Validation loss not decreasing. Loss is Nan - PyTorch.

Validation loss not decreasing The model is updating weights but loss is constant. So I tried to train the data on a simple 3 layer CNN just to see if I screwed something up while implementing VGG. I have tried increasing the number of layers in my RNN and adding DropOut to my model, but no success. Your validation loss is varying wildly because your validation set is likely not representative of the whole dataset. I used learning rate of 0. 02). The problem is no matter what architecture I use for the Model, the validation loss would not be less than ~0. My data includes (238 images including 23 set validation). They are transformed into 128 * 128px before being Training and Validation loss are same but not decreasing for LSTM model. Why is accuracy and loss staying exactly the same while training? 0. Losses of keras CNN model is not decreasing. LSTM is Showing very low accuracy and large loss. 5 - 0. class Both result in a similar roadblock in that my validation loss never improves from epoch #1. I noticed that my model was overfitting (validation loss increased after some epochs) and hence I used a dropout (p=0. Resetting Adam optimizer reduces validation loss. But while training and when validating the loss on validation set I get these nonsensical results that my loss is over 1000. What does it mean: "Everything looks OK but loss won't decreases!" 2. Hot Network Questions What does the To[1] mean in the concept is_convertible_without_narrowing? My validation loss and training loss were decreasing but accuracy of both remained constant. You should stop training at that point and try to use some tricks to avoid overfitting. Cardinality is 864 You are facing the phenomenon of "overfitting" when your validation loss goes up after decreasing. However this is not the case of the validation data you have. But no strong relation between train loss with validation accuracy. Dataset : I train on the FaceForensics++ dataset, with a full size of 44335 images with a 80/20 You can see that in the case of training loss. Ashwath S Ashwath S. I have a timeseries data and I am doing univariate forecasting using stacked LSTM without any activation function, Like following. this is the train and development cell for multi-label classification task using Roberta (BERT). i checked upto 40 Epochs. 4333 and ending on 4. I would recommend shuffling/resampling the validation set, or using a larger validation fraction. modification from classification loss to regression loss. 2 vs 0. 9] and not decreasing further. Asking for help, clarification, or responding to other answers. I also tried using regularization with lambda = 0. I have used other loss functions as well like dice+binarycrossentropy loss, jacard loss and MSE loss but the loss is almost constant. my question is: why train loss is decreasing step by step, but accuracy doesn’t increase so much? practically, It is normal that its loss is decreasing fast. My issue is that I have stuck at a training accuracy of 62. Tensorflow-loss not decreasing when training. And here’s a viz of the losses over ten epochs of training. g. backward() may prevent some Other answers explain well how accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw output (float) and a class (0 or 1 in the case of binary classification), while accuracy measures the difference between thresholded output (0 or 1) and class. Now the thing that I don't understand at all is how you can explain this outcome given that the I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early stopping, various learning rates, and also regularizers, but none of them have worked properly. Just to validate that my custom dataset was not the issue, I have tried training with the given Pokemon example using the given command (same Overfitting does not make the training loss increase, rather, it refers to the situation where training loss decreases to a small value while the validation loss remains high. majomere But I am concerned why the train loss is not decreasing as I expected. ConvNet : Validation Loss not strongly decreasing but accuracy is improving. cuda:0 Training begin! [1, 500] training loss: 2. 01 or 0. 4) in the last FCN layer. murali_perumalla (murali perumalla) February 22, 2020, 4:54pm 1. I am training three neural networks (2CNNs, 1LSTM) on an EHR dataset, which is to predict diabetes according to (100 labs * 360 days) data. Your training loss is decreasing, and the training accuracy is increasing which seems to point to the fact that your neural network is actually learning, but it may be the case that it does not generalize very well. Ask Question Asked 1 year, 8 months ago. machine-learning; python; deep-learning; keras; tensorflow; Share. No matter what I do, my validation loss doesn’t converge. It starts decreasing then it after a few epochs it starts increasing until the end of the training. Validation accuracy is also following a non-random pattern, Is my assertion for performance expectation true on overfitting on 20 Training and Validation Loss not Decreasing for Large Datasets . 