def trainedANN():
n = FeedForwardNetwork()
n.addInputModule(LinearLayer(4, name='in'))
n.addModule(SigmoidLayer(6, name='hidden'))
n.addOutputModule(LinearLayer(2, name='out'))
n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
n.sortModules()
draw_connections(n)
# d = generateTrainingData()
d = getDatasetFromFile(root.path() + "/res/dataSet")
t = BackpropTrainer(n, d, learningrate=0.001, momentum=0.75)
t.trainOnDataset(d)
# FIXME: I'm not sure the recurrent ANN is going to converge
# so just training for fixed number of epochs
count = 0
while True:
globErr = t.train()
print globErr
if globErr < 0.01:
break
count += 1
if count == 20:
return trainedANN()
exportANN(n)
draw_connections(n)
return n
def trainedRNN():
n = RecurrentNetwork()
n.addInputModule(LinearLayer(4, name='in'))
n.addModule(SigmoidLayer(6, name='hidden'))
n.addOutputModule(LinearLayer(2, name='out'))
n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
n.addRecurrentConnection(NMConnection(n['out'], n['out'], name='nmc'))
# n.addRecurrentConnection(FullConnection(n['out'], n['hidden'], inSliceFrom = 0, inSliceTo = 1, outSliceFrom = 0, outSliceTo = 3))
n.sortModules()
draw_connections(n)
d = getDatasetFromFile(root.path() + "/res/dataSet")
t = BackpropTrainer(n, d, learningrate=0.001, momentum=0.75)
t.trainOnDataset(d)
count = 0
while True:
globErr = t.train()
print globErr
if globErr < 0.01:
break
count += 1
if count == 50:
return trainedRNN()
# exportRNN(n)
draw_connections(n)
return n
def trained_cat_dog_ANN():
n = FeedForwardNetwork()
d = get_cat_dog_trainset()
input_size = d.getDimension('input')
n.addInputModule(LinearLayer(input_size, name='in'))
n.addModule(SigmoidLayer(input_size + 1500, name='hidden'))
n.addOutputModule(LinearLayer(2, name='out'))
n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
n.sortModules()
n.convertToFastNetwork()
print 'successful converted to fast network'
t = BackpropTrainer(n, d, learningrate=0.0001) #, momentum=0.75)
count = 0
while True:
globErr = t.train()
print globErr
count += 1
if globErr < 0.01:
break
if count == 30:
break
exportCatDogANN(n)
return n
def main():
a = 0
for i in range(0, 100):
inLayer = SigmoidLayer(2)
hiddenLayer = SigmoidLayer(3)
outLayer = SigmoidLayer(1)
net = FeedForwardNetwork()
net.addInputModule(inLayer)
net.addModule(hiddenLayer)
net.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
net.addConnection(in_to_hidden)
net.addConnection(hidden_to_out)
net.sortModules()
ds = SupervisedDataSet(2, 1)
ds.addSample((1, 1), (0))
ds.addSample((1, 0), (1))
ds.addSample((0, 1), (1))
ds.addSample((0, 0), (0))
trainer = BackpropTrainer(net, ds)
trainer.trainUntilConvergence()
out = net.activate((1, 1))
if (out < 0.5):
a = a + 1
print(str(a) + "/100")
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 trained_cat_dog_RFCNN():
n = RecurrentNetwork()
d = get_cat_dog_trainset()
input_size = d.getDimension('input')
n.addInputModule(LinearLayer(input_size, name='in'))
n.addModule(SigmoidLayer(input_size + 1500, name='hidden'))
n.addOutputModule(LinearLayer(2, name='out'))
n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
n.addRecurrentConnection(FullConnection(n['out'], n['hidden'], name='nmc'))
n.sortModules()
t = BackpropTrainer(n, d, learningrate=0.0001) #, momentum=0.75)
count = 0
while True:
globErr = t.train()
print globErr
count += 1
if globErr < 0.01:
break
if count == 30:
break
exportCatDogRFCNN(n)
return n
def __init__(self, indim, outdim, hiddim=6):
Module.__init__(self, indim, outdim)
self._network = Network()
self._in_layer = LinearLayer(indim + outdim)
self._hid_layer = LSTMLayer(hiddim)
self._out_layer = LinearLayer(outdim)
self._bias = BiasUnit()
self._network.addInputModule(self._in_layer)
self._network.addModule(self._hid_layer)
self._network.addModule(self._bias)
self._network.addOutputModule(self._out_layer)
self._hid_to_out_connection = FullConnection(self._hid_layer,
self._out_layer)
self._in_to_hid_connection = FullConnection(self._in_layer,
self._hid_layer)
self._network.addConnection(self._hid_to_out_connection)
self._network.addConnection(self._in_to_hid_connection)
self._network.addConnection(FullConnection(self._bias,
self._hid_layer))
self._network.sortModules()
