当前位置: 首页>>代码示例>>Python>>正文


Python FeedForwardNetwork.addInputModule方法代码示例

本文整理汇总了Python中pybrain.structure.networks.feedforward.FeedForwardNetwork.addInputModule方法的典型用法代码示例。如果您正苦于以下问题:Python FeedForwardNetwork.addInputModule方法的具体用法?Python FeedForwardNetwork.addInputModule怎么用?Python FeedForwardNetwork.addInputModule使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pybrain.structure.networks.feedforward.FeedForwardNetwork的用法示例。


在下文中一共展示了FeedForwardNetwork.addInputModule方法的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: main

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
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")
开发者ID:Kerzak1408,项目名称:HearthstoneAI,代码行数:35,代码来源:neural_network.py

示例2: __init__

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
    def __init__(self, states, verbose=False, max_epochs=None):
        '''Create a NeuralNetwork instance.

        `states` is a tuple of tuples of ints, representing the discovered subnetworks'
        entrez ids.
        '''
        self.verbose         = verbose
        self.max_epochs      = max_epochs
        self.num_features    = sum(map(lambda tup: len(tup), states))
        self.states          = states

        n = FeedForwardNetwork()
        n.addOutputModule(TanhLayer(1, name='out'))
        n.addModule(BiasUnit(name='bias out'))
        n.addConnection(FullConnection(n['bias out'], n['out']))

        for i, state in enumerate(states):
            dim = len(state)
            n.addInputModule(TanhLayer(dim, name='input %s' % i))
            n.addModule(BiasUnit(name='bias input %s' % i))
            n.addConnection(FullConnection(n['bias input %s' % i], n['input %s' % i]))
            n.addConnection(FullConnection(n['input %s' % i], n['out']))

        n.sortModules()
        self.n = n
开发者ID:mrorii,项目名称:crane,代码行数:27,代码来源:neural_network.py

示例3: buildSharedCrossedNetwork

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def buildSharedCrossedNetwork():
    """ build a network with shared connections. Two hidden modules are
    symmetrically linked, but to a different input neuron than the
    output neuron. The weights are random. """
    N = FeedForwardNetwork('shared-crossed')
    h = 1
    a = LinearLayer(2, name = 'a')
    b = LinearLayer(h, name = 'b')
    c = LinearLayer(h, name = 'c')
    d = LinearLayer(2, name = 'd')
    N.addInputModule(a)
    N.addModule(b)
    N.addModule(c)
    N.addOutputModule(d)

    m1 = MotherConnection(h)
    m1.params[:] = scipy.array((1,))

    m2 = MotherConnection(h)
    m2.params[:] = scipy.array((2,))

    N.addConnection(SharedFullConnection(m1, a, b, inSliceTo = 1))
    N.addConnection(SharedFullConnection(m1, a, c, inSliceFrom = 1))
    N.addConnection(SharedFullConnection(m2, b, d, outSliceFrom = 1))
    N.addConnection(SharedFullConnection(m2, c, d, outSliceTo = 1))
    N.sortModules()
    return N
开发者ID:kortschak,项目名称:pybrain,代码行数:29,代码来源:test_shared_connections.py

示例4: buildXor

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
 def buildXor(self):
     self.params['dataset'] = 'XOR'
     d = ClassificationDataSet(2)
     d.addSample([0., 0.], [0.])
     d.addSample([0., 1.], [1.])
     d.addSample([1., 0.], [1.])
     d.addSample([1., 1.], [0.])
     d.setField('class', [[0.], [1.], [1.], [0.]])
     self.trn_data = d
     self.tst_data = d
     global trn_data
     trn_data = self.trn_data
     nn = FeedForwardNetwork()
     inLayer = TanhLayer(2, name='in')
     hiddenLayer = TanhLayer(3, name='hidden0')
     outLayer = ThresholdLayer(1, name='out')
     nn.addInputModule(inLayer)
     nn.addModule(hiddenLayer)
     nn.addOutputModule(outLayer)
     in_to_hidden = FullConnection(inLayer, hiddenLayer)
     hidden_to_out = FullConnection(hiddenLayer, outLayer)
     nn.addConnection(in_to_hidden)
     nn.addConnection(hidden_to_out)
     nn.sortModules()
     nn.randomize()
     self.net_settings = str(nn.connections)
     self.nn = nn
开发者ID:mfbx9da4,项目名称:neuron-astrocyte-networks,代码行数:29,代码来源:pybrain_ga.py

