import torch.nn as nn

class DogCatClassifier(nn.Module):
    def __init__ (self):
        super().__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.MaxPool2d(2),
            nn.BatchNorm2d(32)
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.MaxPool2d(2),
            nn.BatchNorm2d(64)
        )

        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 128, 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.MaxPool2d(2),
            nn.BatchNorm2d(128)
        )

        self.fc1 = nn.Linear(128 * 4 * 4 , 512)# 2048, lowkey had to calculator it lol
        self.dropout = 0.5 # tunable
        self.fc2 = nn.Linear(512, 1)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        x = nn.functional.sigmoid(x)
        return x