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Python networkwriter.NetworkWriter类代码示例

本文整理汇总了Python中pybrain.tools.customxml.networkwriter.NetworkWriter的典型用法代码示例。如果您正苦于以下问题:Python NetworkWriter类的具体用法?Python NetworkWriter怎么用?Python NetworkWriter使用的例子?那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。


在下文中一共展示了NetworkWriter类的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: train

	def train(self):
		print "Enter the number of times to train, -1 means train until convergence:"
		t = int(raw_input())
		print "Training the Neural Net"
		print "self.net.indim = "+str(self.net.indim)
		print "self.train_data.indim = "+str(self.train_data.indim)

		trainer = BackpropTrainer(self.net, dataset=self.train_data, momentum=0.1, verbose=True, weightdecay=0.01)
		
		if t == -1:
			trainer.trainUntilConvergence()
		else:
			for i in range(t):
				trainer.trainEpochs(1)
				trnresult = percentError( trainer.testOnClassData(), self.train_data['class'])
				# print self.test_data

				tstresult = percentError( trainer.testOnClassData(dataset=self.test_data), self.test_data['class'] )

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

				if i % 10 == 0 and i > 1:
					print "Saving Progress... Writing to a file"
					NetworkWriter.writeToFile(self.net, self.path)

		print "Done training... Writing to a file"
		NetworkWriter.writeToFile(self.net, self.path)
		return trainer
开发者ID:davidlavy88,项目名称:FaceIdentifier,代码行数:30,代码来源:identify.py

示例2: move_function

def move_function(board):
    global net  
    best_max_move = None 
    max_value = -1000
    best_min_move = None
    min_value = 1000

    #value is the chance of black winning
    for m in board.get_moves():
        nextboard = board.peek_move(m)
        value = net.activate(board_to_input(nextboard))
        if value > max_value: 
            max_value = value
            best_max_move = m 
        if value < min_value:
            min_value = value
            best_min_move = m

    ds = SupervisedDataSet(97, 1)
    best_move = None 

    #active player
    if board.active == BLACK:
        ds.addSample(board_to_input(board), max_value)
        best_move = best_max_move
    elif board.active == WHITE: 
        ds.addSample(board_to_input(board), min_value)
        best_move = best_min_move

    trainer = BackpropTrainer(net, ds)
    trainer.train()
    NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_random_black_mini_140.xml')
    NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_random_black_mini_140_copy.xml') 
    return best_move 
开发者ID:johnny-zheng,项目名称:SynapsemonPy,代码行数:34,代码来源:synapsemon_random_black_mini_140.py

示例3: save

 def save(self, filename, desc=None):
     NetworkWriter.writeToFile(self.net, filename + '.xml')
     params = {'labels': self.labels,
               'mean': self.mean.tolist(),
               'std': self.std.tolist()}
     with open(filename + '.yaml', 'w') as f:
         f.write(yaml.dump(params, default_flow_style=False))
开发者ID:Imperoli,项目名称:rockin_at_work_software,代码行数:7,代码来源:classification_network.py

示例4: trainData

def trainData(data, filename):

    net = buildNetwork(data.indim, 40, data.outdim, hiddenclass=TanhLayer, outclass=SigmoidLayer)
    trainer = BackpropTrainer( net, dataset=data, verbose=True, momentum=0.1, weightdecay=0.01)
    _ , valid_errors = trainer.trainUntilConvergence(continueEpochs=2)
    NetworkWriter.writeToFile(net, filename)
    print "Valid error: ", min(valid_errors)
    return net
开发者ID:ericgorlin,项目名称:CS159,代码行数:8,代码来源:ValueNet.py

示例5: main

def main():
    train_file = 'data/train.csv'
    # validation_file = 'data/validation.csv'
    output_model_file = 'model.xml'

    # hidden_size = 4
    epochs = 500

    # load data
    # def loadData():
    train = np.loadtxt(train_file, delimiter=' ')
    Input = train[0:,0:3]
    Output = train[0:,3:5]