示例5: custom_build_network

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def custom_build_network(layer_sizes):
    net = FeedForwardNetwork()
    
    layers = []
    inp = SigmoidLayer(layer_sizes[0], name = 'visible')
    h1 = SigmoidLayer(layer_sizes[1], name = 'hidden1')
    h2 = SigmoidLayer(layer_sizes[2], name = 'hidden2')
    out = SigmoidLayer(layer_sizes[3], name = 'out')
    bias = BiasUnit(name = 'bias')
    
    net.addInputModule(inp)
    net.addModule(h1)
    net.addModule(h2)
    net.addOutputModule(out)
    net.addModule(bias)
    
    net.addConnection(FullConnection(inp, h1))
    net.addConnection(FullConnection(h1, h2))
    net.addConnection(FullConnection(h2, out))
    
    net.addConnection(FullConnection(bias, h1))
    net.addConnection(FullConnection(bias, h2))
    net.addConnection(FullConnection(bias, out))
    
    
    net.sortModules()
    return net
开发者ID:simonhughes22,项目名称:PythonNlpResearch,代码行数:29,代码来源:testPyBrain.py

示例6: buildSubsamplingNetwork

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def buildSubsamplingNetwork():
    """ Builds a network with subsampling connections. """
    n = FeedForwardNetwork()
    n.addInputModule(LinearLayer(6, 'in'))
    n.addOutputModule(LinearLayer(1, 'out'))
    n.addConnection(SubsamplingConnection(n['in'], n['out'], inSliceTo=4))
    n.addConnection(SubsamplingConnection(n['in'], n['out'], inSliceFrom=4))
    n.sortModules()
    return n
开发者ID:davidmiller,项目名称:pybrain,代码行数:11,代码来源:test_subsampling_connection.py

示例7: _buildNetwork

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def _buildNetwork(*layers, **options):
    """This is a helper function to create different kinds of networks.

    `layers` is a list of tuples. Each tuple can contain an arbitrary number of
    layers, each being connected to the next one with IdentityConnections. Due
    to this, all layers have to have the same dimension. We call these tuples
    'parts.'

    Afterwards, the last layer of one tuple is connected to the first layer of
    the following tuple by a FullConnection.

    If the keyword argument bias is given, BiasUnits are added additionally with
    every FullConnection.

    Example:

        _buildNetwork(
            (LinearLayer(3),),
            (SigmoidLayer(4), GaussianLayer(4)),
            (SigmoidLayer(3),),
        )
    """
    bias = options['bias'] if 'bias' in options else False
    use_random_seed = options['use_random_seed'] if 'use_random_seed' in options else False

    net = FeedForwardNetwork()
    layerParts = iter(layers)
    firstPart = iter(next(layerParts))
    firstLayer = next(firstPart)
    net.addInputModule(firstLayer)

    prevLayer = firstLayer

    for part in chain(firstPart, layerParts):
        new_part = True
        for layer in part:
            net.addModule(layer)
            # Pick class depending on whether we entered a new part
            if new_part:
                ConnectionClass = FullConnection
                if bias:
                    biasUnit = BiasUnit('BiasUnit for %s' % layer.name)
                    net.addModule(biasUnit)
                    net.addConnection(FullConnection(biasUnit, layer, use_random_seed=use_random_seed))
            else:
                ConnectionClass = IdentityConnection
            new_part = False
            conn = ConnectionClass(prevLayer, layer)
            net.addConnection(conn)
            prevLayer = layer
    net.addOutputModule(layer)
    net.sortModules()
    return net
开发者ID:theoryno3,项目名称:pybrain,代码行数:55,代码来源:shortcuts.py

