<gh_stars>1-10
import torch.nn as nn
   
class large_cnn(nn.Module):
    # Model
    def __init__(self):
        super(large_cnn, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),  
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(64, 128, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2)
            ) 

        self.classifier = nn.Sequential(
            nn.Linear(512*2*2, 100),
            nn.ReLU(True),
            nn.Linear(100, 2),
            nn.Softmax()
            )
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

class large_cnn_2(nn.Module):
    # Model
    def __init__(self):
        super(large_cnn_2, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),  
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(64),

            nn.Conv2d(64, 128, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(128),

            nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(256),

            nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512),

            nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512)
            ) 

        self.classifier = nn.Sequential(
            nn.Linear(512*2*2, 100),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(100, 2),
            nn.Softmax()
            )
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x
    
import torch.nn as nn
   
class large_cnn(nn.Module):
    # Model
    def __init__(self):
        super(large_cnn, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),  
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(64, 128, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            
            nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2)
            ) 

        self.classifier = nn.Sequential(
            nn.Linear(512*2*2, 100),
            nn.ReLU(True),
            nn.Linear(100, 2),
            nn.Softmax()
            )
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

class large_cnn_2(nn.Module):
    # Model
    def __init__(self):
        super(large_cnn_2, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),  
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(64),

            nn.Conv2d(64, 128, kernel_size=5, padding=2, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(128),

            nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(256),

            nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512),

            nn.Conv2d(512, 512, kernel_size=3, padding=1, stride=1),
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.BatchNorm2d(512)
            ) 

        self.classifier = nn.Sequential(
            nn.Linear(512*2*2, 100),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(100, 2),
            nn.Softmax()
            )
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x
# Model # Model