Python RecurrentNetwork.addOutputModule方法代码示例

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def getNetwork(trndata):
	n = RecurrentNetwork()
	n.addInputModule(LinearLayer(trndata.indim, name='in'))
	n.addModule(SigmoidLayer(100, name='hidden'))
	n.addOutputModule(LinearLayer(trndata.outdim, name='out'))
	n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
	n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
	n.addRecurrentConnection(FullConnection(n['hidden'], n['hidden'], name='c3'))
	n.sortModules()


	# fnn = buildNetwork( trndata.indim, 5, trndata.outdim, outclass=SoftmaxLayer )
	trainer = BackpropTrainer( n, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01)

	# TODO: return network and trainer here. Make another function for training
	# for i in range(20):
		# trainer.trainEpochs(1)
	# trainer.trainUntilConvergence(maxEpochs=100)

	# trnresult = percentError( trainer.testOnClassData(),trndata['class'] )
	# tstresult = percentError( trainer.testOnClassData(dataset=tstdata ), tstdata['class'] )

	# print "epoch: %4d" % trainer.totalepochs, \
	# 	"  train error: %5.2f%%" % trnresult

	# out = fnn.activateOnDataset(tstdata)
	# out = out.argmax(axis=1)  # the highest output activation gives the class
	return (n, trainer)

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def buildNonGravityNet(recurrent = False):
    if recurrent:
        net = RecurrentNetwork()
    else:
        net = FeedForwardNetwork()
    l1 = LinearLayer(2)
    l2 = LinearLayer(3)
    s1 = SigmoidLayer(2)
    l3 = LinearLayer(1)
    net.addInputModule(l1)
    net.addModule(l2)
    net.addModule(s1)
    net.addOutputModule(l3)
    net.addConnection(IdentityConnection(l1, l2, outSliceFrom = 1))
    net.addConnection(IdentityConnection(l1, l2, outSliceTo = 2))
    net.addConnection(IdentityConnection(l2, l3, inSliceFrom = 2))
    net.addConnection(IdentityConnection(l2, l3, inSliceTo = 1))
    net.addConnection(IdentityConnection(l1, s1))
    net.addConnection(IdentityConnection(l2, s1, inSliceFrom = 1))
    net.addConnection(IdentityConnection(s1, l3, inSliceFrom = 1))
    if recurrent:
        net.addRecurrentConnection(IdentityConnection(s1, l1))
        net.addRecurrentConnection(IdentityConnection(l2, l2, inSliceFrom = 1, outSliceTo = 2))
    net.sortModules()
    return net

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
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

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
class MoveBrain:
    def __init__(self):
        self.n = RecurrentNetwork()
        inLayer = LinearLayer(8)
        hiddenLayer = SigmoidLayer(4)
        self.numInputs = 8
        outLayer = LinearLayer(4)
        self.n.addInputModule(inLayer)
        self.n.addModule(hiddenLayer)
        self.n.addOutputModule(outLayer)

        in_to_hidden = FullConnection(inLayer, hiddenLayer)
        hidden_to_out = FullConnection(hiddenLayer, outLayer)

        self.n.addConnection(in_to_hidden)
        self.n.addConnection(hidden_to_out)

        self.n.sortModules()
        self.ds = SupervisedDataSet(8, 4) 
        self.trainer = BackpropTrainer(self.n, self.ds)

    def run(inputs):
        if inputs.size() == self.numInputs:
            self.n.activate(inputs)
        else:
            print "num of inputs do not match"

    def addRule(self,rule):
        self.ds.append(rule)

    def saveNetwork(self):
        fileObject = open('networks/avoidandfindv1', 'w')
        pickle.dump(self.n, fileObject)

        fileObject.close()

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
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

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def main():
    inData=createDataset()
    env = MarketEnvironment(inData)
    task = MaximizeReturnTask(env)
    numIn=min(env.worldState.shape)

    net=RecurrentNetwork()
    net.addInputModule(BiasUnit(name='bias'))
    #net.addOutputModule(TanhLayer(1, name='out'))
    net.addOutputModule((SignLayer(1,name='out')))
    net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
    net.addInputModule(LinearLayer(numIn,name='in'))
    net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
    net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
    net.sortModules()
    # remove bias (set weight to 0)
    #initialParams=append(array([0.0]),net._params[1:])
    #net._setParameters(initialParams)
    #net._setParameters([ 0.0,-0.05861005,1.64281513,0.98302613])
    #net._setParameters([0., 1.77132063, 1.3843613, 4.73725269])
    #net._setParameters([ 0.0, -0.95173719, 1.92989266, 0.06837472])
    net._setParameters([ 0.0, 1.29560957, -1.14727503, -1.80005888, 0.66351325, 1.19240189])

    ts=env.ts
    learner = RRL(numIn+2,ts) # ENAC() #Q_LinFA(2,1)
    agent = LearningAgent(net,learner)
    exp = ContinuousExperiment(task,agent)

