caffe学习3——layers

weixin_33968104

于 2016-01-23 16:27:00 发布

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文章标签：人工智能

原文链接：http://www.cnblogs.com/Wanggcong/p/5153536.html

本文介绍了深度学习中Layer的概念及其重要性。Layer作为模型的基本计算单元，包括卷积、池化、内积等操作，并定义了setup、forward及backward三种核心计算方式。文章还提到了自定义Layer的方法，只需实现这三个部分即可轻松融入网络中。

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1 layer是模型的本质，是计算的基本单元。Layers convolve filters, pool, take inner products, apply nonlinearities like rectified-linear and sigmoid and other elementwise transformations, normalize, load data, and compute losses like softmax and hinge. See the layer catalogue for all operations.

2 每层定义三种计算： setup, forward, and backward. there will be two Forward and Backward functions implemented, one for CPU and one for GPU.

3 Developing custom layers requires minimal effort by the compositionality of the network and modularity of the code. Define the setup, forward, and backward for the layer and it is ready for inclusion in a net.

转载于:https://www.cnblogs.com/Wanggcong/p/5153536.html