3. The model does not generalize well because it does not have enough information. Train loss is good but validation loss is large and not decreasing much. 5, Although I tried different architectures (3D CNN, 2D CNN, Fine-tuning, Inception) with different combinations of regularization (Dropout, However, the loss for the validation data does not decrease any more after some 200 epochs as you can see in the figure. Model: c I am training a known dataset that came splitted into train and validation. Here’s The validation loss is decreasing with training loss but there is a gap between the two functions. Since you are planning to treat it as a regression problem, I would assume both shapes should be identical, but as already explained I don’t fully understand your approach and don’t know if you would depend on broadcasting or want to So, the resulting inferences on the validation set are shown below. Mukesh1729 November 26, 2021, 3:05pm 22. This model is trained on a dataset of 250 images, where 200 are actually used for training while 50 are used for cross-validation. Your "loss" is the value of your loss function (unknown as you do not show your code) Your "acc" is the value of your metrics (in this case accuracy) The val_* simply means that the value corresponds to your validation data. __ini Train loss is I was doing fine-tuning with ResNet50 model for face recognition using data agumentation, but observed that the model accuracy was increasing but validation accuracy from the very starting point is not imporving, i am not getting where is I am training a deep model for MRI segmentation. Loss is not decreasing for convolutional autoencoder. I think your model overfits. The loss is still not changing between epochs. Here's my training results curves: As you can see, the training loss first decreases fastly and then converges, while validation loss starts to increase - indicates overfitting begins; However the validation loss starts to decreases again in a slow pace - which seems like the model still not I am training a LSTM model with a trained Word2Vec and after 3 epochs i started to observe that my training loss starts to increase while validation loss still keeps decreasing. When I check the metrics JSON file, the training loss is always higher than the validation loss! Validation also converges very fast while training loss is still very high. Hot Network Questions Does the pistol grip tool also take drill bits and screwdriver bits or only wrench sockets? But no matter what I try, the loss won't decrease, thus, predicting incorrectly. And it is the same When Validation Loss starts to increase in early beginning like images you added, it means that there's there is something wrong in the model. 1004 - precision: 0. The loss value does not decrease. I'm really not sure. The validation set is not being used during training so it’s loss is not as small as the training loss but it is still decreasing (albeit at a lower rate which is also normal). It is critical for assessing the model’s generalization abilities — its performance on new Is that a reason that my loss does not decrease and testing accuracy is almost 30%? Thank you for any advise. I can get the model to overfit such that training loss approaches zero with MSE (or 100% accuracy if classification), but at no stage does the validation loss decrease. The models I am using are U-Net++ and UNet3+. 2 - and then beginning from the second epoch that loss sticks at the very same value, e. Yes, since a 5D model output and a 4D target could indicate a multi-class segmentation use case for 3D volumes. Hello! I am running a project using Detectron2 (object detection with faster_rcnn_R_50_FPN_1x), where I have training and validation data. 001 to 10000. I just tried training the model without the "Variational" parts. train_dataloader is my train dataset and dev_dataloader is development dataset. Some of them are explained below: Insufficient data: Validation loss does not decrease if the model is trained on a small dataset. In such circumstances, a change in weights after an epoch has a more visible In this scenario, my validation loss, in orange, initially fell faster than my training loss, in blue. Conv2D(8-16-32-64-128-64-32-16-8) Conv2D thank you for your feedback. Loss not reducing in Linear Regression with Pytorch. For this code I used the Pins Face Recognition dataset and here is the code I used below: output: Data preparing. Any comments are highly appreciated! 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, 1) can access resource3(0, 0, I train on batch sizes of [60,128,256] with the training loss fluctuating in the range [0. I assume this is where it would make sense to stop training the model. , it is not underfitting) and that increasing the model's capacity (i. Some possible reasons why I think this happens: I am incorrectly using WeightedRandomSampler. Validation Loss is increase and validation accuracy is decrese on every epochs in my RNN Model. 