def __init__(self, t='fig'):
self.mydata = ECTdata('E:\deeplearning\ECT\数据生成\data', 5000)
self.mydata.initsca(t=t)
print("data init success!")
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True #允许显存增长
set_session(tensorflow.Session(config=config))
optimizer = Adam()
self.discriminator = self.build_discriminator()
self.discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
self.generator = self.build_generator()
self.generator.compile(loss='mean_squared_error', optimizer=optimizer)
# The generator takes noise as input and generates imgs
z = Input(shape=(self.mydata.capsize, ))
img = self.generator(z)
self.discriminator.trainable = False
# The discriminator takes generated images as input and determines validity
validity = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
self.combined = Model(z, validity)
self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)
def __init__(self):
if 'session' in locals() and tensorflow.session is not None:
print('Close interactive session')
tensorflow.session.close()
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True #允许显存增长
tensorflow.keras.backend.set_session(tensorflow.Session(config=config))
print('GPU memory is allowed to growth.')
tensorflow.keras.backend.clear_session()
self.path="E:\deeplearning\程序\ECT\ECT\真实实验";
empty1=scipy.io.loadmat(self.path+'\empty.mat')
efull1=scipy.io.loadmat(self.path+'\efull.mat')
lmc1=scipy.io.loadmat(self.path+'\lmc.mat')
self.fltempty=np.asarray(empty1['Cap']) #空管标定数据
self.fltfull=np.asarray(efull1['Cap']) #满管标定数据
self.lmc =np.asarray(lmc1['S']) #灵敏场
with open(self.path+'\calibration.txt','r') as f:
lines=f.readlines()
strempty=lines[1].split()
intempty=list(map(int,strempty))
self.intempty=np.asarray(intempty)
strfull=lines[3].split()
intfull=list(map(int,strfull))
self.intfull=np.asarray(intfull)
self.intdelt=self.intfull-self.intempty
self.fltdelt=self.fltfull-self.fltempty
index=np.argmax(self.intdelt)
self.k=self.intdelt[index]/self.fltdelt[0][index]
print (self.k)
self.draw=ECTdata('E:\deeplearning\ECT\数据生成\data',size=200)
self.draw.initsca(t='tri')
self.dn=DN()
self.land=Land()
def __init__(self,t='fig'):
self.mydata=ECTdata('E:\deeplearning\ECT\数据生成\data',5000)
self.mydata.initsca(t=t)
print("data init success!")
#关闭上次未完全关闭的会话
if 'session' in locals() and tensorflow.session is not None:
print('Close interactive session')
tensorflow.session.close()
config = tensorflow.ConfigProto()
config.gpu_options.allow_growth = True #允许显存增长
set_session(tensorflow.Session(config=config))
print('GPU memory is allowed to growth.')
def __init__(self):
