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 _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 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 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 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 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 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
from pybrain.datasets.supervised import SupervisedDataSet
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
def __init__(self, dim, sparsity=0.1, beta=1, resetAfterTraining=True, name=None):
SigmoidLayer.__init__(self, dim, name);
self.r = sparsity;
self.beta = beta;
self.resetAfterTraining= resetAfterTraining;
self.resetAverage();
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])
net.activate((1, 0)), net.activate((1, 1)))
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)
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 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)
from pybrain.structure import FeedForwardNetwork 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)
from pybrain.tools.shortcuts import buildNetwork from pybrain.supervised.trainers.backprop import BackpropTrainer from pybrain.datasets.supervised import SupervisedDataSet from BinReader import BinReader from pybrain.utilities import percentError from pybrain.datasets.classification import ClassificationDataSet from pybrain.structure.networks.feedforward import FeedForwardNetwork 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()
from pybrain.structure.modules.linearlayer import LinearLayer
from pybrain.structure.modules.sigmoidlayer import SigmoidLayer
from pybrain.structure.connections.full import FullConnection
from pybrain.structure.modules.biasunit import BiasUnit
# Next we transform the data into a vectorized format so that it can be used as a training set
aramdata = open("ARAMData.txt","r")
#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()
# create an Object to get the data source dataObject = MNIST_Data.MNIST_Processing() traininglist = dataObject.neural_data_set traininglabels = dataObject.neural_label_set # step1 #create neural network fnn = FeedForwardNetwork() #set three layers, input+ hidden layer+ output 28*28=784 # the first feature extraction #inLayer = LinearLayer(784,name='inLayer') # the second feature extraction inLayer = LinearLayer(28, name='inLayer') hiddenLayer = SigmoidLayer(30, name='hiddenLayer0') outLayer = LinearLayer(10, name='outLayer') #There are a couple of different classes of layers. For a complete list check out the modules package. #add these three Layers into neural network fnn.addInputModule(inLayer) fnn.addModule(hiddenLayer) fnn.addOutputModule(outLayer) #create the connections between three layers in_to_hidden = FullConnection(inLayer, hiddenLayer) hidden_to_out = FullConnection(hiddenLayer, outLayer) #add connections into network fnn.addConnection(in_to_hidden)
##The worst
time.sleep(1)
aramdata.close()
# Next we transform the data into a vectorized format so that it can be used as a training set
aramdata = open("ARAMData.txt", "r")
#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(20)
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()
from irisdataset import IRIS_TEST_SET, IRIS_TRAIN_SET
# Because the version of pybrain from pip isn't up to date
class ReluLayer(NeuronLayer):
""" Layer of rectified linear units (relu). """
def _forwardImplementation(self, inbuf, outbuf):
outbuf[:] = inbuf * (inbuf > 0)
def _backwardImplementation(self, outerr, inerr, outbuf, inbuf):
inerr[:] = outerr * (inbuf > 0)
net = FeedForwardNetwork()
inLayer = LinearLayer(4, name="in")
hidden0 = SigmoidLayer(5, name="hidden0")
#hidden1 = SigmoidLayer(5, name="hidden1")
outLayer = SigmoidLayer(3, name="out")
net.addInputModule(inLayer)
net.addModule(hidden0)
#net.addModule(hidden1)
net.addOutputModule(outLayer)
def init_params(conn):
for i in range(len(conn.params)):
conn.params[i] = random.uniform(-0.2, 0.2)
in2Hidden = FullConnection(inLayer, hidden0)
#hidden01 = FullConnection(hidden0, hidden1)
hidden2Out = FullConnection(hidden0, outLayer)