#电池老化率测定的神经网络模型

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

path = 'SOH_Data.xlsx'

#训练集读取及归一化

xTrainData = pd.read_excel(path, sheetname = 0)

yTrainData = pd.read_excel(path, sheetname = 1)

n1 = np.shape(xTrainData)[1]

x_data = np.array(xTrainData).astype('float32')

for i in range(n1):

    x_data[:, i] = (x_data[:, i] - np.amin(x_data[:, i]))/(np.amax(x_data[:, i]) - np.amin(x_data[:, i]))

y_data = np.array(yTrainData).astype('float32')

y_data[:] = (y_data[:] - np.amin(y_data[:]))/(np.amax(y_data[:]) - np.amin(y_data[:]))

#测试集读取及归一化

xTestData = pd.read_excel(path, sheetname = 2)

yTestData = pd.read_excel(path, sheetname = 3)

xTest = np.array(xTestData).astype('float32')

n2 = np.shape(xTrainData)[1]

xTrain = np.array(xTrainData).astype('float32')

for i in range(n2):

    xTest[:, i] = (xTest[:, i] - np.amin(xTest[:, i]))/(np.amax(xTest[:, i]) - np.amin(xTest[:, i]))

yTest = np.array(yTestData).astype('float32')

yTest[:] = (yTest[:] - np.amin(yTest[:]))/(np.amax(yTest[:]) - np.amin(yTest[:]))

#参数概要

def variable_summaries(var):

    with tf.name_scope('summaries'):

        mean = tf.reduce_mean(var)#平均值

        tf.summary.scalar('mean', mean)

        with tf.name_scope('stddev'):

            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))

            tf.summary.scalar('stddev', stddev)#标准差

            tf.summary.scalar('max', tf.reduce_max(var))#最大值

            tf.summary.scalar('min', tf.reduce_min(var))#最小值

            tf.summary.histogram('histogram', var)#直方图

#5层神经网络，每层神经元个数

IHO = [12, 8, 5, 4, 1]

#命名空间

with tf.name_scope('input'):

    #定义两个placeholder

    x = tf.placeholder(tf.float32, [None, 12], name = 'xInput')

    y = tf.placeholder(tf.float32, [None, 1], name = 'y')

#神经元中间层

with tf.name_scope('layer'):

    with tf.name_scope('weights_L1'):

        Weight_L1 = tf.Variable(tf.random_normal([12, 8]), name = 'W1')

        variable_summaries(Weight_L1)

    with tf.name_scope('bias_L1'):

        biases_L1 = tf.Variable(tf.zeros([8]), name = 'b1')

        variable_summaries(biases_L1)

    with tf.name_scope('L_1'):

        Wx_plus_b_L1 = tf.matmul(x, Weight_L1) + biases_L1

        L1 = tf.nn.tanh(Wx_plus_b_L1)

    with tf.name_scope('weights_L2'):

        Weight_L2 = tf.Variable(tf.random_normal([8, 5]), name = 'W2')

        variable_summaries(Weight_L2)

    with tf.name_scope('bias_L2'):

        biases_L2 = tf.Variable(tf.zeros([5]), name = 'b2')

        variable_summaries(biases_L2)

    with tf.name_scope('L_2'):

        Wx_plus_b_L2 = tf.matmul(L1, Weight_L2) + biases_L2

        L2 = tf.nn.tanh(Wx_plus_b_L2)

    with tf.name_scope('weights_L3'):

        Weight_L3 = tf.Variable(tf.random_normal([5, 4]), name = 'W3')

        variable_summaries(Weight_L3)

    with tf.name_scope('bias_L3'):

        biases_L3 = tf.Variable(tf.zeros([4]), name = 'b3')

        variable_summaries(biases_L3)

    with tf.name_scope('L_3'):

        Wx_plus_b_L3 = tf.matmul(L2, Weight_L3) + biases_L3

        L3 = tf.nn.tanh(Wx_plus_b_L3)

#神经元输出层

    with tf.name_scope('weights_L4'):

        Weight_L4 = tf.Variable(tf.random_normal([4, 1]), name = 'W4')

        variable_summaries(Weight_L4)

    with tf.name_scope('bias_L4'):

        biases_L4 = tf.Variable(tf.zeros([1]), name = 'b4')

        variable_summaries(biases_L4)

    with tf.name_scope('prediction'):

        Wx_plus_b_L4 = tf.matmul(L3, Weight_L4) + biases_L4

        prediction = tf.nn.tanh(Wx_plus_b_L4)

#二次代价函数

with tf.name_scope('loss'):

    loss = tf.reduce_mean(tf.square(y - prediction), name = 'loss')

    tf.summary.scalar('loss', loss)

