def SCD(y_true, y_pred):
s_t = y_true - K.mean(y_true, axis=1, keepdims=True)
s_p = y_pred - K.mean(y_true, axis=1, keepdims=True)
return 1 - K.mean(
K.l2_normalize(s_t + K.epsilon(), axis=-1) *
K.l2_normalize(s_p + K.epsilon(), axis=-1))
def SAD(y_true, y_pred):
y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1)
y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1)
# sad = T.acos(K.mean(y_true2 * y_pred2, axis=-1))
sad = T.acos((y_true2 * y_pred2))
# sad = -K.mean(y_true2 * y_pred2, axis=-1)
return sad
def normSAD2(y_true, y_pred):
y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1)
y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1)
mse = K.mean(K.square(y_true - y_pred), axis=-1)
# sad = -K.log(1.0-K.mean(y_true2 * y_pred2/np.pi, axis=-1))
sad = K.mean(y_true2 * y_pred2, axis=-1)
# sid = SID(y_true,y_pred)
return 0.005 * mse - 0.75 * sad
def normMSE(y_true, y_pred):
y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1)
y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1)
mse = K.mean(K.square(y_true - y_pred))
return mse