#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()

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    准备弄个NAS,但还没想好直接买现成,还是自己组装一台,先在虚拟机上体验下OpenMediaVault4和黑群晖.主系统是ubuntu,但刚买的时候这笔记本是装windows的,除了ubuntu的系统 ...

  2. CSS预处器的了解

    到目前为止,在众多优秀的CSS预处理器语言中就属Sass.LESS和Stylus最优秀,讨论的也多,对比的也多. 1.Sass背景介绍 Sass是对CSS(层叠样式表)的语法的一种扩充,诞生于2007 ...

  3. 布局inline-block问题

    当在一行中需要展示多个拥有块级属性的标签元素时,通常选择display:inline-block; 优点:不用设置浮动或定位,浮动脱离文档流还需要清除浮动,定位降低扩展性. 问题: 1.标签元素之间会 ...

  4. webconfig标签收集

    在web项目启动时,很多因为vs没有报错,而页面跑不出来的情况,无法调试找到错误, 可以在webconfig中添加一个标签,运行项目就可以在页面显示错误 <customErrors mode=& ...

  5. Linux - 其他命令

    其他命令 目标 查找文件 find 软链接 ln 打包和压缩 tar 软件安装 apt-get 01. 查找文件 find 命令功能非常强大,通常用来在 特定的目录下 搜索 符合条件的文件 序号 命令 ...

  6. gb2312,gbk,utf8的区别

    GB2312编码大约包含6000多汉字(不包括特殊字符),编码范围为第一位b0-f7,第二位编码范围为a1-fe(第一位为cf时,第二位为a1-d3),计算一下汉字个数为6762个汉字.当然还有其他的 ...

  7. 使用IDEA工具编写Python自动化脚本遇到的小问题

    1.无法编写代码:因为安装了IdeaVim,所以需要按i进入编辑模式才能输入 2.位于同一目录下的py文件,其中一个文件需要引入另一个文件from 模块名 import * 无法找到模块名:点击这两个 ...

  8. mysql 文件

    慢查询日志 log_query_time 查询时间超过这个值则会出现在慢查询日志中,默认值是10 log_slow_queries  是否开启慢查询 log_queries_not_using_ind ...

  9. CentOS 7 安装与卸载MySQL 5.7

    先介绍卸载 防止重装 yum方式 查看yum是否安装过mysql yum list installed mysql* 如或显示了列表,说明系统中有MySQL yum卸载 根据列表上的名字 yum re ...

  10. Find out where to contain the smartforms

    Go to table E071 and give smarforms name and it will give the transport req for that. Run SE03, choo ...