0606 - acc: 0. By monitoring the validation loss, we can halt training when the model's performance ceases to improve, This is particularly useful when dealing with scenarios where the pytorch lightning loss not decreasing can lead to LSTM High Training Loss not decreasing and Learn more about lstm, deep learning, signal processing, classification I need to classify my signals into 4 classes, I am feeding 600 . All seems good until I start training, both val_loss and val_accuracy are NOT changing when training. I hope so it getting stuck with local minima. This is a common problem, but there are a few things you can do to fix it. Loss of Conv-neural-network not decreasing, instead obsoleting. The end results look look as expected and the final validation loss is still higher than the training loss. 6615-0. Training and validation loss sometimes not decreasing in Keras dense layer with the Not necessarily. Hi, As the title says, I am training a custom UNet model on a simple dataset (Oxford IIT Pet). But the validation loss started increasing while the validation accuracy is not improved. ), maybe even look into ways you could alter the dataset. This should give more wheight to the MSE and less to the KL divergence. deep-learning; cross-validation; objective-function; Share. Is there any reason you're not using any pre-trained weights for your resnet backbone? resnet50 has something like 200 million parameters, and including the rest of your network that's a lot of parameters to train. 5213 This is a scatter plot comparing the training and validation dataset. RNN Keras model for NLP takes lot of time while training with no decrease in validation loss. I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Ask Question Asked 3 years ago. 0. 01, 0. from_pretrained('bert-base-uncased') input Hi, I load the Roberta pre-trained model from the transformers library and use it for the sentence-pair classification task. 1k Validation loss being lower than training loss, and loss reduction in Keras. I'm using CNN to do multi-regression jobs, but some wired results confuse me. We'll cover: * Common causes of loss not decreasing * How to debug your PyTorch model * How to adjust I have 190,000 training images and I use 10% of it for validation. The accuracy for the validation does not change much overall. I tried changing the activations, increasing and decreasing the network complexity (adding and removing layers, as well as increasing and decreasing the Conv2D parameters), but none of that worked, so i'm guessing it is an architectural problem that I am missing loss and validation loss decreasing but accuracy and validation accuracy remain static. However, in my case, the validation curve never actually decreases at all. Loss not decrasing and is very high keras. Then my val loss not decrease. The model has two inputs and one output which is a binary segmentation map. 001, 0. Training loss stays constant while validation loss fluctuates heavily. 0: 471: July 19, 2022 And I think that my model is suffering from overfitting since the validation loss is not decreasing yet the training is doing well. 0 while Training Loss and Validation Loss still decreasing. 03, but still didn't help. training data. What I don't understand is why Accuracy of 1. @ptrblck hi ,i have tried vgg16,vgg16, densenet ,resnet and i tired chaging a lot parameters but validation loss doesnt decrease . If MobileNet validation loss not decreasing over time. I have learned keras before and I would like to do the same thing in PyTorch like 'model. The problem is 'during the training the training loss is decreasing while the validation loss is constant. – Felix Kleine Bösing. 075 ran the model with 600 iterations. The accuracy is a measure of how many classes that were correctly classified. Modified 3 months ago. Now, if we look at your 4 metrics during training, we can see that your acc is not so static as you think: its first epochs stays at 0. 063 [1, 2000] training loss: 2. Train loss Since the training loss keeps decreasing that means the model is not stuck in a minima. Do you figure out a way to improve the results for your When I train, the training and validation loss continue to go down as they should, but the accuracy and validation accuracy stay around the same. Modified 1 year, 8 months ago. I'm shuffeling the data during training and do data augmentation on the fly. Imagine that in the first epochs, the neural network considers some ground truth positive examples (ys) with GT == 1 with 55% confidence. I used two hidden layers with size 125, 50. What other modern my immediate suspect would be the learning rate, try reducing it by several orders of magnitude, you may want to try the default value 1e-3 a few more tweaks that may help you debug your code: - you don't have to initialize the hidden state, it's optional and LSTM will do it internally - calling optimizer. zero_grad() right before loss. This means: Generally speaking it is not that hard for a machine learning algorithm to perform exceptionally well on Validation loss not decreasing using dense layers altough training and validation data have the same distribution. 