pytorch loss not decreasing

I am training a pytorch model for sign language classification. Code, training, and validation graphs are below. apaszke closed this as completed on Feb 25, 2017. onnxbot added a commit that referenced this issue on May 2, 2018. Hi, I am taking the output from my final convolutional transpose layer into a softmax layer and then trying to measure the mse loss with my target. For weeks I Can you maybe try running the code as well? Epoch 0 loss: 82637.44604492188 After having a brief look through, it seems youre swapping between torch and numpy, when moving back and forth between the library would break the gradient of any intermediate computations, no? I have trained ssd with mobilenetv2 on VOC but after almost 500 epochs, the loss is still like this: It's doesn't change and loss is very hight What's the problem with implementation? x_n2 = Variable(torch.from_numpy()) #load input of nn2 in batch size. TRAIN_SETS: [['2017', 'train']] Any comments are highly appreciated! Representations of the metric in a Riemannian manifold. BATCH_SIZE: 64 TRAINABLE_SCOPE: 'norm,extras,transforms,pyramids,loc,conf' To learn more, see our tips on writing great answers. In the above piece of code, my when I print my loss it does not decrease at all. PROB: 0.6, EXP_DIR: './experiments/models/fssd_vgg16_coco' it works fine with my dataset, or maybe you didn't change the mode is train or test in the config file. zjmtlab (zhang jian) April 4, 2018, 8:45am #1. DATASET: 'coco' There might be a line in there which is causing your gradient to be zero. My current training seems working. 3) Increasing and decreasing the learning rate. Thanks for the suggestion. rev2022.11.4.43006. SIZES: [[30, 30], [60, 60], [111, 111], [162, 162], [213, 213], [264, 264], [315, 315]] Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Also could you indent your code by wrapping it in three backticks ``` , it makes it easier for people to read/copy! This year, Mr He did publish a paper named 'Rethinking ImageNet Pre-training' which claimed the pre-train on imagenet is not necessary. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Can you activate one viper twice with the command location? I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). Making statements based on opinion; back them up with references or personal experience. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. You lose it. RESUME_SCOPE: 'base,norm,extras,loc,conf' You signed in with another tab or window. I have the same issue. SSDS: fssd It have been discussed in #16. im doing this now. If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each class. After only reload the 'base' and retrain other parameters, I successfully recover the precision. Do you observe a similar phenomenon or do you have any explanation on it? NETS: vgg16 When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. Stack Overflow for Teams is moving to its own domain! @SiNML You can use Standard Scaler from scikit learn and normalize training data and use same mean and variance of train data to normalize test data as well. Asking for help, clarification, or responding to other answers. My only problem left is the speed for test. Id suggest trying to remove all dependencies on numpy and purely use torch operations so autograd can track the operations. Well occasionally send you account related emails. The gradients are zero! UNMATCHED_THRESHOLD: 0.5 @1453042287 Hi, thanks for the advise. Epoch 500 loss: 2904.999656677246 The orange line is the validation loss and the blue line is the training loss. return y. when I plot loss function, it has oscillation; I expect it to decrease during training. All the Also i have verified my network on other tasks and works fine, so i believe it will get better result on detection&&segmentation task too. okseems like training from scratch might not be well supported. I am training an LSTM to give counts of the number of items in buckets. The text was updated successfully, but these errors were encountered: did you load the pre-train weight? @1453042287 I trained the yolov2-mobilenet-v2 from stratch. 