def __init__(self, predefined = None, **kwargs):
        """ For the current implementation, the sequence length
        needs to be fixed, and given at construction time. """
        if predefined is not None:
            self.predefined = predefined
        else:
            self.predefined = {}
        FeedForwardNetwork.__init__(self, **kwargs)
        assert self.seqlen is not None

        # the input is a 1D-mesh (as a view on a flat input layer)
        inmod = LinearLayer(self.inputsize * self.seqlen, name='input')
        inmesh = ModuleMesh.viewOnFlatLayer(inmod, (self.seqlen,), 'inmesh')

        # the output is also a 1D-mesh
        outmod = self.outcomponentclass(self.outputsize * self.seqlen, name='output')
        outmesh = ModuleMesh.viewOnFlatLayer(outmod, (self.seqlen,), 'outmesh')

        # the hidden layers are places in a 2xseqlen mesh
        hiddenmesh = ModuleMesh.constructWithLayers(self.componentclass, self.hiddensize,
                                                    (2, self.seqlen), 'hidden')

        # add the modules
        for c in inmesh:
            self.addInputModule(c)
        for c in outmesh:
            self.addOutputModule(c)
        for c in hiddenmesh:
            self.addModule(c)

        # set the connections weights to be shared
        inconnf = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
        outconnf = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')
        forwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='fconn')
        if self.symmetric:
            backwardconn = forwardconn
            inconnb = inconnf
            outconnb = outconnf
        else:
            backwardconn = MotherConnection(hiddenmesh.componentIndim * hiddenmesh.componentOutdim, name='bconn')
            inconnb = MotherConnection(inmesh.componentOutdim * hiddenmesh.componentIndim, name='inconn')
            outconnb = MotherConnection(outmesh.componentIndim * hiddenmesh.componentOutdim, name='outconn')

        # build the connections
        for i in range(self.seqlen):
            # input to hidden
            self.addConnection(SharedFullConnection(inconnf, inmesh[(i,)], hiddenmesh[(0, i)]))
            self.addConnection(SharedFullConnection(inconnb, inmesh[(i,)], hiddenmesh[(1, i)]))
            # hidden to output
            self.addConnection(SharedFullConnection(outconnf, hiddenmesh[(0, i)], outmesh[(i,)]))
            self.addConnection(SharedFullConnection(outconnb, hiddenmesh[(1, i)], outmesh[(i,)]))
            if i > 0:
                # forward in time
                self.addConnection(SharedFullConnection(forwardconn, hiddenmesh[(0, i - 1)], hiddenmesh[(0, i)]))
            if i < self.seqlen - 1:
                # backward in time
                self.addConnection(SharedFullConnection(backwardconn, hiddenmesh[(1, i + 1)], hiddenmesh[(1, i)]))

        self.sortModules()

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

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

 def __init__(self, boardSize, convSize, numFeatureMaps, **args):
     inputdim = 2
     FeedForwardNetwork.__init__(self, **args)
     inlayer = LinearLayer(inputdim*boardSize*boardSize, name = 'in')
     self.addInputModule(inlayer)
     
     # we need some treatment of the border too - thus we pad the direct board input.
     x = convSize/2
     insize = boardSize+2*x
     if convSize % 2 == 0: 
         insize -= 1            
     paddedlayer = LinearLayer(inputdim*insize*insize, name = 'pad')
     self.addModule(paddedlayer)
     
     # we connect a bias to the padded-parts (with shared but trainable weights).
     bias = BiasUnit()
     self.addModule(bias)
     biasConn = MotherConnection(inputdim)
     
     paddable = []
     if convSize % 2 == 0: 
         xs = range(x)+range(insize-x+1, insize)
     else:
         xs = range(x)+range(insize-x, insize)
     paddable.extend(crossproduct([range(insize), xs]))
     paddable.extend(crossproduct([xs, range(x, boardSize+x)]))
     
     for (i, j) in paddable:
         self.addConnection(SharedFullConnection(biasConn, bias, paddedlayer, 
                                                 outSliceFrom = (i*insize+j)*inputdim, 
                                                 outSliceTo = (i*insize+j+1)*inputdim))
             
     for i in range(boardSize):
         inmod = ModuleSlice(inlayer, outSliceFrom = i*boardSize*inputdim, 
                             outSliceTo = (i+1)*boardSize*inputdim)
         outmod = ModuleSlice(paddedlayer, inSliceFrom = ((i+x)*insize+x)*inputdim, 
                              inSliceTo = ((i+x)*insize+x+boardSize)*inputdim)
         self.addConnection(IdentityConnection(inmod, outmod))
         
     self._buildStructure(inputdim, insize, paddedlayer, convSize, numFeatureMaps)
     self.sortModules()
                     

    def training(self,d):
        """
        Builds a network ,trains and returns it
        """

        self.net = FeedForwardNetwork()

        inLayer = LinearLayer(4) # 4 inputs
        hiddenLayer = SigmoidLayer(3) # 5 neurons on hidden layer with sigmoid function
        outLayer = LinearLayer(2) # 2 neuron as output layer


        "add layers to NN"
        self.net.addInputModule(inLayer)
        self.net.addModule(hiddenLayer)
        self.net.addOutputModule(outLayer)