1) Does anything in the construction of the network look incorrect? this is the train and development cell for multi-label classification task using Roberta (BERT). Xavier initialisation is derived with the goal of ensuring that the variance of the output at each neuron is expected to be 1. Say that the validation data (conventionally the part of the dataset that the accuracy is calculated on) only contains data from one class. I am training a model and the accuracy increases in both the training and validation sets. And after every epoch it is not decreasing. It's overfitting (validation accuracy goes down) even though it's not really learning to classify properly the train. Validation loss is not decreasing. Since beta = 1 would be a normal VAE you could try to make beta smaller then one. no_grad() I have used the TFBertModel and AutoModel from the transformer library for training a two-class classification task and the training loss is not decreasing. 2. 6942 - binary_accuracy: Accuracy of 1. Try moving the train/test split However, my model loss is not converging as in the code provided. I normalize the image, and then fit it to the model. I have tried increasing I was running this on google colab with GPU enabled. 0 and decreases with loss. These two classes are very MobileNet validation loss not decreasing over time. Viewed 528 times 0 . The RPN loss is decreasing, but the box classifier loss is increasing, what can be the reason for this? I have also included an image of the average precision for each class. 11. You could check the Could it be that you are using an Autoencoder and in the loss there is a KL Divergence term? In a (Beta-) VAE the loss is Loss = MSE + beta * KL. Siamese network loss not decreasing. Training loss. – AveryLiu Commented Apr 30, 2021 at 5:35 Tensorflow-loss not decreasing when training. 5% and a loss of around 0. Furthermore, since training loss has stopped reflecting on validation loss, this means that whatever new things that the model is learning isn't beneficial Dear All, I am training my network, I can figure out that I am not overfitting as the validation loss can still decrease as well as the training loss. I have an The strange behaviour is that, whilst the training loss decreases over time, the validation loss increases right from the start. epoch 100 validation loss 3. I have queries regarding why loss of network is not decreasing, I have doubt whether I am using correct loss function or not. 9 Keras: Training loss decrases (accuracy increase) while validation loss increases (accuracy decrease) Related questions. Help Hey, so I'm having a really weird issue. Viewed 226 times 0 $\begingroup$ I am trying to train a MobileNetV2 on a custom dataset, to image Classification task. This means that your model has sufficient capacity to learn I am wondering why validation loss of this regression problem is not decreasing while I have implemented several methods such as making the model simpler, adding early I am currently working on training an RNN to map sequential data to real-valued positive values, but unfortunately my validation loss is not decreasing. bert = TFBertModel. I have even experimented with learning rates ranging from 0. Then if the NN only classifies everything to that one class, the validation data would have 100% accuracy! @ChinmayShendye We need a plot for the loss also, not only accuracy. Keras loss is not decreasing. I had this issue - while training loss was decreasing, the validation loss was not decreasing. Thank you @TimNagle-McNaughton for your answer. loss/val_loss decrease but Your validation loss is still going down (as is your training loss), so there is likely more to be gained (and you haven’t started overfitting yet). Why could this be? Could my model be too deep? Do I need to reduce my learning rate even more? Or do I just not have enough data? For reference I am trying to mimic the Tiny YoloV2 architecture My NN has 3 fully connected layers, followed by an LSTM layer. any suggestions would be appreciated. compile function you have defined a loss function and a metrics function. What does this mean? The model is not overfitting as the validation loss is decreasing, but is something wrong with the model as there is a gap between the two functions? I'm new to this, so please help. Hot Network Questions differential equation and limits Could you genetically engineer cells to be able to use electricity instead of ATP as an energy source? Now during training the loss goes down in the first epoch from about 0. Hot Network Questions Gather on first list, apply to second list A superhuman character only damaged by a nuclear blast’s fireball. Loss is Nan - PyTorch. PyTorch Loss Not Decreasing: How to Fix If you're using PyTorch and your loss is not decreasing, you're not alone. I have a custom image set that I am using. Training and validation accuracy suddenly drops in convolutional neural network (CNN) 8. Based on this, I think the model is improving and I’m not calculating validation loss correctly, but I can’t figure out anything I’m doing wrong! Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. i tried with loss functions: adam,SGD, lr_schedulars: reduceonpleatue , stepLR. I followed your suggestion for simpler cases; I used a very small dataset of original and I can see the drop in loss of approx 0. Is there anything else that I should Because model should not be learning anything but both my train & val loss are decreasing. 