神经网络理论基础及Python实现

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tanh ( x )

def logistic ( x ) :

return 1 / ( 1 + np . exp ( - x ))

def logistic_derivative ( x ) :

return logistic ( x ) * ( 1 - logistic ( x ))

设计BP神经网络的形式（几层，每层多少单元个数），用到了面向对象，主要是选择哪种非线性函数，以及初始化权重。layers是一个list，里面包含每一层的单元个数。

class NeuralNetwork :

def __init__ ( self , layers , activation = 'tanh' ) :

"""

:param layers: A list containing the number of units in each layer.

Should be at least two values

:param activation: The activation function to be used. Can be

" logistic " or " tanh "

"""

if activation == 'logistic' :

self . activation = logistic

self . activation_deriv = logistic_derivative

elif activation == 'tanh' :

self . activation = tanh

self . activation_deriv = tanh_deriv

self . weights = []

for i in range ( 1 , len ( layers ) - 1 ) :

self . weights . append (( 2 * np . random . random (( layers [ i - 1 ] + 1 , layers [ i ] + 1 )) - 1 ) * 0.25 )

self . weights . append (( 2 * np . random . random (( layers [

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