本文的目标是能够了解 Caffe 基本的使用流程。
接下来重点看一下 Caffe Tutorial 了解基本数据结构
Deep networks are compositional models that are naturally represented as a collection of inter-connected layers that work on chunks of data. Caffe defines a net layer-by-layer in its own model schema. The network defines the entire model bottom-to-top from input data to loss. As data and derivatives flow through the network in the forward and backward passes Caffe stores, communicates, and manipulates the information as blobs: the blob is the standard array and unified memory interface for the framework. The layer comes next as the foundation of both model and computation. The net follows as the collection and connection of layers. The details of blob describe how information is stored and communicated in and across layers and nets.
Solving is configured separately to decouple modeling and optimization.
We will go over the details of these components in more detail.