When optical coherence tomography (OCT) is used for retinal disease diagnosis, it is critical to detect and classify the biomarkers from the OCT B-scans of patients. In this paper, we propose a novel weakly supervised approach that utilizes healthy data and image-level labels for biomarker detection and classification. The proposed approach is based on a hybrid network which integrates adversarial generative network and guided attention into one framework. The framework includes an anomaly detection network and a classification network. The anomaly detection network reconstructs an input image with biomarkers to a reconstructed image and the reconstructed image is compared with the input image to locate the biomarkers. Inspired by the guided attention inference network, we utilize the discriminator trained in the anomaly detection network as a classifier twice to reduce model parameters and obtain a complete attention map with class information to get biomarker classes. Experimental results with a large dataset demonstrate the effectiveness of the proposed detection and classification framework.