模型无关元学习/Model-Agnostic Meta-Learning（MAML）源码解读

最近看了好久的MAML源代码，也学了TF，想写篇文章将MAML源码总结出来，揭开MAML的神秘面纱。

论文

摘要

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two fewshot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

总结：MAML找到一个好的初始参数，而不是0，极大减少训练时间和样本数。

算法

如图，我们有初始参数θ，有3个task，每个task有最好的参数θ*。此时θ可以有3个梯度下降的方向，但是我们没有选择梯度下降，而是往这3个点共有的方向前进了一步。这样新得到的θ只需要几步就能达到其他任务的 θ*

具体地：算法第6步计算出每个task的θ*（也不是θ*，因为只走了几步没走完）,假设θ走到了这一步，变成了θi*，再求这一点的梯度，加起来平均，得到一个中和的方向，往这个方向走，即第8步更新原始的θ，就得到了一个meta-learner

源码

我找的源码并不是这篇论文给的源码，而是“dragen1860”根据官方源码实现的简易版源码，链接，可以看人家的highlight：

• adopted from cbfin’s official implementation with equivalent performance on mini-imagenet
• clean, tiny code style and very easy-to-follow from comments almost every lines
• faster and trivial improvements, eg. 0.335s per epoch comparing with 0.563s per epoch, saving up to 3.8 hours for total 60,000 training process

文件结构

meta-learn的算法流程

1. 从main函数入口进去
2. 设定参数

• nway：5，分类数，如猫的、狗的、马的……
• kshot：1，样本数
• kquery：15，？
• meta_batchsz：4，元学习中的batch数，即task的数目
• K：5，为了找到每个task最好的θ*，MAML可以进行K次梯度下降，并不是固定一次

3. 生成数据（张量）

这里先回顾下 support set和query set：每个task就是传统机器学习的任务，包含了训练集和测试集，但是容易搞混，我们不这么叫了，叫support set和query set（那这两个set 设置多大比较好？），然后4个task作为meta-train用的，叫做train set，4个task作为meta-test用的，叫做test set

以下就是4个任务在meta-train阶段的support set和query set

# image_tensor: [4, 80, 84*84*3]
support_x = tf.slice(image_tensor, [0, 0, 0], [-1,  nway *  kshot, -1], name='support_x')
query_x = tf.slice(image_tensor, [0,  nway *  kshot, 0], [-1, -1, -1], name='query_x')
support_y = tf.slice(label_tensor, [0, 0, 0], [-1,  nway *  kshot, -1], name='support_y')
query_y = tf.slice(label_tensor, [0,  nway *  kshot, 0], [-1,<