main.py

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

class lenNet(nn.Module):
    def __init__(self):
        super(lenNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # Updated input size here
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), 2, 2)
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # Exclude batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = lenNet()
print(net)

input = torch.randn(1, 1, 32, 32)
print('\nImage batch shape:')
print(input.shape)

output = net(input)
print('\nRaw output:')
print(output)
print(output.shape)