Binary Jaccard Index in Lasagne

binary_jaccard_index.py

def binary_jaccard_index(predictions,targets):
    """Computes the binary (generalized) Jaccard index between predictions and targets.
    .. math:: L_i  = \\sum_i{\\min(p_i,t_i} / \\sum_i{\\max(p_i,t_i}
    Parameters
    ----------
    predictions : Theano tensor
        Predictions in [0, 1], such as a sigmoidal output of a neural network,
        giving the probability of the positive class
    targets : Theano tensor
        Targets in {0, 1}, such as ground truth labels.
    Returns
    -------
    Theano tensor
        An expression for the jaccard similarity coefficient(accuracy) in [0, 1]
    Notes
    -----
    This objective is also known as (generalized) Jaccard similarity coefficient. 
    This objective function should not be used with a gradient calculation;It is intended as a convenience for
    validation and testing, not training.
    To obtain the average accuracy, call :func:`theano.tensor.mean()` on the
    result, passing ``dtype=theano.config.floatX`` to compute the mean on GPU.
    """    
    intersection = theano.tensor.minimum(predictions, targets)
    union = theano.tensor.maximum(predictions, targets)
    axes = tuple(range(1,4))
    return intersection.sum(axis=axes) / union.sum(axis=axes)