Keras categorical cross entropy loss nan. Compile your model with focal loss as sample: Binary model.

Keras categorical cross entropy loss nan compile (loss= [binary_focal_loss (alpha=. Keras documentation: Probabilistic metricsComputes the crossentropy metric between the labels and predictions. Computes the cross-entropy loss between true labels and predicted labels. Saves you that to_categorical step which is common with TensorFlow/Keras models! Adam is an update to the RMSProp optimizer. Jun 1, 2021 · In particular you could compare the methods used in nll_loss_out_frame and nll_loss2d_forward_out_frame, which might have a different order of accumulation (and the latter seems to over-/underflow in float16) and is why they promote to float32 using amp. CategoricalCrossentropy tf. The loss functions, metrics, and optimizers can be customized and configured like so: from keras import optimizers from keras import losses from keras import metrics Jun 16, 2020 · What's confusing me is that categorical_crossentropy expects a 1-hot encoded setup as its true parameter. Use this cross-entropy loss for binary (0 or 1) classification applications. This loss function performs the same type of loss - categorical crossentropy loss - but works on integer targets instead of one-hot encoded ones. UPD: Actually f1 is slowly growing (10-100 epochs vs 1 epoch reaching max accuracy), seems like it's because my undersampled classes are TOO low in count. The following deduction is from tf. When training a neural network with keras for the categorical_crossentropy loss, how exactly is the loss defined? I expect it to be the average over all samples of $$\\textstyle\\text{loss}(p^\\text{t Sep 27, 2023 · What is cross-entropy loss? Cross-entropy Loss, often called “cross-entropy,” is a loss function commonly used in machine learning and deep learning, particularly in classification tasks. This involves taking the log of the prediction which diverges as the prediction approaches zero. tf. 0, axis=-1 ) I'm implementing a neural network with Keras, but the Sequential model returns nan as loss value. Tested this with the mnist_cnn example code aswell as with self designed conv networks. You will first calculate the cross entropy loss for a binary classification problem and then for a classification problem with ten classes. Jan 10, 2019 · When I attempt to perform one-hot encoding, I get an OOM error, which is why I'm using sparse categorical cross entropy as my loss function instead of regular categorical cross entropy. The target values are binary 1 or zero and are stored as floats in a numpy arra. The lower the binary cross-entropy value, the better the model’s predictions align with the true labels. However, after fitting the compiled model to my training sets and for validation, I am getting loss: nan values. It quantifies the dissimilarity between the predicted probability distribution and the actual probability distribution (ground truth) of a set of events or classes. Dec 11, 2015 · Like previously stated in issue #511 Keras runs into not a number losses while training on GPU. 25, gamma=2)], metrics= ["accuracy"], optimizer=adam) Categorical model. The formula you posted is reformulated to ensure stability and avoid underflow. g. Jul 5, 2017 · This is my code i typed to classify some classes consisting of birds, dogs and cats. e, a single floating-point value which either represents a logit, (i. You can often tell if this is the case if the loss begins to increase and then diverges to infinity. Mar 16, 2018 · A naive implementation of Binary Cross Entropy will suffer numerical problem on 0 output or larger than one output, eg log (0) -> NaN. I have tried different methods I know, but the result still remain the same. Initially x. Computes focal cross-entropy loss between true labels and predictions. It measures the dissimilarity between the target and output probabilities or logits. sigmoid_cross_entropy_with_logits. losses. The sparse categorical cross-entropy loss is similar to categorical cross-entropy, but it is used when the target tensor contains integer class labels instead of one-hot encoded vectors. This is not specific to focal loss, all keras loss functions take y_true and y_pred, you do not need to worry where those parameters are coming from, they are fed by keras automatically. This is the crossentropy metric class to be used when there are multiple label classes (2 or more). k Use this crossentropy loss function when there are two or more label classes. Posted by: Chengwei 7 years, 1 month ago (5 Comments) In this quick tutorial, I am going to show you two simple examples to use the sparse_categorical_crossentropy loss function and the sparse_categorical_accuracy metric when compiling your Keras model. Then I run y = y. Computes the crossentropy metric between the labels and predictions. Saves you that to_categorical step which is common with TensorFlow/Keras models! Jul 6, 2020 · Tried to train UNet on GPU to create binary classified image. config. Test case: import tensorflow as tf import tensorflow. compile (loss= [categorical_focal_loss (alpha= [ [. floatX == 'float64': eps Computes the crossentropy loss between the labels and predictions. Suggestions on what to do will greatly be appreciated. Inherits From: Loss View aliases Main