src/similarity/siamese.py [118:138]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
model = cnn(args.similarity_dims, 152)
optimizer = OPTIMIZER[args.optimizer](model.parameters(), lr= args.learning_rate)

# Save arguments used to create model for restoring the model later
with open(MODEL_INFO_PATH, 'wb') as f:
    model_info = {
        'simililarity-dims': args.similarity_dims
    }
    torch.save(model_info, f)
        
###############################################################################
# Training code
###############################################################################

def contrastive_loss(distance, labels):
    
    is_diff = (labels > 0.0).float()
    loss = torch.mean(((1-is_diff) * torch.pow(distance, 2)) +
                        ((is_diff) * torch.pow(torch.abs(labels - distance), 2)))
        
    return loss
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



src/similarity/siamese2.py [99:119]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    model = cnn(args.similarity_dims, 152)

optimizer = OPTIMIZER[args.optimizer](model.parameters(), lr= args.learning_rate)

# Save arguments used to create model for restoring the model later
with open(MODEL_INFO_PATH, 'wb') as f:
    model_info = {
        'simililarity-dims': args.similarity_dims
    }
    torch.save(model_info, f)
        
###############################################################################
# Training code
###############################################################################

def contrastive_loss(distance, labels):
    
    is_diff = (labels > 0.0).float()
    loss = torch.mean(((1-is_diff) * torch.pow(distance, 2)) +
                        ((is_diff) * torch.pow(torch.abs(labels - distance), 2)))
    return loss
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -