amount_data = X.shape[0]
hidden_neurons = 64
output_neurons = 1
nn = NeuralNetwork(lr=lr,
epochs=epochs,
amount_data=amount_data,
hidden_neurons=hidden_neurons,
output_neurons=output_neurons)
X_train, X_validate, Y_train, Y_validate = nn.split_data(X, Y, 0.3)
loss_train, acc_train, loss_validate, acc_validate =\
nn.train(
X_train = X_train,
Y_train = Y_train,
X_validate = X_validate,
Y_validate = Y_validate)
graph = Graph(file="nn1",
amount_data=amount_data,
hidden_neurons=hidden_neurons,
output_neurons=output_neurons,
W1=nn.W1,
W2=nn.W2,
b1=nn.b1,
b2=nn.b2)
plt.figure()
plt.plot(loss_train, label="train")
plt.plot(loss_validate, label="validate")
class AppWindow(QMainWindow):
def __init__(self):
QMainWindow.__init__(self)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setWindowTitle(progname)
self.init_ui()
self.network = None
self.isDataLoaded = False
self.training_data = None
self.test_data = None
self.predict_y = None
self.networkSettings = None
def init_ui(self):
self.init_menus()
self.init_toolbar(QSize(40, 40))
mainWidget = QWidget(self)
mainbox = QHBoxLayout(mainWidget)
# self.setLayout(mainbox)
topFrame = QFrame(mainWidget)
topFrame.setFrameShape(QFrame.StyledPanel)
btmFrame = QFrame(mainWidget)
btmFrame.setFrameShape(QFrame.StyledPanel)
splitter = QSplitter(Qt.Vertical)
splitter.addWidget(topFrame)
splitter.addWidget(btmFrame)
# logText 30%, Plot 70%
splitter.setStretchFactor(0, 4)
splitter.setStretchFactor(1, 1)
mainbox.addWidget(splitter)
self.init_plot_area(topFrame)
vboxLog = QVBoxLayout(btmFrame)
self.logTextEdit = QPlainTextEdit("")
self.logTextEdit.appendHtml("""<font size='4'>欢迎使用{}</font><p>""".format(progname))
self.logTextEdit.setReadOnly(True)
vboxLog.addWidget(self.logTextEdit)
mainWidget.setFocus()
self.setCentralWidget(mainWidget)
self.statusBar().showMessage("Ready")
self.setWindowIcon(QIcon('res/load_network.png'))
self.show()
def init_plot_area(self, parent):
hboxPlot = QHBoxLayout(parent)
# errplot_labels = {'t': u'供水温度预测误差', 'x': u'时间', 'y': u'误差百分比(%)'}
# predplot_labels = {'t': u'供水温度预测值', 'x': u'时间', 'y': u'供水温度(℃)'}
errplot_labels = {'t': 'Prediction Errors', 'x': 'Time', 'y': 'Error Percent(%)'}
predplot_labels = {'t': 'Predicted Temperature', 'x': 'Time', 'y': 'Temperature(℃)'}
self.errPlot = StaticMplotCanvas(parent, labels=errplot_labels)
self.predPlot = StaticMplotCanvas(parent, labels=predplot_labels)
hboxPlot.addWidget(self.errPlot)
hboxPlot.addWidget(self.predPlot)
def init_toolbar(self, iconSize):
# data file
self.loadDataAct = QAction(QIcon('res/load_data.png'), 'Import Training Data', self)
self.loadDataAct.setShortcut('Ctrl+L')
self.loadDataAct.triggered.connect(self.loadTrainingDataFile)
self.saveDataAct = QAction(QIcon('res/save_data.png'), 'Export Predicted Data', self)
self.saveDataAct.setShortcut('Ctrl+E')
self.saveDataAct.triggered.connect(self.savePredictDataToFile)
self.saveDataAct.setEnabled(False)
# network
self.loadNetworkAct = QAction(QIcon('res/load_network.png'), 'Load Trained Network', self)
self.loadNetworkAct.setShortcut('Ctrl+N')
self.loadNetworkAct.triggered.connect(self.restoreNeuralNetwork)
self.loadNetworkAct.setEnabled(False)
self.saveNetworkAct = QAction(QIcon('res/save_network.png'), 'Save Trained Network', self)
self.saveNetworkAct.setShortcut('Ctrl+S')
self.saveNetworkAct.triggered.connect(self.saveNeuralNetwork)
self.saveNetworkAct.setEnabled(False)
# run & predict
self.runTrainingAct = QAction(QIcon('res/train_network.png'), 'Train Network', self)
self.runTrainingAct.setShortcut('Ctrl+R')
self.runTrainingAct.triggered.connect(self.runNetworkTraining)
self.runTrainingAct.setEnabled(False)
self.predictDatakAct = QAction(QIcon('res/predict.png'), 'Predict Data', self)
self.predictDatakAct.setShortcut('Ctrl+P')
self.predictDatakAct.triggered.connect(self.predictData)
self.predictDatakAct.setEnabled(False)
# clear
self.resetAct = QAction(QIcon('res/clear.png'), 'Clear data and network', self)
self.resetAct.setEnabled(False)
self.resetAct.triggered.connect(self.clearDataAndNetwork)
dataToolbar = self.addToolBar('Data ToolBar')
