Current-based　mechanical　fault　diagnosis　is　more　convenient　and　low　cost　since　additional　sensors　are　not　required.　However,　it　is　still　challenging　to　achieve　this　goal　due　to　the　weak　fault　information　in　current　signals.　In　this　paper,　a　dual-loss　convolutional　neural　network　(DLCNN)　is　proposed　to　implement　the　intelligent　bearing　fault　diagnosis　via　current　signals.　First,　a　novel　similarity　loss　(SimL)　function　is　developed,　which　is　expected　to　maximize　the　intra-class　similarity　and　minimize　the　inter-class　similarity　in　the　model　optimization　operation.　In　the　loss　function,　a　weight　parameter　is　further　introduced　to　achieve　a　balance　and　leverage　the　performance　of　SimL　function.　Second,　the　DLCNN　model　is　constructed　using　the　presented　SimL　and　the　cross-entropy　loss.　Finally,　the　two-phase　current　signals　are　fused　and　then　fed　into　the　DLCNN　to　provide　more　fault　information.　The　proposed　DLCNN　is　tested　by　experiment　data,　and　the　results　confirm　that　the　DLCNN　achieves　higher　accuracy　compared　to　the　conventional　CNN.　Meanwhile,　the　feature　visualization　presents　that　the　samples　of　different　classes　are　separated　well.