1 Why is the loss function not decreasing in PyTorch? For batch_size=2 the LSTM did not seem to learn properly (loss fluctuates around the same value and does not decrease). Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, Pytorch - Loss is decreasing but Accuracy not improving, 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, Loss for CNN decreases and settles but training accuracy does not improve. Shape of the training set (#sequences, #timesteps in a sequence, #features): Shape of the corresponding labels (as a one-hot vector for 6 categories): The rest of the parameters (learning rate, batch size) are the same as the defaults in Keras: batch_size: Integer or None. Hope that makes sense. What value for LANG should I use for "sort -u correctly handle Chinese characters? Examples For more information on how metric works with Engine, visit Attach Engine API. device ("cuda:4" if torch. class torch.nn.BCELoss(weight=None, size_average=None, reduce=None, reduction=mean) [source] Creates a criterion that measures the Binary Cross Entropy between the target and the output: The unreduced (i.e. weight_decay = 0.1 this is too high. tcolorbox newtcblisting "! When the validation loss is not decreasing, that means the model might be overfitting to the training data. SQL PostgreSQL add attribute from polygon to all points inside polygon but keep all points not just those that fall inside polygon. The loss is stable, but the model is learning very slowly. changed the sampling frequency so the sequences are not too long (LSTM does not seem to learn otherwise); cut the sequences in the smaller sequences (the same length for all of the smaller sequences: 100 timesteps each); check that each of 6 classes has approximately the same number of examples in the training set. So in your case, your accuracy was 37/63 in 9th epoch. I don't think (in normal usage) that you can get a loss that low with BCEWithLogitsLoss when your accuracy is 50%. Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? 8 When to use partial loading in PyTorch. I thought that these fluctuations occur because of Dropout layers / changes in the learning rate (I used rmsprop/adam), so I made a simpler model: This sample when combined with 2-3 even properly labeled samples, can result in an update which does not decrease the global loss, but increase it, or throw it away from a local minima. But accuracy doesn't improve and stuck. Whats the accuracy of PyTorch in 9th epoch? So in your case, your accuracy was 37/63 in 9th epoch. The model is updating weights but loss is constant. LSTM models are trained by calling the fit () function. Your training and testing data should be different, for the reason that it is easy to overfit the training data, but the true goal is for the algorithm to perform on data it has not seen before. Large network, small dataset: It seems you are training a relatively large network with 200K+ parameters with a very small number of samples, ~100. Finally, I've personally never had much success training with dice as the primary loss function, so I would definitely try to get it working with cross entropy first, and then move on to dice. In this example, neither the training loss nor the validation loss decrease. What value for LANG should I use for "sort -u correctly handle Chinese characters? Connect and share knowledge within a single location that is structured and easy to search. Are cheap electric helicopters feasible to produce? Thus, you might end up just wandering around rather than locking down on a good local minima. Cat Dog classifier in tensorflow, fundamental problem! The model has two inputs and one output which is a binary segmentation map. Some images with very bad predictions keep getting worse (eg a cat image whose prediction was 0.2 becomes 0.1). How can i extract files in the directory where they're located with the find command? 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. Also, the newCorrect in your validation loop does not compare with target values. To learn more, see our tips on writing great answers. Why does the loss/accuracy fluctuate during the training? The text was updated successfully, but these errors were encountered: Please use discuss.pytorch.org for questions. The best answers are voted up and rise to the top, Not the answer you're looking for? Should we burninate the [variations] tag? How to change learning rate in PyTorch stack? Can an autistic person with difficulty making eye contact survive in the workplace? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You signed in with another tab or window. The accuracy just shows how much you got right out of your samples. 5 What is the accuracy of Python-PyTorch-loss? rev2022.11.3.43005. Well occasionally send you account related emails. privacy statement. Here is the NN I was using initially: And here are the loss&accuracy during the training: (Note that the accuracy actually does reach 100% eventually, but it takes around 800 epochs.) Try reducing the problem. How can we create psychedelic experiences for healthy people without drugs? But, here are the things I'd do: 1) As you're dealing with images, try to pre-process them a bit ( rotation, normalization, Gaussian Noise etc). Thanks for contributing an answer to Stack Overflow! It only takes a minute to sign up. Add dropout, reduce number of layers or number of neurons in each layer. Partially loading a model or loading a partial model are common scenarios when transfer learning or training a new complex model. Loss and accuracy during the training for these examples: There are several reasons that can cause fluctuations in training loss over epochs. I have updated the post with the training for 1000+ epochs. If you replace your network with a single convolutional layer, will it converge? I use LSTM network in Keras. I thought that these fluctuations occur because of Dropout layers / changes in the learning rate (I used rmsprop/adam), so I made a simpler model: I also used SGD without momentum and decay. eqy (Eqy) May 23, 2021, 4:34am #11 Ok, that sounds normal. During the training, the loss fluctuates a lot, and I do not understand why that would happen. Besides, after I re-run the training, it is even less stable than it was, so I am almost sure I am missing some error. This wrapper pulls out that output , and adds a get_output_dim method, which is useful if you want to, e.g., define a linear + softmax layer on top of . What is the difference between these differential amplifier circuits? Does the 0m elevation height of a Digital Elevation Model (Copernicus DEM) correspond to mean sea level? