Tensorflow loss nan. However, I keep getting a loss: nan output.

Tensorflow loss nan Feb 28, 2025 · NaNs can occur during training ML models and mess it up. Dec 20, 2024 · These NaN values can complicate the process of fine-tuning models and prevent them from converging properly. In this article, we learn the common causes and fixes we can apply. fit, but turns to nan after fitting the model. 1 and tensorflow-gpu 2. Sometimes, your term inside the log might go <=0 and sometimes, it might not. During the first few rounds, the loss seemed alright. 0 (v2. Try scaling your data (though unscaled data will usually cause infinite losses rather than NaN loses). 18. 6. 0-0-g2b96f3662b) Python version: 3. 3 to ~3. The code I'm using works perfectly fine on a single GPU. Else, try Dec 11, 2015 · Here is the net-part of my code. Try adding sigmoid in the loss function (like sigmoid (self. . scope():. fit, the training loss turns to nan. debug. Loss function returns nan on time series dataset using tensorflow Ask Question Asked 7 years ago Modified 7 years ago Jun 2, 2020 · I have recently set up a new machine at home for deep learning; Ubuntu 18. 1 CUDA Here my tra Jun 24, 2024 · I've developed a TensorFlow model for an artificial intelligence project, but I'm having a problem with NaN in the loss function during training. But after the step 10210 the loss varies and ends up becoming NaN. May 15, 2016 · I was running into my loss function suddenly returning a nan after it go so far into the training process. 0 Custom code Yes OS platform and distribution Linux (google colab default) Mobile devic Nov 16, 2021 · tf. Mar 14, 2017 · So I define this custom loss function in Keras using a Tensorflow backend to minimize a background extraction autoencoder. That's why you might not be getting NaN in colab. I have wrapped all of my code inside the scope of a tf. Is there someone who has the same problem? TensorFlow 2. I created a “minimal working example” of a model with only 2 layers which May 8, 2025 · 注意，不是最后的loss比较大就没事，而是不要让任何有用的值都大规模的接近0或者变成0（这里我不太确定原理，希望有大佬可以指出）这样可以在对结果不造成什么太大影响的情况下，保证不会出现Nan的情况中间踩坑一般出现Nan都可以通过减小lr来解决，但是这次无效判断方法：lr直接设置为0 May 18, 2018 · Found the solution! Turns out previous checkpoints of the model were conflicting with the current training session, so I deleted everything in the folder my model saves checkpoints to and now it's training without any NaN loss errors. I was still getting loss that eventually turned into a nan and I was getting quite fustrated. Here's an extract from my code: import os os. 5. It seems that just one NaN in a tensor will turn all of it to NaN, you can't have some NaNs and some non NaNs. math. debug_mnist_v2 This TF2 program creates a multi-layer perception (MLP) and trains it to recognize MNIST images. This example purposefully uses the low-level API of TF2 to define custom layer constructs, loss function, and training loop, because the likelihood of NaN bugs is higher when we use this more flexible but more error-prone API than when we Oct 11, 2022 · Tensorflow Loss function is NAN when using GPU Asked 2 years, 11 months ago Modified 2 years, 9 months ago Viewed 1k times Jan 26, 2020 · After I begin training, the loss is normal for 1 ~ 2 steps for the first epoch. Mar 12, 2021 · Hello everyone, I am new in AI and I trying to learn object detection using tensorflow 2. python. However, I keep getting a loss: nan output. I figured out that the problem appears with losses that make the operation tf. I have tried increasing the dataset's size, increasing the… Mar 17, 2021 · A single null value will cause the loss to be NaN. My network model the TensorFlow BinaryCrossentropy loss quickly reaches NaN Asked 3 years, 8 months ago Modified 3 years, 7 months ago Viewed 1k times Sep 11, 2020 · I followed the code in the book 'hands-on machine learning with scikit-learn and tensorflow' to build a multiple outputs neural network in Keras. py --pipeline_config_path=training The first 295 mini-batches, the loss steadily decreased from ~4. I get NaN loss values in this case, even though I should be returning 0 for the loss when thr ground truth is NaN. During the training, the loss is printed, but the May 19, 2017 · The weird thing is: after the first training step, the loss value is not nan and is about 46 (which is oddly low. I checked the relus, the optimizer, the loss function, my dropout in accordance with the relus, the size of my network and the shape of the network. math. subtract(true_labels, predicted_labels)))) where true_labels and predicted_labels have shape (batch_size, 6), and only entire rows of either matrix can be NaN. In this article, we will explore various techniques to identify and handle these NaN problems effectively. If i reduce the learning rate by a lot I'm able to sometimes keep it from going loss NaN. It could result in a nan, inf or -inf "value". However, whenever I call model. reduce_mean(tf. zeros_like(true_labels), tf. I've actually had gradient contributing be the cause of NaN loss before. Whenever I tried to run following command in cmd: python model_main_tf2. 14 training loops with step-by-step code examples and debugging strategies. 001 or something even smaller) and/or removing gradient clipping. Jan 11, 2023 · When running the model (using both versions) tensorflow-cpu, data generation is pretty fast (almost instantly) and training happens as expected with proper loss values But when using the tensorflow-gpu, The model loading is too long, then epochs start after another 7-10 minutes and the loss generated is Nan, I’ve tried to Feb 1, 2022 · My losses are nan when I'm running on GPU nodes in a cluster while in CPU is working fine, I'm using a conda environment with tensorflow 2. 