Python supervised.BackpropTrainer类代码示例

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

class EightBitBrain(object):
    
    def __init__(self, dataset, inNodes, outNodes, hiddenNodes, classes):
        self.__dataset = ClassificationDataSet(inNodes, classes-1)
        for element in dataset:
            self.addDatasetSample(self._binaryList(element[0]), element[1])
        self.__dataset._convertToOneOfMany()
        self.__network = buildNetwork(inNodes, hiddenNodes, self.__dataset.outdim, recurrent=True)
        self.__trainer = BackpropTrainer(self.__network, learningrate = 0.01, momentum = 0.99, verbose = True)
        self.__trainer.setData(self.__dataset)

    def _binaryList(self, n):
        return [int(c) for c in "{0:08b}".format(n)]
    
    def addDatasetSample(self, argument, target):
        self.__dataset.addSample(argument, target)

    def train(self, epochs):
        self.__trainer.trainEpochs(epochs)
    
    def activate(self, information):
        result = self.__network.activate(self._binaryList(information))
        highest = (0,0)
        for resultClass in range(len(result)):
            if result[resultClass] > highest[0]:
                highest = (result[resultClass], resultClass)
        return highest[1]

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 training(d):
    # net = buildNetwork(d.indim, 55, d.outdim, bias=True,recurrent=False, hiddenclass =SigmoidLayer , outclass = SoftmaxLayer)
    net = FeedForwardNetwork()
    inLayer = SigmoidLayer(d.indim)
    hiddenLayer1 = SigmoidLayer(d.outdim)
    hiddenLayer2 = SigmoidLayer(d.outdim)
    outLayer = SigmoidLayer(d.outdim)

    net.addInputModule(inLayer)
    net.addModule(hiddenLayer1)
    net.addModule(hiddenLayer2)
    net.addOutputModule(outLayer)

    in_to_hidden = FullConnection(inLayer, hiddenLayer1)
    hidden_to_hidden = FullConnection(hiddenLayer1, hiddenLayer2)
    hidden_to_out = FullConnection(hiddenLayer2, outLayer)

    net.addConnection(in_to_hidden)
    net.addConnection(hidden_to_hidden)
    net.addConnection(hidden_to_out)

    net.sortModules()
    print net

    t = BackpropTrainer(net, d, learningrate = 0.9,momentum=0.9, weightdecay=0.01, verbose = True)
    t.trainUntilConvergence(continueEpochs=1200, maxEpochs=1000)
    NetworkWriter.writeToFile(net, 'myNetwork'+str(time.time())+'.xml')
    return t

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 train(self, **kwargs):

        if "verbose" in kwargs:
            verbose = kwargs["verbose"]
        else:
            verbose = False

        """t = BackpropTrainer(self.rnn, dataset=self.trndata, learningrate = 0.1, momentum = 0.0, verbose = True)
        for i in range(1000):
            t.trainEpochs(5)

        """
       # pdb.set_trace()
        #print self.nn.outdim, " nn | ", self.trndata.outdim, " trndata "
        trainer = BackpropTrainer(self.nn, self.trndata, learningrate = 0.0005, momentum = 0.99)
        assert (self.tstdata is not None)
        assert (self.trndata is not None)
        b1, b2 = trainer.trainUntilConvergence(verbose=verbose,
                              trainingData=self.trndata,
                              validationData=self.tstdata,
                              maxEpochs=10)
        #print b1, b2
        #print "new parameters are: "
        #self.print_connections()

        return b1, b2

  def train(self, params):
    """
    Train TDNN network on buffered dataset history
    :param params:
    :return:
    """
    # self.net = buildNetwork(params['encoding_num'] * params['num_lags'],
    #                         params['num_cells'],
    #                         params['encoding_num'],
    #                         bias=True,
    #                         outputbias=True)

    ds = SupervisedDataSet(params['encoding_num'] * params['num_lags'],
                           params['encoding_num'])
    history = self.window(self.history, params['learning_window'])

    n = params['encoding_num']
    for i in xrange(params['num_lags'], len(history)):
      targets = numpy.zeros((1, n))
      targets[0, :] = self.encoder.encode(history[i])

      features = numpy.zeros((1, n * params['num_lags']))
      for lags in xrange(params['num_lags']):
        features[0, lags * n:(lags + 1) * n] = self.encoder.encode(
          history[i - (lags + 1)])
      ds.addSample(features, targets)

    trainer = BackpropTrainer(self.net,
                              dataset=ds,
                              verbose=params['verbosity'] > 0)

    if len(history) > 1:
      trainer.trainEpochs(params['num_epochs'])

