- AI – A Game Changer for Marketing
- Siamese Neural Network ( With Pytorch Code Example )
- Infographic: How AI is Transforming the Workplace

© 2019 Innovation Incubator Group of Companies. All Rights Reserved.

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), nn.Conv2d(96, 256, kernel_size=5,stride=1,padding=2), nn.ReLU(inplace=True), nn.LocalResponseNorm(5,alpha=0.0001,beta=0.75,k=2), nn.MaxPool2d(3, stride=2), nn.Dropout2d(p=0.3), nn.Conv2d(256,384 , kernel_size=3,stride=1,padding=1), nn.ReLU(inplace=True), nn.Conv2d(384,256 , kernel_size=3,stride=1,padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(3, stride=2), nn.Dropout2d(p=0.3), ) # Defining the fully connected layers self.fc1 = nn.Sequential( # First Dense Layer nn.Linear(30976, 1024), nn.ReLU(inplace=True), nn.Dropout2d(p=0.5), # Second Dense Layer nn.Linear(1024, 128), nn.ReLU(inplace=True), # Final Dense Layer nn.Linear(128,2)) def forward_once(self, x): # Forward pass output = self.cnn1(x) output = output.view(output.size()[0], -1) output = self.fc1(output) return output def forward(self, input1, input2): # forward pass of input 1 output1 = self.forward_once(input1) # forward pass of input 2 output2 = self.forward_once(input2) # returning the feature vectors of two inputs return output1, output2

class ContrastiveLoss(torch.nn.Module): def __init__(self, margin=2.0): super(ContrastiveLoss, self).__init__() self.margin = margin def forward(self, output1, output2, label): # Find the pairwise distance or eucledian distance of two output feature vectors euclidean_distance = F.pairwise_distance(output1, output2) # perform contrastive loss calculation with the distance loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) + (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2)) return loss_contrastive

def oneshot(model,img1,img2): # Gives you the feature vector of both inputs output1,output2 = model(img1.cuda(),img2.cuda()) # Compute the distance euclidean_distance = F.pairwise_distance(output1, output2) #with certain threshold of distance say its similar or not if eucledian_distance > 0.5: print("Orginal Signature") else: print("Forged Signature")