6.2神经网络算法实现--python机器学习

来源：互联网发布：淘宝销售数据编辑：程序博客网时间：2024/05/20 18:19

参考彭亮老师的视频教程：转载请注明出处及彭亮老师原创
视频教程： http://pan.baidu.com/s/1kVNe5EJ

1. 关于非线性转化方程(non-linear transformation function)

sigmoid函数(S 曲线)用来作为activation function:

1.1 双曲函数(tanh)

1.2 逻辑函数(logistic function)

2. 实现一个简单的神经网络算法

import numpy as np

def tanh(x):

return np.tanh(x)

def tanh_deriv(x):

return 1.0 - np.tanh(x)*np.tanh(x)

def logistic(x):

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

def logistic_derivative(x):

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

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[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 # adding the bias unit to the input layer

X = temp

y = np.array(y)

for k in range(epochs):

i = np.random.randint(X.shape[0])

a = [X[i]]

for l in range(len(self.weights)): #going forward network, for each layer

a.append(self.activation(np.dot(a[l], self.weights[l]))) #Computer the node value for each layer (O_i) using activation function

error = y[i] - a[-1] #Computer the error at the top layer

deltas = [error * self.activation_deriv(a[-1])] #For output layer, Err calculation (delta is updated error)

#Staring backprobagation

for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer

#Compute the updated error (i,e, deltas) for each node going from top layer to input layer

deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))

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[l]))

return a

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