mdl_full.py 文件源码

def __call__(self, x, y, t):
        self.clear()
        hR = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convR1(x))), 3, stride=2)
        hR = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convR2(hR))), 3, stride=2)
        hR = F.relu(self.convR3(hR))
        hR = F.relu(self.convR4(hR))
        hR = F.max_pooling_2d(F.relu(self.convR5(hR)), 3, stride=2)
        hR = F.dropout(F.relu(self.fcR6(hR)), train=self.train)
        hR = F.dropout(F.relu(self.fcR7(hR)), train=self.train)
        hD = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convD1(y))), 3, stride=2)
        hD = F.max_pooling_2d(F.relu(
            F.local_response_normalization(self.convD2(hD))), 3, stride=2)
        hD = F.relu(self.convD3(hD))
        hD = F.relu(self.convD4(hD))
        hD = F.max_pooling_2d(F.relu(self.convD5(hD)), 3, stride=2)
        hD = F.dropout(F.relu(self.fcD6(hD)), train=self.train)
        hD = F.dropout(F.relu(self.fcD7(hD)), train=self.train)
        h = F.dropout(F.relu(self.fc8(hR, hD)), train=self.train)
        h = self.fc9(h)

        self.loss = F.softmax_cross_entropy(h, t)
        self.accuracy = F.accuracy(h, t)
        return self.loss