diff --git a/tianshou/utils/net/discrete.py b/tianshou/utils/net/discrete.py
index ae6a1ef..afed6df 100644
--- a/tianshou/utils/net/discrete.py
+++ b/tianshou/utils/net/discrete.py
@@ -49,10 +49,6 @@ class DQN(nn.Module):
         super(DQN, self).__init__()
         self.device = device
 
-        self.conv1 = nn.Conv2d(4, 32, kernel_size=8, stride=4)
-        self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
-        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
-
         def conv2d_size_out(size, kernel_size=5, stride=2):
             return (size - (kernel_size - 1) - 1) // stride + 1
 
@@ -68,16 +64,22 @@ class DQN(nn.Module):
         convw = conv2d_layers_size_out(w)
         convh = conv2d_layers_size_out(h)
         linear_input_size = convw * convh * 64
-        self.fc = nn.Linear(linear_input_size, 512)
-        self.head = nn.Linear(512, action_shape)
+
+        self.net = nn.Sequential(
+            nn.Conv2d(4, 32, kernel_size=8, stride=4),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(32, 64, kernel_size=4, stride=2),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, 64, kernel_size=3, stride=1),
+            nn.ReLU(inplace=True),
+            nn.Flatten(),
+            nn.Linear(linear_input_size, 512),
+            nn.Linear(512, action_shape)
+        )
 
     def forward(self, x, state=None, info={}):
         r"""x -> Q(x, \*)"""
         if not isinstance(x, torch.Tensor):
             x = torch.tensor(x, device=self.device, dtype=torch.float32)
         x = x.permute(0, 3, 1, 2)
-        x = F.relu(self.conv1(x))
-        x = F.relu(self.conv2(x))
-        x = F.relu(self.conv3(x))
-        x = self.fc(x.reshape(x.size(0), -1))
-        return self.head(x), state
+        return self.net(x), state