Tutorial_LeNet.py

import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNOrm, Loss functions

# LeNet architecture
# 1x32x32 Input -> (5x5), s=1, p=0 -> avg pool s=2, p=0 -> (5x5), s=1, p=0 -> avg pool s=2, p=0
# -> Conv 5x5 to 120 channels x Linear 120 -> 84 x Linear 10

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.relu = nn.ReLU()
        self.pool = nn.AvgPool2d(kernel_size=(2,2),stride=(2,2))
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=(5,5),stride=(1,1),padding=(0,0))
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=(5,5),stride=(1,1),padding=(0,0))
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=(5, 5), stride=(1, 1), padding=(0, 0))
        self.linear1 = nn.Linear(120, 84)
        self.linear2 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.pool(x)
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.relu(self.conv3(x)) # num_examples x 120 x 1 x 1 -> num_examples x 120
        x = x.reshape(x.shape[0], -1)
        x = self.relu(self.linear1(x))
        x = self.linear2(x)
        return x

x = torch.randn(64, 1, 32, 32)
model = LeNet()
print(model(x).shape)