def contrastive_loss(y_true, y_pred):
'''Contrastive loss from Hadsell-et-al.'06
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
'''
square_pred = K.square(y_pred)
margin_square = K.square(K.maximum(margin - y_pred, 0))
return K.mean(y_true * square_pred + (1 - y_true) * margin_square)
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 f1_loss(y_true, y_pred):
tp = K.sum(K.cast(y_true * y_pred, 'float'), axis=0)
tn = K.sum(K.cast((1 - y_true) * (1 - y_pred), 'float'), axis=0)
fp = K.sum(K.cast((1 - y_true) * y_pred, 'float'), axis=0)
fn = K.sum(K.cast(y_true * (1 - y_pred), 'float'), axis=0)
p = tp / (tp + fp + K.epsilon())
r = tp / (tp + fn + K.epsilon())
f1 = 2 * p * r / (p + r + K.epsilon())
f1 = tf.where(tf.math.is_nan(f1), tf.zeros_like(f1), f1)
return 1 - K.mean(f1)
def surface_loss(y_true, y_pred):
y_true_dist_map = tf.py_function(func=calc_dist_map_batch,
inp=[y_true],
Tout=tf.float32)
multipled = y_pred * y_true_dist_map
return K.mean(multipled)
def call(self, y_true, y_pred):
square_pred = keras.square(y_pred)
margin_square = keras.square(keras.maximum(self.margin - y_pred, 0))
return keras.mean(y_true * square_pred + (1 - y_true) * margin_square)