googlenetbn.py 文件源码

def __call__(self, x, t):
        self.clear()
        test = not self.train

        h = F.max_pooling_2d(
            F.relu(self.norm1(self.conv1(x), test=test)),  3, stride=2, pad=1)
        h = F.max_pooling_2d(
            F.relu(self.norm2(self.conv2(h), test=test)), 3, stride=2, pad=1)

        h = self.inc3a(h)
        h = self.inc3b(h)
        h = self.inc3c(h)
        h = self.inc4a(h)

        a = F.average_pooling_2d(h, 5, stride=3)
        a = F.relu(self.norma(self.conva(a), test=test))
        a = F.relu(self.norma2(self.lina(a), test=test))
        a = self.outa(a)
        self.loss1 = F.softmax_cross_entropy(a, t)

        h = self.inc4b(h)
        h = self.inc4c(h)
        h = self.inc4d(h)

        b = F.average_pooling_2d(h, 5, stride=3)
        b = F.relu(self.normb(self.convb(b), test=test))
        b = F.relu(self.normb2(self.linb(b), test=test))
        b = self.outb(b)
        self.loss2 = F.softmax_cross_entropy(b, t)

        h = self.inc4e(h)
        h = self.inc5a(h)
        h = F.average_pooling_2d(self.inc5b(h), 7)
        h = self.out(h)
        self.loss3 = F.softmax_cross_entropy(h, t)

        self.loss = 0.3 * (self.loss1 + self.loss2) + self.loss3
        self.accuracy = F.accuracy(h, t)
        return self.loss