Lenet.py

import torch.nn as nn
import torch.nn.functional as F

class Lenet(nn.Module):
    def __init__(self):
        super(Lenet, self).__init__()
        self.conv1 = nn.Conv2d(1,6,5,stride=1,padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1   = nn.Linear(400, 120)
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 10)
        self.T_revision = nn.Linear(10, 10, False)
        
    def forward(self, x, revision=True):
        correction = self.T_revision.weight
        out = F.relu(self.conv1(x))
        out = F.max_pool2d(out, 2)
        out = F.relu(self.conv2(out))
        out = F.max_pool2d(out, 2)
        out = out.view(out.size(0), -1)
        out = F.relu(self.fc1(out))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        if revision == True:
            return out, correction
        else:
            return out