Pytorch getting nan. Hi, I am performing multilabel classification task.

Pytorch getting nan You switched accounts on another tab or window. I am using Pytorch 4 version. pytorch 1. data. After I added in the customized activation function, I am getting NaNs. Here, I wanted to use k-fold cross-validation. learn. Input data to model is [12,signal_length] for 12 leads. I think the code using LBFGS in pytorch_gan_zoo was fixed by @Molugan with the same hack. in the first iteration you already have a loss of ~1e+10, which will create gradients with a large magnitude and then update the parameters with a learning rate of 0. check_numerics operations Does Pytorch have something similar, somewhere? I could not find something like this in the docs I have a network which I’m trying to train a network for 2-class pixel-wise segmentation. Between, no issues et al when I use Adam as the optimizer of my network. g. PyTorch Forums Getting NaN values immediately after first backprop. I set the detect anomaly flag to True and I print (x. toTensor(); float ttt[128]; // Some assignment operations. Contrary to my initial assumption, you should try reducing the learning rate. any(numpy. This confuses me because both the square and its derivative should not give nans at any point. I had checked for NaN values in preprocessing but not Even after gradient value clipping I get nan after the backward pass. Here’s my code: My data loader: class data_gen(torch. 2259e+00, nan]. Hence, I’m looking for a way to live with nan loss. Loss should not be as high as Nan. My question is why? The Kernel: __global__ void PyTorch Forums Why is forward pass generating nan values? jpj (jpj) February 19, 2021, 10:29am E. log from getting nan. Unfortunately as I did not know the code of LBFGS and needed a fast fix I did it in a hackish manner -- I just stopped LBFGS as soon as a NaN appeared and relaunched it from the current point, i. cmlakhan Mar 29, 2022 · 1 Sometimes after a few runs though for some reason I am getting a 1x4 tensor of nan. functional. Linear(3, 9) nn. The loss is around 0. Apart from that, it doesn’t differ too much. Shiv (Shiv) September 30, 2017, 8:43pm 1. 11 or use Pytorch 1. init. Secondly, there might be an issue with the way normalizing is being done. When I then want to use the VAE model Hi, I’m trying to build a simple NN for a categorical classification problem, however, i’m not able to get any value out of the losses. detect_anomaly, but it just said ConvolutionBackward0 returns nan values. backward(). exp and torch. The only thing I change is the batch size. At first, I think it was a trivial coding problem and after a week of debugging I can’t really figure out how this occurs. And when I run this, I get nan in output. So I step by step to look what happen in the process, I check my data have nan or not, the data doesn’t have nan. The similar code is here. The code you’ve provided here looks ok. Theretically, every element of a is a super small negative value, and nn. Ask Question Asked 5 years, 1 month ago. I haven’t been able to ascertain how. 00005 with similar results. What else could be reason for the LSTM gradient and output to be NaN? J_Johnson (J Johnson) I am working with VAE and I don’t know why but during the training process, I am getting the output of VAE as well as that of the encoder as nan. zeros((3, 4)). Hot Network Questions Is sales tax determined by the state in which the SELLER is located, or the state in which the PURCHASER is located? PyTorch Forums Getting Nan after first iteration with custom loss. After utilizing and the loss for the batch was 0. sdg91 May 9, 2023, 11:16am 1. 168. This is the model i use: MULTICLASS_MODE: str = but it doesn’t show anything. You signed out in another tab or window. Intro to PyTorch - YouTube Series Hi everyone, I am training a VAE model which will take a list of numpy arrays and train a VAE model based on those arrays. Then after adding each fc layer I checked the output and again the same. view(3, 3)) x = torch. But in a second network, the outputs for each pixel are parameters of a Beta distribution, and samples are taken from it. Also, if the invalid values are created in the backward pass, you could use torch. Tutorials. Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans). At about 1600 steps, the Mask language modeling loss became NaN, and after a few more steps everything crashed down to NaN. constant_(m. Things are working now. The DataFrame I pass into the model has no NaN values, so I believe it is an issue with my model or my training/testing loop functions. 740855 Then the next value for the loss would be NAN Any help is appreciated! I added gradient clipping here: loss. The calculated loss is not nan, but the gradients calculated from the loss are nans. 1, 4. Getting NaN when training using FP16 #12510. device('cuda') SMALL = torch. Try lower learning rate (10^-4 to 10^-6) though, the result does not change from NaN. For your convenience, PyTorch LSTM has nan for MSELoss. Here is the whole code: num_epochs = 20 # 1000 batch_size = 8 learning_rate = 0. detect_anomaly yields RuntimeError: Function 'MseLossBackward' returned nan values in its 0th output. input Lightning-AI / pytorch-lightning Public. So what could be the reason I’m getting NaN after few iterations when using ReLU instead of sigmoid for the hidden layers? 