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

# define your CNN architecture here
class Scratch_net(nn.Module):
    # Included code below is just an example 
    def __init__(self,num_classes):
        super(Scratch_net, self).__init__()
        ## Define layers of a CNN
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(),nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(),nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(),nn.MaxPool2d(kernel_size=3, stride=2),
        )
        
        self.avgpool = nn.AdaptiveAvgPool2d((6,6))
        
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Linear(4096, num_classes)
        )

    
    def forward(self, x):
        ## Define forward behavior
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x