dataToolbar.addAction(self.loadDataAct)
dataToolbar.addAction(self.saveDataAct)
dataToolbar.setIconSize(iconSize)
networkToolbar = self.addToolBar('Network ToolBar')
networkToolbar.addAction(self.loadNetworkAct)
networkToolbar.addAction(self.runTrainingAct)
networkToolbar.addAction(self.predictDatakAct)
networkToolbar.addAction(self.saveNetworkAct)
networkToolbar.setIconSize(iconSize)
resetToolbar = self.addToolBar('Reset ToolBar')
resetToolbar.addAction(self.resetAct)
resetToolbar.setIconSize(iconSize)
def init_menus(self):
# File
settingAct = QAction(QIcon('res/settings.png'), '设置', self)
settingAct.triggered.connect(self.showSettingDialog)
exitAct = QAction(QIcon('exit.png'), '退出', self)
exitAct.setShortcut('Ctrl+Q')
exitAct.triggered.connect(self.fileQuit)
menubar = self.menuBar()
fileMenu = menubar.addMenu('&File')
fileMenu.addAction(settingAct)
fileMenu.addSeparator()
fileMenu.addAction(exitAct)
# Help
helpMenu = QMenu('&Help', self)
self.menuBar().addMenu(helpMenu)
helpMenu.addAction('使用说明', self.usage)
helpMenu.addAction('关于', self.about)
@pyqtSlot()
def loadTrainingDataFile(self):
fname, _ = QFileDialog.getOpenFileName(self, 'Open Training Data', '.', 'Data File(*.csv)')
if not fname:
# self.logStatus("加载数据文件{}失败!".format(fname), 'red', 'E')
return
dl = DataHandler(fname)
self.training_data, self.test_data = dl.load(cb=self.logStatus)
self.isDataLoaded = True
self.runTrainingAct.setEnabled(True)
self.loadNetworkAct.setEnabled(True)
self.resetAct.setEnabled(True)
self.logStatus("加载数据文件{}成功".format(fname))
self.logStatus('请训练神经网络或者加载已经训练的神经网络模型', '#FF8C00', 'T')
@pyqtSlot()
def savePredictDataToFile(self):
if self.predict_y is None:
# self.logStatus('没有未保存的预测数据, 请先进行数据预测!', 'red', 'E')
return
fname, _ = QFileDialog.getSaveFileName(self, 'Save Predicted Data', '.', 'Data File(*.csv)')
if not fname:
self.logStatus('保存预测数据文件{}失败!'.format(fname), 'red', 'E')
return
test_x, _ = DataHandler.split_xy(self.test_data)
status = DataHandler.save(np.concatenate((test_x, self.predict_y), axis=0), fname)
if status:
self.logStatus('保存预测数据文件{}成功'.format(fname))
@pyqtSlot()
def restoreNeuralNetwork(self):
fname, _ = QFileDialog.getOpenFileName(self, 'Open Network File', '.', 'Network File(*.nf)')
if not fname:
# self.logStatus('打开神经网络文件{}失败!'.format(fname), 'red', 'E')
return
# clear previous plots
self.clearPlots()
training_x, training_y = DataHandler.split_xy(self.training_data)
self.network = NeuralNetwork(training_x, training_y)
try:
self.network.load(fname)
except ShapeError as e:
self.logStatus('加载神经网络文件{}失败!'.format(fname), 'red', 'E')
QMessageBox.warning(self, '警告', '加载神经网络文件失败, 请检查文件格式是否正确!')
return
self.logStatus('神经网络文件{}加载成功'.format(fname))
self.predictDatakAct.setEnabled(True)
self.logStatus('请执行数据预测', '#FF8C00', 'T')
@pyqtSlot()
def saveNeuralNetwork(self):
fname, _ = QFileDialog.getSaveFileName(self, 'Save Network File', '.', 'Network File(*.nf)')
if not fname:
# self.logStatus('保存神经网络文件{}失败!'.format(fname), 'red', 'E')
return
self.network.dump(fname)
self.logStatus('保存神经网络文件{}成功'.format(fname))
@pyqtSlot()
def runNetworkTraining(self):
if self.network is not None:
ans = QMessageBox.question(self, '警告',
'系统中已存在训练好的神经网络，请问您需要重新训练神经网络吗?')
if ans == QMessageBox.No:
return
# clear previous plots
self.clearPlots()
self.logStatus("正在初始化神经网络结构...", 'blue', 'I')
training_x, training_y = DataHandler.split_xy(self.training_data)
# retrieve settings
epoch0 = 2000
tol0 = 0.1
retry_num = 3
h0size = 4
h1size = 4
if self.networkSettings:
epoch0 = self.networkSettings['epoch']
tol0 = self.networkSettings['tol']
retry_num = self.networkSettings['retry']
h0size = self.networkSettings['h0size']
h1size = self.networkSettings['h1size']
self.logStatus("神经网络信息：layer1={}, layer2={}, epoch0={}, retry={}, tol0={}"
.format(h0size, h1size, epoch0, retry_num, tol0), 'blue', 'I')
net = [(training_x.shape[0], ''), (h0size, 'sigmoid'), (h1size, 'sigmoid'), (1, 'feedthrough')]
try:
self.network = NeuralNetwork(training_x, training_y, sizes=net)
except ShapeError as e:
self.logStatus('初始化神经网络结构失败!')
