# Loss Functions¶

## MSELoss¶

class recoder.losses.MSELoss(confidence=0, reduction='elementwise_mean')[source]

Computes the weighted mean squared error loss.

The weight for an observation x:

$w = 1 + confidence \times x$

and the loss is:

$\ell(x, y) = w \cdot (y - x)^2$
Parameters: confidence (float, optional) – the weighting of positive observations. reduction (string, optional) – Specifies the reduction to apply to the output: ‘none’ | ‘elementwise_mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘elementwise_mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Default: ‘elementwise_mean’

## MultinomialNLLLoss¶

class recoder.losses.MultinomialNLLLoss(reduction='elementwise_mean')[source]

Computes the negative log-likelihood of the multinomial distribution.

$\ell(x, y) = L = - y \cdot \log(softmax(x))$
Parameters: reduction (string, optional) – Specifies the reduction to apply to the output: ‘none’ | ‘elementwise_mean’ | ‘sum’. ‘none’: no reduction will be applied, ‘elementwise_mean’: the sum of the output will be divided by the number of elements in the output, ‘sum’: the output will be summed. Default: ‘elementwise_mean’