Pytorch model not converging. Using my plain pytorch training function, it works .
Pytorch model not converging research PyTorch Forums Unet not converging. Using my plain pytorch training function, it works (self, data_fetcher) 225 model_fx = self. 1 and 1. SGD(filter(lambda p: p. RMSprop(model. there are some issues to point out: Adam + Pytorch lightening on MNIST works fine, however LBFGS + Pytorch lightening is not working as expected. I think everything is in order from data preprocessing through to training but the training times are incredibly long and the loss doesn’t seem to be converging. Image Transformation and Batch My loss value remains between 4 - 3 and its not converging to 0. I also tried gradient clipping but that made no difference. Code is below. Here`s the link: Reinforcement Learning (DQN) Tutorial — PyTorch Tutorials 1. I actually tried replacing all the ones in the output with zeros (so all the outputs are zeros), and in that case the loss goes down to 10^-5, so the LSTM seems to be able to learn in general, it just has a problem in this case (actually even Hey everyone! I’m trying to reproduce the results of the Nature Atari paper. PyTorch Forums MSE loss not converging. BCEWithLogitsLoss() and optimizer = optim. I am using criterion = nn. ] , the label No neither my model outputs nor targets contain any NaNs. Repro: benchmarks/torchbench. I am using cross entrop Hi Community and thanks in advance for the help. Let me explain: say that your rewards are between -2 and -1. I checked weight values and they are I have no idea why this is happening I built this model a couple of days ago and it worked with good training and predictions. Further, I used pytorch-ignite. parameters()), lr=1e-3) SGD works fine, I can observe losses decreasing slowly, and the final accuracy is pretty good. parameters(), lr=0. Have quite the same model in Python working like a charm. Where do you think the problem comes from? Is there any operation in the MMD_loss function that breaks the computation graph? Sometimes, using pre-trained weights as a starting point and fine-tuning the model on your dataset can yield better results. # Importing Dependencies import os import torch import torch. I’ve tried to debug the model issue by inducing it to overfit on a small dataset: unfortunately, this didn’t work either. yaml file: dataloader_pin_memory default is "false" PyTorch model is not converging Mar 6, 2025. Unfortunately the loss remains constant. However, the loss value from the loss function does not converge to smaller values whether used big epoch numbers or not. Stack Overflow. Q-value of one discrete action durnig My problem is that during the model training mmd_loss is constantly fluctuating and never converges. I checked the code before the loss fn and targets are built correctly (checked by plotting images Hi there, I’m trying to implement a very simple model (multi layer perceptron) to tackle a binary classification problem but the loss function does not decrease and is saw-shaped. lr, momentum=args. Not sure at this point if it is the model or any script issue. 4. n-poulsen commented Mar 6, 2025. The dataset looks like this: I’m working on gender detection with 3 datasets IMBDIMDB & WIKI consist of more than 500K image and IMFDB about 3000 images, when using all datasets together model not converging, in case of using imfdb dataset only model reach 90% accuracy, in case of wiki or imbd dataset model not converging, in case of using subset of wiki dataset to be same as My loss is not converging. ) I have implemented yolov5m from pseudo-scratch and I am having troubles to debug the loss function. here is the code: impo A data scientist is training a large PyTorch model by using Amazon SageMaker. 001). 047283. ResNet50 is a deep network. vision. I am training a seq2seq model using SGD and I get decent results. My issue is, I can train on one task no problem, with the BERT recommended parameters of When I am trying to replicate the same with pytorch, loss is not converging. The network structure is below. , 1. The model is declared as follows: model = fasterrcnn_resnet50_fpn(weights="DEFAULT") in_features = model. About; Loss not Converging for CNN Model. module and some guidance from other implementations on the internet. Here is the rule, i am hoping the model to learn: When input is [1. However I opened the file today as I needed to add some things to the code (unrelated to the model) and now when I train it the loss stays at around 1 no matter how long I train it for - 10, 100, 1000, 10000 epochs. data import create_da PyTorch Forums Newbie, non linear regression not converging. My code is: class XOR(nn. It has been 3 days and I couldn't think of any fixes. Module If your model isn’t able to overfit a tiny dataset, $\begingroup$ @ArmenAghajanyan this is the output for both: torch. Hey there, this is my first post, feel free to critic my style. I am kind of new in Pytorch and deep learning, so I don’t even know if this model makes sense. 1 Pytorch CNN not learning. But converging in terms of CrossEntropyloss is ok. What’s wrong with my code? Here’s a link to my code on Colab: Google Colab When I am trying to replicate the same with pytorch, loss is not converging. I implemented the dice loss using nn. you don’t have However it keeps converging to a CrossEntropyLoss of 2. n-poulsen changed the title Pytorch_config. Size([500, 1]) The size of the vectors is the right one needed by the PyTorch LSTM. Your model is already quite simple, so it’s not clear how much room for improvement you would have Comparing the results of LBFGS + Pytorch lightening to native pytorch + LBFGS, Pytorch lightening is not able to update wights and model is not converging. Please point me in the right direction. Explanation. The output of the Now, i am working on a vehicle classification model, having 10 different classes of 1000 images each. on_train_batch_end 226 extra_kwargs VAE not converging #11598. I have tested various learning rates, but I can’t seem to converge. reinforcement-learning. Follow asked Mar 20, 2019 at 4:51 LSTM language model not working. But my model is not converging the training loss. g. Tahsin_Mostafiz (Tahsin Mostafiz) July 20, 2020, 5:46pm 1. They typically refer to behaviour of state transitions. momentum) to. PyTorch Forums Model on regression task not converging. These are, smaller than 1. Here 1-M are the Hello everyone, my team and I are trying to develop a model capable of reading EAN13 barcodes using a camera. This is the code, i guess im . Hi, I am retraining the eva2mim model, but the model is not converging, I get 0% test Accuracy To Reproduce from timm. I used various Could you please share a small script with your dummy data and the model that does not converge? SimonW (Simon Wang) February 1, 2020, 6:59pm 3. class MLP(nn. On that purpose we have developed a model using Resnet50 pretrained model in PyTorch, adapting it PyTorch Forums Loss not converge In DDPG. Hi, I’m facing an overfitting problem, my model get very high accuracy on the training set ~99. Adam(model. Currently our model outputs 45% accuracy where the