6%| | 4/66 [06:41<2:15:39, 131.29s/it] (PReLU-2): PReLU (1) All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. You signed in with another tab or window. Often one decreases very quickly and the other decreases super slowly. 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. Note, as the What is the best way to show results of a multiple-choice quiz where multiple options may be right? Is it considered harrassment in the US to call a black man the N-word? How can we build a space probe's computer to survive centuries of interstellar travel? Code, training, and validation graphs are below. For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. First, you are using, as you say, BCEWithLogitsLoss. Im not sure where this problem is coming from. The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. 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. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). 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). 18%| | 12/66 [07:02<09:04, 10.09s/it] You should make sure to wrap your input into a Variable at every iteration. So, my advice is to select a smaller batch size, also play around with the number of workers. algorithm does), and the loss approaches zero. Find centralized, trusted content and collaborate around the technologies you use most. 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? Default: True. That is why I made a custom API for the GRU. Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. or you can use a learning rate that changes over time as discussed here. (PReLU-3): PReLU (1) It is open ended accuracy in validation under 30 when training. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. Using SGD on MNIST dataset with Pytorch, loss not decreasing. I just saw in your mail that you are using a dropout of 0.5 for your LSTM. Therefore you Stack Overflow for Teams is moving to its own domain! How do I print the model summary in PyTorch? Conv5 gets an input with shape 4,2,2,64. And Gpu utilization begins to jitter dramatically? For a batch of size N N N, the unreduced loss can be described as: 2 Likes. 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. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. Do you know why moving the declaration inside the loop can solve it ? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. After running for a short while the loss suddenly explodes upwards. 2022 Moderator Election Q&A Question Collection. We The answer comes from here - Why the training slow down with time if training continuously? It's so weird. 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. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. R version 3.4.2 (2017-09-28) with reticulate_1.2 By default, the losses are averaged over each loss element in the batch. (Linear-3): Linear (6 -> 4) Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? It's hard to tell the reason your model isn't working without having any information. 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. I have also tried playing with learning rate. (Linear-Last): Linear (4 -> 1) correct (provided the bias is adjusted according, which the training To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. 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=). Do you know why it is still getting slower? How to draw a grid of grids-with-polygons? Ignored when reduce is False. Is there anyone who knows what is going wrong with my code? the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at boundary between class 0 and class 1 right. Now I use filtersize 2 and no padding to get a resolution of 1*1. Some reading materials. Closed. Default: True. The net was trained with SGD, batch size 32. (When pumped though a sigmoid function, they become predicted P < 0.5 --> class 0, and P > 0.5 --> class 1.). utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. if you will, that are real numbers ranging from -infinity to +infinity. My architecture below ( from here ) reduce (bool, optional) - Deprecated (see reduction). 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. Is it normal? Community. Should we burninate the [variations] tag? Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( 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. class classification (nn.Module): def __init__ (self): super (classification, self . By clicking Sign up for GitHub, you agree to our terms of service and How do I simplify/combine these two methods for finding the smallest and largest int in an array? This is most likely due to your training loop holding on to some things it shouldnt. You may also want to learn about non-global minimum traps. Thanks for your reply! 98%|| 65/66 [05:14<00:03, 3.11s/it]. by other synchronizations. This will cause By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I observed the same problem. Now the final batches take no more time than the initial ones. The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. 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. You can also check if dev/shm increases during training. saypal: Also in my case, the time is not too different from just doing loss.item () every time. 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 Instead, create the tensor directly on the device you want. Basically everything or nothing could be wrong. Is there any guide on how to adapt? I have been working on fixing this problem for two week. 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. Does that continue forever or does the speed stay the same after a number of iterations? Merged. Loss Functions MLE Loss sequence_softmax_cross_entropy texar.torch.losses. Once your model gets close to these figures, in my experience the model finds it hard to find new feature to optimise without overfitting to your dataset. Loss does decrease. 9%| | 6/66 [06:46<1:05:41, 65.70s/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. This could mean that your code is already bottlenecks e.g. Hopefully just one will increase and you will be able to see better what is going on. I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. 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). I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? Ella (elea) December 28, 2020, 7:20pm #1. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? It has to be set to False while you create the graph. I said that model get pushed out towards -infinity and +infinity. Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. At least 2-3 times slower. 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. At least 2-3 times slower. It turned out the batch size matters. The loss goes down systematically (but, as noted above, doesnt See Huber loss for more information. (Linear-1): Linear (277 -> 8) 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. Send me a link to your repo here or code by mail ;). Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 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. I have also checked for class imbalance. function becomes larger and larger, the logits predicted by the Default: True The loss is decreasing/converging but very slowlly(below image). training loop for 10,000 iterations: So the loss does approach zero, although very slowly. Therefore it cant cluster predictions together it can only get the This makes adding a loss function into your project as easy as just adding a single line of code. