Python FeedForwardNetwork.sortModules方法代码示例

# 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 sortModules [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")

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [as 别名]
def createNet():
    net = FeedForwardNetwork()
    modules = add_modules(net)
    add_connections(net, modules)
    # finish up
    net.sortModules()
    gradientCheck(net)
    return net

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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)

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [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

# 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 sortModules [as 别名]

#.........这里部分代码省略.........
    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
    )

    net.addConnection(FullConnection(h4, h5))
    net.addConnection(FullConnection(h5, outp))

    # finish up
    net.sortModules()
    logger.info("Done building network")
    return net