QMessageBox.warning(self, '警告', '初始化神经网络结构失败, 请重试!')
return
# training
mu0 = 0.1
beta = 10
retry = 0
self.logStatus("使用LM算法开始训练神经网络...", 'blue', 'I')
while retry < retry_num:
residual, mu, citer, msg = \
self.network.train(retry=retry, epoch=epoch0, mu0=mu0,
beta=beta, tol=tol0, cb=self.logStatus)
if residual is None:
if retry == (retry_num - 1):
self.logStatus("训练失败!".format(msg), 'red', 'E')
return
else:
self.logStatus("训练失败:{}, 重试中...".format(msg), '#FFA07A', 'I')
self.network.randomize_wb()
# continue
elif residual > tol0:
if retry == (retry_num - 1):
self.logStatus("训练失败!".format(msg), 'red', 'E')
return
else:
self.logStatus("训练失败: 运算未能收敛, 重试中...", '#FFA07A', 'I')
self.network.randomize_wb()
# continue
else:
self.logStatus("神经网络训练完成, 迭代次数={1}, 最终残差={0}"
.format(residual, citer+retry*epoch0), 'blue', 'I')
break
retry += 1
self.predictDatakAct.setEnabled(True)
self.saveNetworkAct.setEnabled(True)
self.logStatus('请执行数据预测', '#FF8C00', 'T')
@pyqtSlot()
def predictData(self):
# don't forget to clear previous plots
self.clearPlots()
self.logStatus("开始进行数据预测...", 'blue', 'I')
test_x, test_y = DataHandler.split_xy(self.test_data, False)
self.predict_y = self.network.predict(test_x)
self.logStatus("开始计算误差...", 'blue', 'I')
self.predPlot.update_plot(x=np.arange(len(self.predict_y[0])),
y=self.predict_y[0])
# error plot
err_percent = (self.predict_y - test_y) * 100.0 / test_y
self.errPlot.error_plot(x=np.arange(len(err_percent[0])),
y=err_percent[0])
abs_err = np.abs(err_percent)
self.logStatus("数据预测完成, 最大绝对值误差={}%, 平均绝对值误差={}%"
.format(abs_err.max(), abs_err.mean()), 'blue', 'I')
self.saveDataAct.setEnabled(True)
@pyqtSlot()
def clearDataAndNetwork(self):
ans = QMessageBox.question(self, '警告',
'您希望删除所有的数据和已经训练好的神经网络吗?')
if ans == QMessageBox.No:
return
# reset
self.network = None
self.isDataLoaded = False
self.training_data = None
self.test_data = None
# update UI
self.loadNetworkAct.setEnabled(False)
self.runTrainingAct.setEnabled(False)
self.saveDataAct.setEnabled(False)
self.saveNetworkAct.setEnabled(False)
self.predictDatakAct.setEnabled(False)
self.resetAct.setEnabled(False)
self.logTextEdit.clear()
# clear plots
self.clearPlots()
@pyqtSlot(dict)
def updateSettings(self, settings):
self.networkSettings = settings
@pyqtSlot()
def showSettingDialog(self):
dlg = SettingDialog(self, self.networkSettings)
dlg.show()
@pyqtSlot()
def fileQuit(self):
self.close()
@pyqtSlot()
def about(self):
QMessageBox.about(self, "关于",
"""<b>{}</b><p>版本号: {}""".format(progname, progversion)
)
@pyqtSlot()
def usage(self):
dlg = UsageDialog(self)
dlg.show()
def closeEvent(self, ce):
self.fileQuit()
def logStatus(self, text, color='green', tag='S'):
self.logTextEdit.appendHtml("<p><font color='{0}'><b>[{1}]:</b> {2}"
"</font></p>".format(color, tag, text))
# force UI update. An alternative is to use QThread
QApplication.processEvents()
def clearPlots(self):
self.predPlot.clear()
self.errPlot.clear()
errplot_labels = {'t': 'Prediction Errors', 'x': 'Time', 'y': 'Error Percent(%)'}
predplot_labels = {'t': 'Predicted Temperature', 'x': 'Time', 'y': 'Temperature(℃)'}
self.errPlot.initial_figure(errplot_labels)
self.predPlot.initial_figure(predplot_labels)