human
What is a the Python code you requested given the following:
I am working on the file "FADL1/L7CA_lesson7-cifar10.ipynb".
The first task is:
We have around 30,000 parameters in the ConvNet, about a quarter that in the simple FullNet, and our accuracy is around 57%, up from 47%.
4. Refactored
We're going to refactor the ConvNet slightly so that we put less stuff in the forward pass. For instance, calling relu each loop isn't ideal. So we'll create a new class called ConvLayer which contains a convolution with a kernel size of 3 and a stride of two, and with padding. Padding becomes especially important when you're down to small layer sizes in later convolutions where throwing away a potential convolution around the edge will lose a significant amount of information.
The relu will be inside the ConvLayer class, making it easier to edit and prevent bugs.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class ConvLayer(nn.Module):
    def __init__(self, ni, nf):
        super().__init__()
        self.conv = nn.Conv2d(ni, nf, kernel_size=3, stride=2, padding=1)
        
    def forward(self, x): return F.relu(self.conv(x))

class ConvNet2(nn.Module):
    def __init__(self, layers, c):
        super().__init__()
        self.layers = nn.ModuleList([ConvLayer(layers[i], layers[i + 1]) 
                                     for i in range(len(layers) - 1)])
        self.out = nn.Linear(layers[-1], c)
        
    def forward(self, x):
        for λ in self.layers: x = λ(x)
        x = F.adaptive_max_pool2d(x, 1) # F is nn.Functional
        x = x.view(x.size(0), -1)
        return F.log_softmax(self.out(x), dim=-1)