Sequential module - init method

Having learned about the sequential module, now is the time to see how you can convert a neural network that doesn't use sequential modules to one that uses them. We are giving the code to build the network in the usual way, and you are going to write the code for the same network using sequential modules.

class Net(nn.Module):
    def __init__(self, num_classes):
        super(Net, self).__init__()

        self.conv1 = nn.Conv2d(in_channels=1, out_channels=5, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(in_channels=5, out_channels=10, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(in_channels=20, out_channels=40, kernel_size=3, padding=1)

        self.relu = nn.ReLU()

        self.pool = nn.MaxPool2d(2, 2)

        self.fc1 = nn.Linear(7 * 7 * 40, 1024)
        self.fc2 = nn.Linear(1024, 2048)
        self.fc3 = nn.Linear(2048, 10)

We want the pooling layer to be used after the second and fourth convolutional layers, while the relu nonlinearity needs to be used after each layer except the last (fully-connected) layer. For the number of filters (kernels), stride, passing, number of channels and number of units, use the same numbers as above.

Declare all the layers needed for feature extraction in the self.features.

Exercise

Sequential module - init method

Instructions 1/2