What is left is the actual research code: the model, the optimization and the data loading. Before we debug this code, we will organize it into the Lightning format. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. It may help to know that I feel like this has happened with other projects of mine in the past. Dropout is used during testing, instead of only being used for training. How do I check if PyTorch is using the GPU? My model doesn't seem to learn anything. I will run your notebook with HMDB51 for 10 epochs and show to you a log of the training. Can I spend multiple charges of my Blood Fury Tattoo at once? For your own datasets you would have to compute it yourself. You signed in with another tab or window. I have solved the problem, because my training data has very small boxes, so the smoothed l1 loss(log(0)=-inf) become -Inf. Add dropout, reduce number of layers or number of neurons in each layer. Define a loss function. PyTorch Lightning has logging to TensorBoard built in. If loss decreases, means its a hyper parameter problem with SGD. It can be every epoch or if this is too costly because the dataset is huge it can be each N epoch. These nasty bugs are hard to track down. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. The amount of parameters of the tutorial model and my net are about the same at ~62k. This one here computes the histogram of the input data before it goes into the training step. What is a good way to debug this? . A PyTorch library for easily training Faster RCNN models With the introduction of torcheval, does it make sense to Visualizing word embeddings using pytorch. I am able to fix the above issue, but now I am getting another issue. Hi, I am taking the output from my final convolutional transpose layer into a softmax layer and then trying to measure the mse loss with my target. I can try to reproduce it, since I am working on a similar project. 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 . Hi @sacmehta , have you tried a smaller learning rate? If you shift your training loss curve a half epoch to the left, your losses will align a bit better. Have a question about this project? Regex: Delete all lines before STRING, except one particular line. https://pytorch.org/docs/stable/nn.html#torch.nn.SmoothL1Loss, Got nan Regression loss and inf Classification loss on pytorch 1.5. I am trying to reproduce your results, but validation regression loss is infinte. A fast learning rate means you descend down quickly because you likely are far away from any minimum. THIS was the reason my loss was not decreasing. if not, its a problem with code or data. Lets have a look at a technique that lets us detect such errors very quickly. Thank you. This can be diagnosed from a plot where the training loss is lower than the validation loss, and the validation loss has a trend that suggests further improvements are possible. I have train the X3D and SlowFast on the HMDB51 by mmaction2(default config samples one clip from one video), top1 acc is also about 30%, and validation loss can decrease. Symptoms: validation loss is consistently lower than the training loss, the gap between them remains more or less the same size and training loss has fluctuations. What is the difference between the following two t-statistics? Pytorch identifying batch size as number of channels in Conv2d layer. When the validation loss is not decreasing, that means the model might be overfitting to the training data. If you provide a colab short script that reproduces the problem I will look at it. Then I tried to train hmdb51 without pretrained, the evaluation accuracy is as follows: Did I miss any key points during finetuning or could you give any clues about this? If these conditions are met, the model passes the test. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Because I used the same function for my own dataset and got the same problem. Asking for help, clarification, or responding to other answers. Well occasionally send you account related emails. Would be great if you can provide s. Batchsize is 4 and image resolution is 32*32 so inputsize is 4,32,32,3 I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss function. I figured the problem is using the softmax in the last layer. I reproduced your example (tweaking a bit the code, there are typos here and there), and I don't even see a change in the loss: it is stuck at 2.303. Sign in Are Githyanki under Nondetection all the time? 2018-12-01 12:38:51,741 - root - INFO - Epoch: 0, Step: 300, Average Loss: 7.1205, Average Regression Loss 2.2209, Average Classification Loss: 4.8996 For example, in PyTorch I would mix up the NLLLoss and CrossEntropyLoss as the former requires a softmax input and the latter doesn't. 20. Validation loss not decreasing. If something is not working the way we expect it to work, it is likely a bug in one of these three parts of the code. Modified 2 years ago. This might just be an issue with how I fundamentally build my networks. Viewed 616 times 1 $\begingroup$ So I am currently trying to . The log says the regression loss is Inf. Making statements based on opinion; back them up with references or personal experience. If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each Code, training, and validation graphs are below. For the benefit of clarity, the code for the callbacks shown here is very simple and may not work right away with your models. Your learning rate and momentum combination is too large for such a small batch size, try something like these: Update: I just realized another problem is you are using a relu activation at the end of the network. Any idea what might go wrong? I dont remember it. I've managed to get the model to train but my loss is not decreasing over time. Extending TorchVisions Transforms to Object Detection Getting Started with Facial Keypoint Detection using Deep is it possible to use several different pytorch models on Press J to jump to the feed. Let's say that we observe that the validation loss has not decreased for 5 consecutive epochs. 