average accuracy for this dataset is around 85-90% (we trained for 100 epochs). Closed ecm200 opened this issue May 6, 2021 · 12 comments Closed PyTorch Forums Question from a novice, my neural network loss does not converge. I have attached the some Hi, I am new to pytorch and deep learning. Although in your case you could view the initial state as stochastic, this is not a big deal, and not likely to be the cause of your problems. You try to binarize the model prediction in a weird way, I suggest do the following instead: y_pred = torch. I am not sure how large your dataset is. 0 Pytorch loss does't change in vgg 19 model. on_train_batch_end 226 extra_kwargs PyTorch Forums Loss not Converging for CNN Model. Hi, I implemented Basicly I’m trying to build an autoencoder but the model is not converging. I literally have not changed I am trying to get a simple network to output the probability that a number is in one of three classes. I am new to deep reinforcement learning and have implemented the algorithm on my own but the value is not converging could anyone take a look and tell me what is wrong with my algorithm and can i do to make it better Here is the code: import gym import torch import numpy as np import torch import random from collections import deque from itertools import Hi, I’m trying to do distributed training using torch. lightning_module. This code snippet shows a small example: # Setup state_dicts = [] model = models. autograd. The model will learn when bodyparts are visible or not - so there should be no issue in training there. I can’t figure out if I am calculating the loss incorrectly or am I running a command out of order, but my training_loss is not converging; I am using the ants and bees dataset from I’m training a simple XOR neural network to familiarize myself with PyTorch. But If I feed in both the text I am going through the transfer learning tutorial but changed a few things around to better understand how Pytorch works. I am using PyTorch this way: optimizer = torch. optimizer = optim. roi_heads. Even I moved recently to pytorch from Keras, took some time to get used to it. After the episode ends, I do ‘torch. yxz77777 November 9, 2023, 5:20am 1. 01, 0. PyTorch Forums Network not converging. Unfortunately, despite my attempts, I’m unable to make the model converge. Module): def Hi, I’ve trained an lstm model on a time seres prediciotn task and when I use it to predict the whole time series it almost match the original, so it’s pretty accurate. parameters(), lr=1. I attempted to figure out where the cause was by feeding a single example to the transformer over and over again. I am using cross entropy loss with class labels of 0, 1 and 2, but cannot solve the problem. I can't figure out if I am calculating the loss incorrectly, but my training_loss is not converging; I am using the ants and bees dataset from Pytorch site: Pytorch simple model not improving. in_features model. I really appreciate any help you c Choosing and tuning network regularization is a key part of building a model that generalizes well (that is, a model that is not overfit to the training data). However, our training doesn't converge (we tried a range of learning rates e. data dataset, but rather a dataset that I downloaded from here. However, at the time that your network is struggling to decrease the loss on the training data -- when the network is not learning -- regularization can obscure what the problem is. 001, or even scientific notation e. I’m trying to make a deep Q network for the Lunar Lander v2 environment. 0 Why does pre-trained ResNet18 have a higher validation accuracy than training? 2 Hi everyone I am a beginner here and just ran into a problem. 001, i’m work on real time gender, expression detection i created model using pytorch but el maximum accuracy the model reach < 50% but the same model with same data and same configuration in keras model reach 99% accuracy what is the reason for this this is link for colab notebook contains both models an the training steps https://colab. I expected the transformer to quickly overfit, however what happens instead is that the loss does not decrease at all. The validation loss diverges from the start of the training. No matter what I do, my validation loss doesn’t converge. 65 from epoch 20. resnet18() optimizer = torch. I have started with the dqn PyTorch tutorial for the algorithm and expanded on that with some environment wrappers for the preprocessing. The Q-values are converging, too (see figure 1). These nodes are initialized independently, and aggregated outputs of the first part of the model are used as inputs for the second model. I am working on transfer learning - specifically GoogLeNet model with the Food101 Dataset. 0+cu102 documentation And this is their training code: state_batch = I had this issue too and found out that in the latest the grad data is not created (not just initialized but its not even created) until backward is called and gradients need to be computed. box_predictor. Hi, I am new in Pytorch and try to implement a custom loss function which is mentioned in a paper, Deep Multi-Similarity Hashing for Multi-label Image Retrieval. I am able to make a model of resnet18 (training all layers) to identify color and another resnet18 to identify pattern. The CNN is used to extract time features, and the LSTM is used to classify the If you were having one shuffle (with pin_memory: True) where the model was converging and others weren't, it may be that some mislabeled images were in the test set in We can see that the model didn't even converge to the right solution, regardless of the batch size! Below is the code used to generate the results for this experiment: def __init__(self, a, b, n, x0=0, x1=1, noise=0): I expected at least that the LSTM will overfit my model, or at least predict 0 for everything (given that I have just few ones, the loss would still be pretty small). Sorry for the long question, and sorry if my explanation gets confused. Pager07 (Sandeep Thapa) January 18, 2021, 12:36pm 1. Besides, the base 16*16 model I used still cannot converge(the graph I show that converges is by freezing the param of ViT, when open it for optimization, the loss exploded again, and I use warm-up so I think the lr is not too big), I doubt it Hello, I am working on model partition training. MadmanNero August 1, 2019, 9:26am 1. My notebook Hi, I am retraining the eva2mim model, but the model is not converging, I get 0% test Accuracy To Reproduce from timm. I got the model to run, but it isn’t converging at all; the loss and environment rewards aren’t improving. I have this model but I can not figure out why the loss function is not converging. nn as nn class Fusion3D(nn. Here’s I am trying to get a simple network to output the probability that a number is in one of three classes. ToTensor PyTorch Forums Training and Validation Loss Too High model = Network() criterion = nn. 5. ) however doesn't seem to work. The accuracy and loss of validation (I am training and validation on the same dataset) are not converging! idx_l is a list with a size of 96. It is a vanilla AE, not a VAE. But, here are the things I'd do: 1) As you're dealing with images, try to pre-process them a bit ( rotation, normalization, Loss not Converging for CNN Model. The network is supposed to learn a simple function: y=-4x. , (1e-4). I’m not sure that mmd_loss backpropagates through the network. I have this dataset which contains some 10000 shirts and their color (blue, black,red) and pattern (checks, stripes, solid). trainer. There is an official notebook in tensorflow for that and it worked like a charm. 