#使用梯度下降法训练

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

#合并所有summary

merged = tf.summary.merge_all()

with tf.Session() as sess:

    #变量初始化

    sess.run(tf.global_variables_initializer())

    writer = tf.summary.FileWriter('logs/', sess.graph)

    for i in range(10000):

        summary, _ = sess.run([merged, train_step], feed_dict = {x: x_data, y: y_data})

        writer.add_summary(summary, i)

        curr_loss = sess.run(loss, feed_dict = {x: x_data, y: y_data})

        if (i + 1)%100 == 0:

            print('第%d次迭代loss:'%(i + 1), curr_loss)

    #训练集预测集

    prediction_value = sess.run(prediction, feed_dict = {x: x_data})

    #测试集预测集

    prediction_value_test = sess.run(prediction, feed_dict = {x: xTest})

    test_loss = sess.run(loss, feed_dict = {x: xTest, y: yTest})

    print('测试误差：', test_loss)

    print(prediction_value_test)
#电池老化率测定的神经网络模型

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from numpy import *

import os

np.set_printoptions(suppress=True)

np.set_printoptions(threshold=np.NaN)

BATCH_SIZE = 256

with open("D:\\bs\\finall_data\\marry2.11\\train\\nc_all.txt","rb") as fa,open("D:\\bs\\finall_data\\marry2.11\\train\\calipso_all.txt","rb") as fb,open("D:\\bs\\finall_data\\marry2.11\\test\\nc_text.txt","rb") as fc,open("D:\\bs\\finall_data\\marry2.11\\test\\calipso_text.txt","rb") as fd:

    #训练集读取及归一化

    # xTrainData = pd.read_excel(path, sheetname = 0)

    # yTrainData = pd.read_excel(path, sheetname = 1)

    # # print(xTrainData)

    # # print(yTrainData)

    # n1 = np.shape(xTrainData)[1]

    # # print(n1)

    # x_data = np.array(xTrainData).astype('float32')

    # print(x_data)

    #

    # def Polyfit(x, y, degree):

    #     results = {}

    #     coeffs = np.polyfit(x, y, degree)

    #     # results['polynomial'] = coeffs.tolist()

    #

    #     # r-squared

    #     p = np.poly1d(coeffs)

    #     # print(p)

    #     # # fit values, and mean

    #     # yhat = p(x)  # or [p(z) for z in x]

    #     # ybar = np.sum(y) / len(y)  # or sum(y)/len(y)

    #     # ssreg = np.sum((yhat - ybar) ** 2)  # or sum([ (yihat - ybar)**2 for yihat in yhat])

    #     # sstot = np.sum((y - ybar) ** 2)  # or sum([ (yi - ybar)**2 for yi in y])

    #     # results['determination'] = ssreg / sstot  # 准确率

    #     return results

    list_x = []

    for i in fa.readlines():

        # print(str(i))

        x_data_1 = str(i).split(" ")[2:18]

        for x_data_12 in x_data_1:

            x_data_12=float(x_data_12)

            list_x.append(x_data_12)

    mat_x = mat(list_x)

    x_data = mat_x.reshape(-1, 16)

    for i in range(16):

        x_data[i, :] = (x_data[i, :] - np.amin(x_data[i, :])) / (np.amax(x_data[i, :]) - np.amin(x_data[i, :]))

    list_y=[]

    for v in fb.readlines():

        y_data_1 = str(v).split("   ")[2].split(" ")[0]

        y_data_1=1/(1+float(y_data_1))

        # print(y_data)

        list_y.append(float(y_data_1))

    # print(list_y)

    mat_y=mat(list_y)

    y_data = mat_y.reshape(-1, 1)

    # print(y_data)

    # y_data = np.array(yTrainData).astype('float32')

    # y_data[:] = (y_data[:] - np.amin(y_data[:]))/(np.amax(y_data[:]) - np.amin(y_data[:]))

    #

    #

    # # print(y_data[:])

    # z1 = Polyfit(x_data, y_data, 2)

    # plt.plot(x_data, y_data, 'o')

    # # plt.plot(x_data, np.polyval(z1, x_data))

    # plt.show()

    #

    # #测试集读取及归一化

    list_t_x = []

    for m in fc.readlines():

        x_data_t_1 = str(m).split(" ")[2:18]

        for x_data_t_12 in x_data_t_1:

            # print(x_data_t_12)

            x_data_t_12 = float(x_data_t_12)

            list_t_x.append(x_data_t_12)

    mat_t_x = mat(list_t_x)

    xTest = mat_t_x.reshape(-1, 16)

