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

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 6, 5),
            nn.BatchNorm2d(6),
            nn.ReLU(True)
        )
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Sequential(
            nn.Conv2d(6, 16, 5),
            nn.BatchNorm2d(16),
            nn.ReLU(True)
        )
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(self.conv1(x))
        x = self.pool(self.conv2(x))
        x = torch.flatten(x, 1) # flatten all dimensions except batch
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x