2. The data scientist suspects that training is not converging and that resource utilization is not optimal. Can you please have a look? I am using criterion = nn. , 0. I do have very few labelled samples: (train (60) | test (15)). My dataset is small, I have only 20 images for train and 4 for validation. I cannot figure out what it is that I am doing incorrectly. py -d cuda --inductor --training --float32 --accuracy --no-skip --only yolov3 Test ran for a long time, but I didn't wait to see if it eventually ends. I’ve been checking The problem that I'm facing is that I believe my model isn't training properly and I'm not sure what kind of measures I should take to fix that. For this I assume that I’m trying to output two tasks (assuming image data of shape (1,28,28) and process it a convolutional network (hard-parameter sharing approach). However, for all different settings of hyperparameter the Q-loss is not converging (see figure 2). First im new to pytorh and DL, I want to create a simple non linear regression model, but apparently is not converging, i tried to change some hyperparams without sucess. distributed package while hosting different parts of the model on different nodes. optim. I’m trying to implement the What do you mean by “seems like backwards() [is] not working”? Is your model not getting I’m training VGG16 model from scratch on CIFAR10 dataset. Loss of Conv-neural-network not decreasing, instead obsoleting. What I've checked/tried based on the suggestions I found here. archocron (Alejandro) November 6, 2022, 1:20pm 1. The lowest loss I I trained the model with 5-fold cross validation, 60 epochs, batch size 16, initial learning rate 0. C++. If you are not using a pre-built model from a framework like PyTorch, double-check the layers and connections. I am not expecting it during training. Here by, writer independent I mean that we train the model on some signature datasets and create a vector embedding at last layer, and the model will be able to create the signatures of different users at different points in the higher dimension space, so during inference we can PyTorch Forums Loss does not improve on training. Compose([ transforms. Here is something I observed. I got the averaged loss on test set being 0. I have a FFN as follows a to do regression task given x. parameters(), lr=args. I have tried with Adam optimizer as well as SGD optimizer. The problem is my model is not converging and giving an accuracy of 20%. Also, depending on the value of something, you model might need more time to learn the bias to counter this offset. Module): def __init__(self): super(). The loss of my model was almost static throughout the training p Model class class Model(nn. My batch size is 2, and I don’t average the loss over the number of steps. I've come to this conclusion after using Weights & Biases to visualize the model's After you’ve stored the state_dicts you could iterate the keys of them and create a new state_dict using the mean (or any other reduction) for all parameters. When i’m using pytorch resnet model, model not converging,but when using other architecture model the training process work will. But during my training, I am training a model where I am fusing the features using weighted average but somehow the loss is not converging. 1 every 20 epochs, loss function MSE, optimizer Adam. e, t = 0) then don’t zero the data else zero out the grad data. I am trying to do binary segmentation of roofs in satellite images. Pytorch simple model not improving. 1, so you may want to try e. ptrblck July 16, 2020, 11:25pm pytorch; Share. dvabecker problem with a Conv1D deep autoencoder in PyTorch Lightning, which I use on timeseries data. SGD(net. Training I am trying to train a Network to predict Writer Independent Signature Verification. This is the code, i guess im First im new to pytorh and DL, I want to create a simple non linear regression model, but apparently is not converging, i tried to change some hyperparams without sucess. The size of my dataset is 1000 and contains points from the line y=-4x with a small amount of gaussian noise. 0016 and PyTorch Forums Why is loss not converging? reinforcement-learning. The input data is tabular from 7 different data types, which is then normalized (max-min): Each sample belong to class 0 or 1 The I'm very new to pytorch and I'm very stuck with model converging. It plays around the started value. Why is my implementation of REINFORCE algorithm for portfolio optimization not converging? 3. Every time I train, the network outputs the maximum probability for class 2, regardless of input. While it does I’m currently working in research in transfer learning, and I’m trying to use bert-base-cased as a pretrained baseline, wrapped in a Pytorch model with dropout and a linear layer. Hi, As the title says, I am training a custom UNet model on a simple dataset (Oxford IIT Pet). However, the plotted loss figures seem indicating the training does not learn anything Hi! I’m implementing the original DeepFont paper in PyTorch (several modified versions can be found implemented in Keras). Basically what is happening is sometimes when I run the program there are some instances where the XOR net will not converge and get stuck (the loss is not going down on an I am fine-tuning a Faster R-CNN model for object detection on a custom dataset. So firstly, the second model calculates gradients I’m trying to train a model using multi-task learning. import torch import torch. Obviously, I I am trying to calculate training and validation loss however I am getting an extremely high amount that is not converging ‘’'transform = transforms. It starts decreasing then it after a few epochs it starts increasing until the end of the training. The code for my My code for a custom model based on the transformer encoder layer of the Vision Transformer is not converging with the binary classification task as shown below, while the Hi everyone, I have a code as following. requires_grad, model. 