Python utilities.percentError函数代码示例

	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

def main():
  trndata, tstdata = createDS()
  for repeat in xrange(repeats):
    iter_trn_results = []
    iter_tst_results = []
    nn = createNNLong(trndata)
    hiddenAstrocyteLayer, outputAstrocyteLayer = associateAstrocyteLayers(nn)
    trainer = BackpropTrainer(nn, dataset=trndata, learningrate=0.01,
                              momentum=0.1, verbose=False, weightdecay=0.0)
    for grand_iter in xrange(iterations):
      trainer.trainEpochs(1)
      trnresult = percentError(trainer.testOnClassData(), trndata['class'])
      iter_trn_results.append(trnresult)
      tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
      iter_tst_results.append(tstresult)
      
      if not grand_iter%20:
        print 'epoch %4d' %trainer.totalepochs, 'train error %5.2f%%' %trnresult, \
            'test error %5.2f%%' %tstresult
            
      inputs  = list(trndata['input'])
      random.shuffle(inputs)
      for inpt in trndata['input']:
        nn.activate(inpt)
        for minor_iter in range(hiddenAstrocyteLayer.astrocyte_processing_iters):
          hiddenAstrocyteLayer.update()
          outputAstrocyteLayer.update()
        hiddenAstrocyteLayer.reset()
        outputAstrocyteLayer.reset()
    all_trn_results.append(iter_trn_results)
    all_tst_results.append(iter_tst_results)
  plotResults(all_trn_results)
  plotResults(all_tst_results)
  plt.show()

    def train(self):

        #self.init_iri()
        self.init_image()
        self.ds = ClassificationDataSet(self.IN, 1, nb_classes=128)
        #classifier.init_image()
        self.load_data()
        print "Number of trianing patterns: ", len(self.trndata)
        print "Input and output dimensions: ", self.trndata.indim, self.trndata.outdim
        print "First sample (input, target, class):"
        print self.trndata['input'][0], self.trndata['target'][0], self.trndata['class'][0]
        print self.trndata.indim, self.trndata.outdim
        self.net = buildNetwork(self.trndata.indim, 7, self.trndata.outdim)


        trainer = BackpropTrainer(self.net, dataset=self.trndata, momentum=0.1, verbose=True, weightdecay=0.01)

        """
        for i in range(200):
            trainer.trainEpochs(1)
            trnresult = percentError(trainer.testOnClassData(), self.trndata['class'])
            tstresult = percentError(trainer.testOnClassData(dataset = self.tstdata), self.tstdata["class"])
            print "epch: %4d" %  trainer.totalepochs, \
                " train error: %5.2f%%" % trnresult, \
                " test error: %5.2f%%" % tstresult
        """
        trainer.trainUntilConvergence()
        trnresult = percentError(trainer.testOnClassData(), self.trndata['class'])
        tstresult = percentError(trainer.testOnClassData(dataset = self.tstdata), self.tstdata["class"])
        print "epch: %4d" %  trainer.totalepochs, \
            " train error: %5.2f%%" % trnresult, \
            " test error: %5.2f%%" % tstresult

	def testBPHasLearned(self):
		trnresult = percentError(self.trainer.testOnClassData(),
			self.trn_d['class'])
		tstresult = percentError(self.trainer.testOnClassData(dataset=self.tst_d),
			self.tst_d['class'])
		print 'trn perc error', trnresult
		print 'tst perc error', tstresult

def calculateANNaccuracy(model, trndata, tstdata, trainer,iterations=20,maxEpochs=10):
    
    trn_sum=0
    tst_sum=0
    trnfinal=[]
    tstfinal=[]
           
    for i in range(iterations):

            trainer.trainUntilConvergence(maxEpochs=maxEpochs, continueEpochs=1)
            trnresult = percentError( trainer.testOnClassData(),
                              trndata['class'] )
            tstresult = percentError( trainer.testOnClassData(
           dataset=tstdata ), tstdata['class'] )
            trnres=100-trnresult
            tstres=100-tstresult
            trn_sum=trn_sum+trnres
            tst_sum=tst_sum+tstres
            trnfinal.append(trnres)
            tstfinal.append(tstres)
            
    trn_avg=trn_sum/(i+1)
    tst_avg=tst_sum/(i+1)
    print "Train average accuracy: %5.2f%%" % trn_avg
    print "Test average accuracy: %5.2f%%" % tst_avg

