Keras loss nan classification. It's possible that you have some

Keras loss nan classification. It's possible that you have some calculation based for example on averaging loss over several time stamps, but one of the time stamps has 0 instances causing a cascade of NaN values. io Nov 23, 2024 · Potential Solutions to NaN Loss Solution 1: Addressing Exploding Gradients. satadru5. Let’s get into it! Keras loss functions 101. Issue body actions. loss: nan - val_loss: nan in binary classification #9038. On some datasets, it runs well and calculates the loss, while on others the loss is NaN. For instance, using a regression loss for a classification task might provide NaN values. rand(size) ) to check the model, and ofc got an accuracy of 0. 6780 - val_loss: nan - val_accuracy: 0. frame. Because log(0) is negative infinity, when your model trained enough the output distribution will be very skewed, for instance say I'm doing a 4 class output, in the beginning my probability looks like Apr 29, 2025 · how you can define your own custom loss function in Keras, how to add sample weighing to create observation-sensitive losses, how to avoid nans in the loss, how you can monitor the loss function via plotting and callbacks. 6654 - val_loss: nan - val_accuracy: 0. The loss function requires the following inputs: y_true (true label): This is either 0 or 1. I have tried increasing the dataset's size, increasing the… Computes the cross-entropy loss between true labels and predicted labels. May 15, 2016 · After noticing some CSV files led to nan while others worked, suddenly we looked at the encoding of the files and realized that ascii files were NOT working with keras, leading to nan loss and accuracy of 0. In Keras, loss functions are passed during the compile stage, as shown below. I'll take for stock info process for practice a classification via transformer, targeting a sim Provides a collection of loss functions for training machine learning models using TensorFlow's Keras API. The different datasets are similar in that they are augmented versions of the original one. When that happens, your model will not update its weights and will stop learning, so this situation needs to be avoided. Use this cross-entropy loss for binary (0 or 1) classification applications. Jul 22, 2021 · but when training starts, I got nan for loss and 0 for accuracy! sometimes accuracy starts with a value of 0. core. I am training a machine learning model, but the loss is nan. <class 'pandas. 6+ KB Jul 22, 2021 · In my experience the most common cause for NaN loss is when a validation batch contains 0 instances. See full list on keras. 5300 Epoch 3/50 14 votes, 10 comments. Jul 24, 2019 · Given that there are about 10,000 classes I used sparse_categorical_crossentropy rather than one-hot encoding the classes, however as soon as the network starts training the loss is stuck at one number and after several batches is goes to NaN I tried different scaling of the images and a smaller network but with no luck. The NaN loss might stem from the exploding gradients problem, which is especially prevalent in regression tasks due to unbounded outputs. The training and validation metrics and loss do not change per epoch, which is worrisome (and, I think, a symptom of overfitting), but I'm also concerned about understanding the graphs themselves. 5300 Epoch 2/50 41/41 [=====] - 1s 18ms/step - loss: nan - accuracy: 0. losses import SparseCategoricalCrossentropy keras-team / keras Public. Here are the TensorBoard graphs: The training loss should (roughly) be decreasing per epoch, as should the validation loss. opened on Jan 10, 2018. Description. 0903, and then goes to 0 and stays there. Apr 29, 2025 · Why Keras loss nan happens Most of the time, losses you log will be just some regular values, but sometimes you might get nans when working with Keras loss functions. . Fortunately, there are several strategies to counteract this issue: Ensure the loss function is appropriate for your task. ```python from tensorflow. DataFrame'> Int64Index: 2474 entries, 0 to 5961 Data columns (total 4 columns): Age 2474 non-null int64 Pre_Hospitalization_Disposal 2474 non-null object Injury_to_hospital_time 2474 non-null float64 Discharge_results 2474 non-null int64 dtypes: float64(1), int64(2), object(1) memory usage: 96. Nov 22, 2021 · I have a transformer model almost exactly the same as in the Keras example code for time series data. Feb 26, 2019 · I have some data and wanting to classification. keras. kernel_initializer='uniform' has been untouched and unconsidered in my quest to figure this out. I have sigmoid activation function in the output layer to squeeze output between 0 and 1, but maybe Aug 10, 2019 · loss: nan could have something to do with my loss function being binary_crossentropy and maybe some values are giving that loss function a hard time. Copy link. May 4, 2021 · I'm using keras-bert for classification. 0000e+00; however, utf-8 and utf-16 files were working! Breakthrough. I'm implementing a neural network with Keras, but the Sequential model returns nan as loss value. 33 (the chance accuracy, three classes). Sample output Epoch 1/50 41/41 [=====] - 7s 51ms/step - loss: nan - accuracy: 0. If you're training for cross entropy, you want to add a small number like 1e-8 to your output probability. Review the loss to make sure there are no divisions by zero or log of zero within the function. random. Im getting Nan loss from epoch 1 through 50. I generated matrices of random values ( np. boj hkoav jmfd ymqgar xbhdk gbqmkwr vbp tszc zqjrzr kqnd