    # print(xTest.shape)                        #(1598,16)

    # xTestData = pd.read_excel(path, sheetname = 2)

    # yTestData = pd.read_excel(path, sheetname = 3)

    # xTest = np.array(xTestData).astype('float32')

    # n2 = np.shape(xTrainData)[1]

    # xTrain = np.array(xTrainData).astype('float32')

    for i in range(16):

        xTest[i, :] = (xTest[i, :] - np.amin(xTest[i, :]))/(np.amax(xTest[i, :]) - np.amin(xTest[i, :]))

    list_t_y = []

    for n in fd.readlines():

        y_data_t_1 = str(n).split("   ")[2].split(" ")[0]

        # print(y_data)

        y_data_t_1=1/(1+float(y_data_t_1))

        list_t_y.append(float(y_data_t_1))

    # print(list_y)

    mat_t_y = mat(list_t_y)

    yTest = mat_t_y.reshape(-1, 1)

    # print(yTest)

    # yTest = np.array(yTestData).astype('float32')

    # yTest[:] = (yTest[:] - np.amin(yTest[:]))/(np.amax(yTest[:]) - np.amin(yTest[:]))

    # print(np.amax(yTest[:]))

    # print(np.amax(y_Test[:]))

    #参数概要

    def variable_summaries(var):

        with tf.name_scope('summaries'):

            mean = tf.reduce_mean(var)#平均值

            tf.summary.scalar('mean', mean)

            with tf.name_scope('stddev'):

                stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))

                tf.summary.scalar('stddev', stddev)#标准差

                tf.summary.scalar('max', tf.reduce_max(var))#最大值

                tf.summary.scalar('min', tf.reduce_min(var))#最小值

                tf.summary.histogram('histogram', var)#直方图

    #5层神经网络，每层神经元个数

    IHO = [16, 8, 5, 4, 1]

    #命名空间

    with tf.name_scope('input'):

        #定义两个placeholder

        x = tf.placeholder(tf.float32, [None, 16], name = 'xInput')

        y = tf.placeholder(tf.float32, [None, 1], name = 'y')

    #神经元中间层

    with tf.name_scope('layer'):

        with tf.name_scope('weights_L1'):

            Weight_L1 = tf.Variable(tf.random_normal([16, 8]), name = 'W1')

            variable_summaries(Weight_L1)

        with tf.name_scope('bias_L1'):

            biases_L1 = tf.Variable(tf.zeros([8]), name = 'b1')

            variable_summaries(biases_L1)

        with tf.name_scope('L_1'):

            Wx_plus_b_L1 = tf.matmul(x, Weight_L1) + biases_L1

            L1 = tf.nn.sigmoid(Wx_plus_b_L1)

        with tf.name_scope('weights_L2'):

            Weight_L2 = tf.Variable(tf.random_normal([8, 5]), name = 'W2')

            variable_summaries(Weight_L2)

        with tf.name_scope('bias_L2'):

            biases_L2 = tf.Variable(tf.zeros([5]), name = 'b2')

            variable_summaries(biases_L2)

        with tf.name_scope('L_2'):

            Wx_plus_b_L2 = tf.matmul(L1, Weight_L2) + biases_L2

            L2 = tf.nn.sigmoid(Wx_plus_b_L2)

        with tf.name_scope('weights_L3'):

            Weight_L3 = tf.Variable(tf.random_normal([5, 4]), name = 'W3')

            variable_summaries(Weight_L3)

        with tf.name_scope('bias_L3'):

            biases_L3 = tf.Variable(tf.zeros([4]), name = 'b3')

            variable_summaries(biases_L3)

        with tf.name_scope('L_3'):

            Wx_plus_b_L3 = tf.matmul(L2, Weight_L3) + biases_L3

            L3 = tf.nn.sigmoid(Wx_plus_b_L3)

    #神经元输出层

        with tf.name_scope('weights_L4'):

            Weight_L4 = tf.Variable(tf.random_normal([4, 1]), name = 'W4')

            variable_summaries(Weight_L4)

        with tf.name_scope('bias_L4'):

            biases_L4 = tf.Variable(tf.zeros([1]), name = 'b4')

            variable_summaries(biases_L4)

        with tf.name_scope('prediction'):

            Wx_plus_b_L4 = tf.matmul(L3, Weight_L4) + biases_L4

            prediction = tf.nn.sigmoid(Wx_plus_b_L4)

    #二次代价函数

    with tf.name_scope('loss'):

        loss = tf.reduce_mean(tf.square(y - prediction), name = 'loss')

        tf.summary.scalar('loss', loss)

