def tversky_loss(y_true, y_pred, alpha=0.3, beta=0.7, smooth=1e-10):
""" Tversky loss function.
Parameters
----------
y_true : keras tensor
tensor containing target mask.
y_pred : keras tensor
tensor containing predicted mask.
alpha : float
real value, weight of '0' class.
beta : float
real value, weight of '1' class.
smooth : float
small real value used for avoiding division by zero error.
Returns
-------
keras tensor
tensor containing tversky loss.
"""
y_true = K.flatten(y_true)
y_pred = K.flatten(y_pred)
truepos = K.sum(y_true * y_pred)
fp_and_fn = alpha * K.sum(y_pred * (1 - y_true)) + beta * K.sum((1 - y_pred) * y_true)
answer = (truepos + smooth) / ((truepos + smooth) + fp_and_fn)
return -answer # might be 1 -
def jaccard_coef_logloss(y_true, y_pred, smooth=1e-10):
""" Loss function based on jaccard coefficient.
Parameters
----------
y_true : keras tensor
tensor containing target mask.
y_pred : keras tensor
tensor containing predicted mask.
smooth : float
small real value used for avoiding division by zero error.
Returns
-------
keras tensor
tensor containing negative logarithm of jaccard coefficient.
"""
y_true = K.flatten(y_true)
y_pred = K.flatten(y_pred)
truepos = K.sum(y_true * y_pred)
falsepos = K.sum(y_pred) - truepos
falseneg = K.sum(y_true) - truepos
jaccard = (truepos + smooth) / (smooth + truepos + falseneg + falsepos)
return -K.log(jaccard + smooth) # might be 1 -