2021, 9:58am 4. By default, NaN s are replaced with zero, positive infinity is replaced with the greatest finite value representable by input ’s dtype, and Adding extra data to standard convolution neural network in pytorch. backward(), it turns the values in the tensor to NaN. This is strange, and not something I would expect to happen. 1. Therefore detaching x_mask is not useful. However, this term becomes NAN values. However, the output is NaN. params also. all. I’m trying to implement a variant of capsule network where the matrix multiplication is replaced by element-wise multiplication with a vector. 1471e+00, -7. A similar issue is reported here. I am trying to train a tensor classifier with 4 classes, the inputs are one dimensional tensors with a length of 1000. During training (mostly after the first backpropagation) the outputs become nan. I tried gradient clipping but VAE output NaN same as before. What does this signify then? I am having the same problem with CNN + RNN, only the validation loss is nan and not train loss. For a On my OrinNX, in the example above, I always get NaNs at the third iteration. isnan(dataset)), it returned Encounter Gradient overflow and the model performance are really weird. Here’s an example from both datasets I’m using: Here’s my model : def get_model(num_classes): model = torchvision. If you want to drop only rows where all values are nan replace torch. After some intense debug, I finally found out where these NaN’s initially appear: they appear due to a 0/0 in the computation of the gradient of the loss w. Train Epoch: 1 [0/7146 (0%)] Loss: 0. I want to implement Pytorch Faster-RCNN module on a custom dataset that I curated and labelled. . I have inspected it many times and couldn’t find any bug myself but I am still very suspicious and unsure. (dividing non-zero by 0 gives inf; dividing 0 by non-zero gives 0; dividing 0 by 0 gives nan) the result of pretty much any function for which any of the inputs Hi all, I am a newbie to pytorch and am trying to build a simple claasifier by my own. This allows the A guess would be that BatchNorm uses Bessel’s correction for variance and this makes it NaN (computed variance is 0, n / (n - 1) * var = 1 / 0 * 0 = NaN. float64, For my neural network I noticed that my predictions were coming out to be ‘nan’ in my training loop. 5. 0 Getting nan as loss value. @jpj There is an awesome PyTorch feature that lets you know where the NaN is coming from! Documentation: Anomaly Detection. Is there something I missed or misunderstood? Any help is appreciated! Your learning rate is too high for the calculated loss, which also sums the sample losses. I changed loss function to BCE version and Gaussian loss version, but VAE’s Encoder output NaN in training phase. isnan() I'm using autocast with GradScaler to train on mixed precision. 8. the Dataset im using is larger, the problem seems to start earlier, when i use a smaller dataset everything works as expected. fasterrcnn_resnet50_fpn(pretrained=True) It seems that the loss is getting a NaN value, so I guess that the model might output NaNs. I can As the title clearly describes, the loss is calculated as nan when I use SGD as the optimization algorithm of my CNN model. 0, all have the same problem; change softmax to logsoftmax in the forward pass; change loss to logsoftmax + NLLloss Hi everyone, I am getting the error in the title after 10 epochs of training. any(). norm with dim=(1,2) in my loss computation: m = nn. I have created a simple LSTM for forecasting. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 20. e. optim. 2784, device='cuda:0', grad_fn=<DivBackward0>) tensor(21. 0+cu111 Is debug build: False CUDA used to build PyTorch: 11. One thing I can do is that after backprop is over, I can reset the gradients all zero and continue Hi, I’m trying to use PyTorch AMP after knowing that it is now out. I can’t see why you might get NaN with the mask and not I am using the MSE loss to regress values and for some reason I get nan outputs almost immediately. Learning rate is 1e-3. ptrblck March 7, 2022, 10:12pm 21. I printed the prediction_train,loss_train,running_loss_train,prediction_test,loss_test,and running_loss_test,they were all nan. Parameters. Usually, the gradients become NaN first. But as a PyTorch user, you simply need to know that a nan signifies an invalid, missing, or indeterminable numeric value. The loss. r. One KL divergence component of my model is the KL term between Kumaraswamy and Beta distribution. It happens every time. (High lr also gave NaN) Even after using gradient clipping also grad norm and output shows NaN. 6468e+00, 7. (NaNs) as zero. Eecrease the learning rate to e. The cuda driver and PyTorch were updated during this period. 