示例8: buildSlicedNetwork

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def buildSlicedNetwork():
    """ build a network with shared connections. Two hiddne modules are symetrically linked, but to a different 
    input neuron than the output neuron. The weights are random. """
    N = FeedForwardNetwork('sliced')
    a = LinearLayer(2, name = 'a')
    b = LinearLayer(2, name = 'b')
    N.addInputModule(a)
    N.addOutputModule(b)
    
    N.addConnection(FullConnection(a, b, inSliceTo=1, outSliceFrom=1))
    N.addConnection(FullConnection(a, b, inSliceFrom=1, outSliceTo=1))
    N.sortModules()
    return N
开发者ID:HKou,项目名称:pybrain,代码行数:15,代码来源:test_sliced_connections.py

示例9: __init__

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
class PyBrainANNs:
    def __init__(self, x_dim, y_dim, hidden_size, s_id):
        self.serialize_id = s_id
        self.net = FeedForwardNetwork()

        in_layer = LinearLayer(x_dim)
        hidden_layer = SigmoidLayer(hidden_size)
        out_layer = LinearLayer(y_dim)
        self.net.addInputModule(in_layer)
        self.net.addModule(hidden_layer)
        self.net.addOutputModule(out_layer)

        in_to_hidden = FullConnection(in_layer, hidden_layer)
        hidden_to_out = FullConnection(hidden_layer, out_layer)
        self.net.addConnection(in_to_hidden)
        self.net.addConnection(hidden_to_out)

        self.net.sortModules()

    def _prepare_dataset(self, x_data, y_data):
        assert x_data.shape[0] == y_data.shape[0]

        if len(y_data.shape) == 1:
            y_matrix = np.matrix(y_data).T
        else:
            y_matrix = y_data.values

        assert x_data.shape[1] == self.net.indim
        assert y_matrix.shape[1] == self.net.outdim

        data_set = SupervisedDataSet(self.net.indim, self.net.outdim)
        data_set.setField("input", x_data)
        data_set.setField("target", y_matrix)

        return data_set

    def train(self, x_data, y_data):
        trainer = BackpropTrainer(self.net, self._prepare_dataset(x_data, y_data))
        trainer.train()

    def score(self, x_data, y_datas):
        return ModuleValidator.validate(regression_score, self.net, self._prepare_dataset(x_data, y_datas))

    def predict(self, x_data):
        return np.array([self.net.activate(sample) for sample in x_data])

    def save(self, path):
        joblib.dump(self.net, path)

    def load(self, path):
        self.net = joblib.load(path)
开发者ID:erdincay,项目名称:ScoreGrass,代码行数:53,代码来源:PyBrainANNs.py

示例10: createNN

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def createNN():
	nn = FeedForwardNetwork()
	inLayer = TanhLayer(4, name='in')
	hiddenLayer = TanhLayer(6, name='hidden0')
	outLayer = ThresholdLayer(3)
	nn.addInputModule(inLayer)
	nn.addModule(hiddenLayer)
	nn.addOutputModule(outLayer)
	in_to_hidden = FullConnection(inLayer, hiddenLayer)
	hidden_to_out = FullConnection(hiddenLayer, outLayer)
	nn.addConnection(in_to_hidden)
	nn.addConnection(hidden_to_out)
	nn.sortModules()
	return nn
开发者ID:mfbx9da4,项目名称:neuron-astrocyte-networks,代码行数:16,代码来源:testmain.py

