training loss not decreasing tensorflow

Training loss not decreasing if both mixed-precision and XLA/JIT is @RyanStout, I'm using exactly the same model, loss and optimizer as in. That's a good idea. TensorBoard reads log data from the log directory hierarchy. 84/84 [00:17<00:00, 5.72it/s] Training Loss: 0.7922, Accuracy: 0.83 To learn more, see our tips on writing great answers. I did the following steps and I have two problems. When I train my model on roughly 1500 samples, I always get my training and validation accuracy completely overlapping and virtually equal, reflected in the graph below. An iterative approach is one widely used method for reducing loss, and is as easy and efficient as walking down a hill.. TensorBoard Scalars: Logging training metrics in Keras Tensorflow: loss decreasing, but accuracy stable Ensure that your model has enough capacity by overfitting the training data. Do US public school students have a First Amendment right to be able to perform sacred music? 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. Unfortunately, the ReLU activation function is not perfect. Hi all, I'm training a neural network with both CNN and RNN, but I found that although the training loss is consistently decreasing, the validation loss remains as NaN. The Keras progress bars look nice if you are training 20 epochs, but no one wants an infinite scroll in their logs of 300 epochs progress bars (I find it disgusting). Having issues with neural network training. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Multiplication table with plenty of comments, Replacing outdoor electrical box at end of conduit. Current elapsed time 2m 24s, ---------- training: 100%|| Validation loss is not decreasing - Data Science Stack Exchange Should we burninate the [variations] tag? I have already tried different learning rates, optimizers, and batch sizes, but these did not affect the result very much as well. vocab size: 33001 training data size: 518G ( dupe factor: 10) max_seq_length: 512 3 gram maskin. Evaluate the model's effectiveness. Should we burninate the [variations] tag? Find centralized, trusted content and collaborate around the technologies you use most. Correct handling of negative chapter numbers. rev2022.11.3.43004. First one is a simplest one. I calculated the mean and standard deviation of the training data and added this augmentation to my data loader. I'll create a simple base and compare results to UNet and VGG16. i use: I've normalized the data using the transforms.functional.normalize function. 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. precision and recall values kept unchanged for some training steps. Why does the loss/accuracy fluctuate during the training? (Keras, LSTM) jeeter juice live resin real vs fake; are breast fillers safe; Newsletters; ano ang pagkakatulad ng radyo at telebisyon brainly; handheld game console with builtin games How can I best opt out of this? For example, for a batch size of 64 we do 1024/64=16 steps, summing the 16 gradients to find the overall training gradient. How many characters/pages could WordStar hold on a typical CP/M machine? First, we store the new log values into our data structure: Then, we create a graph for each metric, which will include the train and validation metrics. tensorflow 1.15.5, I have to use tensorflow 1.15 in order to be able to use DirectML because i have AMD GPU, followed this tutorial: Thanks for contributing an answer to Stack Overflow! I will vote your answer up as soon as I have enough reputation points. This is usually visualized by plotting a curve of the training loss. I don't think anyone finds what I'm working on interesting. Hence, for example, two training examples that deviate from their ground truths by 1 unit would lead to a loss of 2, while a single training example that deviates from its ground truth by 2 units would lead to a loss of 4, hence having a larger impact. I'm guessing I have something wrong with the model. I get at least 91% accuracy using random forest. It makes it difficult to get a sense of the progress of training, and its just bad practice (at least if youre training from a Jupyter Notebook). To train a model, we need a good way to reduce the model's loss. Tensorflow - loss not decreasing Ask Question 2 Lately, I have been trying to replicate the results of this post, but using TensorFlow instead of Keras. It was extremely helpful with structure and data loading. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 3.I used ssd_inception_v2_coco.config. