yansudong
commented
5 years ago This is my custom loss function:
def my_newloss(y_true, y_pred, alpha=0.8):
mseloss = K.mean(K.square(y_pred - y_true), axis=-1)
num = (K.int_shape(y_true)[0]) * (K.int_shape(y_true)[1])
samm = K.zeros(num)
n = 0
for x in range(K.int_shape(y_true)[0]):
for y in range(K.int_shape(y_true)[1]):
z = K.reshape(y_pred[ x, y,:], [-1])
sa = K.reshape(y_true[x, y,:], [-1])
tem1=K.dot(z,sa)
tem2=(K.linalg.norm(z))*(K.linalg.norm(sa))
samm[n]=K.acos(tem1/tem2)
n=n+1
SAM=(K.mean(samm))*180/math.pi
samloss = SAM
return alpha*mseloss + (1-alpha)*samloss
Who can help me solve it? The main problem is the calculation and coding of num and samm.
ymodak
When I customize the loss function, I want to get the specific value of the y_true dimension. I tried y_true.shape, y_true.get_shape(), y_true.get_shape().as_list(), and K.shape(y_true), but Will report an error because of data format and other issues. Does anyone have relevant experience? Who can help me? thank you very much!