1.NeuralNetwork.py
#coding:utf-8
import numpy as np
#定义双曲函数和他们的导数
def tanh(x):
return np.tanh(x)
def tanh_deriv(x):
return 1.0 - np.tanh(x)**2
def logistic(x):
return 1/(1 + np.exp(-x))
def logistic_derivative(x):
return logistic(x)*(1-logistic(x))
#定义NeuralNetwork 神经网络算法
class NeuralNetwork:
#初始化，layes表示的是一个list，eg[10,10,3]表示第一层10个神经元，第二层10个神经元，第三层3个神经元
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 = []
#循环从1开始，相当于以第二层为基准，进行权重的初始化
for i in range(1, len(layers) - 1):
#对当前神经节点的前驱赋值
self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i] + 1))-1)*0.25)
#对当前神经节点的后继赋值
self.weights.append((2*np.random.random((layers[i] + 1, layers[i + 1]))-1)*0.25)
#训练函数 ，X矩阵，每行是一个实例 ，y是每个实例对应的结果，learning_rate 学习率，
# epochs，表示抽样的方法对神经网络进行更新的最大次数
def fit(self, X, y, learning_rate=0.2, epochs=10000):
X = np.atleast_2d(X) #确定X至少是二维的数据
temp = np.ones([X.shape[0], X.shape[1]+1]) #初始化矩阵
temp[:, 0:-1] = X # adding the bias unit to the input layer
X = temp
y = np.array(y) #把list转换成array的形式
for k in range(epochs):
#随机选取一行，对神经网络进行更新
i = np.random.randint(X.shape[0])
a = [X[i]]
#完成所有正向的更新
for l in range(len(self.weights)):
a.append(self.activation(np.dot(a[l], self.weights[l])))
#
error = y[i] - a[-1]
deltas = [error * self.activation_deriv(a[-1])]
#开始反向计算误差，更新权重
for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last 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)
#预测函数
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 a2、基于NeuralNetwork的手写数字识别
#-*-coding:utf-8-*-
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import numpy as np
from sklearn.datasets import load_digits
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelBinarizer
from NeuralNetwork import NeuralNetwork
from sklearn.cross_validation import train_test_split
digits = load_digits()
X = digits.data
y = digits.target
X -= X.min() # normalize the values to bring them into the range 0-1
X /= X.max()
###############################训练模型########################
nn = NeuralNetwork([64,100,10],'logistic')
X_train, X_test, y_train, y_test = train_test_split(X, y)
labels_train = LabelBinarizer().fit_transform(y_train)
labels_test = LabelBinarizer().fit_transform(y_test)
print "start fitting"
nn.fit(X_train,labels_train,epochs=3000)
###############预测结果############################### predictions = [] for i in range(X_test.shape[0]): o = nn.predict(X_test[i] ) predictions.append(np.argmax(o)) ###############混淆矩阵#####################################
print confusion_matrix(y_test,predictions)
print classification_report(y_test,predictions)
#################打印预测结果#####################
# for each in predictions:
# print each
# for each in y_test:
# print each3、运行结果：
start fitting
[[44 0 0 0 0 0 0 0 0 0] [ 0 44 0 0 0 1 0 0 2 0] [ 0 1 39 0 0 0 0 0 0 0] [ 0 1 0 49 0 0 0 2 2 0] [ 0 2 0 0 34 0 0 2 1 0] [ 0 2 0 0 1 44 1 0 0 3] [ 1 2 0 0 0 0 43 0 0 0] [ 0 0 0 0 0 0 0 41 0 0] [ 0 4 0 0 0 1 0 1 31 2] [ 0 4 0 0 0 0 0 1 1 43]]
precision recall f1-score support
0 0.98 1.00 0.99 44
1 0.73 0.94 0.82 47
2 1.00 0.97 0.99 40
3 1.00 0.91 0.95 54
4 0.97 0.87 0.92 39
5 0.96 0.86 0.91 51
6 0.98 0.93 0.96 46
7 0.87 1.00 0.93 41
8 0.84 0.79 0.82 39
9 0.90 0.88 0.89 49
avg / total 0.92 0.92 0.92 450
Process finished with exit code 0
