Few-shot relation classification has attracted great attention recently and is regarded as an ef-fective way to tackle the long-tail problem in relation classification. Most previous works onfew-shot relation classification are based on learning-to-match paradigms which focus on learn-ing an effective universal matcher between the query and one target class prototype based oninner-class support sets. However the learning-to-match paradigm focuses on capturing the sim-ilarity knowledge between query and class prototype while fails to consider discriminative infor-mation between different candidate classes. Such information is critical especially when targetclasses are highly confusing and domain shifting exists between training and testing phases. Inthis paper we propose the Global Transformed Prototypical Networks(GTPN) which learns tobuild a few-shot model to directly discriminate between the query and all target classes with bothinner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between allcandidate classes and therefore leads to better classification performance. We conducted exper-iments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance onthe official leaderboard of FewRel 2.0 1.