# 神经网络的架构

![](http://neuralnetworksanddeeplearning.com/images/tikz11.png)\
神经网络分为：输入层、输出层、中间层(hidden layer)

**输入层**： 每个特征作为一个输入神经元。\
取值为\[0,1]\
**输出层**：\
输出值为(0,1)\
输出值 < 0.5代表否\
输出值 > 0.5代表是\
**中间层**：\
中间层的设计需要要一些技巧，如果网络深度与训练时间之间的平衡。\
本书后面会这个话题。

**feedforward神经网络**：这一层的输出是下一层的输入，中间没有循环\
**recurrent(复发的)神经网络**：网络中有循环。这种情况下的神经元只在特征条件下才会fire。

recurrent不如feedforward有影响力。但recurrent更接近大脑的工作方式。\
本书仅讲解feedforward神经网络。


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