In this paper, we propose a novel meta learning approach, namely LGM-Net, for few-shot classification. The approach learns transferable prior knowledge across tasks and directly produces network parameters for similar unseen tasks with training samples. LGM-Net includes two key modules: TargetNet and MetaNet. The TargetNet module is a neural network for solving a specific task. The MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples. A new intertask normalization strategy which makes use of common information shared across tasks is utilized during training. Experimental results demonstrate that LGM-Net adapts well to similar unseen tasks and achieves state-of-the-art performance on Omniglot and \textit{mini}ImageNet datasets. And experiments on synthetic datasets are given to show that the transferable prior knowledge is learned by the MetaNet which can help to solve unseen tasks through mapping training data to functional weights. The proposed approach achieves the goal of fast learning and adaptation since no further tuning steps are required in comparison with other exisiting meta learning approaches.