电机振动故障检测tensorflow神经网络

# -*- coding: utf-8 -*- """ Created on Mon Apr 2 11:46:50 2018 @author: FDS """ #电机故障数据检测（只针对一圈） #一组数据一组数据进行训练 # # import tensorflow as tf import numpy as np import scipy.io as sio import random #输入数据部分 # data_inter=sio.loadmat('C:/Users/FDS/Desktop/data2018/data_prepare_inter.mat')['data_E']#故障数据 data_normal=sio.loadmat('C:/Users/FDS/Desktop/data2018/data_prepare_normal.mat')['data_Enormal']#正常总数据 a=np.array(data_inter[0:100]) b=np.array(data_normal[0:450]) train_data=np.concatenate((a,b),axis=0) c=np.array(data_inter[100:]) d=np.array(data_normal[450:]) test_data=np.concatenate((c,d),axis=0) y_train_data=np.concatenate((np.ones(a.shape[0]),np.zeros(b.shape[0])),axis=0)#训练标签 y_train_data.shape=(y_train_data.shape[0],1) y_test_data=np.concatenate((np.ones(c.shape[0]),np.zeros(d.shape[0])),axis=0)#测试标签 y_test_data.shape=(y_test_data.shape[0],1) e=np.zeros((550,2)) #全部训练数据的标签 for i in range(0,100): e[i][1]=1 for i in range(100,550): e[i][0]=1 e_test=np.zeros((57,2)) for i in range(0,22): e_test[i][1]=1 for i in range(22,57): e_test[i][0]=1 #模型搭建部分 x_data=tf.placeholder("float32",[None,8]) weight=tf.Variable(tf.ones([8,2])) bias=tf.Variable(tf.ones([2])) y_model=tf.nn.softmax(tf.matmul(x_data,weight)+bias) y_data=tf.placeholder("float32",[None,2]) loss=tf.reduce_sum(tf.pow((y_model-y_data),2)) train_step=tf.train.GradientDescentOptimizer(0.01).minimize(loss) init=tf.global_variables_initializer() sess=tf.Session() sess.run(init) temp_x=np.zeros((50,8))#随机取50组数据进行训练 temp_y=np.zeros((50,2))#随机50组数据的标签值 #训练 for i in range(1000): rand=random.randint(0,499) #产生随机数 for k in range(0,50): temp_x[k]=train_data[rand+k]#训练值 temp_y[k]=e[rand+k]#标签值 sess.run(train_step,feed_dict={x_data:temp_x,y_data:temp_y}) if i%5==0: correct_prediction=tf.equal(tf.argmax(y_model,1),tf.argmax(y_data,1)) accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float")) print(sess.run(accuracy,feed_dict={x_data:test_data,y_data: e_test})) print(sess.run(weight)) #输出权重 print(sess.run(bias)) #输入偏离值