<h1>前言</h1>
该 修改的都修改了看看效果吧。
<h1>代码</h1>
# -*- coding: utf-8 -*-
# @Time : 2017/11/27 上午10:23
# @Author : SkullFang
# @Email : yzhang.private@gmail.com
# @File : FiveDemo.py
# @Software: PyCharm
import random
import json
import sys
import numpy as np
class QuadraticCost(object):
@staticmethod #静态函数注解
def fn(a,y):
return 0.5 * np.linalg.norm(a-y) **2
@staticmethod
def delta(z,a,y):
return (a-y) * sigmoid_prime(z)
class CrossEntropyCost(object):
"""
交叉商
"""
@staticmethod
def fn(a,y):
return np.sum(np.nan_to_num(-y * np.log(a) - (1-y) * np.log(1-a)))
@staticmethod
def delta(z,a,y):
"""
:param z:无用
:param a:
:param y:
:return:
"""
return a-y
class Network(object):
def __init__(self, sizes,cost=CrossEntropyCost):
# 网络层数
self.num_layers = len(sizes)
# 网络每层神经元个数
self.sizes = sizes
self.default_weight_initializer()
#损失函数
self.cost=cost
def default_weight_initializer(self):
# 初始化每层的偏置
self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]
# 初始化每层的权重
self.weights = [np.random.randn(y, x)/np.sqrt(x)
for x, y in zip(self.sizes[:-1], self.sizes[1:])]
def large_weight_initialzer(self):
# 初始化每层的偏置
self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]
# 初始化每层的权重
self.weights = [np.random.randn(y, x)
for x, y in zip(sizes[:-1], self.sizes[1:])]
def feedforward(self, a):
for b, w in zip(self.biases, self.weights):
a = sigmoid(np.dot(w, a) + b)
return a
# 随机梯度下降
def SGD(self, training_data, epochs, mini_batch_size, eta,lmbda,
test_data=None):
if test_data: n_test = len(test_data)
# 训练数据总个数
n = len(training_data)
# 开始训练 循环每一个epochs
for j in xrange(epochs):
# 洗牌 打乱训练数据
random.shuffle(training_data)
# mini_batch
mini_batches = [training_data[k:k + mini_batch_size]
for k in range(0, n, mini_batch_size)]
# 训练mini_batch
for mini_batch in mini_batches:
self.update_mini_batch(mini_batch, eta,lmbda,n)
# print "Epoch {0} complete".format(j)
#
#
# if test_data:
# print "Epoch {0}: {1} / {2}".format(
#
# j, self.evaluate(test_data), n_test)
print "Epoch %s training complete" % j
cost = self.total_cost(training_data, lmbda)
print "Cost on training data: {}".format(cost)
accuracy = self.accuracy(training_data, convert=True)
print "Accuracy on training data: {} / {}".format(accuracy, n)
if test_data:
cost = self.total_cost(test_data, lmbda, convert=True)
print "Cost on test data: {}".format(cost)
accuracy = self.accuracy(test_data)
print "Accuracy on test data: {} / {}".format(accuracy, len(test_data))
# 更新mini_batch
def update_mini_batch(self, mini_batch, eta,lmbda,n):
# 保存每层偏倒
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
# 训练每一个mini_batch
for x, y in mini_batch:
delta_nable_b, delta_nabla_w = self.update(x, y)
# 保存一次训练网络中每层的偏倒
nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_nable_b)]
nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
# 更新权重和偏置 Wn+1 = wn - eta * nw
self.weights = [w -eta*(lmbda/n)*w-(eta / len(mini_batch)) * nw
for w, nw in zip(self.weights, nabla_w)]
self.biases = [b - (eta / len(mini_batch)) * nb
for b, nb in zip(self.biases, nabla_b)]
# 前向传播
def update(self, x, y):
# 保存每层偏倒
nabla_b = [np.zeros(b.shape) for b in self.biases]
nabla_w = [np.zeros(w.shape) for w in self.weights]
activation = x
# 保存每一层的激励值a=sigmoid(z)
activations = [x]
# 保存每一层的z=wx+b
zs = []
# 前向传播
for b, w in zip(self.biases, self.weights):
# 计算每层的z
z = np.dot(w, activation) + b
# 保存每层的z
zs.append(z)
# 计算每层的a
activation = sigmoid(z)
# 保存每一层的a
activations.append(activation)
# 反向更新了
# 计算最后一层的误差
# delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
delta=(self.cost).delta(zs[-1],activations[-1],y)
# 最后一层权重和偏置的倒数
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
# 倒数第二层一直到第一层 权重和偏置的倒数
for l in range(2, self.num_layers):
z = zs[-l]
sp = sigmoid_prime(z)
# 当前层的误差
delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
# 当前层偏置和权重的倒数
nabla_b[-l] = delta
nabla_w[-l] = np.dot(delta, activations[-l - 1].transpose())
return (nabla_b, nabla_w)
def evaluate(self, test_data):
test_results = [(np.argmax(self.feedforward(x)), y)
for (x, y) in test_data]
return sum(int(x == y) for (x, y) in test_results)
def accuracy(self, data,convert=False):
"""
计算准确率
:param data:
:param convert: 看是不是测试集，因为训练集的标签是10维向量。测试集是数字
:return:
"""
if convert:
results = [(np.argmax(self.feedforward(x)), np.argmax(y))
for (x, y) in data]
else:
results = [(np.argmax(self.feedforward(x)), y)
for (x, y) in data]
return sum(int(x == y) for (x, y) in results)
def total_cost(self,data,lmbda,convert=False):
"""
计算总损失
:param data:
:param lmbda:
:param convert:
:return:
"""
cost=0.0
for x,y in data:
a=self.feedforward(x)
if convert:
y= vectorized_result(y)
cost+=self.cost.fn(a,y)/len(data)
cost +=0.5*(lmbda/len(data))*sum(np.linalg.norm(w)**2 for w in self.weights)
return cost
def cost_derivative(self, output_activation, y):
return (output_activation - y)
def save(self,filename):
"""
模型保存
:param filename: 文件名
:return:
"""
data ={ "sizes": self.sizes, #模型结构
"weights": [w.tolist() for w in self.weights], #tolist转换为列表类型
"biases": [b.tolist() for b in self.biases],
"cost": str(self.cost.__name__) #保存一下损失函数
}
f=open(filename,"w")
json.dump(data,f)
f.close()
def vectorized_result(j):
"""Return a 10-dimensional unit vector with a 1.0 in the j'th position
and zeroes elsewhere. This is used to convert a digit (0...9)
into a corresponding desired output from the neural network.
"""
e = np.zeros((10, 1))
e[j] = 1.0
return e
def load(filename):
"""
加载模型
:param filename:
:return:
"""
f=open(filename,"r")
data=json.load(f)
f.close()
cost=getattr(sys.modules[__name__],data["cost"]) #找对象
net=Network(data["sizes"],cost=cost)
net.weights=[np.array(w) for w in data["weights"]]
net.biases=[np.array(b) for b in data["biases"]]
return net
def sigmoid(z):
return 1.0 / (1.0 + np.exp(-z))
def sigmoid_prime(z):
return sigmoid(z) * (1 - sigmoid(z))
if __name__ == '__main__':
from ProjectOne import mnist_loader
traning_data, validation_data, test_data = mnist_loader.load_data_wrapper()
net = Network([784, 60,10, 10])
net.SGD(traning_data, 30, 10, 0.5, 5.0,test_data=test_data)
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