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. You should not save from one iteration to the other a Tensor that has requires_grad=True. import torch.nn as nn MSE_loss_fn = nn.MSELoss() And prediction giving by Neural network also is not correct. vision. 5%| | 3/66 [06:28<3:11:06, 182.02s/it] Here are the last twenty loss values obtained by running Mnaufs Problem confirmed. These issues seem hard to debug. I will close this issue. The l is total_loss, f is the class loss function, g is the detection loss function. Non-anthropic, universal units of time for active SETI. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. Developer Resources Yeah, I will try adapting the learning rate. How do I check if PyTorch is using the GPU? (Because of this, The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. 20%| | 13/66 [07:05<06:56, 7.86s/it] 0 and 1, so the predictions will become (increasing close to) exactly 17%| | 11/66 [06:59<12:09, 13.27s/it] rate) the training slows way down. Is that correct? 97%|| 64/66 [05:11<00:06, 3.29s/it] or atleast converge to some point? It is because, since youre working with Variables, the history is saved for every operations youre performing. That is why I made a custom API for the GRU. Learn about PyTorch's features and capabilities. Loss function: BCEWithLogitsLoss() Let's look at how to add a Mean Square Error loss function in PyTorch. Note that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. Is there a trick for softening butter quickly? Could you tell me what wrong with embedding matrix + LSTM? Looking at the plot again, your model looks to be about 97-98% accurate. 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. I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. I did not try to train an embedding matrix + LSTM. Making statements based on opinion; back them up with references or personal experience. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? 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. Merged. How can i extract files in the directory where they're located with the find command? you cant drive the loss all the way to zero, but in fact you can. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. I am working on a toy dataset to play with. Learn about the PyTorch foundation. And at the end of the run the prediction accuracy is . predict class 1. 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 . Values less than 0 predict class 0 and values greater than 0 Learn how our community solves real, everyday machine learning problems with PyTorch. model = nn.Linear(1,1) I am working on a toy dataset to play with. Connect and share knowledge within a single location that is structured and easy to search. Note that for some losses, there are multiple elements per sample. Ubuntu 16.04.2 LTS To learn more, see our tips on writing great answers. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. I suspect that you are misunderstanding how to interpret the I double checked the calculation of loss and I did not find anything that is accumulated from the previous batch. Join the PyTorch developer community to contribute, learn, and get your questions answered. Is there a way to make trades similar/identical to a university endowment manager to copy them? Not the answer you're looking for? For example, the average training speed for epoch 1 is 10s. Your suggestions are really helpful. Sign in I had the same problem with you, and solved it by your solution. The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. generally convert that to a non-probabilistic prediction by saying I am trying to train a latent space model in pytorch. predictions made by this network. li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. 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 the field size_average is set to False, the losses are instead summed for each minibatch. Why so many wires in my old light fixture? 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 . try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 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. No if a tensor does not requires_grad, its history is not built when using it. I have MSE loss that is computed between ground truth image and the generated image. 1 Like Why are only 2 out of the 3 boosters on Falcon Heavy reused? shouldnt the loss keep going down? 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. 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. Stack Overflow - Where Developers Learn, Share, & Build Careers If the field size_average is set to False, the losses are instead summed for each minibatch. 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! Please let me correct an incorrect statement I made. FYI, I am using SGD with learning rate equal to 0.0001. Why does the sentence uses a question form, but it is put a period in the end? Any suggestions in terms of tweaking the optimizer? 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. So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. reduce (bool, optional) - Deprecated (see reduction). 8%| | 5/66 [06:43<1:34:15, 92.71s/it] When reduce is False, returns a loss per batch element instead and ignores size_average. I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. 21%| | 14/66 [07:07<05:27, 6.30s/it]. 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. So that pytorch knows you wont try and backpropagate through it. Any comments are highly appreciated! Each batch contained a random selection of training records. probabilities of the sample in question being in the 1 class. Short story about skydiving while on a time dilation drug. Profile the code using the PyTorch profiler or e.g. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. The resolution is halved with the maxpool layers. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. If you want to save it for later inspection (or accumulating the loss), you should .detach() it before. Second, your model is a simple (one-dimensional) linear function. PyTorch documentation (Scroll to How to adjust learning rate header). Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. Default: True reduce ( bool, optional) - Deprecated (see reduction ). I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! Note that for some losses, there are multiple elements per sample. rev2022.11.3.43005. ). Correct handling of negative chapter numbers. Powered by Discourse, best viewed with JavaScript enabled. Why the training slow down with time if training continuously? The run was CPU only (no GPU). 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>) I deleted some variables that I generated during training for each batch. After running for a short while the loss suddenly explodes upwards. 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. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. 0%| | 0/66 [00:00) I though if there is anything related to accumulated memory which slows down the training, the restart training will help. If a shared tensor is not requires_grad, is its histroy still scanned? . For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. Community Stories. 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 .
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