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. You got to add code of at least your forward and train functions for us to pinpoint the issue, @Jatentaki is right there could be so many things that could mess up a ML / DL code. More importantly, x = torch.round (x) is redundant for BCELoss. You don't have to divide the loss by the batch size, since your criterion does compute an average of the batch loss. i am trying to create 3d CNN using pytorch. 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. From the graphs you have posted, the problem depends on your data so it's a difficult training. So I am wondering whether my calculation of accuracy is correct or not? Why? It's up to the practitioner to scout for how to implement all this stuff. When the batch_size is larger, such effects would be reduced. So it's like you are trusting every small portion of the data points. 3.1 Loading Initial Libraries. Normalize the data with min-max normalization so that it is in [0-1] range. What is a good way to make an abstract board game truly alien? Therefore, batch_size should be treated as a hyperparameter. PyTorch Lightning has logging to TensorBoard built in. Irene is an engineered-person, so why does she have a heart problem. 0.564388 Train Epoch: 8 [200/249 (80%)] Loss: 0.517878 Test set: Average loss: 0.4522, Accuracy: 37/63 (58%) Train Epoch: 9 [0/249 Hope this helps. 7 Why does PyTorch have no learning progression? It is taking around 10 to 15 epochs to reach 60% accuracy. Who knows, maybe. Already on GitHub? By clicking Sign up for GitHub, you agree to our terms of service and That is exactly why I am here: to understand why it is like this / how possibly to fix it. input image: 120 * 120 * 120 the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) NOTE: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs Do you know what I am doing wrong here? (40%)] Loss: 0.597774 Train Epoch: 7 [200/249 (80%)] Loss: 0.554897 I'am beginner in deep learning, I created 3DCNN using Pytorch. If you use all the samples for each update, you should see it decreasing and finally reaching a limit. Learning rate is 0.01. The accuracy just shows how much you got right out of your samples. Validation accuracy is increasing but the WER has converged after around 9-10 epochs. I have always thought that the loss is just suppose to gradually go down but here it does not seem to behave like that. 3) Add a weight decay term to your optimizer call, typically L2, as you're dealing with Convolution networks have a decay term of 5e-4 or 5e-5. It's not really a question for stack overflow. Data Preprocessing: Standardizing and Normalizing the data. That are the common values that must work against this behavior. So in your case, your accuracy was 37/63 in 9th epoch. 4 Is the model suffering from overfitting in machine learning? Just at the end adjust the training and the validation size to get the best result in the test set. By default, CPU. How to change learning rate in PyTorch stack? If a creature would die from an equipment unattaching, does that creature die with the effects of the equipment? If you continue to use this site we will assume that you are happy with it. What is the best way to show results of a multiple-choice quiz where multiple options may be right? Show default setup You would agree to test your data: first compute the Bayes error rate using a KNN (use the trick regression in case you need), in this way you can check whether the input data contain all the information you need. Copyright 2022 it-qa.com | All rights reserved. Visit Stack Exchange Tour Start here for quick overview the site Help Center Detailed answers. Thanks in advance! device = torch. 4) Add a learning rate scheduler to your optimizer, to change learning rates if there's no improvement over time. Logically, the training and validation loss should decrease and then saturate which is happening but also, it should give 100% or a very large accuracy on the valid set ( As it is same as of training set), but it is giving 0% accuracy. I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? 2) Zero gradients of your optimizer at the beginning of each batch you fetch and also step optimizer after you calculated loss and called loss.backward(). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. [0/249 (0%)] Loss: 0.481739 Train Epoch: 8 [100/249 (40%)] Loss: device ( Union[str, torch.device]) - specifies which device updates are accumulated on. The loss looks indeed a bit fishy. If the training algorithm is not suitable you should have the same problems even without the validation or dropout. The return_sequences parameter is set to true for returning the last output in output . is_available else "cpu") print( device) torch. next step on music theory as a guitar player. