def __init__(self,tty,serialSpeed,outputPath,upthreshold=5,downthreshold=5,isThresholdRelativeToMean=True,preLength=10,postLength=120,mode='threshold'):
self.ser=serial.Serial(tty,serialSpeed)
self.isThresholdRelativeToMean=isThresholdRelativeToMean
self.upthreshold=upthreshold
self.outputPath=outputPath
self.downthreshold=downthreshold
self.__isReading=False
self.__inputLisners=[]
self.maxPlotLen=500
self.plotY=deque([0.0]*self.maxPlotLen)
self.plotX=np.arange(0,self.maxPlotLen)
self.figure=plt.figure()
self.minY=200
self.maxY=300
self.plt= plt.axes(xlim=(0, self.maxPlotLen), ylim=(self.minY,self.maxY))
self.plt.grid(True)
self.line,=self.plt.plot([],[],lw=2)
self.mean=-1
self.buffer=deque()
self.inCapturing=False
self.pre=preLength
self.post=postLength
self.__caputredSignalListner=[]
self.fff=0;
self.socketController=socketController('localhost',8080)
init()
def init(learningRate, numOfEpochs, batchSize, dimension, model_iter):
nn.init(learningRate, numOfEpochs, batchSize, dimension, model_iter)
def init(learningRate, numOfEpochs, batchSize, dimension): nn.init(learningRate, numOfEpochs, batchSize, dimension)
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import nn
learnRate = 0.1
if len(sys.argv) > 1: #use given settings
dropout = float(sys.argv[1])
momentum = float(sys.argv[2])
decay = float(sys.argv[3])
(model, optimizer, criterion) = nn.init(learnRate,
momentum=momentum,
dropout=dropout,
wd=decay)
(accv, errv, losst) = nn.fullTrain(model, optimizer, criterion)
testAccuracy = nn.test(model)
print('Test Accuracy:\t' + str(testAccuracy.item() * 100.) + "%\n")
fig, ax1 = plt.subplots()
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Validation Error', color='tab:red')
ax1.plot(errv, color='tab:red')
ax1.tick_params(axis='y', labelcolor='tab:red')
ax2 = ax1.twinx()
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import nn
if len(sys.argv) < 2:
learnRates = [1., 0.1, 0.01, 0.001, 0.0001]
else:
learnRates = [float(sys.argv[1])]
for learnRate in learnRates:
print("######## LEARNING RATE = " + str(learnRate) + " ########\n")
(model, optimizer, criterion) = nn.init(learnRate)
(accv, errv, losst) = nn.fullTrain(model, optimizer, criterion)
testAccuracy = nn.test(model)
print('Test Accuracy:\t' + str(testAccuracy.item() * 100.) + '%\n')
fig, ax1 = plt.subplots()
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Validation Error', color='tab:red')
ax1.plot(errv, color='tab:red')
ax1.tick_params(axis='y', labelcolor='tab:red')
ax2 = ax1.twinx()
ax2.set_ylabel('Training Loss', color='tab:blue')
ax2.plot(losst, color='tab:blue')
ax2.tick_params(axis='y', labelcolor='tab:blue')