    print(net._params)
    exp.doInteractionsAndLearn(len(ts)-1)
    print(net._params)

    outData=DataFrame(inData['RETURNS']/100)
    outData['ts']=[i/100 for i in ts]
    outData['cum_log_ts']=cumsum([log(1+i) for i in outData['ts']])

    outData['Action_Hist']=env.actionHistory
    outData['trading rets']=pE.calculateTradingReturn(outData['Action_Hist'],outData['ts'])
    outData['cum_log_rets']=cumsum([log(1+x) for x in outData['trading rets']])

    paramHist=learner.paramHistory
    plt.figure(0)
    for i in range(len(net._params)):
        plt.plot(paramHist[i])
    plt.draw()

    print(pE.percentOfOutperformedMonths(outData['trading rets'],outData['ts']))


    #ax1.plot(sign(actionHist),'r')
    plt.figure(1)
    outData['cum_log_ts'].plot(secondary_y=True)
    outData['cum_log_rets'].plot(secondary_y=True)
    outData['Action_Hist'].plot()
    plt.draw()
    plt.show()

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def runNeuralLearningCurveSimulation(dataTrain, dataTest, train_tfidf, test_tfidf, outFile):
    print 'running neural learning curve'
    outFile.write('-------------------------------------\n')
    outFile.write('train==> %d, %d \n'%(train_tfidf.shape[0],train_tfidf.shape[1]))
    outFile.write('test==>  %d, %d \n'%(test_tfidf.shape[0],test_tfidf.shape[1]))
    
    trainDS = getDataSetFromTfidf(train_tfidf, dataTrain.target)
    testDS = getDataSetFromTfidf(test_tfidf, dataTest.target)
    
    print "Number of training patterns: ", len(trainDS)
    print "Input and output dimensions: ", trainDS.indim, trainDS.outdim
    print "First sample (input, target, class):"
    print len(trainDS['input'][0]), trainDS['target'][0], trainDS['class'][0]
    '''
    with SimpleTimer('time to train', outFile):
        net = buildNetwork(trainDS.indim, trainDS.indim/2, trainDS.indim/4, trainDS.indim/8, trainDS.indim/16, 2, hiddenclass=TanhLayer, outclass=SoftmaxLayer)
        trainer = BackpropTrainer( net, dataset=trainDS, momentum=0.1, verbose=True, weightdecay=0.01, batchlearning=True)
    '''
    net = RecurrentNetwork()
    net.addInputModule(LinearLayer(trainDS.indim, name='in'))
    net.addModule(SigmoidLayer(trainDS.indim/2, name='hidden'))
    net.addModule(SigmoidLayer(trainDS.indim/4, name='hidden2'))
    net.addOutputModule(SoftmaxLayer(2, name='out'))
    net.addConnection(FullConnection(net['in'], net['hidden'], name='c1'))
    net.addConnection(FullConnection(net['hidden'], net['out'], name='c2'))
    net.addRecurrentConnection(FullConnection(net['hidden'], net['hidden'], name='c3'))
    net.addRecurrentConnection(FullConnection(net['hidden2'], net['hidden'], name='c4'))
    net.sortModules()
    trainer = BackpropTrainer( net, dataset=trainDS, momentum=0.01, verbose=True, weightdecay=0.01)
    
    outFile.write('%s \n' % (net.__str__()))
    epochs = 200
    with SimpleTimer('time to train %d epochs' % epochs, outFile):
        for i in range(epochs):
            trainer.trainEpochs(1)
            trnresult = percentError( trainer.testOnClassData(),
                                  trainDS['class'] )
            tstresult = percentError( trainer.testOnClassData(
               dataset=testDS ), testDS['class'] )
    
            print "epoch: %4d" % trainer.totalepochs, \
                  "  train error: %5.2f%%" % trnresult, \
                  "  test error: %5.2f%%" % tstresult
                  
    outFile.write('%5.2f , %5.2f \n' % (100.0-trnresult, 100.0-tstresult))
                  
    predicted = trainer.testOnClassData(dataset=testDS)
    results = predicted == testDS['class'].flatten()
    wrong = []
    for i in range(len(results)):
        if not results[i]:
            wrong.append(i)
    print 'classifier got these wrong:'
    for i in wrong[:10]:
        print dataTest.data[i], dataTest.target[i]
        outFile.write('%s %d \n' % (dataTest.data[i], dataTest.target[i]))

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def createRecurrent(inputSize,nHidden):
    n = RecurrentNetwork()
    n.addInputModule(LinearLayer(inputSize, name='in'))
    n.addModule(SigmoidLayer(nHidden, name='hidden'))
    n.addOutputModule(LinearLayer(1, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.addRecurrentConnection(FullConnection(n['hidden'], n['hidden'], name='c3'))
    n.sortModules()
    return n