    # validation = np.loadtxt(validation_file, delimiter=',')
    # train = np.vstack((train, validation))

    # x_train = train[:, 0:-1]
    # y_train = train[:, -1]
    # y_train = y_train.reshape(-1, 1)

    # input_size = x_train.shape[1]
    # target_size = y_train.shape[1]

    # prepare dataset
    # def prepare dataset(input_size, target_size):
    ds = SDS(Input,Output)
    # ds.addSample(input_size)
    # ds.setField('input', x_train)
    # ds.setField('target', y_train)

    # init and train
    # def initTrain(input_size, hidden_size, input, output):
    # net = buildNetwork(input_size, hidden_size, target_size, bias=True)
    net = buildNetwork(3,  # input layer
                                 4,  # hidden0
                                 2,  # output
                                 hiddenclass=SigmoidLayer,
                                 outclass=SigmoidLayer,
                                 bias=True
                                 )
    net = NetworkReader.readFrom('model.xml')
    for i,o in zip(Input,Output):
        ds.addSample(i,o)
        print i, o

    trainer = BackpropTrainer(net, ds)
        
    print "training for {} epochs...".format(epochs)

    for i in range(epochs):
        mse = trainer.train()
        rmse = sqrt(mse)
        print "training RMSE, epoch {}: {}".format(i + 1, rmse)
        if os.path.isfile("../stopfile.txt") == True:
            break
    
    NetworkWriter.writeToFile(net, output_model_file)
开发者ID:amaneureka,项目名称:iResQ,代码行数:58,代码来源:train.py

示例6: writetrainedinfo

 def writetrainedinfo(self, neuralnetwork):
     """
     # Using the Python pickle
     fileObject = open('traininfo', 'w')
     pickle.dump(neuralnetwork, fileObject)
     fileObject.close()
     """
     # Writing file using the NetworkWriter
     NetworkWriter.writeToFile(neuralnetwork, 'xtrainedinfo.xml')
开发者ID:casyazmon,项目名称:mars_city,代码行数:9,代码来源:xtraining.py

示例7: SaveNetwork

    def SaveNetwork(self):
        """
        Creating dump of network.
        """
        FCLogger.debug('Saving network to PyBrain xml-formatted file...')

        NetworkWriter.writeToFile(self.network, self.networkFile)

        FCLogger.info('Network saved to file: {}'.format(os.path.abspath(self.networkFile)))
开发者ID:chrinide,项目名称:FuzzyClassificator,代码行数:9,代码来源:PyBrainLearning.py

示例8: save

def save( history, net ):
    """
    This function gets called after each training/testing block or when the
    script gets closed. It saves the neural net and RL history of the agent so
    that it can be restored or reused in another model.
    """
    base = os.path.splitext( sys.argv[2] )[0]
    print 'Saving network to: ' + base + '.xml'
    NetworkWriter.writeToFile( net, base + '.xml' )
    fileObject = open( base + '.history', 'w' )
    pickle.dump( history, fileObject )
    fileObject.close()
开发者ID:RyanHope,项目名称:PyDistractorRatio,代码行数:12,代码来源:model-nfq.py

示例9: save_network_to_file

    def save_network_to_file(self, filename):
        """Save Network to File

        Saves the neural network including all connection weights into a
        NetworkWriter format xml file for future loading.

        Arguments:
            filename: The filename into which the network should be saved.
        """
        NetworkWriter.writeToFile(self._network, filename)

        return
开发者ID:evansneath,项目名称:surgicalsim,代码行数:12,代码来源:network.py

示例10: trainNetwork

def trainNetwork():
	print "[Training] Network has Started..."
	inputSize = 0
	with open('file1.txt', 'r') as f:			#automatically closes file at the end of the block
  		#first_line = f.readline()
  		#inputSize = len(first_line)
		dataset = SupervisedDataSet(4, 1)	 #specify size of data and target
		f.seek(0) 							#Move back to beginnning of file
		#iterate through the file. 1 picture per line
		for line in f:
			mylist = json.loads(line)		#list object
	    	target = mylist[-1]				#retrieve and then delete the target classification
	    	del mylist[-2:]
	    	#print target
	    	dataset.addSample(tuple(mylist), (target,))
	        #print json.loads(line)
	if os.path.isfile('annModel.xml'):
		skynet = NetworkReader.readFrom('annModel.xml')#for use if individual sample files used
	else:
		skynet = buildNetwork(dataset.indim, 8, dataset.outdim, bias=True, hiddenclass=TanhLayer) #input,hidden,output
	#SoftmaxLayer, SigmoidLayer, LinearLayer, GaussianLayer
	#Note hidden neuron number is arbitrary, can try 1 or 4 or 3 or 5 if this methods doesnt work out
	trainer = BackpropTrainer(skynet, dataset,learningrate = 0.3, weightdecay = 0.01,momentum = 0.9)
	#trainer.trainUntilConvergence()
	for i in xrange(1000):
		trainer.train()
    #trainer.trainEpochs(1000)
    #Save the now trained neural network
	NetworkWriter.writeToFile(skynet,'annModel.xml')
	print "[Network] has been Written"