示例11: buildnet

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def buildnet(modules):
    net = FeedForwardNetwork(name='mynet');
    net.addInputModule(modules['in'])
    net.addModule(modules['hidden'])
    net.addOutputModule(modules['out'])
    net.addModule(modules['bias'])
    net.addConnection(modules['in_to_hidden'])
    net.addConnection(modules['bias_to_hidden'])
    net.addConnection(modules['bias_to_out'])
    if ('hidden2' in modules):
        net.addModule(modules['hidden2'])
        net.addConnection(modules['hidden_to_hidden2'])
        net.addConnection(modules['bias_to_hidden2'])
        net.addConnection(modules['hidden2_to_out'])
    else:
        net.addConnection(modules['hidden_to_out'])
    net.sortModules()
    return net
开发者ID:gnrhxni,项目名称:CS542,代码行数:20,代码来源:nettalk_modules.py

示例12: buildIris

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
 def buildIris(self):
     self.params['dataset'] = 'iris'
     self.trn_data, self.tst_data = pybrainData(0.5)
     global trn_data
     trn_data = self.trn_data
     nn = FeedForwardNetwork()
     inLayer = TanhLayer(4, name='in')
     hiddenLayer = TanhLayer(6, name='hidden0')
     outLayer = ThresholdLayer(3, name='out')
     nn.addInputModule(inLayer)
     nn.addModule(hiddenLayer)
     nn.addOutputModule(outLayer)
     in_to_hidden = FullConnection(inLayer, hiddenLayer)
     hidden_to_out = FullConnection(hiddenLayer, outLayer)
     nn.addConnection(in_to_hidden)
     nn.addConnection(hidden_to_out)
     nn.sortModules()
     nn.randomize()
     self.net_settings = str(nn.connections)
     self.nn = nn
开发者ID:mfbx9da4,项目名称:neuron-astrocyte-networks,代码行数:22,代码来源:pybrain_ga.py

示例13: buildParity

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
 def buildParity(self):
     self.params['dataset'] = 'parity'
     self.trn_data = ParityDataSet(nsamples=75)
     self.trn_data.setField('class', self.trn_data['target'])
     self.tst_data = ParityDataSet(nsamples=75)
     global trn_data
     trn_data = self.trn_data
     nn = FeedForwardNetwork()
     inLayer = TanhLayer(4, name='in')
     hiddenLayer = TanhLayer(6, name='hidden0')
     outLayer = ThresholdLayer(1, name='out')
     nn.addInputModule(inLayer)
     nn.addModule(hiddenLayer)
     nn.addOutputModule(outLayer)
     in_to_hidden = FullConnection(inLayer, hiddenLayer)
     hidden_to_out = FullConnection(hiddenLayer, outLayer)
     nn.addConnection(in_to_hidden)
     nn.addConnection(hidden_to_out)
     nn.sortModules()
     nn.randomize()
     self.net_settings = str(nn.connections)
     self.nn = nn
开发者ID:mfbx9da4,项目名称:neuron-astrocyte-networks,代码行数:24,代码来源:pybrain_ga.py

示例14: FeedForwardNetwork

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
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()


print 'build set'

alldata = ClassificationDataSet(dim, 1, nb_classes=2)
开发者ID:343829084,项目名称:AliRecommendProject,代码行数:33,代码来源:BPNetwork.py

示例15: _build_network

# 需要导入模块: from pybrain.structure.networks.feedforward import FeedForwardNetwork [as 别名]
# 或者: from pybrain.structure.networks.feedforward.FeedForwardNetwork import addInputModule [as 别名]
def _build_network():
    logger.info("Building network...")