# if 'session' in locals() and tensorflow.session is not None:
# print('Close interactive session')
# tensorflow.session.close()
# config = tensorflow.ConfigProto()
# config.gpu_options.allow_growth = True #允许显存增长
# tensorflow.keras.backend.set_session(tensorflow.Session(config=config))
# print('GPU memory is allowed to growth.')
# tensorflow.keras.backend.clear_session()
self.path = "E:\deeplearning\程序\ECT\ECT\真实实验"
empty1 = scipy.io.loadmat(self.path + '\empty.mat')
efull1 = scipy.io.loadmat(self.path + '\efull.mat')
lmc1 = scipy.io.loadmat(self.path + '\lmc.mat')
self.fltempty = np.asarray(empty1['Cap']) #空管标定数据
self.fltfull = np.asarray(efull1['Cap']) #满管标定数据
self.lmc = np.asarray(lmc1['S']) #灵敏场
self.path = "E:\会议项目\第九届流态化会议\处理数据\加料高度\\v15l168\\2016-12-13_13-04-28.203"
with open(self.path + '\calibration.txt', 'r') as f:
lines = f.readlines()
strempty = lines[1].split()
intempty = list(map(int, strempty))
self.intempty = np.asarray(intempty)
strfull = lines[3].split()
intfull = list(map(int, strfull))
self.intfull = np.asarray(intfull)
with open("E:\会议项目\第九届流态化会议\处理数据\\app\\option\\calibration\\efull.txt",
'r') as f:
l = f.readline()
strfull = l.split()
fltfull = list(map(float, strfull))
self.oldfltfull = np.asarray(fltfull)
with open("E:\会议项目\第九届流态化会议\处理数据\\app\\option\\calibration\\empty.txt",
'r') as f:
l = f.readline()
strempty = l.split()
fltempty = list(map(float, strempty))
self.oldfltempty = np.asarray(fltempty)
self.intdelt = self.intfull - self.intempty
self.fltdelt = self.fltfull - self.fltempty
self.oldfltdelt = self.oldfltfull - self.oldfltempty
index = np.argmax(self.intdelt)
self.k = self.intdelt[index] / self.fltdelt[0][index]
self.k2 = self.intdelt[index] / self.oldfltdelt[index]
print(self.k)
print(self.k2)
self.draw = ECTdata('E:\deeplearning\ECT\数据生成\data', size=200)
self.draw.initsca(t='tri')
self.dn = DN()
self.land = Land()
def __init__(self, t='tri', sample=20000):
self.mydata = ECTdata('E:\deeplearning\ECT\数据生成\data', sample)
self.mydata.initsca(t=t)
self.sample = sample
print("data init success!")
#print(self.mydata.lmc.shape) 28,702
self.Srow = np.sum(self.mydata.lmc, axis=0) #702
self.Scol = np.sum(self.mydata.lmc, axis=1) #28
self.SLBP = np.zeros([self.mydata.capsize, self.mydata.imgsize])
self.SLAND = np.zeros([self.mydata.capsize, self.mydata.imgsize])
for i in range(self.mydata.imgsize):
for j in range(self.mydata.capsize):
self.SLBP[j][i] = self.mydata.lmc[j][i] / self.Srow[i]
self.Srow2 = np.sum(self.SLBP, axis=1)
for i in range(self.mydata.capsize):
for j in range(self.mydata.imgsize):
self.SLAND[i][j] = self.SLBP[i][j] / self.Srow2[i]
self.yLBP = np.zeros([4, self.mydata.imgsize])
self.yLAND = np.zeros([4, self.mydata.imgsize])
def __init__(self, t='fig'):
self.mydata = ECTdata('E:\deeplearning\ECT\数据生成\data', 20000)
self.mydata.initsca(t=t)
print("data init success!")
from loaddata import ECTdata
import tensorflow
from tensorflow.keras.models import Sequential,Model
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras import Input
import matplotlib.pyplot as mp
#双向训练
mydata=ECTdata('E:\deeplearning\ECT\数据生成\data',10000)
mydata.initsca()
print("data init success!")
input=Input(shape=(mydata.imgsize,))
encoded = Dense(140, activation='relu')(input)
midnet=Dense(mydata.capsize, activation='relu')
mid = midnet(encoded)
midoutput=midnet.output
decoded = Dense(140, activation='relu')(mid)
output=Dense(mydata.imgsize, activation='relu')(decoded)
model=Model(inputs=input,outputs=[midoutput,output])
model1=Model(inputs=input,outputs=output)
model.compile(optimizer='adadelta', loss='mean_squared_error')
model.fit(mydata.imgtrain,[mydata.captrain,mydata.imgtrain],epochs=500,shuffle=True)
p=model.evaluate(mydata.imgtest,[mydata.captest,mydata.imgtest])
print("整个网络的损失为%f %f"%(p[0],p[1]))
mid2=Input(shape=(mydata.capsize,))
decoded1=model.layers[-2](mid2)
from loaddata import ECTdata
import matplotlib.pyplot as mp
import numpy as np
mydata = ECTdata('E:\deeplearning\ECT\数据生成\datatest')
mydata.initsca(t='tri')
for i in range(2):
mydata.drawsca(mydata.images[i], t='tri')
mp.show()