9 GPU model and memory: Google Colab TPU I've found that sometimes fitting model on TPU leads to NaN loss, while fitting on CPU d Feb 8, 2023 · There is a 2-class classification problem, and my loss function is custom. 04 RTX 2080 Ti graphics card After installing the drivers and CUDA/CuDNN I have found that there is a consistent issue when training many (completely different) models; the loss becomes NaN after some number of epochs (which varies randomly). Dec 17, 2024 · Detecting these anomalies early in your TensorFlow graphs ensures that they don't propagate and affect the model training and results. They usually don't appear at once, but rather after a phase where the loss increases suddenly and within a few steps reaches inf. May 2, 2025 · Learn practical solutions to fix NaN values in TensorFlow 2. You could try lowering the learning rate (0. distribute. Sep 6, 2025 · In this guide, we will walk through the reasons you might be facing this issue and how to correct NaN values in your data using TensorFlow. Jun 26, 2022 · The loss function you're using is BCELoss which gives "NaN" when the value inside log is less than or equal to 0. p_pred) instead of self. Oct 20, 2016 · I just meant that you can look in implementation of add_check_numerics_ops to see which op determines if a variable is NaN, and use that op – Yaroslav Bulatov Oct 21, 2016 at 19:04 Possible duplicate of Tensorflow Nan loss reasons – Seanny123 Jun 1, 2017 at 4:54 Aug 25, 2020 · I'm training my custom model with EfficientDet D0 but after 700 steps I am getting loss as nan value. I had some check where it was supposed to filter them out, the check was right but it turns out the whole tensor just went NaN. The code: I'm implementing a neural network with Keras, but the Sequential model returns nan as loss value. when i run a logistic regression model, the first loss value is about ~3600). is_nan(true_labels), tf. Dec 10, 2024 · This article explores the common causes and solutions for encountering "NaN loss" during deep learning model training. How to May 20, 2019 · My Question is why my training accuracy is not improving and why loss is nan also why without one-hot encoding the softmax in the output is working fine? Note1: I apologize that my data is big so I cannot share it here but if there are some way to share it here then I am ready to do that. I am training a machine learning model, but the loss is nan. 14 votes, 10 comments. check_numerics() function. This helps identify and handle these numerical issues effectively. Checking for NaNs and Infinities in TensorFlow TensorFlow provides several methods for inspecting your tensors during graph execution. It's supposed to ensure that the prediction x_hat doesn't stray to far fro Feb 25, 2022 · python -m tensorflow. MirroredStrategy using with strategy. reduce_mean. examples. debugging. 2. Discover the causes of NaN loss values in TensorFlow and learn effective strategies to resolve them in this comprehensive, easy-to-follow guide. But after that, it suddenly becomes NaN loss. I tried to see the model weight before and after training, and it turns out the weight was exist before calling model. I have sigmoid activation function in the output layer to squeeze output between 0 and 1, but maybe Aug 24, 2024 · NaN values can be a challenging issue to tackle, but with careful analysis and debugging techniques, they can be addressed to enhance the reliability and performance of TensorFlow models. is _ nan bookmark_border On this page Used in the notebooks Args Returns numpy compatibility Oct 27, 2021 · It computes the output as a float, and calculate the loss value as expected. Jun 12, 2017 · Quite often, those NaN come from a divergence in the optimization due to increasing gradients. KerasやTensorFlowを使っているときに、突然損失関数でnanが出てその特定にとても困ることがあります。ディープラーニングはブラックボックスになりがちなので、普通プログラムのデバッグよりもかなり大変です。この方法は、データに問題がある場合限定の方法ですが、単純では May 13, 2017 · I was doing CIFAR-10 training on CPU with Tensorflow. Feb 10, 2025 · Issue type Bug Have you reproduced the bug with TensorFlow Nightly? No Source source TensorFlow version 2. But I have an issue while train a model. the keras backend is tensorflow. where(tf. I've looked into this before, and often times it's because the loss suddenly starts growing due to exploding gradients or a learning rate that is too large. I have also tested this tf. environ[' Aug 29, 2023 · When training with GPU i see fairly large fluctuations in loss and val-loss and quite fast i get loss NaN. Then for no apparent reason, the loss became nan, and stayed nan for the rest of the training. v2. Tensorflow模型训练过程中，很多情况下，在训练一段时间后，输出的loss为nan，幸运的可能是一闪而过，接着正常训练，多数是持续nan下去，训练失败。 Mar 30, 2016 · I was running into my loss function suddenly returning a nan after it go so far into the training process. 1 . The labels are categorical, and the final activation function is Softmax. This will ensure the term is in (0,1) range. This issue only happens when using multiple GPUs. 3. 0 in TensorFlow doesn't result in a division by zero exception. Jul 22, 2020 · System information TensorFlow version (use command below): 2. The reason for nan, inf or -inf often comes from the fact that division by 0. Apr 25, 2021 · when training the model to predict the 3 labels (candidate, false positive, confirmed) the loss is always nan and the accuracy stabilizes very fast on a specific value. Callback that terminates training when a NaN loss is encountered. Example 1: Identifying NaN values in a TensorFlow tensor To debug NaN values in TensorFlow, you can use the tf. p_pred. 4. I'll try other loss functions, but they take some time to provide useful evidence, since you can't determine when the loss turns into 'nan'. 0 with GTX 1060 10. square(tf. svczn tns gvhs ckhu vib3 ijf ru r02x efxi vj5uto

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