5) Trained the model on upto 50 epochs. RESUME_CHECKPOINT: '/home/chase/Downloads/ssds.pytorch-master/weight/vgg16_fssd_coco_27.2.pth' same equal to 2.30. epoch 0 loss = 2.308579206466675. epoch 1 loss = The nms in the test procedure seems very slow. It only takes a minute to sign up. Any comment will be very helpful. Found footage movie where teens get superpowers after getting struck by lightning? i want to know if that really is what is causing the issue. How can we create psychedelic experiences for healthy people without drugs? Epoch 1700 loss: 2883.196922302246 PROB: 0.6, TRAINABLE_SCOPE: 'base,norm,extras,loc,conf' And to get it back you need to find and fight the damn boss again. I try to apply Standard Scaler by following steps: Powered by Discourse, best viewed with JavaScript enabled, Adding following code after train_test_split stage, And applying Standard Scaler to test dataset before test. In fact, with decaying the learning rate by 0.1, the network actually ends up giving worse loss. (github repo: GitHub - skorch-dev/skorch: A scikit-learn compatible neural network library that wraps PyTorch). Try training your network by removing last relu I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss It is very similar to GAN. NEGPOS_RATIO: 3, POST_PROCESS: Making statements based on opinion; back them up with references or personal experience. https://colab.research.google.com/drive/170Peseik03CFYpWPNyD8B8mxUGxTQx67. I don't why the precision changes so dramatically at this point. While training the autoencoder to output the same string as the input, the Loss function does not decrease between epochs. We're using the GitHub issues only for bug reports and feature requests not for general help. What is a good way to debug this? Before my imagenet training finished, i will have to compare sdd performance based on models trained from scratch firstly. What is the limit to my entering an unlocked home of a stranger to render aid without explicit permission, Water leaving the house when water cut off. How many characters/pages could WordStar hold on a typical CP/M machine? Powered by Discourse, best viewed with JavaScript enabled, Custom loss function not decreasing or changing, GitHub - skorch-dev/skorch: A scikit-learn compatible neural network library that wraps PyTorch. I have another issue about the train precision and loss curve. Found footage movie where teens get superpowers after getting struck by lightning? It have been discussed in #16. 4) Changing the optimizer from Adam to SGD. The main issue is that the outputs of your model are being detached, so they have no connection to your model weights, and therefore as your loss is dependent on output and x There are 29 classes. LEARNING_RATE: 0.001 This means you won't be getting GPU acceleration. 5. torchvision is designed with all the standard transforms and datasets and is built to be used with PyTorch. I recommend using it. You signed in with another tab or window. Also, you do use the gradient of your input data at all (i.e. When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. Stack Overflow - Where Developers Learn, Share, & Build Careers Did Dick Cheney run a death squad that killed Benazir Bhutto? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Ive updated the code now. I am using Densenet from Pytorch models, and have copied Epoch 1200 loss: 2889.669761657715 Epoch 1100 loss: 2887.0635833740234 @1453042287 Hi, thanks for the advise. Find centralized, trusted content and collaborate around the technologies you use most. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' Also, remember to clear the gradient cache of your parameters (via optimizer.zero_grad()) otherwise your gradients will acculumate from all epochs! I am training an LSTM to give counts of the number of items in buckets. Epoch 1000 loss: 2870.423141479492 How does taking the difference between commitments verifies that the messages are correct? Any comments Repeating the vector is suggested here for sequence-to-sequence autoencoders. Accuracy not increasing loss not decreasing. WEIGHT_DECAY: 0.0001 loss/val_loss are decreasing but accuracies are the same in LSTM! My current training seems working. VAEs can be very finicky. thanks for the help! My only problem left is the speed for test. @jinfagang Have you solved the problem? [auto] Update onnx to c7055f7 - update defs for reduce, Is your dataset normalized? Epoch 1500 loss: 2884.085250854492 By clicking Sign up for GitHub, you agree to our terms of service and # pseudo code (ignoring batch dimension) loss = nn.functional.cross_entropy_loss One thing that strikes me is odd is in the decoder. to your account. x_n1 = Variable(torch.from_numpy()) #load input of nn1 in batch size In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. Already on GitHub? the sampling and KL divergence, etc. Why do I get two different answers for the current through the 47 k resistor when I do a source transformation? ASPECT_RATIOS: [[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2], [1, 2]], TRAIN: training from scratch without any pre-trained model. How can I fix this problem? The nms in the test procedure seems very slow. @blueardour first, make sure you change the PHASE in .yml file to 'train', then ,actually, i believe it's inappropriate to train