    net = FeedForwardNetwork()
    inp = LinearLayer(IMG_WIDTH * IMG_HEIGHT * 2)
    h1_image_width = IMG_WIDTH - FIRST_CONVOLUTION_FILTER + 1
    h1_image_height = IMG_HEIGHT - FIRST_CONVOLUTION_FILTER + 1
    h1_full_width = h1_image_width * CONVOLUTION_MULTIPLIER * NUMBER_OF_IMAGES
    h1_full_height = h1_image_height * CONVOLUTION_MULTIPLIER
    h1 = SigmoidLayer(h1_full_width * h1_full_height)

    h2_width = h1_full_width / 2
    h2_height = h1_full_height / 2
    h2 = LinearLayer(h2_width * h2_height)

    h3_image_width = h2_width / CONVOLUTION_MULTIPLIER / NUMBER_OF_IMAGES - SECOND_CONVOLUTION_FILTER + 1
    h3_image_height = h2_height / CONVOLUTION_MULTIPLIER - SECOND_CONVOLUTION_FILTER + 1
    h3_full_width = h3_image_width * (CONVOLUTION_MULTIPLIER * 2) * NUMBER_OF_IMAGES
    h3_full_height = h3_image_height * (CONVOLUTION_MULTIPLIER * 2)
    h3 = SigmoidLayer(h3_full_width * h3_full_height)

    h4_full_width = h3_image_width - MERGE_FILTER
    h4_full_height = h3_image_height - MERGE_FILTER
    h4 = SigmoidLayer(h4_full_width * h4_full_height)

    logger.info("BASE IMG: %d x %d" % (IMG_WIDTH, IMG_HEIGHT))
    logger.info("First layer IMG: %d x %d" % (h1_image_width, h1_image_height))
    logger.info("First layer FULL: %d x %d" % (h1_full_width, h1_full_height))
    logger.info("Second layer FULL: %d x %d" % (h2_width, h2_height))
    logger.info("Third layer IMG: %d x %d" % (h3_image_width, h3_image_height))
    logger.info("Third layer FULL: %d x %d" % (h3_full_width, h3_full_height))
    logger.info("Forth layer FULL: %d x %d" % (h3_image_width, h3_image_height))
    outp = SoftmaxLayer(2)

    h5 = SigmoidLayer(h4_full_width * h4_full_height)

    # add modules
    net.addOutputModule(outp)
    net.addInputModule(inp)
    net.addModule(h1)
    net.addModule(h2)
    net.addModule(h3)
    net.addModule(h4)
    net.addModule(h5)

    # create connections

    for i in range(NUMBER_OF_IMAGES):
        _add_convolutional_connection(
            net=net,
            h1=inp,
            h2=h1,
            filter_size=FIRST_CONVOLUTION_FILTER,
            multiplier=CONVOLUTION_MULTIPLIER,
            input_width=IMG_WIDTH * 2,
            input_height=IMG_HEIGHT,
            output_width=h1_full_width,
            output_height=h1_full_height,
            offset_x=h1_image_width * i,
            offset_y=0,
            size_x=h1_image_width,
            size_y=h1_image_height
        )

    _add_pool_connection(
        net=net,
        h1=h1,
        h2=h2,
        input_width=h1_full_width,
        input_height=h1_full_height
    )

    for i in range(NUMBER_OF_IMAGES * CONVOLUTION_MULTIPLIER):
        for j in range(CONVOLUTION_MULTIPLIER):
            _add_convolutional_connection(
                net=net,
                h1=h2,
                h2=h3,
                filter_size=SECOND_CONVOLUTION_FILTER,
                multiplier=CONVOLUTION_MULTIPLIER,
                input_width=h2_width,
                input_height=h2_height,
                output_width=h3_full_width,
                output_height=h3_full_height,
                offset_x=h3_image_width * i,
                offset_y=h3_image_height * j,
                size_x=h3_image_width,
                size_y=h3_image_height
            )

    _merge_connection(
        net=net,
        h1=h3,
        h2=h4,
        filter_size=MERGE_FILTER,
        input_width=h3_full_width,
        input_height=h3_full_height,
        output_width=h4_full_width,
        output_height=h4_full_height
    )
#.........这里部分代码省略.........
开发者ID:ShadowswordPL,项目名称:PowerRecruiter,代码行数:103,代码来源:neural_network.py


注:本文中的pybrain.structure.networks.feedforward.FeedForwardNetwork.addInputModule方法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。