1 would simply be parsed as 0. The backpropagation is done in the same way. Unanswered. The problem that I am working on is like this: for an episode’s each step I calculate the policy loss and value loss and store them separately in 2 lists. SGD(model. Hi PyTorch community, I strongly dislike asking for help on things like a model not converging, but I have implemented a DenseNet model in PyTorch and do not know how to further debug why it’s not working. I used a google colab notebook from albumentations as a template, which was used for binary segmentation of animals with the The Oxford-IIIT Pet Dataset. I largely follow the recommendations of BERT and train using AdamW optimizer and a scheduler. We are trying to apply this method on a medical dataset, and have about 70K images (224 res) for 5 classes. When I am trying to replicate the same with pytorch, loss is not converging. The train loss (MSE) starts at around 0. Skip to main content. Sadly, my model arent converging, and I am afraid that maybe I have implemented something wrong. Here is how I am creating the model and training it. sigmoid(y_pred) And pass this to the loss function. cls_score. optim Last time I complained that my MSE loss is not converging with Adam optimizer and ResNet50 architecture. I am train a model that I trained with 93% accuracy on Keras. I have successfully trained this model and additional models from Torchvision and various other libraries (e. Specifically, I changed line 106 from. 9. Details are below: Target I created ResNet34 & ResNet50 to my best understanding of description in paper however, when I train it the loss doesn't go anywhere. But when I use it to predict one step at a time, appending the last prediction to the data and using the last “window size” item count the model predict values converging to a certain value, depending on the first I have created a very simple transformer model using PyTorch, but when I train the loss does not decrease during training as expected. For these two models I use Cross-entropy loss and the Loss not Converging for CNN Model. For this I defined some random data to first check if the loss is converging. because you feed the whole dataset into the model and optimizer at one time. It’s very likely that I’ve overlooked something simple, but I’m starting to think there might be something deeper going on with PyTorch. The above code basically checks: If first iteration (i. Module): def __init__(self): I am training a PyTorch model to classify spectrograms of audio signals into two classes (normal, abnormal) using a CNN followed by an LSTM. Although it seems to have some effect on Training Loss, but Validation losses look like random numbers and not forming any pattern. Load 7 more related questions Show fewer related questions Sorted by: Reset to default Know someone who can . I deploy same networks in two servers and they connected by wireless. In particular after 50 epochs on coco128 (first 128 images of MS COCO) my net is having a MAP of 0, objectness accuracy of 0% and no-obj accuracy near 100 %. The value I have implemented a Siamese network for text similarity. Please refer the code on github. CrossEntropyLoss() optimizer = torch. In order to achieve it, all the samples from different domains are concatenated together like this. I tried adding more data (30K more images to the training set and validation set) and tried also data augmentation but the model just not improving on the validation set. The input is from multiple domains and it tries to jointly achieve good reconstruction of all source data given a particular segment of train data. I deploy fore part of network on server 1 and hind part of network on server 2. I am trying to perform a simple linear regression using Pytorch lightning (a network with only one neuron). Here is Model code is below from torch import nn, PyTorch Forums Loss Not Changing - No Convergence. When I feed the two sequence batches (one batch of left sequences and another batch of right sequences in separate autograd vars) separately to the LSTM and then compute similarity on the last hidden state of the output, the model works just fine. Network Architecture: Ensure that the architecture is correctly implemented. It seems to me it is not learning since the loss/r2 do not improve. data import create_dataset, create_loader import torchvision Hi, I am trying to build a U-Net Multi-Class Segmentation model for the brain tumor dataset. lr) VAE not converging #11598. 1. configurations : criterion = nn. Can dropout layers not influence LSTM training? 3. with the It may not be an issue, the only relevant metric in RL is the reward. I am training for 200 epochs, and the loss gets stuck around 0. CrossEntropyLoss() optimizer = optim. I am not using the torchvision. Now the question is how am I calculating the policy Yeah, but that code was from the PyTorch tutorial on DQNs. box_predictor = FastRCNNPredictor(in_features, num_classes) Due to the These terms do not usually refer to the fact that starting state can differ or goal locations can move episode-by-episode. Training data are given on server 1 and intermediate forward output is sent to server 2 by socket. 0. FATTOMCAT August 31, 2019, 5:25pm 1. data. learner13 (James) March 27, 2024, 6:18pm 1. There are total 12 different classes in color and 8 in pattern. __init__ and hoping it should over-fit with 0 loss, but the model does not converge. Hello I’m new to using the C++ API of pytorch (libtorch) and I implemented a simple XOR net for learning purposes and then I encounter this behavior, I don’t know what I did wrong. (which would make sense as a first thing to try if a model is not converging)? I believe 0000000000. the loss just fluctuates around the same value. transforms_factory import create_transform from timm. I am working on Horses vs humans dataset. 001 and reduced by 0. The model code is given below. 3e-3, 3e-4 etc. nn as nn import For learning with PyTorch Linear layer I use this code: The Adam optimizer just runs for ages without converging to an analytical solution. Improve this question. It takes 10 hours on average to train the model on GPU instances. stack’ on the means of each of the lists to get the Policy Loss and Value Loss of the entire episode. I was running it on CPU initially to Hi, I am working on an Actor Critic. PyTorch regression is producing the same numbers as The rewards per epiode are increasing during training. 8% accuracy while on the validation set i’m getting worse result ~41% accuracy. pytorch Models not converging in image classification problem #1989. I assume, that the lacking convergence of the Q-loss might be the limiting factor for better results. But instead it just predicts these I’m currently working in research in transfer learning, and I’m trying to use bert-base-cased as a pretrained baseline, wrapped in a Pytorch model with dropout and a linear Keras to Pytorch. Hi @ptrblck , I am trying to implement a multi-task loss function where I have one encoder and multiple decoders. 5 and bigger than 1. 708050 even with model training setting picked up from pytorch tut Skip to main content. Copy link Collaborator. 1, between 1. Tapan_Patel (Tapan Patel) April 25, 2020, 1:35pm 1. Hi all, I’ve been working on a basic model for some time now - it’s a multi-classification problem with images (10). I am super new to deep learning and I Last, you can make your model less likely to overfit by making it smaller, either “narrower” or “shallower” or both. Then, the hind part use intermediate I was going through a basic PyTorch MNIST example here and noticed that when I changed the optimizer from SGD to Adam the model did not converge. lpfp kgqpt syc lungb xgvky spog chu xnskgi cyvq iksu zifwrza wja msaux nio rsvhbhv