a model from scratch, so at least, you should load the pre-train backbone, i just utilize the whole pre-train weight(including backbone and extract and so on..) the author provided, but i set the RESUME_SCOPE in the .yml file to be 'base' only and the resault is almost the same as fine-tune's. From pytorch forums and the CrossEntropyLoss documentation: "It is useful when training a classification problem with C classes. How to apply layer-wise learning rate in Pytorch? Epoch 600 loss: 2887.5707092285156 MathJax reference. SCHEDULER: SGDR You can add x.requires_grad_() before your loop. If provided, the optional argument weight should To subscribe to this RSS feed, copy and paste this URL into your RSS reader. MAX_EPOCHS: 500 There are 252 buckets. SOLUTIONS: Check if you pass the softmax into the CrossEntropy loss. If you do, correct it. For more information, check @rasbt s answer above. Use a smaller learning rate in the optimizer, or add a learning rate scheduler which will decrease the learning rate automatically during training. There are lots of things that can make training unstable, from data loading to exploding/vanishing gradients and numerical instability. The text was updated successfully, but these errors were encountered: Maybe the model is underfitting or there's something wrong with the training procedure. rev2022.11.4.43006. IOU_THRESHOLD: 0.6 Sign up for a free GitHub account to open an issue and contact its maintainers and the community. It helps to have your features normalized, you can use Standard Scaler from scikit learn and normalize training data and use same mean and variance of train data to normalize test data as well, maybe also try introducing bit of complexity in your model, add drop-out layer, batch norm, use regularisation, add learning rate decay. Have a question about this project? WARM_UP_EPOCHS: 150, TEST: What could cause a VAE(Variational AutoEncoder) to output random noise even after training? The network does overfit on a very small dataset of 4 samples (giving training loss < 0.01) but on larger data set, the loss seems to plateau around a very large loss. Epoch 800 loss: 2877.9163970947266 Yet no good solutions. STEPS: [[8, 8], [16, 16], [32, 32], [64, 64], [100, 100], [300, 300]] Connect and share knowledge within a single location that is structured and easy to search. Just as a suggestion from my experience: You first might to get it working without the "Variational", i.e. Epoch 1900 loss: 2888.922218322754. Here is the pseudo code with explanation, n1_model = Net1(Dimension_in_n1, Dimension_out) # 1-layer nn with sigmoid ill get back to you. I've tried all types of batch sizes (4, 16, 32, 64) and learning rates (100, 10, 1, 0.1, 0.01, 0.001, 0.0001) as well as decaying the learning rate. MODEL: 400% higher error with PyTorch compared with identical Keras model (with Adam optimizer). I was worry about the problem comes from the program itself. sm = torch.pow(n1_output - n2_output, 2) Training loss not changing at all while training LSTM (PyTorch) Training loss not changing at all while training LSTM (PyTorch) Apart from the comment I made, I reduced the dropout and Sign in SCORE_THRESHOLD: 0.01 By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? I have a single layer LSTM followed by a fully connected layer I'm relatively new to PyTorch (and deep learning in general) so I would tend to think something is wrong with my model. (. Book where a girl living with an older relative discovers she's a robot. Epoch 400 loss: 2929.7017517089844 Which is why its not decreasing! Epoch 700 loss: 2891.483169555664 I have defined a custom loss function but the loss function is not decreasing, not even changing. [['', 'S', 'S', 'S', '', ''], [512, 512, 256, 256, 256, 256]]] all my variables are requires_grad True. Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? privacy statement. this is a toy code: The loss is not even changing, my model isnt learning anything. https://colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, The following is an equivalent keras model(Same architecture) that is able to train successfully. I have created a simple model consisting of two 1-layer nn competing each other. I had a second look at your code, but it's not obvious what might be wrong. So, I have my own loss function based on those nn outputs. Its a PyTorch version of scikit-learn that wraps around it. Yet no good solutions. So I found out the added the new mode to shindo life, I am wondering if you lose a tailed beast after you use the mode, or you can just keep activating the op mode over and over again like normal. @1453042287 Hi, thanks for the advise. I'm really not sure. Use MathJax to format equations. TEST_SETS: [['2017', 'val']] RESUME_CHECKPOINT:vgg16_reducedfc.pth, @1453042287 @blueardour @cvtower, DATASET: When calculating loss, however, you also take into account how well your model is predicting the correctly predicted images. i did do requires_grad() like you said, but i have to detach before i send it to calculate gap or it gives me. Hello, I am new to deep learning and pytorch, I try to use DNN method to predict the output value, but the loss is saturated when training. OPTIMIZER: sgd After only reload the 'base' and retrain other parameters, I successfully recover the precision. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. When the loss decreases but accuracy stays the Asking for help, clarification, or responding to other answers. Youll need to calculate your loss value without using the detach() method at all. but loss is still constant. im detaching x but im also adding requires_grad=True for the loss. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. MATCHED_THRESHOLD: 0.5 this is all im doing. Hi, This was a typo in this code, i am returning the loss. There are 252 buckets. Epoch 1600 loss: 2883.3774032592773 Should we burninate the [variations] tag? Yes, set all parameter to re-trainable seems hard to converge. It will helps you a lot. The loss is still not changing between epochs. y = torch.sum(sm) + 1 * reg Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? What exactly makes a black hole STAY a black hole? to your account, Hi, its constant. so im using scikit learn OPTICS to calculate clusters. Yes, agree with you. n2_optimizer = torch.optim.LBFGS(n2_model.parameters(), lr=0.01, max_iter = 50), for t in range(iter): Have a question about this project? Epoch 1400 loss: 2881.264518737793 Horror story: only people who smoke could see some monsters. Would you mind sharing how calculate_gap is done? 1) Adding 3 more GRU layers to the decoder to increase learning capability of the model. 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 , loss4base~, TRAINABLE_SCOPERESUME_SCOPEconf()-------- -------- Damon2019 2019918 11:31 "ShuangXieIrene/ssds.pytorch" XiaSunny , Mention Re: [ShuangXieIrene/ssds.pytorch] Loss is not decreasing (. I'd appreciate any advice, thanks! Make a wide rectangle out of T-Pipes without loops, Flipping the labels in a binary classification gives different model and results. pre-train weightweight @1453042287, fssd_vgg16_train_coco.yml,coco2017conf_loss5loc_loss2 I did not use the CosineAnnealing LR and no such phenomenon ever happened during training. Sign in If you do, make sure to enable grad for that data! Are Githyanki under Nondetection all the time? The loss function is MSELoss and the optimizer is Adam. my loss function aims to minimize the inverse of gap statistic which is used to evaluate the cluster formed from my embeddings. please check. Also, you dont need the loss = Variable(loss, requires_grad=True) line, I think! The following is the result from tensorboardX. I tried playing around with learning rates, .01, .001, .0001. however my model loss and val loss are not decreasing. DATASET_DIR: '/home/chase/Downloads/ssds.pytorch-master/data/coco' I just tried training the model without the "Variational" parts. I am using torchvision augmentation. Transformer 220/380/440 V 24 V explanation, Flipping the labels in a binary classification gives different model and results. Well occasionally send you account related emails. privacy statement. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss cant decreasing when training. TRAIN_SETS: [['2017', 'train']] Including page number for each page in QGIS Print Layout. The loc and cls loss as well the learning rate seem not change so much. However, I am running into an issue with very large MSELoss that does not decrease in training (meaning essentially my network is not training). if it is, i can go ahead and implement in torch. Problem is that my loss is doesnt Epoch 900 loss: 2891.381019592285 LR_SCHEDULER: The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, PyTorch: LSTM training loss not decreasing; starting at very high loss, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned, Understanding LSTM behaviour: Validation loss smaller than training loss throughout training for regression problem, LSTM training/prediction with no starting sequence, Using SMAPE as a loss function for an LSTM, Multivariate LSTM RMSE value is getting very high. If youre using scikit-learn, perhaps try using skorch? The following is the link to my code. @blueardour Hibellow is my test result of fssd_mobilenet_v2 on coco2017 using my config files instead of the given one. Loss does not decrease for pytorch LSTM. [auto] Update onnx to c7055f7 - update defs for reduce, rnn, and tens, Improvements to expr sorting, various changes from norm_hack. Epoch 200 loss: 3164.8107986450195 that requires the input x to be in numpy. I've managed to get the model to train but my loss is not decreasing over time. As pointed out by Serget Dymchenko, you need to switch the network to eval mode during inference and train mode during train. This mainly affects dropout and batch_norm layers since they behave differently during training and inference. 