Stack Overflow for Teams is moving to its own domain! I find default works fine for most cases. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. The replies from @knoriy explains your situation better and is something that you should try out first. There are only four parameters that are changing in the current program. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. Why does the sentence uses a question form, but it is put a period in the end? I have also checked for class imbalance. This makes adding a loss function into your project as easy as just adding a single line of code. Connect and share knowledge within a single location that is structured and easy to search. By default, the losses are averaged over each loss element in the batch. Merged. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] 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. Using SGD on MNIST dataset with Pytorch, loss not decreasing. if you will, that are real numbers ranging from -infinity to +infinity. Community Stories. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( Does that continue forever or does the speed stay the same after a number of iterations? try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 How do I print the model summary in PyTorch? I though if there is anything related to accumulated memory which slows down the training, the restart training will help. (Because of this, This is most likely due to your training loop holding on to some things it shouldnt. prediction accuracy is perfect.) generally convert that to a non-probabilistic prediction by saying I deleted some variables that I generated during training for each batch. 21%| | 14/66 [07:07<05:27, 6.30s/it]. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Some reading materials. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. After running for a short while the loss suddenly explodes upwards. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? When use Skip-Thoughts, I can get much better result. Profile the code using the PyTorch profiler or e.g. It's hard to tell the reason your model isn't working without having any information. Well occasionally send you account related emails. And prediction giving by Neural network also is not correct. 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. This will cause All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. (PReLU-1): PReLU (1) 11%| | 7/66 [06:49<46:00, 46.79s/it] boundary is somewhere around 5.0. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. You may also want to learn about non-global minimum traps. P < 0.5 --> class 0, and P > 0.5 --> class 1.). I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! The l is total_loss, f is the class loss function, g is the detection loss function. Sign in li-roy mentioned this issue on Jan 29, 2018. add reduce=True argument to MultiLabelMarginLoss #4924. I will close this issue. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. reduce (bool, optional) - Deprecated (see reduction). Is it considered harrassment in the US to call a black man the N-word? The resolution is halved with the maxpool layers. 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>) At least 2-3 times slower. Im experiencing the same issue with pytorch 0.4.1 I find default works fine for most cases. Default: True to tweak your code a little bit. . 5%| | 3/66 [06:28<3:11:06, 182.02s/it] The run was CPU only (no GPU). you cant drive the loss all the way to zero, but in fact you can. Each batch contained a random selection of training records. Why so many wires in my old light fixture? As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). The net was trained with SGD, batch size 32. Is there anyone who knows what is going wrong with my code? This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. That is why I made a custom API for the GRU. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). And prediction giving by Neural network also is not correct. Asking for help, clarification, or responding to other answers. Ignored when reduce is False. go to zero). Learn about PyTorch's features and capabilities. Non-anthropic, universal units of time for active SETI. The answer comes from here - Why the training slow down with time if training continuously? Problem confirmed. So, my advice is to select a smaller batch size, also play around with the number of workers. How can i extract files in the directory where they're located with the find command? Any suggestions in terms of tweaking the optimizer? Please let me correct an incorrect statement I made. ). I tried a higher learning rate than 1e-5, which leads to a gradient explosion. Correct handling of negative chapter numbers. Loss function: BCEWithLogitsLoss() By clicking Sign up for GitHub, you agree to our terms of service and 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. 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. The text was updated successfully, but these errors were encountered: With the VQA 1.0 dataset the question model achieves 40% open ended accuracy. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. 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. I suspect that you are misunderstanding how to interpret the the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at Moving the declarations of those tensors inside the loop (which I thought would be less efficient) solved my slowdown problem. Ella (elea) December 28, 2020, 7:20pm #1. 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. (Linear-2): Linear (8 -> 6) We The loss is decreasing/converging but very slowlly(below image). How do I simplify/combine these two methods for finding the smallest and largest int in an array? 12%| | 8/66 [06:51<32:26, 33.56s/it] add reduce=True arg to SoftMarginLoss #5071. 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. It is because, since youre working with Variables, the history is saved for every operations youre performing. Merged. predict class 1. class classification (nn.Module): def __init__ (self): super (classification, self . Should we burninate the [variations] tag? Community. Ignored when reduce is False. 14%| | 9/66 [06:54<23:04, 24.30s/it] The reason for your model converging so slowly is because of your leaning rate (1e-5 == 0.000001), play around with your learning rate. Make a wide rectangle out of T-Pipes without loops. Learn about the PyTorch foundation. 8%| | 5/66 [06:43<1:34:15, 92.71s/it] At least 2-3 times slower. I am working on a toy dataset to play with. Developer Resources 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. The loss goes down systematically (but, as noted above, doesnt Why the training slow down with time if training continuously? 2 Likes. R version 3.4.2 (2017-09-28) with reticulate_1.2 model = nn.Linear(1,1) I am working on a toy dataset to play with. Learning rate affects loss but not the accuracy. From your six data points that If the field size_average is set to False, the losses are instead summed for each minibatch. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? Yeah, I will try adapting the learning rate. shouldnt the loss keep going down? 2%| | 1/66 [05:53<6:23:05, 353.62s/it] training loop for 10,000 iterations: So the loss does approach zero, although very slowly. Basically everything or nothing could be wrong. sigmoid saturates, its gradients go to zero, so (with a fixed learning Add reduce arg to BCELoss #4231. wohlert mentioned this issue on Jan 28, 2018. It's so weird. I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. 17%| | 11/66 [06:59<12:09, 13.27s/it] Making statements based on opinion; back them up with references or personal experience. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. FYI, I am using SGD with learning rate equal to 0.0001. 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. 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 . If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? 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. Thanks for your reply! 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. Find centralized, trusted content and collaborate around the technologies you use most. Ignored when reduce is False. 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. What is the right way of handling this now that Tensor also tracks history? 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 rate) the training slows way down. However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. Second, your model is a simple (one-dimensional) linear function. Loss with custom backward function in PyTorch - exploding loss in simple MSE example. I have MSE loss that is computed between ground truth image and the generated image. If the field size_average is set to False, the losses are instead summed for each minibatch. Note, as the No if a tensor does not requires_grad, its history is not built when using it. 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. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Here are the last twenty loss values obtained by running Mnaufs 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 did not try to train an embedding matrix + LSTM. Code, training, and validation graphs are below. When reduce is False, returns a loss per batch element instead and ignores size_average. And at the end of the run the prediction accuracy is How many characters/pages could WordStar hold on a typical CP/M machine? And Gpu utilization begins to jitter dramatically. model get pushed out towards -infinity and +infinity. or you can use a learning rate that changes over time as discussed here. Your suggestions are really helpful. The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. I have been working on fixing this problem for two week. As the weight in the model the multiplicative factor in the linear Can I spend multiple charges of my Blood Fury Tattoo at once? How can I track the problem down to find a solution? Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? 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. I have observed a similar slowdown in training with pytorch running under R using the reticulate package. How do I check if PyTorch is using the GPU? print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=
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