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When should be use weighted cross-entropy loss function?
Cross-entropy loss is used when adjusting model weights during training. The aim is to minimize the loss, i.e, the smaller the loss the better the model. A perfect model has a cross-entropy loss of 0.
What is weighted loss?
The weighted loss function proposed works by generating a weight map [10], which is calculated based on the predicted value and error obtained for each instance. The hypothesis is that the deep learning models using dynamically weighted loss function will learn more effectively compared to a standard loss function.
Why cross-entropy is used?
Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions.
How is cross entropy loss used in classification?
The lower the loss the better the model. Cross-Entropy loss is a most important cost function. It is used to optimize classification models. The understanding of Cross-Entropy is pegged on understanding of Softmax activation function. I have put up another article below to cover this prerequisite
How is binary cross entropy loss different from Softmax loss?
Binary Cross-Entropy Loss Also called Sigmoid Cross-Entropy loss. It is a Sigmoid activation plus a Cross-Entropy loss. Unlike Softmax loss it is independent for each vector component (class), meaning that the loss computed for every CNN output vector component is not affected by other component values.
Why do we need weighted cross entropy in Python?
We could also have penalized the loss based on the estimated labels by simply defining and the rest of the code need not change thanks to broadcasting magic. In the general case, you would want weights that depend on the kind of error you make.
Which is better binary cross entropy or categorical cross entropy?
TensorFlow: softmax_cross_entropy. Is limited to multi-class classification. In this Facebook work they claim that, despite being counter-intuitive, Categorical Cross-Entropy loss, or Softmax loss worked better than Binary Cross-Entropy loss in their multi-label classification problem.