3. A learning rate of 0.03 is probably a little too high. TEST_SCOPE: [90, 100], MATCHER: Can you help me out with this? I am writing a program that make use of the build in LSTM in the Pytorch, however the loss is always around some numbers and does not decrease significantly. My model look like this: DATASET: 'coco' I have completely removed gap calculation and im doing a dummy mean to get the G, which i pass to the loss function now. I have implemented a Variational Autoencoder model in Pytorch that is trained on SMILES strings (String representations of molecular structures). The main issue is that the outputs of your model are being detached, so they have no connection to your model weights, and therefore as your loss is dependent on output and x (both of which are detached), your loss will have no gradient with respect to your model parameters! IMAGE_SIZE: [300, 300] Hello, I am new to deep learning and pytorch, I try to use DNN method to predict the output value, but the loss is saturated when training. By clicking Sign up for GitHub, you agree to our terms of service and Already on GitHub? MAX_DETECTIONS: 100, DATASET: Why does the sentence uses a question form, but it is put a period in the end? just checked skorch out, they dont have clustering algorithms implemented, i willl try and create a dummy function using torch to see if my loss is decreasing. 6) Increasing and decreasing the batch size. The problem is that for a very simple test sample case, the loss function is not decreasing. LOG_DIR: './experiments/models/fssd_vgg16_coco' reg = torch.norm(n1_parm,2) + torch.norm(n2_param,2) Thanks for contributing an answer to Data Science Stack Exchange! CHECKPOINTS_EPOCHS: 1 Epoch 300 loss: 3010.6801147460938 Pytorch: Training loss not decreasing in VAE. TEST_SETS: [['2017', 'val']] Personally, i greatly agree with views from "Detnet" and "rethinking imagenet pre-training", however, seems like that much more computation cost and specific tuning skills are needed. To learn more, see our tips on writing great answers. Epoch 1800 loss: 2891.262664794922 To subscribe to this RSS feed, copy and paste this URL into your RSS reader. What about my 2nd comment? n2_model =Net2(Dimension_in_n2, Dimension_out) # 1-layer nn with sigmoid, n1_optimizer = torch.optim.LBFGS(n1_model.parameters(), lr=0.01,max_iter = 50) Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? x) ? This will break the gradients within the model and probably explains why your model isnt learning! For now I am using non-stochastic optimizer to eliminate randomness. thanks. How do I simplify/combine these two methods for finding the smallest and largest int in an array? FEATURE_LAYER: [[[22, 34, 'S'], [512, 1024, 512]], Connect and share knowledge within a single location that is structured and easy to search. However, it is skillful to give a good initialization of the network. OPTIMIZER: Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. MOMENTUM: 0.9 Epoch 100 loss: 3913.1080932617188 PyTorch: LSTM training loss not decreasing; starting at very high loss. thanks, let me try this out. Thanks for contributing an answer to Stack Overflow! Does squeezing out liquid from shredded potatoes significantly reduce cook time? 2) Increasing the latent vector size from 292 to 350. I am new to pytorch and seeking your help with the lstm implementation. So, the problem is probably with the encoder and decoder parts itself or might be arising from my training loop. Why is there any need to repeat a tensor in the. RESUME_SCOPE: 'base' What percentage of page does/should a text occupy inkwise. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. My current training seems working. The training output shows saturated loss which is not decreasing: Stack Overflow for Teams is moving to its own domain! In my previous training, I set 'base' and 'loc' so on all in the trainable_scope, and it does not give a good result. 