trainepocs.py

def train_epocs(model, epochs=10, lr=0.01, wd=0.0):
    optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
    model.train()
    for i in range(epochs):
        usernames = torch.LongTensor(train_df.UserId.values)
        game_titles = torch.LongTensor(train_df.TitleId.values)
        ratings = torch.FloatTensor(train_df.Userscore.values)
        y_hat = model(usernames, game_titles)
        loss = F.mse_loss(y_hat, ratings)
        optimizer.zero_grad()  # reset gradient
        loss.backward()
        optimizer.step()
        print(loss.item())
    test(model)

Hello do you mind sharing the dataset that you used on your blogpost https://betterprogramming.pub/building-a-recommendation-engine-with-pytorch-d64be4856fe7