mean_absolute_error

mean_absolute_error(logits, levels, reduction='mean')

Computes the mean absolute error of ordinal predictions.

Parameters

• logits : torch.tensor, shape(num_examples, num_classes-1)

  Outputs of the CONDOR layer.

• levels : torch.tensor, shape(num_examples, num_classes-1)

  True labels represented as extended binary vectors (via condor_pytorch.dataset.levels_from_labelbatch).

• reduction : str or None (default='mean')

  If 'mean' or 'sum', returns the averaged or summed loss value across all data points (rows) in logits. If None, returns a vector of shape (num_examples,)

Returns

• loss : torch.tensor

  A torch.tensor containing a single loss value (if reduction='mean' or 'sum') or a loss value for each data record (if reduction=None).

Examples

    >>> import torch
    >>> levels = torch.tensor(
    ...    [[1., 1., 0., 0.],
    ...     [1., 0., 0., 0.],
    ...    [1., 1., 1., 1.]])
    >>> logits = torch.tensor(
    ...    [[2.1, 1.8, -2.1, -1.8],
    ...     [1.9, -1., -1.5, -1.3],
    ...     [1.9, 1.8, 1.7, 1.6]])
    >>> mean_absolute_error(logits, levels)
    tensor(0.)