self.offset = self._network.offset
self.backprojectionFactor = 0.01
def importCatDogANN(fileName=root.path() + "/res/recCatDogANN"):
n = FeedForwardNetwork()
n.addInputModule(LinearLayer(7500, name='in'))
n.addModule(SigmoidLayer(9000, name='hidden'))
n.addOutputModule(LinearLayer(2, name='out'))
n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
n.sortModules()
params = np.load(root.path() + '/res/cat_dog_params.txt.npy')
n._setParameters(params)
return n
def trained3ONN():
n = FeedForwardNetwork()
inp = LinearLayer(176850, name='input')
hid = LinearLayer(3, name='hidden')
out = LinearLayer(1, name='output')
#add modules
n.addOutputModule(out)
n.addInputModule(inp)
n.addModule(hid)
#add connections
n.addConnection(FullConnection(inp, hid, inSliceTo=100, outSliceTo=1))
n.addConnection(
FullConnection(inp,
hid,
inSliceFrom=100,
inSliceTo=5150,
outSliceFrom=1,
outSliceTo=2))
n.addConnection(FullConnection(inp, hid, inSliceFrom=5150, outSliceFrom=2))
n.addConnection(FullConnection(hid, out))
n.sortModules()
print "Network created"
d = load3OrderDataSet()
print "Data loaded"
t = BackpropTrainer(n, d, learningrate=0.001, momentum=0.75)
# FIXME: I'm not sure the recurrent ANN is going to converge
# so just training for fixed number of epochs
print "Learning started"
count = 0
while True:
globErr = t.train()
print "iteration #", count, " error = ", globErr
if globErr < 0.01:
break
count = count + 1
# if (count == 100):
# break
# for i in range(100):
# print t.train()
exportANN(n)
return n
def buildSimpleLSTMNetwork(peepholes=False):
N = RecurrentNetwork('simpleLstmNet')
i = LinearLayer(100, name='i')
h = LSTMLayer(10, peepholes=peepholes, name='lstm')
o = LinearLayer(1, name='o')
b = BiasUnit('bias')
N.addModule(b)
N.addOutputModule(o)
N.addInputModule(i)
N.addModule(h)
N.addConnection(FullConnection(i, h, name='f1'))
N.addConnection(FullConnection(b, h, name='f2'))
N.addRecurrentConnection(FullConnection(h, h, name='r1'))
N.addConnection(FullConnection(h, o, name='r1'))
N.sortModules()
return N
def _establishRecurrence(self):
""" Adds a recurrent full connection from the output layer to the first
hidden layer.
"""
network = self.network
outlayer = self.getOutputLayer()
hid1layer = self.getFirstHiddenLayer()
network.addRecurrentConnection(FullConnection(outlayer, hid1layer))
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 __init__(self, dims, **args):
""" The one required argument specifies the sizes of each dimension (minimum 2) """
assert len(dims) == 2
SwipingNetwork.__init__(self, dims=dims, **args)
if self.mariopos == None:
self.mariopos = (dims[0] / 2, dims[1] / 2)
pdims = product(dims)
# the input is a 2D-mesh (as a view on a flat input layer)
inmod = LinearLayer(self.insize * pdims, name='input')
inmesh = ModuleMesh.viewOnFlatLayer(inmod, dims, 'inmesh')
# the output is a 2D-mesh (as a view on a flat sigmoid output layer)
outmod = self.outcomponentclass(self.outputs * pdims, name='output')
outmesh = ModuleMesh.viewOnFlatLayer(outmod, dims, 'outmesh')
if self.componentclass is MDLSTMLayer:
c = lambda: MDLSTMLayer(self.hsize, 2, self.peepholes).meatSlice()
hiddenmesh = ModuleMesh(c, (self.size, self.size, 4),
'hidden',
baserename=True)
else:
hiddenmesh = ModuleMesh.constructWithLayers(
self.componentclass, self.hsize,
tuple(list(dims) + [self.swipes]), 'hidden')
self._buildSwipingStructure(inmesh, hiddenmesh, outmesh)
o = LinearLayer(self.outputs)
self.addConnection(IdentityConnection(outmesh[self.mariopos], o))
self.outmodules = []
self.addOutputModule(o)
# add the identity connections for the states
for m in self.modules:
if isinstance(m, MDLSTMLayer):
tmp = m.stateSlice()
index = 0
for c in list(self.connections[m]):
if isinstance(c.outmod, MDLSTMLayer):
self.addConnection(
IdentityConnection(
tmp,
c.outmod.stateSlice(),
outSliceFrom=self.hsize * (index),
outSliceTo=self.hsize * (index + 1)))
index += 1
# special inputs
self.addInputModule(LinearLayer(2, name='specialin'))
self.addConnection(FullConnection(self['specialin'], o))
self.sortModules()
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 __init__(self, outdim, hiddim=15):
""" Create an EvolinoNetwork with for sequences of dimension outdim and
hiddim dimension of the RNN Layer."""
