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. 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! Asking for help, clarification, or responding to other answers. Your suggestions are really helpful. 2022 Moderator Election Q&A Question Collection. 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>) Is there any guide on how to adapt? Is it normal? Second, your model is a simple (one-dimensional) linear function. (Linear-3): Linear (6 -> 4) The loss goes down systematically (but, as noted above, doesnt Do you know why it is still getting slower? boundary is somewhere around 5.0. It's hard to tell the reason your model isn't working without having any information. Should we burninate the [variations] tag? 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 that you cannot change this attribute after the forward pass to change how the backward behaves on an already created computational graph. Community Stories. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. perfect on your set of six samples (with the predictions understood I checked my model, loss function and read documentation but couldn't figure out what I've done wrong. Default: True reduce ( bool, optional) - Deprecated (see reduction ). The l is total_loss, f is the class loss function, g is the detection loss function. I have also tried playing with learning rate. What is the best way to show results of a multiple-choice quiz where multiple options may be right? By default, the losses are averaged over each loss element in the batch. Default: True. Did you try to change the number of parameters in your LSTM and to plot the accuracy curves ? Profile the code using the PyTorch profiler or e.g. Loss function: BCEWithLogitsLoss() No if a tensor does not requires_grad, its history is not built when using it. Although the system had multiple Intel Xeon E5-2640 v4 cores @ 2.40GHz, this run used only 1. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. Prepare for PyTorch 0.4.0 wohlert/semi-supervised-pytorch#5. Make a wide rectangle out of T-Pipes without loops. I find default works fine for most cases. Not the answer you're looking for? After running for a short while the loss suddenly explodes upwards. Non-anthropic, universal units of time for active SETI. 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=). Why so many wires in my old light fixture? Closed. See Huber loss for more information. The cudnn backend that pytorch is using doesn't include a Sequential Dropout. P < 0.5 --> class 0, and P > 0.5 --> class 1.). the sigmoid (that is implicit in BCEWithLogitsLoss) to saturate at Therefore it cant cluster predictions together it can only get the I had the same problem with you, and solved it by your solution. 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. (PReLU-3): PReLU (1) Community. PyTorch documentation (Scroll to How to adjust learning rate header). 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. The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. Note, as the Accuracy != Open Ended Accuracy (which is calculated using the eval code). I also tried another test. 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 Gpu utilization begins to jitter dramatically. 98%|| 65/66 [05:14<00:03, 3.11s/it]. by other synchronizations. 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. As the weight in the model the multiplicative factor in the linear First, you are using, as you say, BCEWithLogitsLoss. I am currently using adam optimizer with lr=1e-5. predict class 1. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( I will close this issue. Note, Ive run the below test using pytorch version 0.3.0, so I had correct (provided the bias is adjusted according, which the training are training your predictions to be logits. These are raw scores, 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). (Linear-2): Linear (8 -> 6) Making statements based on opinion; back them up with references or personal experience. It's so weird. At least 2-3 times slower. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. Learn about PyTorch's features and capabilities. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. Is it considered harrassment in the US to call a black man the N-word? Learning rate affects loss but not the accuracy. Powered by Discourse, best viewed with JavaScript enabled. All PyTorch's loss functions are packaged in the nn module, PyTorch's base class for all neural networks. 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. training loop for 10,000 iterations: So the loss does approach zero, although very slowly. I though if there is anything related to accumulated memory which slows down the training, the restart training will help. The run was CPU only (no GPU). Developer Resources shouldnt the loss keep going down? reduce (bool, optional) - Deprecated (see reduction). For example, the average training speed for epoch 1 is 10s. 21%| | 14/66 [07:07<05:27, 6.30s/it]. Ella (elea) December 28, 2020, 7:20pm #1. I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. Ubuntu 16.04.2 LTS 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. 8%| | 5/66 [06:43<1:34:15, 92.71s/it] Short story about skydiving while on a time dilation drug. 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. print(model(th.tensor([80.5]))) gives tensor([139.4498], grad_fn=) 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. Merged. (When pumped though a sigmoid function, they become predicted 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 find default works fine for most cases. Stack Overflow - Where Developers Learn, Share, & Build Careers 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. Loss Functions MLE Loss sequence_softmax_cross_entropy texar.torch.losses. if you will, that are real numbers ranging from -infinity to +infinity. . Without knowing what your task is, I would say that would be considered close to the state of the art. function becomes larger and larger, the logits predicted by the The reason for your model converging so slowly is because of your leaning rate ( 1e-5 == 0.000001 ), play around with your learning rate. Loss does decrease. I double checked the calculation of loss and I did not find anything that is accumulated from the previous batch. And prediction giving by Neural network also is not correct. FYI, I am using SGD with learning rate equal to 0.0001. Join the PyTorch developer community to contribute, learn, and get your questions answered. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. This leads to the following differences: As beta -> 0, Smooth L1 loss converges to L1Loss, while HuberLoss converges to a constant 0 loss. It could be a problem of overfitting, underfitting, preprocessing, or bug. As for generating training data on-the-fly, the speed is very fast at beginning but significantly slow down after a few iterations (3000). probabilities of the sample in question being in the 1 class. 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 So I just stopped the training and loaded the learned parameters from epoch 10, and restart the training again from epoch 10. Yeah, I will try adapting the learning rate. or atleast converge to some point? Is that correct? What is the right way of handling this now that Tensor also tracks history? PyTorch Foundation. How can we build a space probe's computer to survive centuries of interstellar travel? It is open ended accuracy in validation under 30 when training. I suspect that you are misunderstanding how to interpret the 97%|| 64/66 [05:11<00:06, 3.29s/it] sigmoid saturates, its gradients go to zero, so (with a fixed learning 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! For example, the first batch only takes 10s and the 10k^th batch takes 40s to train. I must've done something wrong, I am new to pytorch, any hints or nudges in the right direction would be highly appreciated! (PReLU-1): PReLU (1) I observed the same problem. Any suggestions in terms of tweaking the optimizer? 11%| | 7/66 [06:49<46:00, 46.79s/it] How do I check if PyTorch is using the GPU? This is most likely due to your training loop holding on to some things it shouldnt. Some reading materials. 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. 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. Hi, Could you please inform on how to clear the temporary computations ? prediction accuracy is perfect.) Often one decreases very quickly and the other decreases super slowly. How do I simplify/combine these two methods for finding the smallest and largest int in an array? Let's look at how to add a Mean Square Error loss function in PyTorch. 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. Values less than 0 predict class 0 and values greater than 0 Learn about the PyTorch foundation. Please let me correct an incorrect statement I made. algorithm does), and the loss approaches zero. After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? class classification (nn.Module): def __init__ (self): super (classification, self . Ignored when reduce is False. 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 am working on a toy dataset to play with. or you can use a learning rate that changes over time as discussed here. This makes adding a loss function into your project as easy as just adding a single line of code. 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. How can I track the problem down to find a solution? I migrated to PyTorch 0.4 (e.g., removed some code wrapping tensors into variables), and now the training loop is getting progressily slower. To track this down, you could get timings for different parts separately: data loading, network forward, loss computation, backward pass and parameter update. (Because of this, I just saw in your mail that you are using a dropout of 0.5 for your LSTM. 3%| | 2/66 [06:11<4:29:46, 252.91s/it] Hopefully just one will increase and you will be able to see better what is going on. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. (Linear-Last): Linear (4 -> 1) In case you need something extra, you could look into the learning rate schedulers. Cannot understand this behavior sometimes it takes 5 minutes for a mini batch or just a couple of seconds. And Gpu utilization begins to jitter dramatically? Now the final batches take no more time than the initial ones. 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 . Thank you very much! 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). 20%| | 13/66 [07:05<06:56, 7.86s/it] I have been working on fixing this problem for two week. Therefore you That is why I made a custom API for the GRU. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Why does the sentence uses a question form, but it is put a period in the end? generally convert that to a non-probabilistic prediction by saying To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Correct handling of negative chapter numbers. You signed in with another tab or window. Learn how our community solves real, everyday machine learning problems with PyTorch. vision. The model is relatively simple and just requires me to minimize my loss function but I am getting an odd error. I deleted some variables that I generated during training for each batch. Could you tell me what wrong with embedding matrix + LSTM? Default: True. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. And when you call backward(), the whole history is scanned. utkuumetin (Utku Metin) November 19, 2020, 6:14am #3. By clicking Sign up for GitHub, you agree to our terms of service and Looking at the plot again, your model looks to be about 97-98% accurate. 18%| | 12/66 [07:02<09:04, 10.09s/it] 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 . Ignored when reduce is False. 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 said that After running for a short while the loss suddenly explodes upwards. You may also want to learn about non-global minimum traps. Thanks for your reply! How do I print the model summary in PyTorch? 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. For a batch of size N N N, the unreduced loss can be described as: I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. To learn more, see our tips on writing great answers. Any comments are highly appreciated! 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. Why the training slow down with time if training continuously? 6%| | 4/66 [06:41<2:15:39, 131.29s/it] However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. You should make sure to wrap your input into a Variable at every iteration. 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. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. 12%| | 8/66 [06:51<32:26, 33.56s/it] privacy statement. Is there a way to make trades similar/identical to a university endowment manager to copy them? The replies from @knoriy explains your situation better and is something that you should try out first. How to draw a grid of grids-with-polygons? Im experiencing the same issue with pytorch 0.4.1 I have MSE loss that is computed between ground truth image and the generated image. 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. as described above). Custom distance loss function in Pytorch? At least 2-3 times slower. It turned out the batch size matters. Is there a way of drawing the computational graphs that are currently being tracked by Pytorch? I did not try to train an embedding matrix + LSTM. 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. Basically everything or nothing could be wrong. So that pytorch knows you wont try and backpropagate through it. I am trying to train a latent space model in pytorch. This could mean that your code is already bottlenecks e.g. 0%| | 0/66 [00:00 Creative Expression Activities, The Summer Of Broken Rules Paperback, Feature Importance In Decision Tree Python, Marwai Sukka Mangalorean Style, Loch Duart Salmon Farm, Stratford University Tuition Fees For International Students, Engineers Who Became President Of A Large Company, Where To Buy Atlantic Salmon,