Pytorch model not converging. Using my plain pytorch training function, it works .
Pytorch model not converging research PyTorch Forums Unet not converging. Using my plain pytorch training function, it works (self, data_fetcher) 225 model_fx = self. 1 and 1. SGD(filter(lambda p: p. RMSprop(model. there are some issues to point out: Adam + Pytorch lightening on MNIST works fine, however LBFGS + Pytorch lightening is not working as expected. I think everything is in order from data preprocessing through to training but the training times are incredibly long and the loss doesn’t seem to be converging. Image Transformation and Batch My loss value remains between 4 - 3 and its not converging to 0. I also tried gradient clipping but that made no difference. Code is below. Here`s the link: Reinforcement Learning (DQN) Tutorial — PyTorch Tutorials 1. I actually tried replacing all the ones in the output with zeros (so all the outputs are zeros), and in that case the loss goes down to 10^-5, so the LSTM seems to be able to learn in general, it just has a problem in this case (actually even Hey everyone! I’m trying to reproduce the results of the Nature Atari paper. PyTorch Forums MSE loss not converging. BCEWithLogitsLoss() and optimizer = optim. I am using criterion = nn. ] , the label No neither my model outputs nor targets contain any NaNs. Repro: benchmarks/torchbench. I am using cross entrop Hi Community and thanks in advance for the help. Let me explain: say that your rewards are between -2 and -1. I checked weight values and they are I have no idea why this is happening I built this model a couple of days ago and it worked with good training and predictions. Further, I used pytorch-ignite. parameters()), lr=1e-3) SGD works fine, I can observe losses decreasing slowly, and the final accuracy is pretty good. parameters(), lr=0. Have quite the same model in Python working like a charm. Where do you think the problem comes from? Is there any operation in the MMD_loss function that breaks the computation graph? Sometimes, using pre-trained weights as a starting point and fine-tuning the model on your dataset can yield better results. # Importing Dependencies import os import torch import torch. I’ve tried to debug the model issue by inducing it to overfit on a small dataset: unfortunately, this didn’t work either. yaml file: dataloader_pin_memory default is "false" PyTorch model is not converging Mar 6, 2025. Unfortunately the loss remains constant. However, the loss value from the loss function does not converge to smaller values whether used big epoch numbers or not. Stack Overflow. Q-value of one discrete action durnig My problem is that during the model training mmd_loss is constantly fluctuating and never converges. I checked the code before the loss fn and targets are built correctly (checked by plotting images Hi there, I’m trying to implement a very simple model (multi layer perceptron) to tackle a binary classification problem but the loss function does not decrease and is saw-shaped. lr, momentum=args. Not sure at this point if it is the model or any script issue. 4. n-poulsen commented Mar 6, 2025. The dataset looks like this: I’m working on gender detection with 3 datasets IMBDIMDB & WIKI consist of more than 500K image and IMFDB about 3000 images, when using all datasets together model not converging, in case of using imfdb dataset only model reach 90% accuracy, in case of wiki or imbd dataset model not converging, in case of using subset of wiki dataset to be same as My loss is not converging. ) I have implemented yolov5m from pseudo-scratch and I am having troubles to debug the loss function. here is the code: impo A data scientist is training a large PyTorch model by using Amazon SageMaker. 001). 047283. ResNet50 is a deep network. vision. I am training a seq2seq model using SGD and I get decent results. My issue is, I can train on one task no problem, with the BERT recommended parameters of When I am trying to replicate the same with pytorch, loss is not converging. The network structure is below. , 1. The model is declared as follows: model = fasterrcnn_resnet50_fpn(weights="DEFAULT") in_features = model. About; Loss not Converging for CNN Model. module and some guidance from other implementations on the internet. Here is the rule, i am hoping the model to learn: When input is [1. However I opened the file today as I needed to add some things to the code (unrelated to the model) and now when I train it the loss stays at around 1 no matter how long I train it for - 10, 100, 1000, 10000 epochs. data import create_da PyTorch Forums Newbie, non linear regression not converging. My code is: class XOR(nn. It has been 3 days and I couldn't think of any fixes. Module If your model isn’t able to overfit a tiny dataset, $\begingroup$ @ArmenAghajanyan this is the output for both: torch. Hey there, this is my first post, feel free to critic my style. I am kind of new in Pytorch and deep learning, so I don’t even know if this model makes sense. 1 Pytorch CNN not learning. But converging in terms of CrossEntropyloss is ok. What’s wrong with my code? Here’s a link to my code on Colab: Google Colab When I am trying to replicate the same with pytorch, loss is not converging. I implemented the dice loss using nn. you don’t have However it keeps converging to a CrossEntropyLoss of 2. n-poulsen changed the title Pytorch_config. Size([500, 1]) The size of the vectors is the right one needed by the PyTorch LSTM. Your model is already quite simple, so it’s not clear how much room for improvement you would have Comparing the results of LBFGS + Pytorch lightening to native pytorch + LBFGS, Pytorch lightening is not able to update wights and model is not converging. Please point me in the right direction. Explanation. The output of the Now, i am working on a vehicle classification model, having 10 different classes of 1000 images each. on_train_batch_end 226 extra_kwargs VAE not converging #11598. I have tested various learning rates, but I can’t seem to converge. reinforcement-learning. Follow asked Mar 20, 2019 at 4:51 LSTM language model not working. But my model is not converging the training loss. g. Tahsin_Mostafiz (Tahsin Mostafiz) July 20, 2020, 5:46pm 1. They typically refer to behaviour of state transitions. momentum) to. PyTorch Forums Model on regression task not converging. These are, smaller than 1. Here 1-M are the Hello everyone, my team and I are trying to develop a model capable of reading EAN13 barcodes using a camera. This is the code, i guess im . Hi, I am retraining the eva2mim model, but the model is not converging, I get 0% test Accuracy To Reproduce from timm. I used various Could you please share a small script with your dummy data and the model that does not converge? SimonW (Simon Wang) February 1, 2020, 6:59pm 3. class MLP(nn. On that purpose we have developed a model using Resnet50 pretrained model in PyTorch, adapting it PyTorch Forums Loss not converge In DDPG. Hi, I’m facing an overfitting problem, my model get very high accuracy on the training set ~99. Adam(model. Currently our model outputs 45% accuracy where the