recsys9.py

def train(epochs = 10, bs = 64):
    for epoch in range(epochs):
        
        # training the model
        i=0
        total_loss = 0.0
        ct = 0
        while i < len(data[0]):
            x1,x2,y = get_batch(data[0],i,i+bs)
            i+=bs
            ct+=1
            user_ids = torch.LongTensor([user2idx[u] for u in x1]).cuda()
            item_ids = torch.LongTensor([item2idx[b] for b in x2]).cuda()
            y = torch.Tensor(y).cuda()
            # disregard/zero the gradients from previous computation
            model.zero_grad() 
            preds = model(user_ids,item_ids)
            loss = loss_function(preds, y)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        total_loss /= ct
        
        # getting the loss on validation set
        i = 0
        total_val_loss = 0.0
        cv=0
        m = model.eval() # setting the model to evaluation mode
        while i < len(data[1]):
            x11,x21,y1 = get_batch(data[1],i,i+bs)
            i+=bs
            cv+=1
            user_ids = torch.LongTensor([user2idx[u] for u in x11]).cuda()
            item_ids = torch.LongTensor([item2idx[b] for b in x21]).cuda()
            y1 = torch.Tensor(y1).cuda()
            preds = m(user_ids,item_ids)
            loss = loss_function(preds, y1)
            total_val_loss += loss.item()
        total_val_loss /= cv
        
        print('epoch', epoch+1, '   train loss', "%.3f" % total_loss, 
              '   valid loss', "%.3f" % total_val_loss)