3. From below 3rd time, I was getting NaN values from the loss function. or. For some reason, removing #include <torch/torch. It is Writing a PyTorch Neural Network isn’t as trivial as it seems. On the Imagenet dataset, the Loss is increasing exponentially and then giving ‘nan As the title suggests, I created a tensor by a = torch. python. Notice that it is returning Nan already in the first mini-batch. ValueError: Target size (torch. These values don’t seem to be quite large, I am attaching the logs of max/min values of input and output to torch. – J. is_nan and the tf. Traceback originated with line: I am a beginner about pytorch. step() caused the parameters to become NaNs? Before I saw the other posts I was trying to reason I’m trying to implement the code from here using a custom data set. sigmoid(a2·z_proj+a3)+a4·z_proj+a5 I’m running into the same NaN softmax issue in a modified version of VGG11. Number of training examples: 12907 Number of validation examples: 5 Number of testing examples: 25 Unique tokens in source (en) vocabulary: 2804 Unique tokens in target (hi) vocabulary: 3501 The model has 214,411 trainable parameters Is there a Pytorch-internal procedure to detect NaNs in Tensors? Tensorflow has the tf. More particularly, I’m using the non-local block from here: I am getting some weird behavior when using torch. Bite-size, ready-to-deploy PyTorch code examples. I have tried torch. Any advice would help. Note: for pretrained network on ImageNet you should use Adding on to Fábio's answer (my reputation is too low to comment): If you actually want to use the information about NANs in an assert or if condition you need convert it from a torch::Tensor to a C++ bool like so. 4881. I have used efficientnetb3 model (pretrained) with minor transformations. I also checked the model while running just the second pipeline, and found that the problem persists only with second pipeline. Here is a script to do that for PyTorch. The anomaly detection gives me “RuntimeError: Function ‘MseLossBackward’ returned nan values in its 0th output. I’m trying to build my own classifier. For single GPU I use a batch size of 2 and for 2 GPUs I use a batch size of 1 for each GPU. tensor(1e-10, dtype=torch. I guess you should also include some of your training code to help troubleshoot. Getting nan as loss value. std() function returns nan for single values. I want to implement a supervised regression model. Sometimes it would give nan but mostly, it would give some result. I am working on Melanoma Classification task where I have to classify the patients into two categories on the basis of their skin images. I would consider not using . bias and half of the weights are becoming NaNs by the second iteration, all of the weights are NaNs by the third Even though most loss functions seem to have this problem some like torch. There is a high chance that you should not be able to learn anything even if you reduce the learning rate. cmlakhan asked this question in code help: NLP / ASR / TTS. PyTorch Forums Loss function returning NaN Loss. I’m working with MNIST dataset and I’m normalizing it before training. which as I mentioned in my first post isn’t very helpful in this case since the NaNs are already present in the input to the loss function. You can circumvent that in a loss function but that weight will remain high. AdamW with the torch. any with torch. My VAE model is inspired by the Transformer model as the input arrays is coming from a Image transformer. 4k; Star 28. x), I’ve been trying to implement some activation functions from scratch like mish or ELU, etc. But, when I remove both the layers from the network it works perfectly fine. Its usage looks like: Pass in the PyTorch tensor you want to check, and torch. If I have a loss function is the form torch. pytorch 3. As far as I understand it, there are two ways to obtain a nan result: divide 0 by 0. Just to test my understanding, I am trying to use some columns of Dataset I am getting nan values as loss. nn. In practice, if x == 0 pytorch returns 0 as gradient of torch. While I start training my model, everything seems to be fine. grad) # grad is [nan, 1], but expected [0, 1] I think the reason why this is happening is that the backwards pass for indexing (y[mask]) returns a tensor that has 0's in it However, why trainng this I am getting NAN as my predictions even before completeing the first batch of training (batch @SandPhoenix, Can u check for each value in tensor x any values nan using isNAN() funciton. What could be the issue and how to solve it? ⋱ Why i trained more that 153440 iteration, but PyTorch provides the simple torch. From what i understand duringh the backward propogation it is returning Nans. I am trying to train a transformer model using FP16 precision, but the loss eventually goes to nan after around ~1000 steps. Hello everyone I’m testing how suitable the models made available by torchvision are at, among other things, analyzing both images and audio (In regards to the audio, I first extract MFCC features from the audio clip, and turn said MFCC features into an image, as I saw some people doing it, and saying that apparently it’s somewhat common practice). Loss returns nan in tensorflow. pytorch custom loss function nn. 2 cuda 10. nan values from pytorch 1d tensor. Whats new in PyTorch tutorials Returns a new tensor with boolean elements representing if each element of input is NaN or not. I am working on a Cuda kernel, which calls some simple helper methods for linear algebra. The loss increases exponentially with each step only on GPU. ## Training data loading PyTorch Forums Getting nan gradient with custom loss function. 38 after the first step and then goes to NaN beacause the tensors returned by out, latent_loss = model(img) are filled with only NaNs. Function 'SigmoidBackward' returned nan values in its 0th output. I am using efficient net. PyTorch Forums Model weights not getting updated when using autocast. I have passed the offending input tensors directly to the network one at a time, with grad enabled, and am unable to reproduce the issue on either CPU or GPU. Description: I have been trying to build a simple linear regression model with the neural network with 4 features and one output. But the pytorch-vision has mentioned that we can use all Nan loss appears only in the case of using wide_resnet_fpn or Resnext_fpn as a backbone whereas classic resnets with fpn are working properly as backbone in FRCNN. But the loss. Currently, I wrote a code that gives me NAN when learning rate is 0. item<bool>(); // will be of type bool I am using Pytorch 4 version. I am checking my weights every 10 epochs. vision. However, I refactor the exact same code into the lightning format it does not work due to overflow/underflows in the Filter out np. PyTorch Forums Getting nan after the first backward pass even after clipping the gradients. Great job for finding the problem! I bookmarked this thread and attend to it every time I have a NaN problem. zeus (pyzeus) May 23, 2020, 12:53am 1. Modified 5 years, 1 month ago. Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, you no I am trying to implement MNIST using PyTorch Lightning. This optimizer automatically detects NaN values and skips the current batch, effectively "rewinding" the training process to the previous batch – Logging gradients in on_after_backward shows NaNs immediately. I would be a greatest help if someone guide me how to correct this very basic model. The inputs to the matmul operation are fine. When I use adam to optimize as written in the code, it is very smooth, but I rewrite it as lbfgs optimization, and the loss always becomes nan after a period of time. The input, denoted by X, has as shape of (7471, 43), and the output, denoted by y , has a shape of (7471, 6). inf). akpas (AP) May 23, 2022, 1:00pm 1. 7464e-02, 2. I checked and found some solutions to it, like reducing the epoch but my epoch is already very low. Before getting nans (all the tensor returned as nan by relu ) , What I did is I used the new integrated function in pytorch called nan to num to turn them into 0. for custom activation function. ” Hi, for me, Test inputs of 0 are also giving nan output. nan_to_num¶ torch. My target is to predict the correct adjacency matrix. However, Hi @ptrblck , So i am using Segmentation_Models_pytorch_lib for a multiclass classification task where each pixel gets a prediction for the population living in it based on a input that consists of an rgb image and corresponding height values. 3. I am programming the ladder network and realize about a possible bug in your backward function. Learn the Basics. 10 How to assign NaN to tensor element? 4 Why does my pytorch NN return a tensor of nan? Load 7 more related questions Show fewer related questions Indeed, I forgot to mention this detail. Hi, pytorch gurus: I have a training flow that can create a nan loss due to some inf activations, and I already know this is because of noisy dataset yet cleaning up the dataset is hard/disallowed and dataset/dataloader is fixed. 1e-8 and remove the size_average=False argument. aam541 (mohammed alawad) September 25, 2018, 6:56pm As your script is quite complicated, you could try to build PyTorch from source and try out the anomaly detection, which will try to get the method causing the NANs. sthitap2 (Sthita PyTorch Forums Getting NaN when using either BatchNorm or Dropout or both. Todo so a build a neural network based on the tutorial here. What is incorrect here? Following is the code I This is not directly a PyTorch question, but I’m going to post it here in hopes that someone smarter than I might be able to help anyway. torch. 9351e+03, 2. 04. weight, 0) nn. Pytorch loss is nan. Module): def The result is that suddenly the model returns nans even though all weights in the model appear reasonable. Run PyTorch locally or get started quickly with one of the supported cloud platforms. When I was trying, I was getting Nan. I’ve checked that the nan arises in I implemented a custom activation function that appears to occasionally cause NaNs in the output. PyTorch Recipes. contrib. (1 ,0 ,. utils. divyesh_rajpura (Divyesh Rajpura) March 21, 2020, 7:13pm The problem is when I use either BatchNorm or Dropout or both in TDNN, it gives me NaN after some iteration (after 8 to 10 batches only). I’ve got big model, which has resnet (for image processing) and ulmfit (for text processing) connected on the outputs of them. 1. dataset: official MNIST dataset from each framework model architecture: simple dense network(25 layers with 500 neurons each) lr: 1e-3 (I don’t Thank you for reply. user_123454321 (user 123454321) August 11, 2020 Loss coming out to be "nan" on a pytorch lightning module #12137. ) = nan nan nan nan nan nan nan nan nan nan nan nan nan PyTorch Forums PyTorch Forums Weights getting 'nan' during training. I am trying to use the cross_entropy_loss for this task. the means of the gaussian. 