import torch.nn as nn

class CifarNet(nn.Module):
    def __init__(self):
        super(CifarNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3)

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

        self.relu = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(3200, 256)
        
        self.dropout = nn.Dropout(0.5)
        
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool(x)

        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool(x)

        x = x.contiguous().view(-1, 3200)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        
        x = self.relu(self.fc2(x))
        x = self.fc3(x)

        return x