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. machine learning - Tensorflow loss not changing and also computed tensorflow/tensorflow#19138. Code will be useful. Can I spend multiple charges of my Blood Fury Tattoo at once? If provided, the optional argument weight should be a 1D Tensor assigning weight to each of the classes. rev2022.11.3.43004. I trained on TPU-v2-256 but loss is not decreasing. I have tried to run the model but as you've stated, I need to really dig into what the model is doing. Found footage movie where teens get superpowers after getting struck by lightning? My complete code can be seen here. Setup import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers Introduction. I was using cross entropy loss in regression problem which was not correct. Asking for help, clarification, or responding to other answers. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, I feel like I should write an answer to reply to your great comments and questions. This is particularly useful when you have an unbalanced training set.". Training and evaluation with the built-in methods - TensorFlow Here is my Tensorborad samples Making statements based on opinion; back them up with references or personal experience. Thanks you solved my problem. @mkmitchell I doubt you will get any more help from here, unless someone dives into the architecture and gets accommodated with ins and outs, that's why I have proposed to ask the author directly. That's a good suggestion. What is the best way to sponsor the creation of new hyphenation patterns for languages without them? You're right, @JonasAdler, I was not using dropout since "is_training" default value is False, so my output was untouched. Maybe start with smaller and easier model and work you way up from there? The alternative is to have a simple plot, with train and test loss, that updates every epoch or every n steps. Does the 0m elevation height of a Digital Elevation Model (Copernicus DEM) correspond to mean sea level? During validation and testing, your loss function only comprises prediction error, resulting in a generally lower loss than the training set. Can an autistic person with difficulty making eye contact survive in the workplace? Your validation loss is lower than your training loss? This is why! 1. Upd. Also consider a decay rate of 1e-6. WARNING:root:The following classes have no ground truth examples: 0 after that program terminate. The model did not suit my purpose and I don't know enough about them to know why. Problem 1: from step 0 until 3000, my loss has dramatically decreased but after that, it stays constant between 5 to 6 . When the training starts we will initialize all the values. It worked! Effect of batch size on training dynamics | by Kevin Shen | Mini I was using satellite data and multiple indices so had 9 channels, not just the 3. Regex: Delete all lines before STRING, except one particular line. Here is a simple formula: ( t + 1) = ( 0) 1 + t m. Where a is your learning rate, t is your iteration number and m is a coefficient that identifies learning rate decreasing speed. Did you use RGB or higher channels for your training? 84/84 [00:18<00:00, 5.53it/s] Training Loss: 0.7741, Accuracy: 0.84 VGG_19 training loss does not reduce #991 - GitHub Any advice is much appreciated! If you are interested in leveraging fit() while specifying your own training step function, see the . Leading a two people project, I feel like the other person isn't pulling their weight or is actively silently quitting or obstructing it, Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project, Earliest sci-fi film or program where an actor plays themself. Conveniently, we can use tf.utils.shuffle for that purpose, which will shuffle an arbitray array inplace: 9. Share. Find centralized, trusted content and collaborate around the technologies you use most. The most weird thing is that we have the same database and the same model, but just different frameworks. Share How to Diagnose Overfitting and Underfitting of LSTM Models Current elapsed time 2m 42s, ---------- training: 100%|| My loss is not reducing and training accuracy doesn't fluctuate much. Make sure your loss is computed correctly. The loss curve you're seeing on Tensorboard is quite normal. Not the answer you're looking for? I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? Thus, it was not supposed to give completely different behaviours. I took care to use the same parameters used by the author, even those not explicitly shown. What is a good way to make an abstract board game truly alien? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Thank you very much, @Ryan. Does anyone have suggestions about what should I try to solve this problem, please? Calculating the loss by comparing the outputs to the output (or label) Using gradient tape to find the gradients. Even i tried for diffent model eg. 2022 Moderator Election Q&A Question Collection, Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2, Could not find a version that satisfies the requirement tensorflow, CTC loss doesn't decrease using tensorflow, while it decreases using Keras, Tensorflow and Keras show a little different result even though I build exactly same models using same layer modules, error while importing keras ModuleNotFoundError: No module named 'tensorflow.examples'; 'tensorflow' is not a package, Exact model converging on keras-tf but not on keras, Verb for speaking indirectly to avoid a responsibility. 