indim = 0
Module.__init__(self, indim, outdim)
self._network = RecurrentNetwork()
self._in_layer = LinearLayer(indim + outdim)
self._hid_layer = LSTMLayer(hiddim)
self._out_layer = LinearLayer(outdim)
self._bias = BiasUnit()
self._network.addInputModule(self._in_layer)
self._network.addModule(self._hid_layer)
self._network.addModule(self._bias)
self._network.addOutputModule(self._out_layer)
self._in_to_hid_connection = FullConnection(self._in_layer,
self._hid_layer)
self._bias_to_hid_connection = FullConnection(self._bias,
self._hid_layer)
self._hid_to_out_connection = FullConnection(self._hid_layer,
self._out_layer)
self._network.addConnection(self._in_to_hid_connection)
self._network.addConnection(self._bias_to_hid_connection)
self._network.addConnection(self._hid_to_out_connection)
self._recurrent_connection = FullConnection(self._hid_layer,
self._hid_layer)
self._network.addRecurrentConnection(self._recurrent_connection)
self._network.sortModules()
self._network.reset()
self.offset = self._network.offset
self.backprojectionFactor = 0.01
def _backwardImplementation(self, outerr, inerr, inbuf):
FullConnection._backwardImplementation(self, outerr, inerr, inbuf)
def _forwardImplementation(self, inbuf, outbuf):
FullConnection._forwardImplementation(self, inbuf, outbuf)
from pybrain.structure.modules.sigmoidlayer import SigmoidLayer from pybrain.structure.modules.linearlayer import LinearLayer from pybrain.structure.modules.biasunit import BiasUnit from pybrain.structure.connections.full import FullConnection rede = FeedForwardNetwork() camadaEntrada = LinearLayer(2) camadaOculta = SigmoidLayer(3) camadaSaida = SigmoidLayer(1) bias1 = BiasUnit() bias2 = BiasUnit() rede.addModule(camadaEntrada) rede.addModule(camadaOculta) rede.addModule(camadaSaida) rede.addModule(bias1) rede.addModule(bias2) entradaOculta = FullConnection(camadaEntrada, camadaOculta) ocultaSaida = FullConnection(camadaOculta, camadaSaida) biasOculta = FullConnection(bias1,camadaOculta) biasSaida = FullConnection(bias2,camadaSaida) rede.sortModules() print(rede) print(entradaOculta.params) print(ocultaSaida.params) print(biasOculta.params) print(biasSaida.params)
#ChampionDictionary holds all the riot static data about each champion. The Riot IDs are the keys of the dictionary championdictionary = DatabaseActions.CreateChampionDictionary() #Creates a Neural Network of Appropriate size predictionNet = FeedForwardNetwork() inLayer = LinearLayer(len(championdictionary)) hiddenLayer = SigmoidLayer(5) outLayer = SigmoidLayer(1) predictionNet.addInputModule(inLayer) predictionNet.addModule(hiddenLayer) predictionNet.addOutputModule(outLayer) predictionNet.addModule(BiasUnit(name = 'bias')) in_to_hidden = FullConnection(inLayer,hiddenLayer) hidden_to_out = FullConnection(hiddenLayer,outLayer) predictionNet.addConnection(in_to_hidden) predictionNet.addConnection(hidden_to_out) predictionNet.addConnection(FullConnection(predictionNet['bias'],hiddenLayer)) predictionNet.addConnection(FullConnection(predictionNet['bias'],outLayer)) predictionNet.sortModules() trainingSet = SupervisedDataSet(len(championdictionary),1) #Takes each game and turns it into a vector. -1 is stored if the champion is on the opposing team, 1 if the champion is on the player's team #and 0 if it wasn't played. The vector is then fed into the Neural Network's Training Set print "Adding Games to NN" for game in aramdata.readlines():