################## SVM Method #######################
#Change append method in write method for target persistence
	dataX = []
	datay = []
	with open(writeFile, 'r') as f:
		for line in f:
			mylist = json.loads(line)
			target2 = mylist[-1]
			dataX.append(mylist[:-2])
			datay.append(target2)
	#datay = [target2] * len(dataX) #Targets, size is n_samples, for use with indiviual sample files with same target
	print [target2]
	print dataX
	print datay
	clf = svm.LinearSVC()
	clf.fit(dataX,datay)
    #Persist the trained model
	joblib.dump(clf,'svmModel.pkl')
开发者ID:phalax4,项目名称:illumination,代码行数:49,代码来源:writeUnit.py

示例11: Treinar

def Treinar():
    print 'Inicializando o treinamento da Rede......Aguarde'
    ds = SupervisedDataSet(50,1)
    with open('trainning.txt') as f:
        for line in f:
            if line[0] != '#':
                line = line.replace('\n','')
                line = line.split(',')
                exemplo = []
                for x in line: exemplo.append(x)
                ds.addSample(exemplo[1:],exemplo[:1]) # o 1: pega o primeiro valor que e targer.
    ## Dataset
    #trainer = BackpropTrainer(net, learningrate = 0.04, momentum = 0.07, verbose = False)
    trainer = BackpropTrainer(net, learningrate = 0.04, momentum = 0.07, verbose = False)
    trainer.trainOnDataset(ds,10000) 
    NetworkWriter.writeToFile(net, 'filename.xml')
    print 'Treinado e Pronto'
开发者ID:romullofb,项目名称:nnids,代码行数:17,代码来源:nnids.py

示例12: perceptron

def perceptron(hidden_neurons=20, weightdecay=0.01, momentum=0.1):
    INPUT_FEATURES = 200
    CLASSES = 9
    HIDDEN_NEURONS = hidden_neurons
    WEIGHTDECAY = weightdecay
    MOMENTUM = momentum
    
    g = generate_data()
    alldata = g['d']
    testdata = generate_Testdata(g['index'])['d']
    #tstdata, trndata = alldata.splitWithProportion(0.25)
    #print type(tstdata)

    trndata = _convert_supervised_to_classification(alldata,CLASSES)
    tstdata = _convert_supervised_to_classification(testdata,CLASSES)
    trndata._convertToOneOfMany()  
    tstdata._convertToOneOfMany()
    #fnn = NetworkReader.readFrom('ncibig(500+83.85).xml')
    fnn = buildNetwork(trndata.indim, HIDDEN_NEURONS, trndata.outdim,outclass=SoftmaxLayer)
    trainer = BackpropTrainer(fnn, dataset=trndata, momentum=MOMENTUM,verbose=True, weightdecay=WEIGHTDECAY,learningrate=0.01)
    result = 0;
    ssss = 0;
    for i in range(200):
        trainer.trainEpochs(1)
        trnresult = percentError(trainer.testOnClassData(),trndata['class'])
        tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
        out = fnn.activateOnDataset(tstdata)
        ssss = out
        out = out.argmax(axis=1)
        result = out
    df = pd.DataFrame(ssss)
    df.to_excel("ncibigout.xls")
    df = pd.DataFrame(result)
    df.insert(1,'1',tstdata['class'])
    df.to_excel("ncibig.xls")
    error = 0;
    for i in range(len(tstdata['class'])):
        if tstdata['class'][i] != result[i]:
            error = error+1
    #print (len(tstdata['class'])-error)*1.0/len(tstdata['class'])*100
    print AAC(result,tstdata['class'])
    print AUC(np.transpose(tstdata['class'])[0],result.transpose())
    print Fscore(np.transpose(tstdata['class'])[0],result.transpose())
    NetworkWriter.writeToFile(fnn, 'ncibig.xml')
开发者ID:Guosmilesmile,项目名称:pythonstudy,代码行数:44,代码来源:ncirf.py

