model_cnn.py 文件源码

def __call__(self, x, t, predict=False):
        h = self.bn1(self.conv1(x), test=not self.train)
        h = F.max_pooling_2d(F.relu(h), 2, stride=2)
        h = self.bn2(self.conv2(h), test=not self.train)
        h = F.max_pooling_2d(F.relu(h), 2, stride=2)
        h = F.dropout(F.relu(self.conv3(h)), ratio=0.6, train=self.train)
        h = F.max_pooling_2d(F.relu(self.conv4(h)), 2, stride=2)
        h = F.average_pooling_2d(F.relu(self.conv5(h)), 3, stride=1)
        h = F.dropout(F.relu(self.fc6(h)), ratio=0.6, train=self.train)
        h = self.fc7(h)

        self.loss = F.softmax_cross_entropy(h, t)
        self.accuracy = F.accuracy(h, t)
        if predict:
            return h
        else:
            return self.loss