Hi, I met with a strange problem while training deepFM model using ml-1m dataset: if enabling "is_use_fm_part" flag to True, the training process won't converge and the rmse value will become bigger and bigger(and the loss does decrease!). But if switching the flag off, just using dnn only, it seems ok. I only change the deepFM.py a little: For comparing the predicted rating with the GT value, I removed the softmax activation function for the last layer, and then output rmse error instead of auc.
Hi, I met with a strange problem while training deepFM model using ml-1m dataset: if enabling "is_use_fm_part" flag to True, the training process won't converge and the rmse value will become bigger and bigger(and the loss does decrease!). But if switching the flag off, just using dnn only, it seems ok. I only change the deepFM.py a little: For comparing the predicted rating with the GT value, I removed the softmax activation function for the last layer, and then output rmse error instead of auc.