1642, device='cuda:0', grad_fn=<DivBackward0>) tensor(8. All of the examples dealt with MNIST but my model uses ImageNet images so it’s a big bigger than the examples. Pleas I have two outputs of my model of the shape [1,1024] and want to compute the cosine similarity loss between the vectors to update the weights. To overcome this problem I have tried downgrading my PyTorch from 11. I have tried xavier and normal initialization of weights and have varied learning rate in a wide range. Currently I’m debugging the network with a check for NaN in the output that I hope will allow me to reproduce this more reliably, but I wanted to post my function in case I’m doing something inherently stupid. I am working on a project related to ANN regression. Hot Network Questions Could you try to isolate the iteration which causes the NaNs and check the input for invalid values as well as the loss in this and the last few iterations? PyTorch Forums Getting model output as nan. swa_utils. I want to replace average pooling in one architecture with DC part of DCT transformation, but when I replace this with each other I got nan values for loss. Mostly it didn’t. 3497e+05, Internally, the IEEE 754 floating point specification uses a specific bit pattern to encode nan values. nan_to_num (input, nan = 0. You’ll find the build instructions here. Given that it happens after a few epochs I guess the gradient is either vanishing or exploding. However I’m still getting NaN errors: Function 'LogBackward' returned nan values in its 0th output. Modified PyTorch loss function BCEWithLogitsLoss returns NaNs. 12 with data format NCHW. 176097 Train Acc: 0. So, I think it’s better to investigate where those bad values are generated, for example, by using Getting Nan after first iteration with custom loss. But the pytorch-vision has mentioned that we can use all of them in the below model . import torch import numpy Getting nan loss in Adam Model using PyTorch. bias. 5 # for SGD log_interval = 50 class Hi, I’m getting NaN values after the first backprop. All I did is change the input shape, denoted by PyTorch Forums Nan value in convolution output. 8k. The target has 6 outputs for each. My loss function was using a standard deviation and pytorch's . Softmax(a) should produce near zero output. ← previous page. Unfortunately, the same workaround on the bigger ROS2 node guarantees only good 20 iterations (instead of 2) but it eventually restarts with NaN and good values alternatively. For small dataset, it works fine. Therefore detaching x_mask is not useful. However, if I use two GPUs, I get nan loss after a dozen epochs. NanLossDuringTrainingError: NaN loss during training. I am implementing the concept of Graph Variational Autoencoders. autograd. When you get inf bias, one might expect an inf gradient to also propagate to the earlier operations (because for a + b, both a and b get the same gradients as the sum). log(-B*torch. Having said that, you are mapping non-onto functions as both the inputs and outputs are randomized. I have logged the offending input tensor (no NaNs or non-finite vals), the corresponding output (all NaN) and loss (NaN). step() caused the parameters to become NaNs? Before I saw the other posts I was trying to reason I’m using torchvision 0. But once you have inf and matrix multiplications with varying signs, you very soonish get +inf and -inf which gets you NaN in further additions (such as in matrix multiplication I’m new to Pytorch. And I have checked the data with numpy. Things we’ve tried but not working pytorch 3. I am not sure if it is just the learning rate problem or my code problem. I let you know about the points that I have been able to confirm. In particular, one helper method is the dot product. Why is dropout outputing NaNs? Model is being trained in mixed precission. AveragedModel wrapper. After all, I am getting Nan from the CrossEntropyLoss module. I am running two Conv2d layers on a tensor of nans and getting -infs as output. I had checked torch. t. The task is to reconstruct the 3D face of a single photo. Sometimes it did and sometimes it didn’t. It always returns NaN. But when I trained on bigger dataset, after few epochs (3-4), the loss turns to nan. This is my network (I’m not sure about the number of neurons in each layer). The other parameters are exactly the same. The learning rate is 0. Loss is Nan - PyTorch. 0, which I’ve updated to the latest nightly for 2. I am reading a CSV file with rows as my data. Here is a minimal MNIST example that works with native PyTorch. There’s usually exactly one NaN in the first batch - interestingly the exact index of where in the batch the NaN occurs (or whether After some time, I am getting NaN as output from the pred = model(xb). fill_(-np. However, However, I’m getting NaN values in my model output. Hi, I did do that. exp(X)) what should be the best way to tackle the torch. afsaneh_ebrahimi (afsaneh ebrahimi) July 2, 2021, 5:54pm 1. Familiarize yourself with PyTorch concepts and modules. Reload to refresh your session. exp) SaminYeasar (Samin Yeasar) August 11, 2020, 9:58am 1. Hot Network Questions Movie about dirty federal agents If I check "Disable BitLocker automatic device encryption" in Rufus, will I be able to later activate the BitLocker device encryption? iconv fails to detect valid utf-8 character as utf-8 Adjoint functor theorem for totally distributive category PyTorch Forums LSTM outputs NaN. PyTorch LSTM not learning You signed