79 5 5 bronze Training loss decreasing while Validation loss is not decreasing. Also after 100th Epoch, the training loss and validation loss are not at all decreasing. Accuracy and Validation Accuracy stay unchanged while both losses reduce. How to avoid stagnating loss function in tensorflow keras. I’m just I’m curious to know if the KL-loss works well in your case or not. The training loss changes insignificantly. So, the problem is probably with the encoder and decoder parts itself or might be arising from my training loop. fit' and plotting a graph containing both training loss and validation loss. Cardinality is 864 Training loss decreasing while Validation loss is not decreasing. Improve this answer. 1. 0019, final training loss was 0. Commented Jan 18 Validation Loss is calculated on a separate dataset not seen by the model during the training phase. The training loss is continually decreasing, but the the validation loss shows a local minimum at epoch ~100 which indicates that there is overfitting starting to happen. I have 368 pictures in the training set and 51 in the validation set. Instead of scaling within range (-1,1), I choose (0,1), this right there reduced my validation loss by magnitude of one order Validation loss is not decreasing. As I usually choose the best model based on minimum validation loss, the question is, should I keep on training the model until the validation loss stops decreasing? I am training a model, and using the original learning rate of the author (I use their github too), I get a validation loss that keeps oscillating a lot, it will decrease but then suddenly jump to a large value and then decrease again, but never really converges as the lowest it gets is 2 (while training loss converges to 0. Thanks in advance. Follow edited Dec 27, 2015 at 3:30. I am not using the torchvision. This generally relies on the additional assumption that your inputs are standardised to have mean 0 and variance of 1, so it is important to also No its not a validation loss. From the code you provided, it is tough to say why the validation loss is constant but I see several problems in your code. My network loss seems to be constant and does not decrease. Using the weights from the first few epochs I am training a U-NET model on 238 satellite images. 0007. mohanadarafe (Mohanad Arafe) April 24, 2022, 2:05am 1. There might be a way to decrease this loss. no_grad(): for images, targets in data_loader: images = list my validation loss increases while training loss decreases, and accuracy increases for both validation and training I’m trying to find a reason why could that be and how to fix it. Can someone How long did you train for? Is your training loss decreasing? If so how quickly? If your code runs then this isn't a to start, and lower the learning rate (1e-3) for Adam is a good start. 63% epoch 2750 validation loss 3. Hot Network Questions Replacement for M355 Shimano hydraulic brake I have tried different combinations of optimizer (SGD, Adam, etc. Therefore I would definitely looked into how you are getting validation loss and ac $\endgroup$ – I am training the UNET image segmentation network on brain tumor dataset from figshare, it is training well, training loss and training dice score both are changing accordingly or in the same tone with validation loss and validation dice score. 2446 with several ups and Proper initialisation of weights is often crucial to getting deeper neural nets to train. You’d have a problem if the validation loss was increasing while the training loss was decreasing. my question is: why train loss is decreasing step by step, but accuracy doesn't increase so much? practically, Can any one explain why accuracy is decreasing while the loss in train and validation is decreasing? This can happen whenever accuracy is not monotonic function of the loss. In this article, we'll walk you through the steps to troubleshoot and resolve this issue. you can observe the masks are not properly occupying the bounding boxes and the bounding boxes are some times offset to some portion of the predictions. 7. below is the learning rate finder plot: And I have tried the learning rate of 2e-01 and 1e-01 but stil my validation loss is fluctation after few cycles (not like as Jeremy mentioned in 3 lecture it increases and then decreases). 1, 0. Tried everything I I experienced such fluctuations when the validation set was too small (in number, not necessarily percentage split between training-validation). 3, despite of the different architectures that I tried. Ask Question Asked 8 months ago. Pytorch loss is nan. I’ve tried all types of batch sizes (4, 16, 32, 64) and learning rates (100, 10, 1, 0. 