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简单的Neural Network神经网络算法

程序员文章站 2022-07-13 11:43:12
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#-- coding:utf-8 --
import numpy as np

def tanh(x):#定义的一个双曲线函数
return np.tanh(x)

def tan_deriv(x):
return 1.0-np.tanh(x)*np.tanh(x)#双曲线的导数

def logistic(x):#逻辑函数
return 1/(1+np.exp(-x))#sigmoid函数的表达式

def logistic_derivative(x):
return logistic(x)*(1-logistic(x))#逻辑函数的导数

class NeuralNetwork:
def init(self,layers,activation=‘tanh’):
if activation==‘logistic’:
self.activation=logistic
self.activation_deriv=logistic_derivative
elif activation==‘tanh’:
self.activation=tanh
self.activation_deriv=tan_deriv

    self.weights=[]
    for i in range(1,len(layers)-1):#设置权重weights
        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[i]+1,layers[i+1]))-1)*0.25)

def fit(self,X,y,learning_rate=0.2,epochs=10000):
    X=np.atleast_2d(X)
    temp=np.ones([X.shape[0],X.shape[1]+1])
    temp[:,0:-1]=X
    X=temp
    y=np.array(y)#array是指数组

    for k in range(epochs):
        i=np.random.randint(X.shape[0])
        a=[X[i]]

        for l in range(len(self.weights)):
            a.append(self.activation(np.dot(a[1],self.weights[1])))
            error=y[i]-a[-1]
            deltas=[error*self.activation_deriv(a[-1])]
            for l in range(len(a)-2,0,-1):
                deltas.append(deltas[-1].dot(self.weights[1].T)*self.activation_deriv(a[1]))
            deltas.reverse()
            for i in range(len(self.weights)):
                layer=np.atleast_2d(a[i])
                delta=np.atleast_2d(deltas[i])
                self.weights[i]+=learning_rate*layer.T.dot(delta)
def predict(self,x):
    x=np.array(x)
    temp=np.ones(x.shape[0]+1)
    temp[0:-1]=x
    a=temp
    for l in range(0,len(self.weights)):
        a=self.activation(np.dot(a,self.weights[1]))
    return  a
相关标签: Neural Network