condor_pytorch version: 1.0.0

condor_negloglikeloss

condor_negloglikeloss(logits, labels, reduction='mean')

computes the negative log likelihood loss described in

condor tbd.

parameters

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

  outputs of the condor layer.

• labels : 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
    >>> labels = 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]])
    >>> condor_negloglikeloss(logits, labels)
    tensor(0.4936)

CondorOrdinalCrossEntropy

CondorOrdinalCrossEntropy(logits, levels, importance_weights=None, reduction='mean')

computes the condor loss described in

condor tbd.

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).

• importance_weights : torch.tensor, shape=(num_classes-1,) (default=none)

  optional weights for the different labels in levels. a tensor of ones, i.e., torch.ones(num_classes-1, dtype=torch.float32) will result in uniform weights that have the same effect as none.

• 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]])
    >>> CondorOrdinalCrossEntropy(logits, levels)
    tensor(0.8259)