示例13: end_function

def end_function(board, lose):
    global net 

    ds = SupervisedDataSet(97, 1)

    if lose:
        if board.active == BLACK:
            ds.addSample(board_to_input(board), 0)
            whiteboard = board_to_input(board)
            whiteboard[96] = 0
            ds.addSample(whiteboard, 1) 
        elif board.active == WHITE: 
            ds.addSample(board_to_input(board), 1)
            blackboard = board_to_input(board)
            blackboard[96] = 1
            ds.addSample(blackboard, 0) 
    else:
        #black loses
        if board.active == BLACK:
            ds.addSample(board_to_input(board), 0)
            whiteboard = board_to_input(board)
            whiteboard[96] = 0
            ds.addSample(whiteboard, 0) 

        #black wins
        elif board.active == WHITE: 
            ds.addSample(board_to_input(board), 1)
            blackboard = board_to_input(board)
            blackboard[96] = 1
            ds.addSample(blackboard, 1) 

    trainer = BackpropTrainer(net, ds)
    trainer.train()

    NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_random_black_mini_140.xml')
    NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_random_black_mini_140_copy.xml') 
开发者ID:johnny-zheng,项目名称:SynapsemonPy,代码行数:36,代码来源:synapsemon_random_black_mini_140.py

示例14: move_function

def move_function(board):
    global net  
    #active player
    #if board.active == BLACK:
    #   ds.addSample(board_to_input(board), max_value)
    #    best_move = best_max_move
    #elif board.active == WHITE: 
    #    ds.addSample(board_to_input(board), min_value)
    #   best_move = best_min_move
    boardString=board_to_input(board)
    black_material=0
    white_material=0
    for i in range(32):
        isKing=boardString[i+64]=='1'
        if boardString[i]=='1':
            if isKing:
                black_material=black_material+2
            else: 
                black_material=black_material+1
        if boardString[i+32]=='1':
            if isKing:
                white_material=white_material+2
            else:
                white_material=white_material+1
    
    board_val = black_material/(black_material+white_material)

    #create a new dataset. Add a sample with board as input, value as output
    ds = SupervisedDataSet(97, 1)
    ds.addSample(boardString, board_val)
    trainer = BackpropTrainer(net, ds)
    trainer.train()
    NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_primer1.xml')
    NetworkWriter.writeToFile(net, 'SynapsemonPie/synapsemon_primer1_copy.xml')

    return random.choice(board.get_moves()) 
开发者ID:johnny-zheng,项目名称:SynapsemonPy,代码行数:36,代码来源:synapsemonpy_primer1.py

示例15: plotLearningCurve

    if(oldtstError==0):
        oldtstError = tstError
                                                                                                                                                    
    if(oldtstError<tstError):
        tstErrorCount = tstErrorCount+1
        print 'No Improvement, count=%d' % tstErrorCount
        print '    Old Validation Error:', oldtstError 
        print 'Current Validation Error:', tstError
                                                                                                                                                    
    if(oldtstError>tstError):
        print 'Improvement made!'
        print '    Old Validation Error:', oldtstError 
        print 'Current Validation Error:', tstError
        tstErrorCount=0
        oldtstError = tstError
        NetworkWriter.writeToFile(TDNNClassificationNet, networkPath)
        plotLearningCurve()
           
          
trainingTime = time.time()-time_start
trainingTime=np.reshape(trainingTime, (1))
np.savetxt("25sigmoid/Trainingtime.txt", trainingTime)


####################
# Manual OFFLINE Test
####################        
# TDNNClassificationNet = NetworkReader.readFrom('25sigmoid/TrainUntilConv.xml')
# print 'Loaded Trained Network!'
#  
# print TDNNClassificationNet.paramdim
开发者ID:dnth,项目名称:long-behavior,代码行数:31,代码来源:tdnn-10.py


注:本文中的pybrain.tools.customxml.networkwriter.NetworkWriter类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。