aliases tf. I am not to familiar with the DNNClassifier but I am guessing it uses the categorical cross entropy cost function. Provides a collection of loss functions for training machine learning models using TensorFlow's Keras API. Jul 23, 2025 · Binary cross-entropy (log loss) is a loss function used in binary classification problems. Mar 27, 2020 · I'm following Aurélion Géron's book on Machine Learning. The categorical cross-entropy loss is commonly used in multi-class classification tasks where each input sample can belong to one of multiple classes. In each epoch, the loss is NaN, due to which to models fails to improve. You can easily copy it to your model code and use it within your neural network. Then it uses tf. Its the same code for the binary classification but when I add another class and changed the loss function of co Use this crossentropy loss function when there are two or more label classes. Got nan loss on each epoch. Oct 22, 2019 · Example code: binary & categorical crossentropy with TF2 and Keras This example code shows quickly how to use binary and categorical crossentropy loss with TensorFlow 2 and Keras. Computes the crossentropy loss between the labels and predictions. categorical_crossentropy, tf. Nov 11, 2025 · I'm trying to make this model work. y_pred (predicted value): This is the model's prediction, i. This leads to nan in your calculation since log(0) is undefined (or infinite). Aug 28, 2023 · In this article, we will be looking at the implementation of the Weighted Categorical Cross-Entropy loss. What is not really documented is that the Keras cross-entropy automatically "safeguards" against this by clipping the values to be inside the range [eps, 1-eps]. v1. Jan 6, 2022 · Sparse Categorical CrossEntropy causing NAN loss Asked 3 years, 10 months ago Modified 3 years, 10 months ago Viewed 2k times Jan 8, 2016 · On the last 5 times I tried, the loss went to nan before the 20th epoch. . CategoricalCrossentropy Compat aliases for migration See Migration guide for more details. In reality, the only difference between the sparse categorical cross entropy and categorical cross entropy is the format of true labels. Goal: In this notebook you will use Keras to set up a CNN for classification of MNIST images and calculate the cross entropy before the CNN was trained. metrics. We expect labels to be provided in a one_hot representation. May 15, 2016 · I was getting nan values for binary classification then I changed the loss function to 'binary cross entropy' from categorical cross-entropy and it worked fine. hinge loss. We learned to write a categorical cross-entropy loss function in Tensorflow using Keras’s base Loss function. There should be num_classes floating point values per feature, i. There should be # classes floating point values per Too high of a learning rate. the data is scaled so that there are no negative numbers in the dataset and contains no NAN values. Arguments name: (Optional) string name of the Mar 30, 2021 · 1 As mentioned in that post, both categorical cross-entropy (cce) and sparse categorical cross-entropy (scc) have the same loss function just except the format of the true label Y. io Jul 23, 2025 · Categorical Cross-Entropy (CCE), also known as softmax loss or log loss, is one of the most commonly used loss functions in machine learning, particularly for classification problems. I have sigmoid activation function in the output layer to squeeze output between 0 and 1, but maybe Jul 22, 2025 · Learn about Keras loss functions: from built-in to custom, loss weights, monitoring techniques, and troubleshooting 'nan' issues. It assumes that labels are one-hot encoded, e. to_numpy() Sep 28, 2022 · We learned what the categorical cross-entropy loss is, how it works and how it generalizes the binary cross-entropy loss. shape is (6703, 56) and y. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. Binary cross-entropy for binary classification Jul 18, 2021 · Any one knows why raw implementation of Categorical Crossentropy function is so different from the tf. Jun 5, 2017 · For the categorical cross-entropy between predictions and targets: L i = ∑ j t i j log ( p i j ) the value p_ij would be in (-1,1), so the loss may be nan when p_ij in (-1,0]. The loss function requires the following inputs: y_true (true label): This is either 0 or 1. e, value in [-inf, inf tf. I am planning on training a NN, however when I test the architecture on a small subset of my data for test purposes, the loss fails to be calculated properly. Nov 1, 2018 · Could you post the rest of your code? by my understanding when using categorical crossentropy as loss function, the last layer should use a softmax activation function, yielding for each output neuron the probability of the input corresponding to said neuron's class, and not directly the one-hot vector. CategoricalCrossentropy Use this crossentropy loss function when there are two or more label classes. Thank you! def add_indicators See full list on keras. Compile your model with focal loss as sample: Binary model. nn. Testing of loss function always produces nan-return. keras's api function? import tensorflow as tf import math tf. Aliases: tf. Keras recommends that you use the default parameters. Aug 2, 2019 · The Keras library already provides various losses like mse, mae, binary cross entropy, categorical or sparse categorical losses cosine… Apr 17, 2018 · 10 Keras binary_crossentropy first convert your predicted probability to logits. 