Given the class implementation of a layer,what is the syntax to build a neural network which will have this layer as one of its many layers?
Going by the TwoLayerNet example, the model is created by a statement as follows:
model = TwoLayerNet(D_in, H, D_out)
This approach is not helpful in my more general setting.
When I look at the PyTorch example for MNIST, I see the following:
...
import torch.nn as nn
import torch.nn.functional as F
...
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

Will my layer be called in the manner F.relu is called?