6803 - accuracy: 0. Hot Network Questions What is the difference in pacing between thriller and mystery genres? Thanks for the suggestion. But for my case, training loss still goes down but validation loss Try to adjust the parameters $\mathbf W$ and $\mathbf b$ to minimize this loss function. Usually when a model overfits, validation loss goes up and training loss goes down from the point of overfitting. Keras high loss, not decreasing with each epoch. Your phenomenon with validation loss and validation accuracy is not something out of the ordinary. There are 252 buckets. Validation Loss and Validation Accuracy do not change during training. Overfitting problem: high accurance and low accurancy validation for image classification. Why the training accuracy stays high but validation accuracy does not change? Hot Network Questions While training my CNN model my validation loss is lower than training loss but almost around the training loss. Tried changing optimizers and lr_schedulers But nothing worked. 0: 285: December 24, 2021 Module is not converging. I'm at epoch 700 at the moment and the loss has been decreasing every epoch very slowly. Provide details and share your research! But avoid . 0001) as well as I do sampling and indeed the image is better, but the result still not really satisfied. 1e9, and it's not decreasing over time. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). 111, validation accuracy 69. First I preprocess dataset so my train and test dataset shapes are: The training loss keeps decreasing while the validation loss reaches a minimum and then starts to increase again. Related. There are numerous reasons for the validation loss of a machine learning model not to decrease after a certain number of epochs. Just be cautious, you may have to change the learning rate when switching the loss function. 234, validation accuracy 57. As you can see from the pink graph, the total loss is not decreasing at all from its initial value (as is the case for the blue and red graph from previous runs). This means that the model starts sticking too much to Validation loss not decreasing. Amir. Epoch 1/15 1011/1011 [=====] - 18s 10ms/step - loss: 0. 5 to about 0. My model looks as follows class DownSample_Block(nn. With that many parameters it is typical for your loss to jump around for quite a few epochs. 0 something - much below 1) Hi, while training my model I can see the validation loss decrease but the training loss fluctuate. I’ve never experimented with long-short-term-memory models, but, in general for real models running on real problems, 1500 epochs is a very small number. Loss and accuracy increasing/decreasing simultaneously on the training data tells you nothing about whether your model is overfitting. this means you're hitting your architecture's limit, training loss will keep decreasing (this is known as overfitting), which will eventually INCREASE validation loss, make changes to the parameters or consider altering your layers (adding, removing, etc. If the loss decreases consistently, then this check has passed. ), loss (MSE, MAE, etc. I think this can both mean the data is not consistent or the model is not complex enough. My model is setup as shown below. 4) Thanks for the help I have a model that I am trying to train where the loss does not go down. Problem in implementing CNN. When I train a certain neural network on smaller amounts of data, like 100000 datapoints, the training loss decays to 0 and the validation loss As shown in plot (link should be available in the post) the loss slightly decreases at the very beginning and then starts increasing. I have the rose-youtu dataset which spec could be found here in details. I get the paths of all the images, read them in and resize them. I am using drop_last=True and I am using the CTC loss criterion. So if raw outputs change, loss changes but accuracy is more If loss decreases when training, the training accuracy will increase. However, when I trained them, there is a common phenomenon shown in the following pictures: training loss is decreasing, validation loss (seems) not decreasing at all, but both average precision score (PR-AUC) are increasing. I am not sure why the loss increases in the finetuning process for the During training, the training loss keeps decreasing and training accuracy keeps increasing slowly. 0001 and training/validation split: 0. Share. Modified 3 years ago. 043 Validating! How should I interpret this? I don't understand the changes in the validation Loss. The graph of the first example in this section shows the validation loss decreasing and you also vouch for loss to decrease even further if the network is trained even more with more epochs. I’ve tried increasing the batch size (still doesn’t get any better), batch norm and L2 reg. The training loss is a smoother curve because it’s an average over a larger set of predictions; the validation loss is noisier because it’s an average over a smaller (relative to training) set of predictions. 1989, and then it stays there "forever", regardless of how many epochs I Since you are in a regression setting, you should not use softmax as activation function of your last layer - a linear activation (which is the default one, if not defined) should be used here. I had this issue - while training loss was decreasing, the validation loss was not decreasing. implementation help. Pytorch tutorial loss is not decreasing as expected. It is not even overfitting on only three training examples. 1 Hi, I am traning the model and I have tried few different learning rates but my validation loss is not decrasing. 