def FocalLoss(y_true, y_pred):
"""
:param y_true: A tensor of the same shape as `y_pred`
:param y_pred: A tensor resulting from a sigmoid
:return: Output tensor.
"""
gamma = 2.0
alpha = 0.25
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
epsilon = K.epsilon()
# clip to prevent NaN's and Inf's
pt_1 = K.clip(pt_1, epsilon, 1. - epsilon)
pt_0 = K.clip(pt_0, epsilon, 1. - epsilon)
return -K.mean(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) \
- K.mean((1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
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 -