optimizers.py

import torch.optim as optim

#define optimizer and loss
optimizer = optim.Adam(model.parameters())
criterion = nn.BCELoss()

#define metric
def binary_accuracy(preds, y):
    #round predictions to the closest integer
    rounded_preds = torch.round(preds)
    
    correct = (rounded_preds == y).float() 
    acc = correct.sum() / len(correct)
    return acc
    
#push to cuda if available
model = model.to(device)
criterion = criterion.to(device)