def buildXor(self):
self.params['dataset'] = 'XOR'
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [[0.], [1.], [1.], [0.]])
self.trn_data = d
self.tst_data = d
global trn_data
trn_data = self.trn_data
nn = FeedForwardNetwork()
inLayer = TanhLayer(2, name='in')
hiddenLayer = TanhLayer(3, name='hidden0')
outLayer = ThresholdLayer(1, name='out')
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
nn.randomize()
self.net_settings = str(nn.connections)
self.nn = nn
def createNN():
nn = FeedForwardNetwork()
inLayer = TanhLayer(4, name='in')
hiddenLayer = TanhLayer(6, name='hidden0')
outLayer = ThresholdLayer(3)
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
return nn
def _buildStructure(self, inputdim, insize, inlayer, convSize, numFeatureMaps):
#build layers
outdim = insize - convSize + 1
hlayer = TanhLayer(outdim * outdim * numFeatureMaps, name='h')
self.addModule(hlayer)
outlayer = SigmoidLayer(outdim * outdim, name='out')
self.addOutputModule(outlayer)
# build shared weights
convConns = []
for i in range(convSize):
convConns.append(MotherConnection(convSize * numFeatureMaps * inputdim, name='conv' + str(i)))
outConn = MotherConnection(numFeatureMaps)
# establish the connections.
for i in range(outdim):
for j in range(outdim):
offset = i * outdim + j
outmod = ModuleSlice(hlayer, inSliceFrom=offset * numFeatureMaps, inSliceTo=(offset + 1) * numFeatureMaps,
outSliceFrom=offset * numFeatureMaps, outSliceTo=(offset + 1) * numFeatureMaps)
self.addConnection(SharedFullConnection(outConn, outmod, outlayer, outSliceFrom=offset, outSliceTo=offset + 1))
for k, mc in enumerate(convConns):
offset = insize * (i + k) + j
inmod = ModuleSlice(inlayer, outSliceFrom=offset * inputdim, outSliceTo=offset * inputdim + convSize * inputdim)
self.addConnection(SharedFullConnection(mc, inmod, outmod))
def buildIris(self):
self.params['dataset'] = 'iris'
self.trn_data, self.tst_data = pybrainData(0.5)
global trn_data
trn_data = self.trn_data
nn = FeedForwardNetwork()
inLayer = TanhLayer(4, name='in')
hiddenLayer = TanhLayer(6, name='hidden0')
outLayer = ThresholdLayer(3, name='out')
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
nn.randomize()
self.net_settings = str(nn.connections)
self.nn = nn
def buildParity(self):
self.params['dataset'] = 'parity'
self.trn_data = ParityDataSet(nsamples=75)
self.trn_data.setField('class', self.trn_data['target'])
self.tst_data = ParityDataSet(nsamples=75)
global trn_data
trn_data = self.trn_data
nn = FeedForwardNetwork()
inLayer = TanhLayer(4, name='in')
hiddenLayer = TanhLayer(6, name='hidden0')
outLayer = ThresholdLayer(1, name='out')
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
nn.randomize()
self.net_settings = str(nn.connections)
self.nn = nn
def buildBMTrainer(self):
x, y = self.readexcel()
# 模拟size条数据：
# self.writeexcel(size=100)
# resx=contrib(x,0.9)
# print '**********************'
# print resx
# x1=x[:,[3,4,5,6,7,8,9,10,11,0,1,2]]
# resx1=contrib(x1)
# print '**********************'
# print resx1
self.realy = y
per = int(len(x))
# 对数据进行归一化处理（一般来说使用Sigmoid时一定要归一化）
self.sx = MinMaxScaler()
self.sy = MinMaxScaler()
xTrain = x[:per]
xTrain = self.sx.fit_transform(xTrain)
yTrain = y[:per]
yTrain = self.sy.fit_transform(yTrain)
# 初始化前馈神经网络
self.__fnn = FeedForwardNetwork()
# 构建输入层，隐藏层和输出层，一般隐藏层为3-5层，不宜过多
inLayer = LinearLayer(x.shape[1], 'inLayer')
hiddenLayer0 = SigmoidLayer(int(self.hiddendim / 3), 'hiddenLayer0')
hiddenLayer1 = TanhLayer(self.hiddendim, 'hiddenLayer1')
hiddenLayer2 = SigmoidLayer(int(self.hiddendim / 3), 'hiddenLayer2')
outLayer = LinearLayer(self.rescol, 'outLayer')
# 将构建的输出层、隐藏层、输出层加入到fnn中
self.__fnn.addInputModule(inLayer)
self.__fnn.addModule(hiddenLayer0)
self.__fnn.addModule(hiddenLayer1)
self.__fnn.addModule(hiddenLayer2)
self.__fnn.addOutputModule(outLayer)
# 对各层之间建立完全连接
in_to_hidden = FullConnection(inLayer, hiddenLayer0)
hidden_to_hidden0 = FullConnection(hiddenLayer0, hiddenLayer1)
hidden_to_hidden1 = FullConnection(hiddenLayer1, hiddenLayer2)
hidden_to_out = FullConnection(hiddenLayer2, outLayer)
# 与fnn建立连接
self.__fnn.addConnection(in_to_hidden)
self.__fnn.addConnection(hidden_to_hidden0)
self.__fnn.addConnection(hidden_to_hidden1)
self.__fnn.addConnection(hidden_to_out)
self.__fnn.sortModules()
# 初始化监督数据集
DS = SupervisedDataSet(x.shape[1], self.rescol)
# 将训练的数据及标签加入到DS中
# for i in range(len(xTrain)):
# DS.addSample(xTrain[i], yTrain[i])
for i in range(len(xTrain)):
DS.addSample(xTrain[i], yTrain[i])
# 采用BP进行训练，训练至收敛，最大训练次数为1000
trainer = BMBackpropTrainer(self.__fnn,
DS,
learningrate=0.0001,
verbose=self.verbose)
if self.myalg:
trainingErrors = trainer.bmtrain(maxEpochs=10000,
verbose=True,
continueEpochs=3000,
totalError=0.0001)
else:
trainingErrors = trainer.trainUntilConvergence(
maxEpochs=10000, continueEpochs=3000, validationProportion=0.1)
# CV = CrossValidator(trainer, DS, n_folds=4, valfunc=ModuleValidator.MSE)
# CV.validate()
# CrossValidator
# trainingErrors = trainer.trainUntilConvergence(maxEpochs=10000,continueEpochs=5000, validationProportion=0.1)
# self.finalError = trainingErrors[0][-2]
# self.finalerror=trainingErrors[0][-2]
# if (self.verbose):
# print '最后总体容差:', self.finalError
self.__sy = self.sy
self.__sx = self.sx
for i in range(len(xTrain)):
a = self.sy.inverse_transform(
self.__fnn.activate(xTrain[i]).reshape(-1, 1))
self.restest.append(
self.sy.inverse_transform(
self.__fnn.activate(xTrain[i]).reshape(-1, 1))[0][0])