from pybrain.structure.modules.sigmoidlayer import SigmoidLayer from pybrain.structure.modules.linearlayer import LinearLayer from pybrain.structure.connections.full import FullConnection from pybrain.tools.xml.networkwriter import NetworkWriter dim = 381 n = FeedForwardNetwork() inLayer = LinearLayer(dim) hiddenLayer = SigmoidLayer(100) outLayer = LinearLayer(1) n.addInputModule(inLayer) n.addModule(hiddenLayer) n.addOutputModule(outLayer) in_to_hidden = FullConnection(inLayer,hiddenLayer) hidden_to_out = FullConnection(hiddenLayer,outLayer) n.addConnection(in_to_hidden) n.addConnection(hidden_to_out) n.sortModules() print 'build set' alldata = ClassificationDataSet(dim, 1, nb_classes=2) (data,label,items) = BinReader.readData(ur'F:\AliRecommendHomeworkData\1212新版\train15_17.expand.samp.norm.bin') #(train,label,data) = BinReader.readData(r'C:\data\small\norm\train1217.bin') for i in range(len(data)):
ds.addSample((0, 0), (0, ))
ds.addSample((0, 1), (1, ))
ds.addSample((1, 0), (1, ))
ds.addSample((1, 1), (0, ))
for input, target in ds:
print(input, target)
#define net
net = RecurrentNetwork()
net.addInputModule(LinearLayer(2, name="il"))
net.addModule(SigmoidLayer(4, name="h1"))
net.addModule(SigmoidLayer(4, name="h2"))
net.addOutputModule(LinearLayer(1, name="ol"))
c1 = FullConnection(net["il"], net["h1"])
c2 = FullConnection(net["h1"], net["h2"])
c3 = FullConnection(net["h2"], net["ol"])
cr1 = FullConnection(net["h1"], net["h1"])
net.addConnection(c1)
net.addConnection(c2)
net.addConnection(c3)
net.addRecurrentConnection(cr1)
net.sortModules()
print(net)
trainer = BackpropTrainer(net, ds)
for i in range(20):
for j in range(1000):
ds = SupervisedDataSet(2, 1)
ds.addSample((0, 0), (0, ))
ds.addSample((0, 1), (1, ))
ds.addSample((1, 0), (1, ))
ds.addSample((1, 1), (0, ))
for input, target in ds:
print(input, target)
#define layers and connections
inLayer = LinearLayer(2)
hiddenLayerOne = SigmoidLayer(4, "one")
hiddenLayerTwo = SigmoidLayer(4, "two")
outLayer = LinearLayer(1)
inToHiddenOne = FullConnection(inLayer, hiddenLayerOne)
hiddenOneToTwo = FullConnection(hiddenLayerOne, hiddenLayerTwo)
hiddenTwoToOut = FullConnection(hiddenLayerTwo, outLayer)
#wire the layers and connections to a net
net = FeedForwardNetwork()
net.addInputModule(inLayer)
net.addModule(hiddenLayerOne)
net.addModule(hiddenLayerTwo)
net.addOutputModule(outLayer)
net.addConnection(inToHiddenOne)
net.addConnection(hiddenOneToTwo)
net.addConnection(hiddenTwoToOut)
net.sortModules()
print(net)
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])
def _backwardImplementation(self, outerr, inerr, inbuf):
FullConnection._backwardImplementation(self, outerr, inerr, inbuf)
def _forwardImplementation(self, inbuf, outbuf):
FullConnection._forwardImplementation(self, inbuf, outbuf)
from pybrain.structure.connections.full import FullConnection
from pybrain.structure.modules.linearlayer import LinearLayer
from pybrain.structure.modules.sigmoidlayer import SigmoidLayer
from pybrain.structure.networks.feedforward import FeedForwardNetwork
from pybrain.supervised.trainers.backprop import BackpropTrainer
network = FeedForwardNetwork() # create network
inputLayer = SigmoidLayer(1) # maybe LinearLayer ?
hiddenLayer = SigmoidLayer(4)
outputLayer = SigmoidLayer(1) # maybe LinearLayer ?
network.addInputModule(inputLayer)
network.addModule(hiddenLayer)
network.addOutputModule(outputLayer)
# Connection
network.addConnection(FullConnection(inputLayer, hiddenLayer))
network.addConnection(FullConnection(hiddenLayer, outputLayer))
network.sortModules()
dataTrain = SupervisedDataSet(1, 1) # input, target
dataTrain.addSample(
1, 0.76
) # it seems to me that input(our x), target(value y) from function sin(x)*sin(2*x)
trainer = BackpropTrainer(
network, dataTrain) # it's back prop, we use our network and our data
print(trainer.train()) # i think it's value trained
print(network.params) # i think that are wights
# print(network)