'It was Ben that found it' v 'It was clear that Ben found it'. Ask Question Asked 2 years ago. 2018-12-01 12:40:18,564 - root - INFO - Epoch: 0, Validation Loss: inf, Validation Regression Loss inf, Validation Classification Loss: 10.0192. Below is the implementation for n = 3: And here is the same in a Lightning Callback: Applying this test to the LitClassifer immediately reveals that it is mixing data. Love podcasts or audiobooks? When I trained Movinet with my own dataset. Hi, @sacmehta, how do you fix the inf problem, I am lost in it. Relu before cross entropy loss throws away information about class scores. 5. Thank you. What you did seems correct, you compute the loss of the whole validation set. But wait! Learn on the go with our new app. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. It is important that you always check the range of the input data. This is not a good solution, because it pollutes the code unnecessarily, fills the terminal and overall takes too much time to repeat it later on should we need to. By clicking Sign up for GitHub, you agree to our terms of service and Is this the case in our example? Well, I rewrote most of the SSD code from here: You can find my implementation here and see if it helps: Thanks for your reply, could you tell me what caused the inf loss? So I tested x = torch.reshape(x, (-1. However, it is not much effort to generalize it. I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss function. Finally got fed up with tensorflow and am in the process of piping a project over to pytorch. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. Deploying A MERN Application To Heroku(A Step-by-Step Guide), Acing the Social Component of Technical Interviews, Boost the flexibility of your Microservice architecture with Spring Cloud, How to Use on Conflict in INSERT Statement in PostgreSQL, RuntimeError: 1only batches of spatial targets supported (3D tensors) but got targets of size: : [64], transforms.Normalize(128, 1) # wrong normalization, transforms.Normalize(mean=0.1307, std=0.3081). Anyone an idea why this might happen? Writing good code starts with organization. Every Deep Learning project is different. After a day of racking my brain trying to figure this out. Pytorch tutorial loss is not decreasing as expected, 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. Stack Overflow for Teams is moving to its own domain! 4. Whats wrong? The curve of loss are shown in the following figure: It also seems that the validation loss will keep going up if I train the model for more epochs. Use a more sophisticated model architecture, such as a convolutional neural network (CNN). Now, it's time to put that data to use. save valuable debugging time with PyTorch Lightning. Well occasionally send you account related emails. and also creates a meaningful label for each histogram. And we can confirm this by looking at the histogram in TensorBoard. The TrainingDataMonitor is a bit nicer because it works with multiple input formats (tuple, dict, list etc.) @sacmehta I have the same issues with you, not only validation loss, sometimes the training loss occurs inf of Average Loss, Average Regression Loss, but the classification loss continues to decline how do you solve it? Have you made sure the logsoftmax is being performed along the correct axis? Try training your network by removing last relu from conv5 and keeping lr=0.01 and momentum=0.9. Horror story: only people who smoke could see some monsters. Make a wide rectangle out of T-Pipes without loops. By rejecting non-essential cookies, Reddit may still use certain cookies to ensure the proper functionality of our platform. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. i have used different epocs 25,50,100 . Can you explain it? Why is the loss function not decreasing in PyTorch? Any comments are highly appreciated! The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing.. Loss is not decreasing. 2018-12-01 12:39:27,837 - root - INFO - Epoch: 0, Step: 500, Average Loss: 6.6482, Average Regression Loss 1.9754, Average Classification Loss: 4.6728 ( see the below image ) The idea is simple: If we change the n-th input sample, it should only have an effect on the n-th output. During training, the training loss keeps decreasing and training accuracy keeps increasing slowly. Reason #3: Your validation set may be easier than your training set or . The regression loss here is Smooth L1 Loss. U-Net pytorch model outputting nan for MSE but not L1? The softmax in line 35 is applied to the wrong dimension: And there you go, the classifier works now! @relot I just realized I have another advice for you, I think its more important. It is separate from your research code; there is no need to modify your LightningModule! When taken at dim=1, the loss hovers around 4.15x, Use Adam optim. It's unlikely these are problem related to part of the code of this repository. Loss is nan while validation accuracy stays consistent in Loss doesn't decrease substantially using Flux.jl Loss of all automation data and audiofx of the dmd when Loss of lines when converting from SketchUp to .GLB. Better: Write a Callback class that does it for us! If model weights and data are of very different magnitude it can cause no or very low learning progression, and in the extreme case lead to numerical instability. We quickly find that there is a problem with normalization in line 41: These two numbers are supposed to be the mean and standard deviation of the input data (in our case, the pixels in the images). It might be helpful if you check out some input data and intermediate values. I've managed to get the model to train but my loss is not decreasing over time. In this example, neither the training loss nor the validation loss decrease. