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UI.py
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UI.py
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# encoding: utf-8
# ******************************************************
# requirement:python3
# Author: chyb
# Last modified: 20181023 14:00
# Email:chyb3.14@gmail.com
# Filename:UI.py
# Description:UI create by pyqt5 for network training and testing
# ******************************************************
import sys
from PyQt5.QtWidgets import (QTextEdit,QGridLayout,QGroupBox,QHBoxLayout,QLabel,QFileDialog,QWidget, QPushButton, QLineEdit, QInputDialog, QApplication)
from PyQt5.QtGui import QImage,QPixmap
import numpy as np
import math
from input_data import get_train_data,get_test_data
import cv2
class BPNetwork(object):
def __init__(self,feature_length,input_numbers,hiden_numbers,output_kinds,base_lr1,base_lr2,base_lr3):
self.feature_length=feature_length
self.input_numbers=input_numbers
self.hiden_numbers=hiden_numbers
self.output_kinds=output_kinds
self.hiden_lr=base_lr1
self.out_lr=base_lr2
self.input_lr=base_lr3
self.w1=0.5*(np.random.random((self.feature_length, self.input_numbers))-0.5)
self.w2=0.5*(np.random.random((self.input_numbers, self.hiden_numbers))-0.5)
self.w3=0.5*(np.random.random((self.hiden_numbers, self.output_kinds))-0.5)
self.input_offset=np.zeros(self.input_numbers)
self.hiden_offset=np.zeros(self.hiden_numbers)
self.output_offset=np.zeros(self.output_kinds)
def sigmoid(self,x):
output=[]
for i in x:
output.append(1/(1+math.exp(-i)))
output=np.array(output)
return output
def forward(self,input_feature):
self.input_feature=input_feature
self.input_val=np.dot(self.input_feature,self.w1)+self.input_offset
self.input_out=self.sigmoid(self.input_val)
self.hiden_val=np.dot(self.input_out,self.w2)+self.hiden_offset
self.hiden_out=self.sigmoid(self.hiden_val)
self.out_val=np.dot(self.hiden_val,self.w3)+self.output_offset
self.out_out=self.sigmoid(self.out_val)
def backward(self,label):
self.erro=label-self.out_out
delta_out=self.erro*self.out_out*(1-self.out_out)
delta_hiden=self.hiden_out*(1-self.hiden_out)*np.dot(self.w3,delta_out)
delta_input=self.input_out*(1-self.input_out)*np.dot(self.w2,delta_hiden)
for i in range(0,self.output_kinds):
self.w3[:,i]+=self.hiden_lr*delta_out[i]*self.hiden_out
for i in range(0,self.hiden_numbers):
self.w2[:,i]+=self.out_lr*delta_hiden[i]*self.input_out
for i in range(0,self.input_numbers):
self.w1[:,i]+=self.input_lr*delta_input[i]*self.input_feature
self.output_offset+=self.out_lr*delta_out
self.hiden_offset+=self.hiden_lr*delta_hiden
self.input_offset+=self.input_lr*delta_input
def reduce_lr(self,lr1,lr2,lr3):
self.hiden_lr=lr1
self.out_lr=lr2
self.input_lr=lr3
class BPUI(QWidget):
def __init__(self):
QWidget.__init__(self)
self.initUI()
def initUI(self):
self.gridGroupBox=QGroupBox("GridLayout")
layout=QGridLayout()
layout.setSpacing(10)
self.btn=QPushButton("Import",self)
self.btn.clicked.connect(self.showDialog)
self.predict_btn=QPushButton("Predict",self)
self.predict_btn.clicked.connect(self.predict)
self.train_btn=QPushButton("Train",self)
self.train_btn.clicked.connect(self.train)
self.test_btn=QPushButton("Test",self)
self.test_btn.clicked.connect(self.test)
self.imageView=QLabel()
layout.addWidget(self.imageView,5,1,2,2)
self.imageName=QLabel("Image:")
self.imageLineEdit=QLineEdit()
self.testName=QLabel("Accuracy:")
self.testLineEdit=QLineEdit()
self.numberLabel = QLabel("number:")
self.numberLineEdit = QLineEdit()
self.iterName=QLabel("Iter:")
self.iterLineEdit=QLineEdit("See command window")
self.lossName=QLabel("Loss:")
self.lossLineEdit=QLineEdit("See command window")
layout.addWidget(self.train_btn,0,0)
layout.addWidget(self.iterName,0,1)
layout.addWidget(self.iterLineEdit,0,2)
layout.addWidget(self.lossName,1,1)
layout.addWidget(self.lossLineEdit,1,2)
layout.addWidget(self.test_btn,2,0)
layout.addWidget(self.testName,2,1)
layout.addWidget(self.testLineEdit,2,2)
layout.addWidget(self.btn,3,0)
layout.addWidget(self.imageName,3,1)
layout.addWidget(self.imageLineEdit,3,2)
layout.addWidget(self.predict_btn,4,0)
layout.addWidget(self.numberLabel,4,1)
layout.addWidget(self.numberLineEdit,4,2)
self.setLayout(layout)
self.setGeometry(200,200,400,400)
self.setWindowTitle("Predict")
self.show()
def showDialog(self):
self.filename, _ = QFileDialog.getOpenFileName(self, 'Open file', './')
if len(self.filename):
self.image = QImage(self.filename)
self.imageView.setPixmap(QPixmap.fromImage(self.image))
self.imageLineEdit.setText(self.filename)
def train(self):
self.iterLineEdit.setText("xcz")
sample, label= get_train_data()
sample = np.array(sample,dtype='float')
sample=(sample)/256.0
samp_num = len(sample)
inp_num = len(sample[0])
out_num = 10
hid_num = 15
loss=0
self.BP=BPNetwork(inp_num,20,hid_num,out_num,0.1,0.1,0.1)
for step in range(0,3):
if step==1:
self.BP.reduce_lr(0.01,0.01,0.01)
elif step==2:
self.BP.reduce_lr(0.001,0.001,0.001)
for i in range(0,samp_num):
train_label = np.zeros(out_num)
train_label[label[i]] = 1
self.BP.forward(sample[i])
self.BP.backward(np.array(train_label))
if i%10000==0:
print(str(i+60000*step))
error=self.BP.erro
loss=0
for j in range(0,len(error)):
loss=loss+abs(error[j])
print(loss)
self.iterLineEdit.setText("ending")
self.lossLineEdit.setText("ending")
def test(self):
correct=0
test_s, test_l = get_test_data()
test_s = np.array(test_s,dtype='float')
test_s =(test_s)/256.0
for i in range(0,len(test_s)):
self.BP.forward(test_s[i])
result=self.BP.out_out
if np.argmax(result) == test_l[i]:
correct+=1
accuracy=float(correct)/float(len(test_s))
self.testLineEdit.setText(str(accuracy))
def predict(self):
im=cv2.imread(self.filename,0)
im=im.reshape((1,784))
L=[]
for i in range(0,784):
L.append(im[0,i])
L=np.array(L)
L=L/256.0
self.BP.forward(L)
result=self.BP.out_out
a=np.argmax(result)
self.numberLineEdit.setText(str(a))
if __name__=="__main__":
app=QApplication(sys.argv)
ex=BPUI()
sys.exit(app.exec_())