    #使用梯度下降法训练

    with tf.name_scope('train'):

        train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

    #合并所有summary

    merged = tf.summary.merge_all()

    with tf.Session() as sess:

        #变量初始化

        sess.run(tf.global_variables_initializer())

        writer = tf.summary.FileWriter('logs/', sess.graph)

        for i in range(1000):

            summary, _ = sess.run([merged, train_step], feed_dict = {x: x_data, y: y_data})

            writer.add_summary(summary, i)

            curr_loss = sess.run(loss, feed_dict = {x: x_data, y: y_data})

            if (i + 1)%100 == 0:

                print('第%d次迭代loss:'%(i + 1), curr_loss)

        #训练集预测集

        prediction_value = sess.run(prediction, feed_dict = {x: x_data})

        #测试集预测集

        prediction_value_test = sess.run(prediction, feed_dict = {x: xTest})

        test_loss = sess.run(loss, feed_dict = {x: xTest, y: yTest})

        print('测试误差：', test_loss)

        # print(len(prediction_value_test))           #text数据的个数

        # print(yTest)

        print(prediction_value_test)
from __future__ import print_function

import numpy as np

np.random.seed(1337)

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers.core import Dense, Dropout, Activation

from keras.optimizers import RMSprop

from keras.utils import np_utils

from keras.optimizers import SGD

batch_size = 128

nb_classes = 10

nb_epoch = 20

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)

X_test = X_test.reshape(10000, 784)

X_train = X_train.astype('float32')

X_test = X_test.astype('float32')

X_train /= 255

X_test /= 255

print(X_train.shape[0], 'train samples')

print(X_test.shape[0], 'test samples')

Y_train = np_utils.to_categorical(y_train, nb_classes)

Y_test = np_utils.to_categorical(y_test, nb_classes)

model1 = Sequential()

model1.add(Dense(256, activation='relu', input_dim=784))

model1.add(Dropout(0.2))

model1.add(Dense(256, activation='relu'))

model1.add(Dropout(0.2))

model1.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model1.compile(loss='categorical_crossentropy',

              optimizer=sgd,

              metrics=['accuracy'])

history1 = model1.fit(X_train, Y_train,

                    batch_size = batch_size,

                    epochs = nb_epoch,

                    verbose = 2,

                    validation_data = (X_test, Y_test))

model2 = Sequential()

model2.add(Dense(256, activation='relu', input_dim=784))

model2.add(Dense(256, activation='relu'))

model2.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model2.compile(loss='categorical_crossentropy',

              optimizer=sgd,

              metrics=['accuracy'])

history2 = model2.fit(X_train, Y_train,

                    batch_size = batch_size,

                    epochs = nb_epoch,

                    verbose = 2,

                    validation_data = (X_test, Y_test))

model3 = Sequential()

model3.add(Dense(256, activation='relu', input_dim=784))

model3.add(Dense(10, activation='softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

model3.compile(loss='categorical_crossentropy',

              optimizer=sgd,

              metrics=['accuracy'])

history3 = model3.fit(X_train, Y_train,

                    batch_size = batch_size,

                    epochs = nb_epoch,

                    verbose = 2,

                    validation_data = (X_test, Y_test))

import matplotlib.pyplot as plt

# list all data in history

print(history.history.keys())

# summarize history for accuracy

plt.plot(history1.history['acc'])

plt.plot(history1.history['val_acc'])

plt.title('model1 accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

plt.plot(history2.history['acc'])

plt.plot(history2.history['val_acc'])

plt.title('model2 accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

plt.plot(history3.history['acc'])

plt.plot(history3.history['val_acc'])

plt.title('model3 accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

plt.plot(history1.history['val_acc'])

plt.plot(history2.history['val_acc'])

plt.plot(history3.history['val_acc'])

plt.title('model accuracy')

plt.ylabel('accuracy')

plt.xlabel('epoch')

plt.legend(['model1', 'model2', 'model3'], loc='upper left')

plt.show()

# summarize history for loss

plt.plot(history1.history['loss'])

plt.plot(history1.history['val_loss'])

plt.title('model1 loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

plt.plot(history2.history['loss'])

plt.plot(history2.history['val_loss'])

plt.title('model2 loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

plt.plot(history3.history['loss'])

plt.plot(history3.history['val_loss'])

plt.title('model3 loss')

plt.ylabel('loss')

plt.xlabel('epoch')

plt.legend(['train', 'test'], loc='upper left')

plt.show()

bp代码的更多相关文章

  1. rcu-bp关键代码解读

    1      什么是TLS 原理在网上资料很多,这里不展开. 简单点说,动态申请的每线程变量.有一类比较熟悉的每线程变量是一个带__thread的每线程变量,两者的区别在于,TLS这类每线程变量是动态 ...

