tfkeraslossesHuber

The tf.keras.losses.Huber class is a part of the TensorFlow Keras API, specifically within the losses module. It implements the Huber loss function, which is a type of loss function used in regression tasks. The Huber loss is less sensitive to outliers in data than the mean squared error loss. This is because it transitions from a quadratic loss to a linear loss beyond a certain threshold, making it more robust to outliers.

The Huber loss is defined as follows:

L(y, f(x)) = 1/2 * (y - f(x))^2 for |y - f(x)| <= delta

L(y, f(x)) = delta * (|y - f(x)| - 1/2 * delta) otherwise

where y is the true value, f(x) is the predicted value, and delta is a hyperparameter that determines the threshold at which the loss transitions from quadratic to linear.

The tf.keras.losses.Huber class can be used as a loss function in a Keras model. It can be instantiated with the delta parameter, which defaults to 1.0 if not specified. The class also supports the reduction parameter, which determines how the loss is reduced across the batch. The available options are 'auto', 'sum', and 'sum_over_batch_size'.

The Huber loss function is particularly useful in regression tasks where the data contains outliers, as it provides a more robust measure of error than the mean squared error loss. It is also computationally efficient, making it a popular choice for many regression tasks.