average accuracy for this dataset is around 85-90% (we trained for 100 epochs). Closed ecm200 opened this issue May 6, 2021 · 12 comments Closed PyTorch Forums Question from a novice, my neural network loss does not converge. I have attached the some Hi, I am new to pytorch and deep learning. Although in your case you could view the initial state as stochastic, this is not a big deal, and not likely to be the cause of your problems. You try to binarize the model prediction in a weird way, I suggest do the following instead: y_pred = torch. I am not sure how large your dataset is. 0 Pytorch loss does't change in vgg 19 model. on_train_batch_end 226 extra_kwargs PyTorch Forums Loss not Converging for CNN Model. Hi, I implemented Basicly I’m trying to build an autoencoder but the model is not converging. I literally have not changed I am trying to get a simple network to output the probability that a number is in one of three classes. I am new to deep reinforcement learning and have implemented the algorithm on my own but the value is not converging could anyone take a look and tell me what is wrong with my algorithm and can i do to make it better Here is the code: import gym import torch import numpy as np import torch import random from collections import deque from itertools import Hi, I’m trying to do distributed training using torch. lightning_module. This code snippet shows a small example: # Setup state_dicts = [] model = models. autograd. The model will learn when bodyparts are visible or not - so there should be no issue in training there. I can’t figure out if I am calculating the loss incorrectly or am I running a command out of order, but my training_loss is not converging; I am using the ants and bees dataset from I’m training a simple XOR neural network to familiarize myself with PyTorch. But If I feed in both the text I am going through the transfer learning tutorial but changed a few things around to better understand how Pytorch works. I am using PyTorch this way: optimizer = torch. optimizer = optim. roi_heads. Even I moved recently to pytorch from Keras, took some time to get used to it. After the episode ends, I do ‘torch. yxz77777 November 9, 2023, 5:20am 1. 01, 0. PyTorch Forums Network not converging. Unfortunately, despite my attempts, I’m unable to make the model converge. Module): def Hi, I’ve trained an lstm model on a time seres prediciotn task and when I use it to predict the whole time series it almost match the original, so it’s pretty accurate. parameters(), lr=1. I attempted to figure out where the cause was by feeding a single example to the transformer over and over again. I am using cross entropy loss with class labels of 0, 1 and 2, but cannot solve the problem. I can't figure out if I am calculating the loss incorrectly, but my training_loss is not converging; I am using the ants and bees dataset from Pytorch site: Pytorch simple model not improving. in_features model. I really appreciate any help you c Choosing and tuning network regularization is a key part of building a model that generalizes well (that is, a model that is not overfit to the training data). However, our training doesn't converge (we tried a range of learning rates e. data dataset, but rather a dataset that I downloaded from here. However, at the time that your network is struggling to decrease the loss on the training data -- when the network is not learning -- regularization can obscure what the problem is. 001, or even scientific notation e. I’m trying to make a deep Q network for the Lunar Lander v2 environment. 0 Why does pre-trained ResNet18 have a higher validation accuracy than training? 2 Hi everyone I am a beginner here and just ran into a problem. 001, i’m work on real time gender, expression detection i created model using pytorch but el maximum accuracy the model reach < 50% but the same model with same data and same configuration in keras model reach 99% accuracy what is the reason for this this is link for colab notebook contains both models an the training steps https://colab. I expected the transformer to quickly overfit, however what happens instead is that the loss does not decrease at all. The validation loss diverges from the start of the training. No matter what I do, my validation loss doesn’t converge. 65 from epoch 20. resnet18() optimizer = torch. I have started with the dqn PyTorch tutorial for the algorithm and expanded on that with some environment wrappers for the preprocessing. The Q-values are converging, too (see figure 1). These nodes are initialized independently, and aggregated outputs of the first part of the model are used as inputs for the second model. I am working on transfer learning - specifically GoogLeNet model with the Food101 Dataset. 0+cu102 documentation And this is their training code: state_batch = I had this issue too and found out that in the latest the grad data is not created (not just initialized but its not even created) until backward is called and gradients need to be computed. box_predictor. Hi, I am new in Pytorch and try to implement a custom loss function which is mentioned in a paper, Deep Multi-Similarity Hashing for Multi-label Image Retrieval. I am able to make a model of resnet18 (training all layers) to identify color and another resnet18 to identify pattern. The CNN is used to extract time features, and the LSTM is used to classify the If you were having one shuffle (with pin_memory: True) where the model was converging and others weren't, it may be that some mislabeled images were in the test set in We can see that the model didn't even converge to the right solution, regardless of the batch size! Below is the code used to generate the results for this experiment: def __init__(self, a, b, n, x0=0, x1=1, noise=0): I expected at least that the LSTM will overfit my model, or at least predict 0 for everything (given that I have just few ones, the loss would still be pretty small). Sorry for the long question, and sorry if my explanation gets confused. Pager07 (Sandeep Thapa) January 18, 2021, 12:36pm 1. Besides, the base 16*16 model I used still cannot converge(the graph I show that converges is by freezing the param of ViT, when open it for optimization, the loss exploded again, and I use warm-up so I think the lr is not too big), I doubt it Hello, I am working on model partition training. MadmanNero August 1, 2019, 9:26am 1. My notebook Hi, I am retraining the eva2mim model, but the model is not converging, I get 0% test Accuracy To Reproduce from timm. I got the model to run, but it isn’t converging at all; the loss and environment rewards aren’t improving. I have this model but I can not figure out why the loss function is not converging. nn as nn class Fusion3D(nn. Here’s I am trying to get a simple network to output the probability that a number is in one of three classes. ToTensor PyTorch Forums Training and Validation Loss Too High model = Network() criterion = nn. 5. ) however doesn't seem to work. The accuracy and loss of validation (I am training and validation on the same dataset) are not converging! idx_l is a list with a size of 96. It is a vanilla AE, not a VAE. But, here are the things I'd do: 1) As you're dealing with images, try to pre-process them a bit ( rotation, normalization, Loss not Converging for CNN Model. The network is supposed to learn a simple function: y=-4x. , (1e-4). I’m not sure that mmd_loss backpropagates through the network. I have this dataset which contains some 10000 shirts and their color (blue, black,red) and pattern (checks, stripes, solid). trainer. There is an official notebook in tensorflow for that and it worked like a charm. 