  2. 利用c++编写bp神经网络实现手写数字识别详解

    利用c++编写bp神经网络实现手写数字识别 写在前面 从大一入学开始,本菜菜就一直想学习一下神经网络算法,但由于时间和资源所限,一直未展开比较透彻的学习.大二下人工智能课的修习,给了我一个学习的契机. ...

  3. Notes on Convolutional Neural Networks

    这是Jake Bouvrie在2006年写的关于CNN的训练原理,虽然文献老了点,不过对理解经典CNN的训练过程还是很有帮助的.该作者是剑桥的研究认知科学的.翻译如有不对之处,还望告知,我好及时改正, ...

  4. Windows调试神器：WinDBG

    Q:WinDBG的Watch窗口中我想要查看长字符串,但是后面的内容都被省略为...了怎么办? A:如图,双击你要查看的内容,出现光标后,移动光标即可查看后面被省略的内容 Q:WinDBG如何给程序设 ...

  5. NPashaP的二分图源码部分

    源码链接:https://github.com/nelsonkuang/ant-admin/blob/master/src/utils/d3-viz.js 的二分图部分. 1.整体的级联结构 整个bp ...

  6. Kinect舒适区范围--UE4 的Blueprint测试范例

    本文章由cartzhang编写,转载请注明出处. 所有权利保留. 文章链接: http://blog.csdn.net/cartzhang/article/details/44748475 作者:ca ...

  7. Python内存管理机制-《源码解析》

    Python内存管理机制 Python 内存管理分层架构 /* An object allocator for Python. Here is an introduction to the layer ...

  8. 神经网络BP算法C和python代码

    上面只显示代码. 详BP原理和神经网络的相关知识,请参阅:神经网络和反向传播算法推导 首先是前向传播的计算: 输入: 首先为正整数 n.m.p.t,分别代表特征个数.训练样本个数.隐藏层神经元个数.输 ...

  9. BP神经网络算法推导及代码实现笔记zz

    一. 前言: 作为AI入门小白,参考了一些文章,想记点笔记加深印象,发出来是给有需求的童鞋学习共勉,大神轻拍! [毒鸡汤]:算法这东西,读完之后的状态多半是 --> “我是谁,我在哪?” 没事的 ...

随机推荐

  1. 解析如何实现微信唤醒默认浏览器下载app教程！

    前言 现如今微信对第三方app下载链接的拦截是越来越严格了,下载链接在微信中分享转发经常会被拦截,一旦被拦截用户就只能复制链接手动打开浏览器粘贴才能访问,如此给用户带来的体验台差,用户量无法有效地累积 ...

  2. C语言语法

    1.函数指针类型定义,然后指针结构体类型定义,最后的调用形式为aaa.bbb();(aaa为结构体变量)或者 aaa->bbb();(aaa为结构体指针),函数指针最好用结构体封装起来变成指针结 ...

  3. linux xfs的一次io异常导致的crash

    returned. [ blocked seconds.----这个默认是120,该环境上是被人为设置1200 [8556407.425653] "echo 0 > /proc/sys ...

  4. 1.express中间件的简介

    express中间件的意思 1, 中间件是一个模块.在js中,模块意味着函数,所以中间件是一个函数.那么这个函数长什么样子? 这还要从中间件的功能说起,它拦截http 服务器提供的请求和响应对象,执行 ...

  5. iOS10以下版本不支持swiper

    解决办法,降低swiper4*版本引用低版本如3.4.2可以兼容iOS8,

  6. windows php exec()不生效问题

    开始 $cmd = 'java -jar C:/xampp/htdocs/dev_env/replace_word_content.jar'; 报错java不是内部命令 换成 $cmd = 'C:\P ...

  7. C语言典型编程1

    关于C的一些小而精的编程,适合希望提升编程能力的初学者学习:关键编程也就几句,但思维可以迁移到其他编程语言.同一问题,算法多种 //阶乘运算(有多种编写方式,编程需要看懂,更要打出来)#include ...

  8. haproxy监控页面添加及参数简介（转）

    环境: [root@localhost 13:55:31 haproxy]# cat /etc/redhat-release CentOS release 6.8 (Final) [root@loca ...

  9. html入门第一天（知识总结）。

    一文本标签:----------双标签.<b>标签 <strong>标签,文本呈现粗体(Html5中建议用strong,strong语义更强)<i>标签 <e ...

  10. C/C++扩展Python的时候数据类型转换的对应：