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 gradientCheck(module, tolerance=0.0001, dataset=None):
    """ check the gradient of a module with a randomly generated dataset,
    (and, in the case of a network, determine which modules contain incorrect derivatives). """
    if module.paramdim == 0:
        print('Module has no parameters')
        return True
    if dataset:
        d = dataset
    else:
        d = buildAppropriateDataset(module)
    b = BackpropTrainer(module)
    res = b._checkGradient(d, True)
    # compute average precision on every parameter
    precision = zeros(module.paramdim)
    for seqres in res:
        for i, p in enumerate(seqres):
            if p[0] == 0 and p[1] == 0:
                precision[i] = 0
            else:
                precision[i] += abs((p[0] + p[1]) / (p[0] - p[1]))
    precision /= len(res)
    if max(precision) < tolerance:
        print('Perfect gradient')
        return True
    else:
        print('Incorrect gradient', precision)
        if isinstance(module, Network):
            index = 0
            for m in module._containerIterator():
                if max(precision[index:index + m.paramdim]) > tolerance:
                    print('Incorrect module:', m, res[-1][index:index + m.paramdim])
                index += m.paramdim
        else:
            print(res)
        return False

    def initializeNetwork(self):
        can1 = NNTrainData.NNTrainData(cv2.imread('NNTrain/can1.png'), self.encodingDict["can"])
        can2 = NNTrainData.NNTrainData(cv2.imread('NNTrain/can2.png'), self.encodingDict["can"])
        can3 = NNTrainData.NNTrainData(cv2.imread('NNTrain/can3.png'), self.encodingDict["can"])
        stain1 = NNTrainData.NNTrainData(cv2.imread('NNTrain/stain1.png'), self.encodingDict["stain"])
        stain2 = NNTrainData.NNTrainData(cv2.imread('NNTrain/stain2.png'), self.encodingDict["stain"])
        stain3 = NNTrainData.NNTrainData(cv2.imread('NNTrain/stain3.png'), self.encodingDict["stain"])
        dirt1 = NNTrainData.NNTrainData(cv2.imread('NNTrain/dirt1.png'), self.encodingDict["dirt"])
        dirt2 = NNTrainData.NNTrainData(cv2.imread('NNTrain/dirt2.png'), self.encodingDict["dirt"])
        dirt3 = NNTrainData.NNTrainData(cv2.imread('NNTrain/dirt3.png'), self.encodingDict["dirt"])

        self.trainData.append(can1)
        self.trainData.append(can2)
        self.trainData.append(can3)
        self.trainData.append(stain1)
        self.trainData.append(stain2)
        self.trainData.append(stain3)
        self.trainData.append(dirt1)
        self.trainData.append(dirt2)
        self.trainData.append(dirt3)

        for x in self.trainData:
            x.prepareTrainData()

        self.net = buildNetwork(4, 3, 3, hiddenclass=TanhLayer, outclass=SoftmaxLayer)
        ds = SupervisedDataSet(4, 3)

        for x in self.trainData:
            ds.addSample((x.contours/100.0, x.color[0]/1000.0, x.color[1]/1000.0, x.color[2]/1000.0), x.output)

        trainer = BackpropTrainer(self.net, momentum=0.1, verbose=True, weightdecay=0.01)
        trainer.trainOnDataset(ds, 1000)
        trainer.testOnData(verbose=True)
        print "\nSiec nauczona\n"

def generate_and_test_nn():
    d = load_training_set()
    n = buildNetwork(d.indim, 13, d.outdim, hiddenclass=LSTMLayer, outclass=SoftmaxLayer, outputbias=False, recurrent=True)
    t = BackpropTrainer(n, learningrate=0.01, momentum=0.99, verbose=True)
    t.trainOnDataset(d, 1000)
    t.testOnData(verbose=True)
    return (n, d)

def testOldTraining(hidden=15, n=None):
    d = XORDataSet()
    if n is None:
        n = buildNetwork(d.indim, hidden, d.outdim, recurrent=False)
    t = BackpropTrainer(n, learningrate=0.01, momentum=0., verbose=False)
    t.trainOnDataset(d, 250)
    t.testOnData(verbose=True)

    def execute(self):
        network = self.networkFactoryMethod()
        trainer = BackpropTrainer(network, learningrate = self.learningrate, momentum = self.momentum)
        trainer.trainOnDataset(self.datasetForTraining, self.epochs)
        averageError = trainer.testOnData(self.datasetForTest)
        self.collectedErrors.append(averageError)

        return averageError

def main():
    print '----- loading train/test datasets -----'
    train_ds, test_ds = create_datasets()
    print '----- building the network -----'
    net = ann_network()
    trainer = BackpropTrainer(net, learningrate=0.1, momentum=0.1, verbose=True)
    print '----- training the model -----'
    trainer.trainOnDataset(train_ds)

def training(d):
    """
    Builds a network and trains it.
    """
    n = buildNetwork(d.indim, 4, d.outdim,recurrent=True)
    t = BackpropTrainer(n, d, learningrate = 0.01, momentum = 0.99, verbose = True)
    for epoch in range(0,500):
        t.train()
    return t