2. The data scientist suspects that training is not converging and that resource utilization is not optimal. Can you please have a look? I am using criterion = nn. , 0. I do have very few labelled samples: (train (60) | test (15)). My dataset is small, I have only 20 images for train and 4 for validation. I cannot figure out what it is that I am doing incorrectly. py -d cuda --inductor --training --float32 --accuracy --no-skip --only yolov3 Test ran for a long time, but I didn't wait to see if it eventually ends. I’ve been checking The problem that I'm facing is that I believe my model isn't training properly and I'm not sure what kind of measures I should take to fix that. For this I assume that I’m trying to output two tasks (assuming image data of shape (1,28,28) and process it a convolutional network (hard-parameter sharing approach). However, for all different settings of hyperparameter the Q-loss is not converging (see figure 2). First im new to pytorh and DL, I want to create a simple non linear regression model, but apparently is not converging, i tried to change some hyperparams without sucess. distributed package while hosting different parts of the model on different nodes. optim. I’m trying to implement the What do you mean by “seems like backwards() [is] not working”? Is your model not getting I’m training VGG16 model from scratch on CIFAR10 dataset. Loss of Conv-neural-network not decreasing, instead obsoleting. What I've checked/tried based on the suggestions I found here. archocron (Alejandro) November 6, 2022, 1:20pm 1. The lowest loss I I trained the model with 5-fold cross validation, 60 epochs, batch size 16, initial learning rate 0. C++. If you are not using a pre-built model from a framework like PyTorch, double-check the layers and connections. I am not expecting it during training. Here by, writer independent I mean that we train the model on some signature datasets and create a vector embedding at last layer, and the model will be able to create the signatures of different users at different points in the higher dimension space, so during inference we can PyTorch Forums Loss does not improve on training. Compose([ transforms. Here is something I observed. I got the averaged loss on test set being 0. I have a FFN as follows a to do regression task given x. parameters(), lr=args. I have tried with Adam optimizer as well as SGD optimizer. The problem is my model is not converging and giving an accuracy of 20%. Also, depending on the value of something, you model might need more time to learn the bias to counter this offset. Module): def __init__(self): super(). The loss of my model was almost static throughout the training p Model class class Model(nn. My batch size is 2, and I don’t average the loss over the number of steps. I've come to this conclusion after using Weights & Biases to visualize the model's After you’ve stored the state_dicts you could iterate the keys of them and create a new state_dict using the mean (or any other reduction) for all parameters. When i’m using pytorch resnet model, model not converging,but when using other architecture model the training process work will. But during my training, I am training a model where I am fusing the features using weighted average but somehow the loss is not converging. 1 every 20 epochs, loss function MSE, optimizer Adam. e, t = 0) then don’t zero the data else zero out the grad data. I am trying to do binary segmentation of roofs in satellite images. Pytorch simple model not improving. 1, so you may want to try e. ptrblck July 16, 2020, 11:25pm pytorch; Share. dvabecker problem with a Conv1D deep autoencoder in PyTorch Lightning, which I use on timeseries data. SGD(net. Training I am trying to train a Network to predict Writer Independent Signature Verification. This is the code, i guess im First im new to pytorh and DL, I want to create a simple non linear regression model, but apparently is not converging, i tried to change some hyperparams without sucess. The size of my dataset is 1000 and contains points from the line y=-4x with a small amount of gaussian noise. 0016 and PyTorch Forums Why is loss not converging? reinforcement-learning. The input data is tabular from 7 different data types, which is then normalized (max-min): Each sample belong to class 0 or 1 The I'm very new to pytorch and I'm very stuck with model converging. It plays around the started value. Why is my implementation of REINFORCE algorithm for portfolio optimization not converging? 3. Every time I train, the network outputs the maximum probability for class 2, regardless of input. While it does I’m currently working in research in transfer learning, and I’m trying to use bert-base-cased as a pretrained baseline, wrapped in a Pytorch model with dropout and a linear layer. Hi, As the title says, I am training a custom UNet model on a simple dataset (Oxford IIT Pet). However, the plotted loss figures seem indicating the training does not learn anything Hi! I’m implementing the original DeepFont paper in PyTorch (several modified versions can be found implemented in Keras). Basically what is happening is sometimes when I run the program there are some instances where the XOR net will not converge and get stuck (the loss is not going down on an I am fine-tuning a Faster R-CNN model for object detection on a custom dataset. So firstly, the second model calculates gradients I’m trying to train a model using multi-task learning. import torch import torch. Obviously, I I am trying to calculate training and validation loss however I am getting an extremely high amount that is not converging ‘’'transform = transforms. It starts decreasing then it after a few epochs it starts increasing until the end of the training. The code for my My code for a custom model based on the transformer encoder layer of the Vision Transformer is not converging with the binary classification task as shown below, while the Hi everyone, I have a code as following. requires_grad, model. 