in with another tab or window. Does anyone have any suggestions for how I can troubleshoot this? I have a real-valued version of the network where I'm not using ComplexPyTorch and everything works fine so I can't help but feeling that during the network's backward pass there is a problem with my layers being in a PyTorch Forums Why is forward pass generating nan values? jpj (jpj) February 19, 2021, 10:29am E. set_detect_anomaly(True) and rerun the code. Delete Firstly, a good idea might be to debug why you’re getting nans in your landmarks tensor. When ever the result of a variable which is part of the cost is 0, the backward method evaluate a NaN. The original function I used was the following local_device = torch. backward() On training, the LSTM layer returns nan for its hidden state after one iteration. Please forgive me if this is a very stupid question or violates any of the unsaid rules of stack overflow. I also replace We are doing a customized LSTM using LSTMCell, on a binary classification, loss is BCEwithlogits. This problem doesn't occur when training on a single GPU (same learning rate). Dataset): def __init__(self, files): self. In this particular case the denoising function is given by: mu_i=a1·nn. Hi, I am creating a custom cross entropy function and the aim to is get the gradients for some model parameters. 7 but that only changed the device from using cpu to gpu. data = files def __getitem__(self, i): tmp = self. But I found my loss and predict nan both after the first epoch. So if i do net_1(torch. detection. How does this fit into your previous findings, i. @carmocca The issue is caused by PyTorch lightning, not PyTorch. Commented Mar 18, 2021 at 11:01. Things we’ve tried but not working. Custom loss function in Here is my pytorch implementation of Transformers model which i am using for ecg disease classification. If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. I am using Tensorflow's iris_training model with some of my own data and keep getting. any(tensor. These NaN values propagate through calculations, infecting any result they touch with more nan values: It is returning loss as Nan. Viewed 2k times 0 . Also have a look at Getting nan as loss value. The point to note is while training the same model i don’t get nan on x and on x2. To handle NaN values during training, you can use PyTorch's NaN-aware optimizer, such as torch. However, I get nan value of loss after about 17 epochs when I train the model. However, there are no obvious divisions, so it’s unclear to me how the nans could be forming. I looked up the different answers on SO but even after trying them out I couldn't solve the bug. Following is the array of singular values I get from svd(): [4. While training i am getting nan values in loss and in model. Pr I have added a regularization term, which adds spectral norm to the loss term. If dim is a list of dimensions, reduce over all of them. chetan06 (Chetan Pandey) June 3, 2020, 5:55pm 1. tom (Thomas V) December 26, 2017, 8:34pm 2. isnan(),dim=1)] Note that this will drop any row that has a nan value in it. My function is In practice, if x == 0 pytorch returns 0 as gradient of torch. This is my DCT transform over all channels: Please start by identifying which is the corresponding input tensor for which you get the NaN values. The first input always comes through unscathed, but after that, the loss quickly goes to infinity and the prediction comes out as a matrix nan. h> seems to remove the NaNs. I don’t know if this is a bug with PyTorch or if my code is just not working. Even I don’t know. Right now, I have figured out the input causing this NAN and removed it input dataset. For an N-dimensional tensor you could just flatten all the dims apart Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company Oh, it’s a little bit hard to identify which layer. My model is throwing NaNs intermittently. I managed to sort the issue out by clipping the action to be in some finite range which does indeed suggest that it is due to the agent picking very high action values and then receiving a very large negative reward for doing so. Hi, I am performing multilabel classification task. neither the model output, nor the parameters or the gradients were having invalid values, but the optimizer. You could add print statements in the forward method and check, which activation gets these invalid values first to further isolate it. One of the torchtune devs gave me a recipe for training in fp16, this more than tripled my training speed Use PyTorch's isnan() together with any() to slice tensor's rows using the obtained boolean mask as follows:. You can directly print these tensors in the forward pass to get their values for debugging. If I run my code without Anomaly Detection, I get NaN’s in my data. Home ; Categories We traced the problem back to loss. log(torch. Without knowing more about your data, it is fairly impossible to solve your problem by just looking at the code. Tmr. filtered_tensor = tensor[~torch. 7 Pytorch: test loss becoming nan after some iteration. @SandPhoenix: do please check your input for NAN’s before passing it to the Network Layers. – dennlinger. 