    print "Train SD = ", np.std(trnfinal)
    print "Test SD = ", np.std(tstfinal)
    print "\n"
    return tst_avg

 def trainNetwork(self,proportion = 0):        
     if proportion != 0:
         tstdata, trndata = self.alldata.splitWithProportion( 0.01*proportion )
     else:
         trndata = self.alldata
     trndata._convertToOneOfMany( )
     if proportion != 0:
         tstdata._convertToOneOfMany( )
     print "Number of training patterns: ", len(trndata)
     print "Input and output dimensions: ", trndata.indim, trndata.outdim
     self.fnn = buildNetwork( trndata.indim, self.hidden_layer_size, trndata.outdim, 
                              hiddenclass=SigmoidLayer,outclass=SoftmaxLayer )
     self.trainer = BackpropTrainer( self.fnn, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01)
     for i in range(self.iterations_number):
         self.trainer.trainEpochs( 1 )
         trnresult = percentError( self.trainer.testOnClassData(),
                                   trndata['class'] )
         if proportion != 0:
             tstresult = percentError( self.trainer.testOnClassData(
                dataset=tstdata ), tstdata['class'] )
     
         if proportion != 0:
             print "epoch: %4d" % self.trainer.totalepochs, \
               "  train error: %5.2f%%" % trnresult, \
               "  test error: %5.2f%%" % tstresult
         else:
             print "epoch: %4d" % self.trainer.totalepochs, \
               "  train error: %5.2f%%" % trnresult

def main():
    images, labels = load_labeled_training(flatten=True)
    images = standardize(images)
    # images, labels = load_pca_proj(K=100)
    shuffle_in_unison(images, labels)
    ds = ClassificationDataSet(images.shape[1], 1, nb_classes=7)
    for i, l in zip(images, labels):
        ds.addSample(i, [l - 1])
    # ds._convertToOneOfMany()
    test, train = ds.splitWithProportion(0.2)
    test._convertToOneOfMany()
    train._convertToOneOfMany()
    net = shortcuts.buildNetwork(train.indim, 1000, train.outdim, outclass=SoftmaxLayer)

    trainer = BackpropTrainer(net, dataset=train, momentum=0.1, learningrate=0.01, weightdecay=0.05)
    # trainer = RPropMinusTrainer(net, dataset=train)
    # cv = validation.CrossValidator(trainer, ds)
    # print cv.validate()
    net.randomize()
    tr_labels_2 = net.activateOnDataset(train).argmax(axis=1)
    trnres = percentError(tr_labels_2, train["class"])
    # trnres = percentError(trainer.testOnClassData(dataset=train), train['class'])
    testres = percentError(trainer.testOnClassData(dataset=test), test["class"])
    print "Training error: %.10f, Test error: %.10f" % (trnres, testres)
    print "Iters: %d" % trainer.totalepochs

    for i in range(100):
        trainer.trainEpochs(10)
        trnres = percentError(trainer.testOnClassData(dataset=train), train["class"])
        testres = percentError(trainer.testOnClassData(dataset=test), test["class"])
        trnmse = trainer.testOnData(dataset=train)
        testmse = trainer.testOnData(dataset=test)
        print "Iteration: %d, Training error: %.5f, Test error: %.5f" % (trainer.totalepochs, trnres, testres)
        print "Training MSE: %.5f, Test MSE: %.5f" % (trnmse, testmse)

def train(args):
  inputs, ys, gc = args
  row_length = len(inputs[0])
  d = ds.ClassificationDataSet(
      row_length, nb_classes=2, class_labels=['Poisonous',
                                              'Edible'])
  d.setField('input', inputs)
  d.setField('target', ys)
  test, train = d.splitWithProportion(.25)
  test._convertToOneOfMany()
  train._convertToOneOfMany()

  hidden = row_length // 2
  print "indim:", train.indim
  net = buildNetwork(train.indim,
                     hidden,
                     train.outdim,
                     outclass=SoftmaxLayer)
  trainer = BackpropTrainer(net,
                            dataset=train,
                            momentum=0.0,
                            learningrate=0.1,
                            verbose=True,
                            weightdecay=0.0)
  for i in xrange(20):
      trainer.trainEpochs(1)
      trnresult = percentError(trainer.testOnClassData(),
                                train['class'])
      tstresult = percentError(
              trainer.testOnClassData(dataset=test),
              test['class'])
      print "epoch: %4d" % trainer.totalepochs, \
            "  train error: %5.2f%%" % trnresult, \
            "  test error: %5.2f%%" % tstresult
  return net, gc