5849 - val_loss: 0. I am training a multi-class neural network by using keras (backend is tensorflow). I am working on Street view house numbers dataset using CNN in Keras on tensorflow backend. The If you want to your network to output a probability distribution, it’s better to use a cross entropy loss (in case you have one_hot target, as for classification, or BCE loss in case When the validation loss stops decreasing, while the training loss continues to decrease, your model starts overfitting. My training loss decreases well but the validation loss increases, so my model is definitely overfitting. Here is the code: Model. 016 and validation was 0. [Orange is validation loss and blue training][1] Train/validation loss not decreasing. The loss function decreases, but accuracy on train set does not change in tensorflow. In this scenario, my training loss has all but converged but the validation loss continues to make big improvements. LSTM: loss value is not changing. 8. By following the code provided by @jhso I determine validation loss by looking at the losses dictionary, sum all of these losses, and at the end average them by the length of the dataloader: def evaluate_loss(model, data_loader, device): val_loss = 0 with torch. lr I have been training a model with nearly 2L images and the validation loss seems to get stuck at 0. Loss vs Val Loss. 5714 and seems to reach an asymptotic limit. The validation loss and accuracy at Epochs 100, 1000, 2000 are. 160 [1, 1000] training loss: 2. training loss reached 0 (100% accuracy) at epoch14; validation loss consistently drops. 25% Unless I've misunderstood or have a bug somewhere, the network is learning. From what i see, the loss when training with predicting x-start is also much higher than the loss when training with predicting epsilon (for me 0. Validation loss does not decrease. And i just can't figure out what the problem is. In that case it may be possible to improve the loss (which is probably the main objective that your algorithm tries to achieve) while the accuracy does not improve or even decreases. I took care to use the same parameters used by the author, even those not explicitly shown. e. Is it overfitting or can it be something else? I am training an LSTM to give counts of the number of items in buckets. I have those results: Train accuracy start at 80% and increases . It's not clear what's it as you didn't show your model. 004 and validation loss was 0. 0 (see here). . Follow asked Feb 7, 2022 at 22:25. 2 LSTM time series prediction - Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can’t decreasing when training. However, when plotting the validation and training losses of these models over time, I find that they all end with a sudden drop in loss, and a permanent plateau. Usually, when the training curve decreases far below the validation curve, this indicates overfitting. here is my code and my outputs: The training loss is continually decreasing, but the the validation loss shows a local minimum at epoch ~100 which indicates that there is overfitting starting to happen. 6619. Modified 8 months ago. As the training loss is decreasing so is the accuracy increasing. Improve this question. CrossEntropyLoss() as my loss function. Pytorch CNN loss is not changing, 0. my val_loss is not decreasing below 0. 01 in each epoch. 0, then on epoch 7 it starts to increase, until epoch 17 where it reaches 0. This can only be determined by comparing loss/accuracy on the validation vs. Sigmoid activations are also highly not advisable for the intermediate layers - Training evaluation: loss: 1. – Validation loss. NN regression loss value not decreasing. Here is the training and validation loss data per epoch: Epoch 1/100 1410/1410 [=====] - 775s 550ms/step - loss: 0. 05% epoch 1000 validation loss 3. 0: 373: December 20, 2021 Validation loss doesn't decrease when using Lightning. So the model can't find a pattern in the data. In order to know whether the model is underfitting or not, I have to plot a graph to compare the training loss and validation loss. So this gave me lesson why sigmoid is used for binary classification. I have already tried different learning rates, optimizers, and batch sizes, but these did not affect the result very much as well. ), batch sizes (32 and 64). However, I see that after training for a while, the validation loss decreases slightly maybe every 11 -12 epochs, at some point I say that it will become steady, however, I can see that it still decreases but then it need long time ( 12 In your model. underfit’ example. When training loss keeps decreasing but your validation loss starts increasing, it is usually a sign of overfitting and an indication to stop training or tinker with learning rate. mat files for every class with 360x1 data points each, I seperated training-test as 70-30. , increasing the number of layers or increasing the number of neurons per starting training loss was 0. Could you add a print of the training loss and validation loss? If they are too far from each If training loss goes down, but validation loss goes up, it is likely that you are running into the problem of overfitting. Getting different predictions might happen when your gradients keep updating during inference so try explicitly "stop" them from updating with torch. Validation and training loss of a model are not stable. The validation and training loss does not go too low instead are stuck on almost 75-80% loss but accuracy achieved is also 76%. My complete code can be seen here. Viewed 24 times 0 $\begingroup$ I am building a model for predicting stock portfolio positions, by minimizing a Sharpe loss function For research, I'm working on face anti-spoofing. These images are 106 x 106 px (black and white) and I have two (2) classes, Bargraph or Gels. 0 I'm running into the issue where my cost is on the order of 1. 