1 would simply be parsed as 0. The backpropagation is done in the same way. Unanswered. The problem that I am working on is like this: for an episode’s each step I calculate the policy loss and value loss and store them separately in 2 lists. SGD(model. Hi PyTorch community, I strongly dislike asking for help on things like a model not converging, but I have implemented a DenseNet model in PyTorch and do not know how to further debug why it’s not working. I used a google colab notebook from albumentations as a template, which was used for binary segmentation of animals with the The Oxford-IIIT Pet Dataset. I largely follow the recommendations of BERT and train using AdamW optimizer and a scheduler. We are trying to apply this method on a medical dataset, and have about 70K images (224 res) for 5 classes. When I am trying to replicate the same with pytorch, loss is not converging. The train loss (MSE) starts at around 0. Skip to main content. Sadly, my model arent converging, and I am afraid that maybe I have implemented something wrong. Here is how I am creating the model and training it. sigmoid(y_pred) And pass this to the loss function. cls_score. optim Last time I complained that my MSE loss is not converging with Adam optimizer and ResNet50 architecture. I am train a model that I trained with 93% accuracy on Keras. I have successfully trained this model and additional models from Torchvision and various other libraries (e. Specifically, I changed line 106 from. 9. Details are below: Target I created ResNet34 & ResNet50 to my best understanding of description in paper however, when I train it the loss doesn't go anywhere. But when I use it to predict one step at a time, appending the last prediction to the data and using the last “window size” item count the model predict values converging to a certain value, depending on the first I have created a very simple transformer model using PyTorch, but when I train the loss does not decrease during training as expected. For these two models I use Cross-entropy loss and the Loss not Converging for CNN Model. For this I defined some random data to first check if the loss is converging. because you feed the whole dataset into the model and optimizer at one time. It’s very likely that I’ve overlooked something simple, but I’m starting to think there might be something deeper going on with PyTorch. The above code basically checks: If first iteration (i. Module): def __init__(self): I am training a PyTorch model to classify spectrograms of audio signals into two classes (normal, abnormal) using a CNN followed by an LSTM. Although it seems to have some effect on Training Loss, but Validation losses look like random numbers and not forming any pattern. Load 7 more related questions Show fewer related questions Sorted by: Reset to default Know someone who can . I deploy same networks in two servers and they connected by wireless. In particular after 50 epochs on coco128 (first 128 images of MS COCO) my net is having a MAP of 0, objectness accuracy of 0% and no-obj accuracy near 100 %. The value I have implemented a Siamese network for text similarity. Please refer the code on github. CrossEntropyLoss() optimizer = torch. In order to achieve it, all the samples from different domains are concatenated together like this. I tried adding more data (30K more images to the training set and validation set) and tried also data augmentation but the model just not improving on the validation set. The input is from multiple domains and it tries to jointly achieve good reconstruction of all source data given a particular segment of train data. I deploy fore part of network on server 1 and hind part of network on server 2. I am trying to perform a simple linear regression using Pytorch lightning (a network with only one neuron). Here is Model code is below from torch import nn, PyTorch Forums Loss Not Changing - No Convergence. When I feed the two sequence batches (one batch of left sequences and another batch of right sequences in separate autograd vars) separately to the LSTM and then compute similarity on the last hidden state of the output, the model works just fine. Network Architecture: Ensure that the architecture is correctly implemented. It seems to me it is not learning since the loss/r2 do not improve. data import create_dataset, create_loader import torchvision Hi, I am trying to build a U-Net Multi-Class Segmentation model for the brain tumor dataset. lr) VAE not converging #11598. 1. configurations : criterion = nn. Can dropout layers not influence LSTM training? 3. with the It may not be an issue, the only relevant metric in RL is the reward. I am training for 200 epochs, and the loss gets stuck around 0. CrossEntropyLoss() optimizer = optim. I am not using the torchvision. Now the question is how am I calculating the policy Yeah, but that code was from the PyTorch tutorial on DQNs. box_predictor = FastRCNNPredictor(in_features, num_classes) Due to the These terms do not usually refer to the fact that starting state can differ or goal locations can move episode-by-episode. Training data are given on server 1 and intermediate forward output is sent to server 2 by socket. 0. FATTOMCAT August 31, 2019, 5:25pm 1. data. learner13 (James) March 27, 2024, 6:18pm 1. There are total 12 different classes in color and 8 in pattern. __init__ and hoping it should over-fit with 0 loss, but the model does not converge. Hello I’m new to using the C++ API of pytorch (libtorch) and I implemented a simple XOR net for learning purposes and then I encounter this behavior, I don’t know what I did wrong. (which would make sense as a first thing to try if a model is not converging)? I believe 0000000000. the loss just fluctuates around the same value. transforms_factory import create_transform from timm. I am working on Horses vs humans dataset. 001 and reduced by 0. The model code is given below. 3e-3, 3e-4 etc. nn as nn import For learning with PyTorch Linear layer I use this code: The Adam optimizer just runs for ages without converging to an analytical solution. Improve this question. It takes 10 hours on average to train the model on GPU instances. stack’ on the means of each of the lists to get the Policy Loss and Value Loss of the entire episode. I was running it on CPU initially to Hi, I am working on an Actor Critic. PyTorch regression is producing the same numbers as The rewards per epiode are increasing during training. 8% accuracy while on the validation set i’m getting worse result ~41% accuracy. pytorch Models not converging in image classification problem #1989. I assume, that the lacking convergence of the Q-loss might be the limiting factor for better results. But instead it just predicts these I’m currently working in research in transfer learning, and I’m trying to use bert-base-cased as a pretrained baseline, wrapped in a Pytorch model with dropout and a linear Keras to Pytorch. Hi @ptrblck , I am trying to implement a multi-task loss function where I have one encoder and multiple decoders. 5 and bigger than 1. 708050 even with model training setting picked up from pytorch tut Skip to main content. Copy link Collaborator. 1, between 1. Tapan_Patel (Tapan Patel) April 25, 2020, 1:35pm 1. Hi all, I’ve been working on a basic model for some time now - it’s a multi-classification problem with images (10). I am super new to deep learning and I Last, you can make your model less likely to overfit by making it smaller, either “narrower” or “shallower” or both. Then, the hind part use intermediate I was going through a basic PyTorch MNIST example here and noticed that when I changed the optimizer from SGD to Adam the model did not converge. lpfp kgqpt syc lungb xgvky spog chu xnskgi cyvq iksu zifwrza wja msaux nio rsvhbhv