def calculateANNaccuracy(model, trndata, tstdata, trainer):
    trn_sum=0
    tst_sum=0
    trnfinal=[]
    tstfinal=[]
    for i in range(20):
        trainer.trainUntilConvergence(maxEpochs=10, continueEpochs=3, validationProportion=0.30)
        trnresult = percentError( trainer.testOnClassData(),
                          trndata['class'] )
        tstresult = percentError( trainer.testOnClassData(
       dataset=tstdata ), tstdata['class'] )
        trnres=100-trnresult
        tstres=100-tstresult
        trn_sum=trn_sum+trnres
        tst_sum=tst_sum+tstres
        trnfinal.append(trnres)
        tstfinal.append(tstres)

    trn_avg=trn_sum/(i+1)
    tst_avg=tst_sum/(i+1)
    '''
    print "Test average accuracy: %5.2f%%" % tst_avg

    print "Test SD = ", np.std(tstfinal)
    '''
    return tst_avg

def drawPic():
    try:
        sampleCount = int(inputEntry.get())
    except:
        sampleCount = 50
        print 'Enter an integer.'
        inputEntry.delete(0, END)
        inputEntry.insert(0, '50')
    # Need column vectors in dataset, not arrays
    for i in range(sampleCount):
        trainer.trainEpochs(1)
        trnresult = percentError(trainer.testOnClassData(), trndata['class'])
        tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata['class'])
        if i % 20 == 0:
            t.delete(1.0, END)
        t.insert(END, "epoch:" + str(trainer.totalepochs) + "  train error:" + str(round(trnresult, 2)) \
                  + "%  test error:" + str(round(tstresult, 2)) + "%\n")
        # Clear the Figure
        drawPic.f.clf()
        drawPic.a = drawPic.f.add_subplot(111, projection='3d')
        drawPic.a.set_title('Training...')
        for a, c, m in [(0, 'r', 'o'), (1, 'b', '^'), (2, 'y', 's')]:
            out = fnn.activateOnDataset(alldata)
            out = out.argmax(axis=1)
            here = (out == a)
            drawPic.a.scatter(alldata['input'][here, 0], alldata['input'][here, 1], alldata['input'][here, 2], c=c, marker=m)
        drawPic.canvas.show()

def measuredLearning(ds):

    trndata,tstdata = splitData(ds,.025)

    #build network


    ###
    # This network has no hidden layters, you might need to add some
    ###
    fnn = buildNetwork( trndata.indim, 22, trndata.outdim, outclass=SoftmaxLayer )
    trainer = BackpropTrainer( fnn, verbose=True,dataset=trndata)
                               
    ####
    #   Alter this to figure out how many runs you want.  Best to start small and be sure that you see learning.
    #   Before you ramp it up.
    ###
    for i in range(150):
        trainer.trainEpochs(5)
   
        
        trnresult = percentError(trainer.testOnClassData(),trndata['class'] )

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

        print "epoch: %4d" % trainer.totalepochs, \
            "  train error: %5.2f%%" % trnresult, \
            "  test error: %5.2f%%" % tstresult
        if(trnresult<.5): 
            return

def trainStep(fnn, trainer, trndata, tstdata):
    trainer.trainEpochs(1)
    trnresult = percentError(trainer.testOnClassData(), trndata["class"])
    tstresult = percentError(trainer.testOnClassData(dataset=tstdata), tstdata["class"])

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

    out = fnn.activateOnDataset(griddata)
    out = out.argmax(axis=1)  # the highest output activation gives the class
    out = out.reshape(X.shape)

    figure(1)
    ioff()  # interactive graphics off
    clf()  # clear the plot
    hold(True)  # overplot on
    for c in [0, 1, 2]:
        here, _ = where(trndata["class"] == c)
        plot(trndata["input"][here, 0], trndata["input"][here, 1], "o")
    if out.max() != out.min():  # safety check against flat field
        contourf(X, Y, out)  # plot the contour
    ion()  # interactive graphics on
    draw()  # update the plot

    figure(2)
    ioff()  # interactive graphics off
    clf()  # clear the plot
    hold(True)  # overplot on
    for c in [0, 1, 2]:
        here, _ = where(tstdata["class"] == c)
        plot(tstdata["input"][here, 0], tstdata["input"][here, 1], "o")
    if out.max() != out.min():  # safety check against flat field
        contourf(X, Y, out)  # plot the contour
    ion()  # interactive graphics on
    draw()  # update the plot

	def test(self,filename,classes,trainer,net):
		testLabels = []

