bool, optional) - Deprecated (see reduction). Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( And Gpu utilization begins to jitter dramatically. Is it considered harrassment in the US to call a black man the N-word? Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. R version 3.4.2 (2017-09-28) with reticulate_1.2 0%| | 0/66 [00:00 4) This makes adding a loss function into your project as easy as just adding a single line of code. 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 . There are only four parameters that are changing in the current program. Problem confirmed. Why the training slow down with time if training continuously? 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. saypal: Also in my case, the time is not too different from just doing loss.item () every time. If a shared tensor is not requires_grad, is its histroy still scanned? 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. Do you know why it is still getting slower? The loss is decreasing/converging but very slowlly(below image). Therefore you I find default works fine for most cases. Make a wide rectangle out of T-Pipes without loops. How do I print the model summary in PyTorch? I deleted some variables that I generated during training for each batch. This will cause To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Loss value decreases slowly. As the weight in the model the multiplicative factor in the linear Not the answer you're looking for? 6%| | 4/66 [06:41<2:15:39, 131.29s/it] Im not sure where this problem is coming from. Well occasionally send you account related emails. It's hard to tell the reason your model isn't working without having any information. Ubuntu 16.04.2 LTS by other synchronizations. So, my advice is to select a smaller batch size, also play around with the number of workers. Correct handling of negative chapter numbers. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. predict class 1. Do you know why moving the declaration inside the loop can solve it ? At least 2-3 times slower. By default, the losses are averaged over each loss element in the batch. 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. Any comments are highly appreciated! Learn how our community solves real, everyday machine learning problems with PyTorch. 2022 Moderator Election Q&A Question Collection. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. It is because, since youre working with Variables, the history is saved for every operations youre performing. PyTorch Foundation. So that pytorch knows you wont try and backpropagate through it. Second, your model is a simple (one-dimensional) linear function. Loss Functions MLE Loss sequence_softmax_cross_entropy texar.torch.losses. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at I have been working on fixing this problem for two week. You should make sure to wrap your input into a Variable at every iteration. import torch.nn as nn MSE_loss_fn = nn.MSELoss() To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? training loop for 10,000 iterations: So the loss does approach zero, although very slowly. 97%|| 64/66 [05:11<00:06, 3.29s/it] I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. 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 solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. Note, Ive run the below test using pytorch version 0.3.0, so I had (Linear-1): Linear (277 -> 8) 1 Like 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. P < 0.5 --> class 0, and P > 0.5 --> class 1.). By default, the losses are averaged over each loss element in the batch. 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. Should we burninate the [variations] tag? 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=). 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! Default: True 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 add reduce=True arg to SoftMarginLoss #5071. Accuracy != Open Ended Accuracy (which is calculated using the eval code). 94%|| 62/66 [05:06<00:15, 3.96s/it] I though if there is anything related to accumulated memory which slows down the training, the restart training will help. After running for a short while the loss suddenly explodes upwards. And Gpu utilization begins to jitter dramatically? rev2022.11.3.43005. Any suggestions in terms of tweaking the optimizer? The loss goes down systematically (but, as noted above, doesnt you cant drive the loss all the way to zero, but in fact you can. I have also tried playing with learning rate. Often one decreases very quickly and the other decreases super slowly. boundary between class 0 and class 1 right. That is why I made a custom API for the GRU. I have MSE loss that is computed between ground truth image and the generated image. The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. The replies from @knoriy explains your situation better and is something that you should try out first. If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. (Linear-Last): Linear (4 -> 1) Sign in How do I check if PyTorch is using the GPU? It is open ended accuracy in validation under 30 when training. 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. you will not ever be able to drive your loss to zero, even if your Non-anthropic, universal units of time for active SETI. 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. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? model = nn.Linear(1,1) I am working on a toy dataset to play with. The net was trained with SGD, batch size 32. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. 20%| | 13/66 [07:05<06:56, 7.86s/it] shouldnt the loss keep going down? 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. 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. For a batch of size N N N, the unreduced loss can be described as: Default: True. The resolution is halved with the maxpool layers. You can also check if dev/shm increases during training. (PReLU-1): PReLU (1) 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. However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. function becomes larger and larger, the logits predicted by the Find centralized, trusted content and collaborate around the technologies you use most. print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) Connect and share knowledge within a single location that is structured and easy to search. I find default works fine for most cases. Your suggestions are really helpful. go to zero). Conv5 gets an input with shape 4,2,2,64. Im experiencing the same issue with pytorch 0.4.1 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 . Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? You signed in with another tab or window. The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. 95%|| 63/66 [05:09<00:10, 3.56s/it] I just saw in your mail that you are using a dropout of 0.5 for your LSTM. Making statements based on opinion; back them up with references or personal experience. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. These issues seem hard to debug. .
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