Hi, does anyone know the difference between these two definitions? Because when I tried to train the network, they had very different performances.
class Net(nn.Module):
        def __init__(self):
		super(Net, self).__init__()
		self.conv1   = nn.Conv2d(3, 16, 5, padding=2)
		self.pool    = nn.MaxPool2d(2, 2)
		self.dropout = nn.Dropout2d(p=0.5)
		self.conv2   = nn.Conv2d(16, 16, 5, padding=2)
		self.conv3   = nn.Conv2d(16, 400, 11, padding=5)
		self.conv4   = nn.Conv2d(400, 200, 1)
		self.conv5   = nn.Conv2d(200, 1, 1)

	def forward(self, x):
		x = self.dropout(self.pool(F.relu(self.conv1(x))))
		x = self.dropout(self.pool(F.relu(self.conv2(x))))
		x = self.conv3(x)
		x = self.conv4(x)
		x = F.relu(self.conv5(x))
		
		return x

and
class Net(nn.Module):
	def __init__(self):
		super(Net, self).__init__()
		self.single5 = nn.Sequential(
						nn.Conv2d(3, 16, 5, padding=2),
						nn.ReLU(),
						nn.MaxPool2d(2, 2),
						nn.Dropout2d(p=0.5),
						nn.Conv2d(16, 16, 5, padding=2),
						nn.ReLU(),
						nn.MaxPool2d(2, 2),
						nn.Dropout2d(p=0.5),
						nn.Conv2d(16, 400, 11, padding=5),
						nn.Conv2d(400, 200, 1)
						)
		self.conv   = nn.Conv2d(200, 1, 1)

	def forward(self, x):
		x = self.single5(x)
		x = F.relu(self.conv(x))
		
		return x