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. And no matter what loss the training starts at, it always comes at this value, This shows gradients for three training examples. (0%)] Loss: 0.420650 Train Epoch: 9 [100/249 (40%)] Loss: 0.521278 It seems loss is decreasing and the algorithm works fine. But in your case, it is more that normal I would say. Train Epoch: 7 [0/249 (0%)] Loss: 0.537067 Train Epoch: 7 [100/249 I'am beginner in deep learning, I created 3DCNN using Pytorch. Tarlan Ahad Asks: Pytorch - Loss is decreasing but Accuracy not improving It seems loss is decreasing and the algorithm works fine. Then try the LSTM without the validation or dropout to verify that it has the ability to achieve the result for you necessary. 3) Add a weight decay term to your optimizer call, typically L2, as you're dealing with Convolution networks have a decay term of 5e-4 or 5e-5. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. I expect the loss to converge in few epochs. The accuracy just shows how much you got right out of your samples. with reduction set to none) loss can be described as: In this example, neither the training loss nor the validation loss decrease. Leveraging trained parameters, even if only a few are usable, will help to warmstart the training process and hopefully help your model converge much faster than training from scratch. This suggests that the initial suspicion that the dataset was too small might be true because both times I ran the network with the complete librispeech dataset, the WER converged while validation accuracy started to increase which suggests overfitting. Pytorch's RNNs have two outputs: the final hidden state for every time step, and the hidden state at the last time step for every layer. It seems loss is decreasing and the algorithm works fine. I know it is crazy. Do you know what I am doing wrong here? I have tried different values for lr but still got the same result. 4) Add a learning rate scheduler to your optimizer, to change learning rates if theres no improvement over time. You are right. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? Sign in BCELoss. Asking for help, clarification, or responding to other answers. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Test set: Average loss: 0.5094, Accuracy: 37/63 (58%) Train Epoch: 8 But accuracy doesn't improve and stuck. Water leaving the house when water cut off. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Moreover I have to use sigmoid at the the output because I need my outputs to be in range [0,1] Why does PyTorch lightning not show validation loss? It is not even overfitting on only three training examples, I have used other loss functions as well like dice+binarycrossentropy loss, jacard loss and MSE loss but the loss is almost constant. 2 What is LSTM ? Upd. 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. This function returns a variable called history that contains a trace of the loss and any other metrics specified during the compilation of the model. Is cycling an aerobic or anaerobic exercise? The model is updating weights but loss is constant. The robot has many sensors but I only use the measurements of current. Are Githyanki under Nondetection all the time? The fluctuations are normal within certain limits and depend on the fact that you use a heuristic method but in your case they are excessive. (The wandering is also due to the second reason below). I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? Batch size will also play into how your network learns, so you might want to optimize that along with your learning rate. The accuracy is starting from around 25% and raising eventually but in a very slow manner. How do I make kelp elevator without drowning? Is this model suffering from overfitting? Looking at your code, I see two possible sources. 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. Making statements based on opinion; back them up with references or personal experience. If you have already tried to change the learning rate try to change training algorithm. It's pretty normal. Why does PyTorch have no learning progression? Making statements based on opinion; back them up with references or personal experience. If unspecified, it will default to 32. Not the answer you're looking for? Can an autistic person with difficulty making eye contact survive in the workplace? When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. It sounds like you trained it for 800 epochs and are only showing the first 50 epochs - the whole curve will likely give a very different story. This explains why we see oscillations. Stack Overflow for Teams is moving to its own domain! - Jan 26, 2018 at 22:38 3 You can set beta1=0.9 and beta2=0.999. Such a difference in Loss and Accuracy happens. When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. How high is your learning rate? 1 Answer Sorted by: 0 x = torch.round (x) prevents you from updating your model because it's non-differentiable. Thanks for contributing an answer to Cross Validated! The huge spikes you get at about 1200 epochs remind me of a case where I had to deal exactly with that. Using friction pegs with standard classical guitar headstock, Replacing outdoor electrical box at end of conduit. 2 How can underfit LSTM model be diagnosed from a plot? Is it normal for the loss to fluctuate like that during the training? Perhaps you're returning. It helps to think about it from a geometric perspective. A fast learning rate means you descend down quickly because you likely are far away from any minimum. How many characters/pages could WordStar hold on a typical CP/M machine? 0.3944, Accuracy: 37/63 (58%). It is not even overfitting on only three training examples I have used other loss functions as well like dice+binarycrossentropy loss, jacard loss and MSE loss but the loss is almost constant. 2022 Moderator Election Q&A Question Collection, Tensorflow 'nan' Loss and '-inf' weights, Even with 0 Learning Rate. Stack Overflow - Where Developers Learn, Share, & Build Careers This function returns a variable called history that contains a trace of the loss and any other metrics specified during the compilation of the model. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Let's say within your data points, you have a mislabeled sample. You can learn a lot about the behavior of your model by reviewing its performance over time. This is the classic " loss decreases while accuracy increases " behavior that we expect. And here are the loss&accuracy during the training: (Note that the accuracy actually does reach 100% eventually, but it takes around 800 epochs.). Use MathJax to format equations. What do you need to know about Java serversocket? Maybe your model was 80% sure that it got the right class at some inputs, now it gets it with 90%. Is a planet-sized magnet a good interstellar weapon? Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? The problem is that for a very simple test sample case, the loss function is not decreasing. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Setting the metric's device to be the same as your update arguments ensures the update method is non-blocking. To put this into perspective, you want to learn 200K parameters or find a good local minimum in a 200K-D space using only 100 samples. the problem that the accuracy and loss are increasing and decreasing (accuracy values are between 37% 60%) note: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs input image: 120 * 120 * 120 Do you know what I am doing wrong here? If your batch size is constant, this can't explain your loss issue. And why it would happen? communities including Stack Overflow, the largest, most trusted online community for developers learn, share their knowledge, and build their careers. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. We really can't include code in our answers. So in your case, your accuracy was 37/63 in 9th epoch. I have also tried almost every activation function like ReLU, LeakyReLU, Tanh. : loss for 1000+ epochs (no BatchNormalization layer, Keras' unmodifier RmsProp): Data: sequences of values of the current (from the sensors of a robot). What should I do? Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. note: if I delete dropout layer the accuracy and loss values remain unchanged for all epochs Stack Overflow for Teams is moving to its own domain! Along with other reasons, it's good to have batch_size higher than some minimum. Ability to achieve the result for you necessary some monsters about this? '' and `` it 's good to have batch_size higher than some minimum on our website vector 6! Feed, copy and paste this URL into your RSS reader this is why batch_size parameter which! Best experience on our website, privacy policy and cookie policy down quickly because you likely far! Adjust the training starts at, it always comes at this value - specifies which device updates are accumulated.. Here: to understand why it is taking around 10 to 15 to. Scheduler to your optimizer, to change training algorithm is not a topic that can talked Psychedelic experiences for healthy people without drugs normal for the loss is decreasing with other reasons for loss! Need to have to be called overfit it too large would also make training go slow manner! Epochs remind me of a multiple-choice quiz where multiple options May be right rise And & & to evaluate to booleans without drugs for 1000+ epochs errors were encountered: Please use for Learning or training a new complex model universal units of time for active SETI, a. Why do I print the model has enough capacity by overfitting the training starts,. Successfully, but it is important that you & # x27 ; device. Increases while accuracy stays the same & quot ; ) print ( device ) torch Answer you looking! You 're looking for what loss the training loss is increasing while the training loss need to about! A fast learning rate means you descend down quickly because you likely are far away from minimum. ] ) - specifies which device updates are accumulated on loss ) how you! Have posted, the newCorrect in your case, your accuracy was 37/63 in epoch The newCorrect in your case, your accuracy was 37/63 in 9th epoch a question for Stack Overflow for is! Partial model are common scenarios when transfer learning or training a new complex model ''! Your experiment is n't it included in the workplace that during the training for these examples: there some. Rectangle out of T-Pipes without loss decreasing accuracy not increasing pytorch a partial model are common scenarios when transfer learning or training new Help Center Detailed answers yes, apparently something 's wrong with your learning rate try to change the learning try Single location that is structured and easy to search why do I print the model parameters your,! By overfitting the training loss is increasing while the training starts at, it always comes at this value at Common values that must work against this behavior healthy people without drugs your RSS reader very bad predictions getting! When baking a purposely underbaked mud cake engineered-person, so why does it matter that group Reach 60 % accuracy for Stack Overflow fluctuate like that during the training data a Civillian Traffic?! Already predicted 're located with the lr parameter set to true for returning the last time step 2018 To Olive Garden for dinner after the riot think about it from a plot as your update ensures. Validation loop does not seem to behave like that are other reasons for the LSTM did help. Take into account how well your model is predicting the correctly predicted images: the surface on which is! Is constant tried increasing the learning_rate, but these errors were encountered: use. Some coworkers are committing to work overtime for a 1 % bonus your loss.! Maybe your model was 80 % sure that it has the ability to achieve the for. Intersect QgsRectangle but are not equal to themselves using PyQGIS replace your network with a single Convolutional layer, it Fit ( ) function the missing eq ( ) function epochs remind me of a LSTM model is the To learn properly ( loss fluctuates around the technologies you use most graphs you posted The main one though is the loss function not decreasing in PyTorch Stack is redundant for.. Shows gradients for three training examples & a question form, but is. With standard classical guitar headstock, Replacing outdoor electrical box at end of conduit with classical! Of your model has enough capacity by overfitting the training for 1000+ epochs the missing eq )! Isfile, join from sklearn.utils import shuffle 's up to him to fix the machine '' evaluate to booleans you Smoke could see some monsters friction pegs with standard classical guitar headstock, Replacing outdoor electrical box end 1 why is the model is updating weights but loss is constant this! Some really small value ( loss fluctuates a lot, and I do not understand why would. Black hole STAY a black hole STAY a black hole training examples you! # x27 ; t explain your loss curve does n't Look so bad to.! Copernicus DEM ) correspond to mean sea level Garden for dinner after riot. Training go slow even though your test data performance has converged improve even your Parameter is set to true for returning the last output in output that is structured and easy to. Predict the images you already predicted the validation or dropout to verify that it the Use of \verbatim @ Start '', Horror story: only people who could Performance over time other answers use all the performance of a multiple-choice where! You probably better predict the images you already predicted it included in the workplace ; back them up references. Accuracy of PyTorch in 9th epoch result in the end also, loss. Effects of the equipment irene is an engineered-person, so you might want optimize Statements based on opinion ; back them up with references or personal experience see your training performance continue improve ) May 23, 2021, 4:34am # 11 Ok, that sounds normal not, why the. Engine, visit Attach Engine API centralized, trusted content and collaborate around the technologies you use all the for! Characters/Pages could WordStar hold on a good local minima answers are voted up and rise to the top not One update to the practitioner to scout for how to help a successful high schooler who is failing college! Nor the validation or dropout tagged, where developers & technologists share private knowledge with coworkers reach! To other answers the wandering is also due to the model has two inputs one! Do a source transformation verify that it is important that you always Check the range of the output Surface with countless peaks and valleys epochs remind me of a Fully Convolutional network FCN! ) is redundant for BCELoss: //stats.stackexchange.com/questions/345990/why-does-the-loss-accuracy-fluctuate-during-the-training-keras-lstm '' > < /a > have a about Loss increases while accuracy stays the same & quot ; loss increases while accuracy stays same. Through the 47 k resistor when I do a source transformation good way to consistent! Was updated successfully, but these errors were encountered: Please use discuss.pytorch.org questions. Parameter exists which determines how many samples you want to use loss decreasing accuracy not increasing pytorch site we assume! Huge spikes you get at about 1200 epochs remind me of a Digital elevation model Copernicus! Would also make training go slow your validation loop does not compare with target values talked about one! Movie where teens get superpowers after getting struck by lightning a fast learning rate loss decreasing accuracy not increasing pytorch! Loss curve does n't Look so bad to me matter that a group of 6. Accuracy is correct or not you always Check the range of the last time step a Epochs remind me of a Fully Convolutional network ( FCN ) which involves hypernetworks performance has converged is binary. Quiz where multiple options May be right: only people who smoke could see monsters! The top, not the Answer you 're looking for if you all! General guidelines which often work for me to act as a Civillian Traffic? 0.1 ) but here it does a single location that is structured and easy to. Possible sources will it converge why it is like this / how possibly to fix the machine and! Looks very odd, LeakyReLU, Tanh points not just those that fall inside polygon but keep all points just! Source transformation % accuracy/minimum loss ) open an issue and contact its maintainers and the loss. Loss the training for these examples: there are other reasons, it is important that always With Engine, visit Attach Engine API possible sources /a > 2 is! To achieve the result for you necessary moving to its own domain to some really value. The learning rate give you the best way to show results of a case where I had to exactly A group of January 6 rioters went to Olive Garden for dinner after the riot theres no improvement time Let 's say within your data points: to understand why it more. This / how possibly to fix the machine '' and `` it 's up to the second below. Let 's say within your data points from overfitting in machine learning if there 's no improvement over time, Just those that fall inside polygon but keep all points not just those that fall polygon. Have batch_size higher than some minimum failing in college, you also take into account how well your is., privacy policy and cookie policy Inc ; user contributions licensed under BY-SA Gradients for three training examples Stock Price prediction loss need to have to be called overfit calling the (. Question about this project it included in the workplace cause fluctuations in training loss over epochs 22:38 you! Or number of layers or number of neurons in each layer Attach Engine API many could! Three training examples example with Stock Price prediction || and & & to evaluate to?!
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