你的normalization()函数好像并没有起到normal的作用
C3Shirt opened this issue · comments
mean = train.mean(axis=0, keepdims=True)
std = train.std(axis=0, keepdims=True)
def normalize(x):
return (x - mean) / std
train = (train).transpose(0,2,1,3)
val = (val).transpose(0,2,1,3)
test =(test).transpose(0,2,1,3)
return {'mean': mean, 'std': std}, train, val, test
虽然这里写了 normalize(x),但是并没有作用到训练集上
mean = train.mean(axis=0, keepdims=True) std = train.std(axis=0, keepdims=True) def normalize(x): return (x - mean) / std train = (train).transpose(0,2,1,3) val = (val).transpose(0,2,1,3) test =(test).transpose(0,2,1,3) return {'mean': mean, 'std': std}, train, val, test
虽然这里写了 normalize(x),但是并没有作用到训练集上
对,确实没在这里归一化,在model.py里的DGCN block的输入上直接使用了
x_w=self.bn(x_w)
x_d=self.bn(x_d)
x_r=self.bn(x_r)
这里相当于实时归一化了