#  define net structure here
        self.conv1 = nn.Sequential(
            nn.Conv2d(5, 8, (3, 3), stride=1, padding=1),
            nn.LeakyReLU(),
            nn.MaxPool2d(2)
        ).cuda()
        self.conv2 = nn.Sequential(
            nn.Conv2d(8, 12, (3, 3), stride=1, padding=1),
            nn.LeakyReLU(),
            nn.MaxPool2d(2)
        ).cuda()
        self.conv3 = nn.Sequential(
            nn.Conv2d(12, 12, (3, 3), stride=1, padding=1),
            nn.LeakyReLU(),
            nn.MaxPool2d(2)
        ).cuda()
        self.conv4 = nn.Sequential(
            nn.Conv2d(12, 12, (3, 3), stride=1, padding=1),
            nn.LeakyReLU()
        ).cuda()
        self.conv5 = nn.Sequential(
            nn.Conv2d(12, 12, (3, 3), stride=1, padding=1),
            nn.LeakyReLU()
        ).cuda()

        n = 12*8*4

        self.linear = nn.Sequential(
            nn.Linear(n, 100),
            nn.Tanh(),
            nn.Linear(100, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, 32),
            nn.Tanh(),
            nn.Linear(32, self.num_outputs),
            nn.Sigmoid(),
        ).cuda()