Sparse Categorical Cross-Entropy: Commonly used in multi-class classification, it quantifies the dissimilarity between predicted class probabilities and true class distribution. Binary Cross-Entropy: ...

Learn how cross entropy and mean squared error impact the learning rate and convergence of your neural network model.

The most common measure of error is called mean squared error. However, there are some research results that suggest using a different measure, called cross entropy error, is sometimes preferable to ...

Graph Neural Networks,Root Mean Square Error,Traffic Flow,Attention Mechanism,Feature Maps,Graph Convolutional Network,Neural Network,Real-world Datasets,Recurrent Neural Network,Arrival ...

Binary cross entropy is a loss function used for binary classification tasks (tasks with only two outcomes/classes). It works by calculating the following average: The above equation can be split into ...

However, there are some research results that suggest using a different measure, called cross entropy error, is sometimes preferable to using mean squared error.

Graph Convolutional Network,Convolutional Neural Network,Mean Absolute Error,Graph Convolution,Mean Absolute Percentage Error,Root Mean Square Error,Self-supervised Learning,Urban Network,Gated ...