L2Loss

class L2Loss[source]

Bases: Loss

This class computes the L2 loss (i.e. squared error) for each sample as:

\[\text{L2Loss}(predict, target) = \sum_{i=0}^{N_{\text{elements}}} (predict_i - target_i)^2.\]

Methods

evaluate(predict, target)[source]

An abstract method for evaluating the loss function. Inputs are expected in a shape of (N, *). Where N is a number of samples. Loss is computed for each sample individually.

Parameters:

  • predict (ndarray) – an array of predicted values using the model.

  • target (ndarray) – an array of the true values.

Returns:

An array with values of the loss function of the shape (N, 1).

Raises:

QiskitMachineLearningError – shapes of predict and target do not match

Return type:

ndarray

gradient(predict, target)[source]

An abstract method for computing the gradient. Inputs are expected in a shape of (N, *). Where N is a number of samples. Gradient is computed for each sample individually.

Parameters:

  • predict (ndarray) – an array of predicted values using the model.

  • target (ndarray) – an array of the true values.

Returns:

An array with gradient values of the shape (N, *). The output shape depends on the loss function.

Raises:

QiskitMachineLearningError – shapes of predict and target do not match.

Return type:

ndarray