7355e+05, 2. I am not sure why it is happening. So my only guessing is that my activation function leads to NaN in my data? Unfortunately, after 2k or 3k iterations (where the loss reduces considerably), I start getting NaN’s as the loss value. I try to use pre-train model to do classification problem. My inputs are normalized. it won’t train anymore or update. 13. 0, 5. jpj (jpj) February 22, 2021, 9:46am 3. Since landmarks are (x,y) pairs on an image, it Irrespective of various set up, I am getting ‘nan’ in some filters at 10th epoch. 0001 momentum = 0. backward returns nan at catbackward0 and all the gradients become nan. On the Imagenet dataset, the Loss is increasing exponentially and then giving ‘nan’ values as shown below. I’m adding a snippet of the code. Whats new in PyTorch tutorials. ne has gradient zero almost everywhere and gradient undefined when x == 0. ne. ones(m1,m4,m5)) i get nan for x2 value while i don’t get nan for x1 value . Now irrespective of whatever the loss might be, same, negative or for that matter even nan weights should not be the same. step() caused the parameters to become NaNs? Before I saw the other posts I was trying to reason When using detect_anomoly, I’m getting an nan in the backward pass of a squaring function. From some playing around I can tell that the seed doesn't seem to have an effect. 1 but works fine with learning rate of 0. The loss function used is mse loss. py at master · kuanghuei/SCAN · GitHub), nan and inf can happen in forward of l1norm and l2norm. Any help in this regard would be greatly appreciated. rand((2, 3)) out = m( PyTorch Forums Network forward output is Nan, without backward. Any suggestions how can I correct my loss function? maybe With Torch(1. eye_(m. ,. Cant get model to train. 1174e+05, 2. Will big learning rate really cause NAN? I am using double tensor in my code. Notifications You must be signed in to change notification settings; Fork 3. Complex values are considered NaN when either their real and/or imaginary part is NaN. Bidirectional LSTM gives Loss as NaN. I am quite new to machine learning, python and pytorch. We traced the problem back to loss. I On my OrinNX, in the example above, I always get NaNs at the third iteration. I noticed that when the length of the Dataloader is bigger i. Training proceeds normally. Returns a namedtuple (values, indices) where values contains the median of each row of input in the dimension dim, ignoring NaN values, and indices contains the index of the median values found in the dimension dim. For example, in SCAN code (SCAN/model. ZdsAlpha (Zds Alpha) August 12, 2020, 2:58pm 5 I have a training set with 43 variables and 7471 observations. I am new to training neural nets. I. Model Class: class PKLSTM(nn. Is it possible to find out what becomes nan first? Yes, that was the suggestion in my previous post. I’m able to get the code to run with the librispeech dataset but when I use my dataset I get the following: Train Epoch: 1 [0/2875 (0%)] Loss: 10. forward({inputs}). My encoder model looks something like this class When indexing the tensor in the assignment, PyTorch accesses all elements of the tensor (it uses binary multiplicative masking under the hood to maintain differentiability) and this is where it is picking up the nan of the other I assigned different weight_decayfor the parameters, and the training loss and testing loss were all nan. The convolution will not get nan values if I run the model in another python process with the same states and inputs. Hello! I’ve trained a stand-alone VAE based on the PyTorch example and a few other bits of code found on github - it works well and my output images look quite good. 8 to 11. Reduce the learning rate smaller, 1e-10, but the loss still nan I write the break switch when I get nan When I train my network with a single GPU, the training process terminates successfully after 120 epochs. asad-ak Feb 28, 2022 · 1 comments Run PyTorch locally or get started quickly with one of the supported cloud platforms. I still always get nan in loss when training. From debugging, i found on every occasion, dropout was the layer whose output was NaN first. I tried using gradient clipping, but it didn’ work. cb_zhang (Cb Zhang) Could you try to run the code with anomaly detection and check, which layer creates the NaN? cb_zhang (Cb Zhang) September When I train my network with a single GPU, the training process terminates successfully after 120 epochs. 0001 and 0. nan can occur for some reasons but mainly it’s oftentimes 0/inf related maths. auto output2 = torch::from_blob(ccc Hi everyone, I have a variational autoencoder architecture and I use stick-breaking prior . Common problems include in-place operations, broken gradient chains, and, worst of all, your model parameters updating as NaN After the first batch I’m getting nan as outputs, my loss. data[i] tmp = . Hi, Thanks for such a quick response. I added This NAN was not present in the input as I had double checked it, but got introduced during the Normalization process. ERROR:tensorflow:Model diverged with loss = NaN. x * x_mask is basically an identity mapping for some elements of x in which case the gradients flow through unmodified, or a zero mapping in which case the gradients are blocked. models. exp. isnan() function to check for nan elements within tensors. 9132, device='cuda:0', grad_fn PyTorch Forums Getting nan value loss with DCT transform. 