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def buildMinimalLSTMNetwork():
    N = RecurrentNetwork('simpleLstmNet')  
    i = LinearLayer(4, name='i')
    h = LSTMLayer(1, peepholes=True, name='lstm')
    o = LinearLayer(1, name='o')
    N.addInputModule(i)
    N.addModule(h)
    N.addOutputModule(o)
    N.addConnection(IdentityConnection(i, h))
    N.addConnection(IdentityConnection(h, o))
    N.sortModules()
    return N

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def build_rec(inp, hid, out):
    n = RecurrentNetwork()
    n.addInputModule(LinearLayer(inp, name='in'))
    n.addModule(TanhLayer(hid, name='hidden'))
    n.addOutputModule(SoftmaxLayer(out, name='out'))
    n.addConnection(FullConnection(n['in'], n['hidden'], name='c1'))
    n.addConnection(FullConnection(n['hidden'], n['out'], name='c2'))
    n.addRecurrentConnection(FullConnection(n['hidden'], n['hidden'], name='c3'))
    n.sortModules()
    #n.randomize()

    return n

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def buildMinimalMDLSTMNetwork():
    N = RecurrentNetwork('simpleMdLstmNet')
    i = LinearLayer(4, name = 'i')
    h = MDLSTMLayer(1, peepholes = True, name = 'mdlstm')
    o = LinearLayer(1, name = 'o')
    N.addInputModule(i)
    N.addModule(h)
    N.addOutputModule(o)
    N.addConnection(IdentityConnection(i, h, outSliceTo = 4))
    N.addRecurrentConnection(IdentityConnection(h, h, outSliceFrom = 4, inSliceFrom = 1))
    N.addConnection(IdentityConnection(h, o, inSliceTo = 1))
    N.sortModules()
    return N

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def build_rnn(input_size, output_size, layers):
    net = RecurrentNetwork()
    layers_list = ["in"]
    net.addInputModule(LinearLayer(input_size, name="in"))
    for i in range(0, layers):
        net.addModule(ReluLayer(input_size, name="hidden"+str(i)))
        layers_list.append("hidden"+str(i))
    net.addOutputModule(TanhLayer(output_size, name="out"))
    layers_list.append("out")

    for i in range(0, len(layers_list)-1):
        net.addConnection(FullConnection(net[layers_list[i]], net[layers_list[i+1]]))

    net.sortModules()
    return net

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def buildMixedNestedNetwork():
    """ build a nested network with the inner one being a ffn and the outer one being recurrent. """
    N = RecurrentNetwork('outer')
    a = LinearLayer(1, name = 'a')
    b = LinearLayer(2, name = 'b')
    c = buildNetwork(2, 3, 1)
    c.name = 'inner'
    N.addInputModule(a)
    N.addModule(c)
    N.addOutputModule(b)
    N.addConnection(FullConnection(a,b))
    N.addConnection(FullConnection(b,c))
    N.addRecurrentConnection(FullConnection(c,c))
    N.sortModules()
    return N

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
 def _CreateRecurentNN():
     net = RecurrentNetwork()
     net.addInputModule(LinearLayer(4, name='in'))
     net.addModule(BiasUnit(name='hidden_bias'))
     net.addModule(TanhLayer(13, name='hidden'))
     #net.addModule(BiasUnit(name='out_bias'))
     net.addOutputModule(SoftmaxLayer(2, name='out_class'))
     #net.addOutputModule(LinearLayer(1, name='out_predict'))
     #net.addConnection(FullConnection(net['out_bias'], net['out_predict']))
     net.addConnection(FullConnection(net['hidden_bias'], net['hidden']))
     net.addConnection(FullConnection(net['in'], net['hidden'], name='fc1'))
     net.addConnection(FullConnection(net['hidden'], net['out_class'], name='fc2'))
     #net.addConnection(FullConnection(net['hidden'], net['out_predict'], name='fc3'))
     net.addRecurrentConnection(FullConnection(net['hidden'], net['hidden'], name='rc3'))
     net.sortModules()
     return net

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import addOutputModule [as 别名]
def buildToddNetwork(hiddenSize):
    net = RecurrentNetwork()
    inLayer = LinearLayer(sampleSize())
    hiddenLayer = SigmoidLayer(hiddenSize)
    outLayer = SigmoidLayer(outputSize())
    net.addInputModule(inLayer)
    net.addModule(hiddenLayer)
    net.addOutputModule(outLayer)
    inRecursive = WeightedPartialIdentityConnection(0.8, pitchCount+1, inLayer, inLayer)
    inToHidden = FullConnection(inLayer, hiddenLayer)
    hiddenToOut = FullConnection(hiddenLayer, outLayer)
    net.addRecurrentConnection(inRecursive)
    net.addConnection(inToHidden)
    net.addConnection(hiddenToOut)
    net.sortModules()
    return net