090 [1, 1500] training loss: 2. 013 - precision: 0. I’m now seeing a higher loss (for both train and val) The train part is working fine, I know that because after model is trained I test the model on the third set (the test set) and the results are as they should be. If our mysterious thermometer somehow managed to measure temperatures using a logarithmic scale, our poor linear model would not have had a chance to fit those measurements and provide us with a Yes validation dataset is taken from a different set of sequences than those used for training. Questions. the first part is training and second part is development (validation). Risk score from Neural Network classifier (more than 2 categories) 0. Below is my validation loss history: The result was that the validation loss decreased for the first few epochs and then increased and stayed around constant. Validation loss not decreasing. We can also see that your loss metric had really few improvement, starting on 4. 5312 Validation evaluation: loss: 1. Means no question of Overfitting. The description is: When I do 10 folders cross-validation, the training loss and validation loss goes down at first 10-15 epochs, but can not go further down after 15 epochs and staying on about (loss: 1. I am using a CPU maybe I required much more stronger machines. If train loss is decreasing and validation accuracy is increasing, then this is what we expect. Viewed 97 times 1 $\begingroup$ I am trying to train a MobileNetV2 on a custom dataset, to image Classification task. loss/val_loss are decreasing but accuracies are the same in LSTM! 0. But if train loss is decreasing while validation accuracy drops to saturation, then overfitting may happend. I use nn. This means that your model has sufficient capacity to learn (i. Tensorflow - Accuracy begins at 1. data dataset, but rather a dataset that I downloaded from here. Here's my model: Keras loss is not decreasing. 67, validation accuracy 12. I tried many ways: Changed activation function Used weighted CE instead of normal CE since the dataset is imbalanced. But when first trained my model and I split training dataset ( sequences 0 to 7 ) into training and validation, validation loss decreases because validation data is taken from the same sequences used for training eventhough it is not the same data for training and evaluating. It is something like this. I will assume you are talking about cross-entropy loss, which can be thought of as a measure of 'surprise'. The curve of loss are shown in the Yep. vision. Follow answered Feb 1 at 5:22. model = Sequential I am currently working on training an RNN to map sequential data to real-valued positive values, but unfortunately my validation loss is not decreasing. Does the fact that the validation Loss is not decreasing as much as the training loss means that I am having overfitting? (just in case, I'm doing 25 epochs, batch size:128, Learning rate:0. I will give my settings and some codes at final position. 6301 - val_loss: The training loss will tell us if our model can fit the training set at all--in other words, if our model has enough capacity to process the relevant information in the data. I am using a pre-trained model as my dataset is very small. However, I cannot compute the right validation loss. But after approximately 40 epochs no improvement in performance measures. 053 [1, 2500] training loss: 2. I visualized the gradients (removed the code because it would just clutter things) and I think something is wrong there? But I'm not sure. But still, even after 20 epochs, for whichever loss function I use, the validation loss remains exactly the same. especially in multi-class classification, the loss may be decreasing while accuracy also decreases. Here’s the loss for the small CNN. Hello, I am working on a Siamese net developed in this paper and trying to reproduce it using batches of 28x28 data. The loss function used to decrease during the training per epoch until the last week, but now even though all of the parameters, including the batch size and the learning rate have the same value, when I train my model the value of the loss function is What I am not sure is if my calculation of training loss and validation loss is correct. Module): def __init__(self,in_ch,out_ch,down_sample=True,batch_norm=True): super(). There is something that I did not understand from ‘3. cajenvr gkzf oulkdp ytbsqv czhnwus xfzyd ftj ynha puvhwwy tvk