Hi, I implemented binary logistic regression using pytorch with one linear layer, one sigmoid layer, and optimized using BCELoss and Adam optimizer. However, it performs worse than sklearn’s implementation of logistic regression with liblinear. More, specifically, as the dimension of sample grows, pytorch’s implementation becomes unstable and seems to be trapped in a local minimum while sklearn performs fine. Is this because pytorch’s optimization algorithm being different from sklearn? In fact adding another layer does not help with the performance. Since I really want to take advantage of pytorch’s GPU support and the ability of building deep nets, I am wondering if anyone has encountered this issue before and how can we get pytorch work as well as sklearn. Thanks