25]], gamma=2)], metrics= ["accuracy"], optimizer=adam) Alpha is used to specify the weight of different categories/labels, the size of the array needs to be Aug 11, 2019 · So no, not depending whether you use sparse categorical cross-entropy, or one hot categorical cross-entropy, there is no difference in how the labels are treated. CategoricalCrossentropy( from_logits=False, label Dec 3, 2018 · I have 7 categories of labels and I am passing 7 in last Dense Layer. It measures the difference between the predicted probability distribution and the actual (true) distribution of classes. enable_eager_execution() y_true =np. Why does this work? keras tensorflow reinforcement-learning loss-function Share Improve this question Follow edited Jun 22, 2020 at 16:08 Daniel asked Jun 9, 2025 · The categorical cross-entropy loss is commonly used in multi-class classification tasks where each input sample can belong to one of multiple classes. Example one - MNIST classification As one of the multi-class, single-label classification datasets, the task is to classify grayscale images Feb 11, 2025 · We start with the binary one, then proceed with categorical cross-entropy and finally discuss how both are different from e. Computes the categorical crossentropy loss. e. keras. However, for the softmax activation, the value pij would be normalized to (0,1], which would not be a problem for categorical_crossentropy. py epsilon is at: if theano. This means that, in your example, Keras gives you a different result because it flat Jun 5, 2017 · For the categorical cross-entropy between predictions and targets: L i = ∑ j t i j log ( p i j ) the value p_ij would be in (-1,1), so the loss may be nan when p_ij in (-1,0]. It quantifies the difference between the actual class labels (0 or 1) and the predicted probabilities output by the model. Oct 6, 2019 · In that case, sparse categorical crossentropy loss can be a good choice. However, when training my U-Net, my loss value is "nan" from start to finish (it initializes as nan and never changes). The states being passed in are not 1-hot encoded, they're just the numerical representation of the state of the universe at that moment. categorical_crossentropy( y_true, y_pred, from_logits=False, label_smoothing=0. Computes the categorical crossentropy loss. , the shape of both y_pred and y_true are [batch_size, num_classes]. The following code tries to evaluate a neural network with a sparse categorical cross entropy loss function, on the Fashion Mnist data set. losses Jul 7, 2020 · I am using the Adam optimisation technique and sparse_categorical_crossentropy for the loss function. , when labels values are [2, 0, 1], then y_true is [[0, 0, 1], [1, 0, 0], [0, 1, 0]]. shape is a binary column having shape (6703, ). In particular, if target is greater than 1 and output is sufficiently large, then bce would be positive and mean(-bce) would be negative. I just updated Keras and checked : in objectives. Jan 24, 2021 · 2 From the comment section for the benefit of the community. It is basically RMSprop with momentum. tf. compat. CategoricalCrossentropy View source on GitHub Computes the crossentropy loss between the labels and predictions. And for that, I used sparse_categorical_crossentropy loss function, but loss becomes "nan". 25, . Dec 19, 2017 · And just for the completeness of the discussion, if, for whatever reason, you insist in using binary cross entropy as your loss function (as I said, nothing wrong with this, at least in principle) while still getting the categorical accuracy required by the problem at hand, you should ask explicitly for categorical_accuracy in the model If target is either 0 or 1, bce is negative, so mean(-bce) is a positive number which is the binary cross entropy loss. Simply if Y is an integer, you would use scc whereas if Y is one-hot, you would use cce. e, value in [-inf, inf Jan 4, 2019 · I've built a u-net architecture using Keras Functional API but I'm having trouble using the sparse categorical cross entropy loss function. sigmoid_cross_entropy_with_logits to calculate cross entropy and return to you the mean of that. Why its giving "nan" for loss? When I use categorical_crossentropy or mean_ Oct 10, 2024 · Comparing with categorical_crossentropy, my f1 macro-average score didn't change at all in first 10 epochs. When I normalize my "masks" by dividing all values by 30 (so they go from 0-1), I get ~0. I also tried Jan 29, 2021 · Tensoflow Keras - Nan loss with sparse_categorical_crossentropy Asked 4 years, 6 months ago Modified 4 years, 6 months ago Viewed 2k times Dec 3, 2020 · The problem is that you are using hard 0s and 1s in your predictions. Feb 7, 2023 · Hello, I am new to machine learning and have a question as to why the model fit function is producing a nan value for loss using the sparse categorical cross entropy loss function. View aliases Main aliases tf. However, if target is not 0 or 1, this logic breaks down. My learning task is multi-class, pixel-wise classification for many 256x256 images. kwemoyv uqub zqfy iipvl smco wvjqq hxetsnqn qzkbjcby bdilp zpjej yjjg spm spgemaq iqkbm qwwnytwo