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by the magnitude of one order When these functions are applied on the wrong dimensions or in the wrong order, we usually get a shape mismatch error, but this is not always the case! In this blog post, we implemented two callbacks that help us 1) monitor the data that goes into the model; and 2) verify that the layers in our network do not mix data across the batch dimension. Find centralized, trusted content and collaborate around the technologies you use most. Did Dick Cheney run a death squad that killed Benazir Bhutto? Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? The text was updated successfully, but these errors were encountered: Can you provide more information? What is a good way to debug this? Make sure the feature map size used for prior generation is the same as feature maps from CNN used for SSD. Pytorch is an open source machine learning framework with a focus on neural networks. Its been a while. The fact that Lightning sanity checks our validation loop at the beginning lets us fix the error quickly, since its obvious now that line 65 should read. Test the network on the test data. Thanks. But the most commonly used method is when the validation loss does not improve for a few epochs. There are many ways to do this. There are a few ways to reduce validation loss: 1. The skill- and mindset that you bring to the project will determine how quickly you discover and adapt to the obstacles that stand in the way of success. The training loss decreased but the validation loss increased from the first epoch. I trained HMDB51 dataset for 20 epochs with modelA0_stream_statedict_v3, the result is as follows: @nguyenquibk1996 I don't think it can converges from the first epoch with many datasets. Parameters optimizer ( Optimizer) - Wrapped optimizer. I don't want to use fully connected (in pytorch linear) layers and I want to add Batch Normalization. What does puncturing in cryptography mean. Already on GitHub? You can optionally divide by its length in order to normalize the loss, so the scale will be the same if you increase the validation set one day. Validation Loss did not decrease in the HMDB51 notebook? privacy statement. Strikes me as a problem. I have some training text data in variable lengths. In that case I have added my training loop here: for batch_idx, (image, label) in enumerate(train_loader): image, label = image.to(device), label.to(device), loss = F.nll_loss(output, label).to(device), (batch_idx*64) + ((epoch-1)*len(train_loader.dataset))), torch.save(model.state_dict(), 'results/model.pth'), torch.save(optimizer.state_dict(), 'results/optimizer.pth'). This is not a bug, its a feature! If I trained Movinet not using pretrained Kinetics with HMDB51 in the notebook sample and my own dataset (i did not save a log of the training), both losses had not decreased. What is the function of in ? When you train Movinet with your dataset, the validation loss decreases or not? 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. Total loss is fixed at around 12. Would be great if you can provide some insights into this issue? The gradient must be zero for all i n (red in the animation above) and nonzero for i = n (green in the animation above). Train the model on the training data. Surprisingly, much of this can be automated. Press question mark to learn the rest of the keyboard shortcuts. I first feed that in an char-based Embedding, then padding using pack_padded_sequence, feeding in LSTM , and finally unpacking with pad_packed_sequence. Organizing it is easy in the beginning, but as the project grows in complexity, more and more time is spent in debugging and sanity checking. One of my nets is a good old fashioned autoencoder I use for anomaly detection . Plz also reference the implementation is PyTorch. Training loss not changing at all while training LSTM (PyTorch) Training loss not changing at all while training LSTM (PyTorch) No Active Events. This was an easy fix because the stack trace told us what was wrong, and it was an obvious mistake. It is portable, so it can be reused for future projects and it requires only changing two lines of code: import the callback, then pass it to Trainer. But the validation loss started increasing while the validation accuracy is not improved. I am not doing any validation as of now. After some time, validation loss started to increase, whereas validation accuracy is also increasing. Say you have some complex surface with countless peaks and valleys. There could be many reasons for this: wrong optimizer, poorly chosen learning rate or learning rate schedule, bug in the loss function, problem with the data etc. Do you have any idea why this might be? Share Follow Use a larger model with more parameters. Hi, So I am trying to sanity-check my binary image classification model. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The training and validation losses quickly decrease. To learn more, see our tips on writing great answers. Now that we have that clear let's understand the training steps:- Move data to GPU (Optional) Clear the gradients using optimizer.zero_grad () Make a forward pass Calculate the loss Perform a backward pass using loss.backward () to calculate the gradients Take optimizer step using optimizer.step () to update the weights At this moment, I have a Variable of BATCH_SIZE*PAD_LENGTH*EMBEDDING_LEN and another Variable of the real length of each. Funny we noticed the other problem at the same time. Using learning rate scheduler, we can gradually decrease the learning rate value dynamically while training. @sacmehta Hi, are you able to share your pretrained PyTorch ImageNet weights? In this post I will show you how you can. Here is the rest of the code. Finally, there is the official PyTorch Lightning Bolts collection of well-tested callbacks, losses, model components and more to enrich your Lightning experience. 3. I met the same problem in my own dataset. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Create notebooks and keep track of their status here. The concept of a callback is a very elegant way of adding arbitrary logic to an existing algorithm. A small contrived . Any suggestion . Loss functions are not measured on the correct scale (for example, cross-entropy loss can be expressed in terms of probability or logits) The loss is not appropriate for the task (for example, using categorical cross-entropy loss for a regression task). Now I use filtersize 2 and no padding to get a resolution of 1*1. The model verification is a bit more sophisticated and also works with multiple in- and outputs. P.S. The training loss decreased but the validation loss increased from the first epoch. Model compelxity: Check if the model is too complex. No matter how much experience you bring with you, there will always be new challenges and unexpected behavior you will struggle with. If the process is all right, you should get a overfitted model with 0 loss. Dropout penalizes model variance by randomly freezing neurons in a layer during model training. Thank you. i trained model almost 8 times with different pretraied models and parameters but validation loss never decreased from 0.84 . To fix this, we add the true mean and standard deviation and also name the arguments to make it clear: We can look these numbers up because for MNIST they are already known. For demonstration, we will use a simple MNIST classifier example that has a couple of bugs: If you run this code, you will find that the loss does not decrease and after the first epoch, the test loop crashes. I changed the intendation so it's runnable with ctrl+c. to your account. Why don't we consider drain-bulk voltage instead of source-bulk voltage in body effect? my dataset os imbalanced so i used weightedrandomsampler but didnt worked . Wrapping this functionality into a callback class has the following advantages: Now with the new callback in action, we can open TensorBoard and switch to the Histograms tab to inspect the distribution of the training data: The targets are in the range [0, 9] which is correct because MNIST has 10 digit classes, but the images have values between -130 and -127, thats wrong! By accepting all cookies, you agree to our use of cookies to deliver and maintain our services and site, improve the quality of Reddit, personalize Reddit content and advertising, and measure the effectiveness of advertising. I have tried different learning rate regimes, but didn't have any luck. Maybe a log of the training. I changed the learningrate but this doesn't seem to be the problem. Use data augmentation to artificially increase the size of the training data set. Each input is of size (64, 1, 28, 28) and the architecture is as follows: self.conv1 = nn.Conv2d(1, 10, kernel_size=5), self.conv2 = nn.Conv2d(10, 20, kernel_size=5), self.fc2 = nn.Linear(50, 10) # (num_features, num_classes), x = F.relu(F.max_pool2d(self.conv1(x), 2)), x = F.relu(F.max_pool2d(self.dropout(self.conv2(x)), 2)). [News] TorchStudio 0.9.8 (IDE for PyTorch) now support PyTorch 1.12 . privacy statement. 320)) but it didn't make a difference to the loss. If other outputs i n also change, the model mixes data and thats not good! I was thinking something different. rev2022.11.3.43005. Increase the size of the training data set. @AlanStark If you use PyTorchs vision api, you should be able to download them by setting the pretrained argument to True. Reddit and its partners use cookies and similar technologies to provide you with a better experience. If we run the above, we immediately get an error message complaining that the sizes dont match in line 65 in the validation step. Sign in loss: 2.270 loss: 2.260 loss: 2.253 loss: 2.250 loss: 2.232 while in the tutorial the loss decreases way faster. Already on GitHub? Best way to get consistent results when baking a purposely underbaked mud cake. Oh, I see. This scheduler reads a metrics quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced. to your account. Also, try a small subset of the training data to verify the process is right. It happens for instance when data augmentations are applied in the wrong order or when a normalization step is forgotten.
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