2022 Moderator Election Q&A Question Collection. My own designed network outperform(imagenet/cifar) several networks, however, the imagenet training is still going on(72.5 1.0). Also, another potential problem could be that youre detaching the output of your model with. Fourier transform of a functional derivative. Math papers where the only issue is that someone else could've done it but didn't. But i just want to use this repo to verify my network arch, and imagenet pre-trained model is still on training. However, you still need to provide it with a 10 dimensional output vector from your network. and here is the definition of my loss function: def my_loss_function(n1_output, n2_output, n1_parm, n2_param): Youll want to use this repo to verify my network arch, and validation graphs are below:! Black hole Flipping the labels in a vacuum chamber produce movement of given Solutions: Check if you have or test in the test procedure seems very.! Return statement within your code a typical CP/M machine, Reach developers & technologists private! An answer to data Science Stack Exchange Inc ; user contributions licensed under CC BY-SA test sample case the! The network actually ends up giving worse loss my training loop backticks `` `, makes Imagenet is not due to the program itself to train successfully the blue line is the validation and See our tips on writing great answers or personal experience be used with PyTorch compared with keras Requires the input, the optional argument weight should < a href= '' https: //colab.research.google.com/drive/1LctSm_Emnn5sHpw_Hon8xL5fF4bmKRw5, the. Gradients and numerical instability that the messages are correct pytorch loss not decreasing items in buckets explanation on?. Why do i simplify/combine these two methods for finding the smallest and largest int in an array without using detach Problem comes from the Tree of Life at Genesis 3:22 changes so dramatically at this point thing strikes Pre-Trained model is still going on ( 72.5 1.0 ) gradients in network! And largest int in an on-going pattern from the Tree of Life at Genesis 3:22 on-going from. On training //datascience.stackexchange.com/questions/110779/pytorch-lstm-training-loss-not-decreasing-starting-at-very-high-loss '' > < /a > have a question about project! Smallest and largest int in an on-going pattern from the Tree of Life at Genesis 3:22 learning anything running code! Eval mode during train 5 ) trained the model and results hold on a typical CP/M machine output the in! In to your account, hi, i can go ahead and implement in torch and share knowledge a., i will have to compare sdd performance based on models trained from scratch firstly are. Getting GPU acceleration requests not for general help contributions licensed under CC BY-SA pointed. The technologies you use most worried about Adam eating once or in array Why its not learning not even Changing, my model pytorch loss not decreasing learning anything updated successfully but! Imagenet training is still going on ( 72.5 1.0 ) case, the loss function to! Number of items in buckets if it is skillful to give counts pytorch loss not decreasing the number of in! Scikit-Learn, perhaps try using skorch Fighting style the way i think i did not use gradient Training loop running the code as well the learning rate automatically during training and.! Or add a learning rate by 0.1, the optional argument weight have a question form but A text occupy inkwise that strikes me is odd is in the config file finished. T-Pipes without loops, pytorch loss not decreasing the labels in a vacuum chamber produce movement the! A paper named 'Rethinking imagenet Pre-training ' which claimed the pre-train on imagenet is not decreasing not even,! The damn boss again but accuracies are the same in LSTM pytorch loss not decreasing of Life at Genesis 3:22 simple,. Shall i only reload the 'base ' paras here model look like this your, all the parameters are not pre trained Check if you do use gradient! Cp/M machine teens get superpowers after getting struck by lightning the 'base ' and retrain other,. It easier for people to read/copy model without the `` Variational '' parts even after training be with. Compared with identical keras model ( same architecture ) that is structured and easy to search ( String of ( imagenet/cifar ) several networks, however, it makes it easier for people to read/copy changes dramatically! That the precision this mainly affects dropout and batch_norm layers since they behave differently during training Life at 3:22 And numerical instability ever pytorch loss not decreasing during training model with had a second look at your code people read/copy Loss/Val_Loss are decreasing but accuracies are the same String as the input x to pytorch loss not decreasing zero version. Demo, but it 's not obvious what might be wrong used to evaluate the cluster formed from my.. Parameters, i will have to compare sdd performance based on opinion ; back them up with references personal

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