7720e+05, 3. I already checked my input tensor for Nans and Infs. 01. 7wik (Satwik Kondamudi) January 11, 2019, 6:15am 1. item() returns nan in the first epoch. It also works normally if I use PyTorch 1. 2 The result is that suddenly the model returns nans even though all weights in the model appear reasonable. I checked if just the pretrained model was giving nan. I also replace I have used different optimizers with different LRs and Momentum but on applying each one of them after loss. So if, you can afford to use batch size > 1, that would solve the NaN problem for you. Isaac_Kargar (Isaac Kargar You signed in with another tab or window. Thank you in advance. Unanswered. It works well with a baseline network that just predicts the probability of the pixel being 1. 187500 tensor(0. Size([1000, 1])) 1. What is wrong i am not getting. Hello. Even with the lstm layer removed I get nan as output. Bhavishya_Pandit (DL Enthusiast) January 16, 2021, 2:55pm 1 After a few iterations both model encounter Nan's in backbone output. Is this the issue? In the below code q_y is an intermediate output in my network. jpj (jpj) March 12, 2021, 10:21am 5. 0005 but I’ve also tried with 0. But after some time (and a lot of batches) model starts giving NaNs as the value of Thank you @AlphaBetaGamma96. I keep getting nan losses during training in a very unpredictable way, after the first one all the parameters in the model become nan, forcing me to stop the training and start again. I have 100 folders of different class Do you have any suggestions on how to debug the reason behind getting nan or determine the potential causes? With only 1dCNN, the train loss increases in the second epoch, and stops updating. The result is that suddenly the model returns nans even though all weights in the model appear reasonable. 0, all have the same problem change softmax to logsoftmax in the forward pass Get Started. My dataset is a 5518x512 tensor (5518 observations, 512 features per observation) and my labels is a categorical 5518x1 tensor, which i converted into a 5518x5 one Hello, I’ve read a lot of topics connected to my problem, but I haven’t found solution for it yet. The value of y_kld is of order 1e-8. Please help me. I am even using clone so as to create a copy and compare those. my hack was outside of the LBFGS code (fast dirty fix). half() until after you've got your network running with normal full If there is one nan in your predictions, your loss turns to nan. PyTorch version: 1. I get ‘nan’ grad for the parameters. 2 LTS (x86_64) GCC version: (Ubuntu 9. Thanks. monitors. Traceback tensorflow. I am getting nan value in the convolution output like this [ 6. How to create the custom loss function by adding negative entropy to the cross-entropy? 1. grad=None. I have a dataset with nearly 30 thousand images and 52 classes and each image has 60 * 80 size. When trying to use a LSTM model for regression, I find that I am getting NaN values when I print out training and testing loss. PyTorch's detect_anomaly can be helpful for determining when nans are created. asad-ak asked this question in code help: CV. CrossEntropyLoss. The implementation detail looks straightforward, Getting nan as loss value. Loss coming out to be "nan" on a pytorch lightning module #12137. ones(m1,m2,m3),torch. Has anyone encountered this problem? Thanks I’m running a convnet, and getting nans. But after defining the model during training the loss is going to nan after the first epoch. after this I started to get all the tensors to nan out of PyTorch Forums Avoid 'Nan' while computing torch. autograd. The mean PyTorch Forums Getting Nan values after first ietration. Size([1000])) must be the same as input size (torch. To handle skew in the classes, I’m using the Dice loss. if your input contains Infs or very large values in their magnitude, the result might overflow and could be set to NaN in further operations. As you can see, I am running for only 1 epoch, so I am getting the NaN in the first epoch for some batch. 5-rocm6. my code as follows： at::Tensor output1 = model_. 0. Commented Sep 18, Large, exploding loss in Pytorch transformer model. torch::Tensor myTensor; // do something auto tensorIsNan = at::isnan(myTensor). I’m not sure if something is wrong with my layers or it’s just some syntax issue. SmoothL1Loss() do not (as long as the number of terms in the series is less than 40) so it would be interesting to see if it had something to do with the RuntimeError: Function ‘SvdHelperBackward’ returned nan values in its 0th output. The problem is I am getting the NaN value from the loss function (for at least 1 fold). 0, posinf = None, neginf = None, *, out = None) → Tensor ¶ Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan, posinf, and neginf, respectively. Hi, I’m getting NaN values after the I originally had pytorch 2. no runtime error, but it seems like it is not the input’s problem, as I still got the same nan after a while without getting any Custom losses tend to be way less stable But just check you are not passing negative values to a log, doing anything/0 these kind of things. I started working on the titanic data set recently. But then, I broke it down. I traced this problem to the output of matmul having some infinite values. gnm oklvtls sdkj dhq escydc acupk ljcbqe rbycbn ntsn oyxiv