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VGG_types = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
class VGG_net(nn.Module):
    def __init__(self, in_channels=3, num_classes=1000):
        super(VGG_net, self).__init__()
        self.in_channels = in_channels

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class AlexNet(nn.Module):
    def __init__(self):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(),
            nn.LocalResponseNorm(size=5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(),

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class Lenet(nn.Module):
  def __init__(self):
    super(Lenet,self).__init__()
    self.tanh = nn.Tanh()      
    self.pool = nn.AvgPool2d(kernel_size=(2,2),stride=(2,2))
    self.conv1 = nn.Conv2d(in_channels=1,out_channels=6,kernel_size=(5,5),stride=(1,1))
    self.conv2 = nn.Conv2d(in_channels=6,out_channels=16,kernel_size=(5,5),stride=(1,1))
    self.conv3 = nn.Conv2d(in_channels=16,out_channels=120,kernel_size=(5,5),stride=(1,1))
    self.linear1 = nn.Linear(120,84)
    self.linear2 = nn.Linear(84,10)

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class Solution(object):
    def longestConsecutive(self, nums):
        """
        :type nums: List[int]
        :rtype: int
        """
        nums = set(nums)
        best = 0
        for x in nums:
            if x-1 not in nums:

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# Load the test dataset
test_dataset = SiameseDataset(training_csv=testing_csv,training_dir=testing_dir,
                                        transform=transforms.Compose([transforms.Resize((105,105)),
                                                                      transforms.ToTensor()
                                                                      ])
                                       )

test_dataloader = DataLoader(test_dataset,num_workers=6,batch_size=1,shuffle=True)
#test the network
count=0

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class ContrastiveLoss(torch.nn.Module):
    """
    Contrastive loss function.
    Based on:
    """

    def __init__(self, margin=1.0):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin

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# Declare Siamese Network
net = SiameseNetwork().cuda()
# Decalre Loss Function
criterion = ContrastiveLoss()
# Declare Optimizer
optimizer = th.optim.Adam(net.parameters(), lr=1e-3, weight_decay=0.0005)
#train the model
def train():
    loss=[] 
    counter=[]

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#create a siamese network
class SiameseNetwork(nn.Module):
    def __init__(self):
        super(SiameseNetwork, self).__init__()
        # Setting up the Sequential of CNN Layers
        self.cnn1 = nn.Sequential(
            nn.Conv2d(1, 96, kernel_size=11,stride=1),
            nn.ReLU(inplace=True),
            nn.LocalResponseNorm(5,alpha=0.0001,beta=0.75,k=2),
            nn.MaxPool2d(3, stride=2),

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# Load the the dataset from raw image folders
siamese_dataset = SiameseDataset(training_csv,training_dir,
                                        transform=transforms.Compose([transforms.Resize((105,105)),
                                                                      transforms.ToTensor()
                                                                      ])
                                       )

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#preprocessing and loading the dataset
class SiameseDataset():
    def __init__(self,training_csv=None,training_dir=None,transform=None):
        # used to prepare the labels and images path
        self.train_df=pd.read_csv(training_csv)
        self.train_df.columns =["image1","image2","label"]
        self.train_dir = training_dir    
        self.transform = transform

    def __getitem__(self,index):