84/84 [00:17<00:00, 5.77it/s] Training Loss: 0.8901, Accuracy: 0.83 My classes are extremely unbalanced so I attempted to adjust training weights based on the proportion of classes within the training data. How can I find a lens locking screw if I have lost the original one? Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? I'm largely following this project but am doing a pixel-wise classification. Is a planet-sized magnet a good interstellar weapon? My complete code can be seen here. 4. What is the deepest Stockfish evaluation of the standard initial position that has ever been done? Try to overfit your network on much smaller data and for many epochs without augmenting first, say one-two batches for many epochs. Thanks. Training is a slow process, you should see a steady drop over time after more iterations. 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. Even i tried for diffent model eg. Here is an example: Current elapsed time 3m 1s. There are many other options as well to reduce overfitting, assuming you are using Keras, visit this link. Initially, the loss will drop very quickly, but will seemingly "bottom out" over time. Etiquette question: a funny way to resign Why bitcoin's generator point does not satisfy Elliptic Curve Cryptography equation? Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I found a bunch of other questions related to this problem here in StackOverflow and StackExchange, but most of them had no answer at all. 3. 0.14233398 0.14176525 Training loss goes down and up again. What is happening? As you know, Facebook's prophet is highly inaccurate and is consistently beaten by vanilla ARIMA, for which we get rewarded with a desperately slow fitting time. 2.Created tfrecord successfully Here we clear the output of our previous epoch, generate a figure with subplots, and plot the graph for each metric, and check if there is an equivalent validation metric: You can run this callback with any verbosity level of any other callback. You can see that illustrated in the Recurrent Neural Network example. Dropout is used during testing, instead of only being used for training. I use your network on cifar10 data, loss does not decrease but increase. 2022 Moderator Election Q&A Question Collection. Short story about skydiving while on a time dilation drug. Did Dick Cheney run a death squad that killed Benazir Bhutto? I get at least 91% accuracy using random forest. Lately, I have been trying to replicate the results of this post, but using TensorFlow instead of Keras. faster_rcnn_inception_resnet_v2_atrous_coco after some steps loss stay constant between 1 and 2 This is making me think there is something fishy going on with my code or in Keras/Tensorflow since the loss is increasing dramatically and you would expect the accuracy to be . How well it performs, were you able to replicate their findings? Image by author Not the answer you're looking for? Within these functions you can do whatever you want, so you can let your imagination run wild and free. I typically find an example that is "close" to what I need then hack away at it while I learn. Python 3.6.13 tensorflow 1.15.5 I have to use tensorflow 1.15 in order to be able to use DirectML because i have AMD GPU Make sure you're minimizing the loss function L ( x), instead of minimizing L ( x). If this one doesn't work, than your model is not capable to model relation between data and desired target or you have an error somewhere. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Notice that larger errors would lead to a larger magnitude for the gradient and a larger loss. MATLAB command "fourier"only applicable for continous time signals or is it also applicable for discrete time signals? Optimizing the variables with those gradients. Build a simple linear model. Stack Overflow for Teams is moving to its own domain! Learning Rate and Decay Rate:Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001. Stack Overflow for Teams is moving to its own domain! Usage of transfer Instead of safeTransfer. Not the answer you're looking for? Tensorflow loss and accuracy during training weird values. But lets stick to this application for now. 18 Tips for Training your own Tensorflow.js Models in the Browser I tried to set it true now, but the problem still happens. I am working on Street view house numbers dataset using CNN in Keras on tensorflow backend. With activation, it can learn something basic. history = model.fit(X, Y, epochs=100, validation_split=0.33) This can also be done by setting the validation_data argument and passing a tuple of X and y datasets. I have queries regarding why loss of network is not decreasing, I have doubt whether I am using correct loss function or not.

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