		#load test data
		tstdata = ClassificationDataSet(103, 1, nb_classes=classes)
		tstdata = self.loaddata(filename, classes)

		testLabels = tstdata['target'];

		# some sort of mandatory conversion
		tstdata._convertToOneOfMany()
		
		# using numpy array
		output = np.array([net.activate(x) for x, _ in tstdata])
		output = output.argmax(axis=1)
		print(output)
		print("on test data",percentError( output, tstdata['class'] ))

		for i, l in enumerate(output):
			print l, '->', testLabels[i][0]

		# alternate version - using activateOnDataset function
		out = net.activateOnDataset(tstdata).argmax(axis=1)
		print out
		return percentError( out, tstdata['class'])

def createnetwork(n_hoglist,n_classlist,n_classnum,n_hiddensize=100):
    n_inputdim=len(n_hoglist[0])
    n_alldata = ClassificationDataSet(n_inputdim,1, nb_classes=n_classnum)
    for i in range(len(n_hoglist)):
        n_input = n_hoglist[i]
        n_class = n_classlist[i]
        n_alldata.addSample(n_input, [n_class])
    n_tstdata, n_trndata = n_alldata.splitWithProportion( 0.25 )
    n_trndata._convertToOneOfMany( )
    n_tstdata._convertToOneOfMany( )

    print "Number of training patterns: ", len(n_trndata)
    print "Input and output dimensions: ", n_trndata.indim, n_trndata.outdim
    print "First sample (input, target, class):"
    print n_trndata['input'][0], n_trndata['target'][0], n_trndata['class'][0]

    n_fnn = buildNetwork(n_trndata.indim,n_hiddensize, n_trndata.outdim, outclass=SoftmaxLayer)
    n_trainer = BackpropTrainer(n_fnn, dataset=n_trndata, momentum=0.1, verbose=True, weightdecay=0.01)

    n_result = 1
    while n_result > 0.1:
        print n_result
        n_trainer.trainEpochs(1)
        n_trnresult = percentError(n_trainer.testOnClassData(),
                                 n_trndata['class'])
        n_tstresult = percentError(n_trainer.testOnClassData(
            dataset=n_tstdata), n_tstdata['class'])

        print "epoch: %4d" % n_trainer.totalepochs, \
            "  train error: %5.2f%%" % n_trnresult, \
            "  test error: %5.2f%%" % n_tstresult
        n_result = n_tstresult

def trainNetwork(inData, numOfSamples, numOfPoints, epochs):
    # Build the dataset
    alldata = createRGBdataSet(inData, numOfSamples, numOfPoints)
    # Split into test and training data
    trndata, tstdata = splitData(alldata)

    # Report  stats
    print "Number of training patterns: ", len(trndata)
    print "Input and output dimensions: ", trndata.indim, trndata.outdim
    print "First sample (input, target, class):"
    print trndata['input'][0], trndata['target'][0], trndata['class'][0]

    # Build and train the network
    fnn = buildNetwork( trndata.indim, 256, trndata.outdim, outclass=SoftmaxLayer )
    trainer = BackpropTrainer( fnn, dataset=trndata, momentum=0.001, verbose=True, weightdecay=0.001)
    #trainer.trainEpochs( epochs )
    trainer.trainUntilConvergence(maxEpochs=epochs)

    # Report results
    trnresult = percentError( trainer.testOnClassData(), trndata['class'] )
    tstresult = percentError( trainer.testOnClassData( dataset=tstdata ), tstdata['class'] )
    print "epoch: %4d" % trainer.totalepochs, \
      "  train error: %5.2f%%" % trnresult, \
      "  test error: %5.2f%%" % tstresult

    # Report results of final network
    checkNeuralNet(trainer, alldata, numOfSamples)
    return fnn