        "create connections"
        in_to_hidden = FullConnection(inLayer, hiddenLayer)
        hidden_to_out = FullConnection(hiddenLayer, outLayer)

        "add connections"
        self.net.addConnection(in_to_hidden)
        self.net.addConnection(hidden_to_out)

        "some unknown but necessary function :)"
        self.net.sortModules()

        print self.net

        "generate big sized training set"
        trainingSet = SupervisedDataSet(4,2)

        trainArr = self.generate_training_set()
        for ri in range(2000):
            input = ((trainArr[0][ri][0],trainArr[0][ri][1],trainArr[0][ri][2],trainArr[0][ri][3]))
            target = ((trainArr[1][ri][0],trainArr[1][ri][1]))
            trainingSet.addSample(input, target)

        "create backpropogation trainer"
        t = BackpropTrainer(self.net,d,learningrate=0.00001, momentum=0.99)
        while True:
            globErr = t.train()
            print "global error:", globErr
            if globErr < 0.0001:
                break

        return self.net

    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 _generate_pybrain_network(self):
     # make network
     self._pybrain_network = FeedForwardNetwork()
     # make layers
     self._in_layer = LinearLayer(self.n_input_neurons, name='in')
     self._hidden_layer = SigmoidLayer(self.n_hidden_neurons, name='hidden')
     self._out_layer = LinearLayer(self.n_output_neurons, name='out')
     self._bias_neuron = BiasUnit(name='bias')
     # make connections between layers
     self._in_hidden_connection = FullConnection(self._in_layer, self._hidden_layer)
     self._hidden_out_connection = FullConnection(self._hidden_layer, self._out_layer)
     self._bias_hidden_connection = FullConnection(self._bias_neuron, self._hidden_layer)
     self._bias_out_connection = FullConnection(self._bias_neuron, self._out_layer)
     # add modules to network
     self._pybrain_network.addInputModule(self._in_layer)
     self._pybrain_network.addModule(self._hidden_layer)
     self._pybrain_network.addOutputModule(self._out_layer)
     self._pybrain_network.addModule(self._bias_neuron)
     # add connections to network
     for c in (self._in_hidden_connection, self._hidden_out_connection, self._bias_hidden_connection, self._bias_out_connection):
         self._pybrain_network.addConnection(c)
     # initialize network with added modules/connections
     self._pybrain_network.sortModules()

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

    net = FeedForwardNetwork()
    layerParts = iter(layers)
    firstPart = iter(layerParts.next())
    firstLayer = firstPart.next()
    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))
            else:
                ConnectionClass = IdentityConnection
            new_part = False
            conn = ConnectionClass(prevLayer, layer)
            net.addConnection(conn)
            prevLayer = layer
    net.addOutputModule(layer)
    net.sortModules()
    return net

 def __init__(self, inputdim, insize, convSize, numFeatureMaps, **args):
     FeedForwardNetwork.__init__(self, **args)
     inlayer = LinearLayer(inputdim * insize * insize)
     self.addInputModule(inlayer)
     self._buildStructure(inputdim, insize, inlayer, convSize, numFeatureMaps)
     self.sortModules()

    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

class MLP:

    data = SupervisedDataSet
    net = FeedForwardNetwork

    def generate_training_set(self):
        random.seed()
        ind = floor(empty((2000,4)))
        outd = floor(empty((2000, 2)))

        res = array((ind,outd))

        print ind
        print
        print outd
        print
        print res

        for i in range(2000):
            n = random.getrandbits(1)
            if n == 0:
                a = random.randint(0,100)
                b = random.randint(0,100)
                c = random.randint(100,5000)
                d = random.randint(100,5000)
                res[0][i][0] = a
                res[0][i][1] = b
                res[0][i][2] = c
                res[0][i][3] = d

                res[1][i][0] = 0
                res[1][i][1] = 1

            else:
                a = random.randint(100,5000)
                b = random.randint(100,5000)
                c = random.randint(0,100)
                d = random.randint(0,100)
                res[0][i][0] = a
                res[0][i][1] = b
                res[0][i][2] = c
                res[0][i][3] = d

                res[1][i][0] = 1
                res[1][i][1] = 0

        for i in range(2000):
            print res[0][i][0],res[0][i][1],res[0][i][2],res[0][i][3], " out", res[1][i][0],res[1][i][1]
        return res

    def getFullDataSet(self):
        res = zeros((50**4, 4))
        a = 0
        b = 0
        c = 0
        d = 0
        for i in range(len(res)):
            if (a % 50 == 0):
                a = 0
            a = a + 1
            if (i % 2 == 0):
                if (b % 50 == 0):
                    b = 0
                b = b + 1

            if (i % 4 == 0):
                if (c % 50 == 0):
                    c = 0
                c = c + 1
            if (i % 8 ==0):
                if (d % 50 == 0):
                    d = 0
                d = d + 1
            res[i][0] = a
            res[i][1] = b
            res[i][2] = c
            res[i][3] = d

        res += 75

        return res

    def make_dataset(self):
        """
        Creates a set of training data with 2-dimensioanal input and 2-dimensional output
        So how dataset have to be looks like?
        """
        self.data = SupervisedDataSet(4,2)

        self.data.addSample((1,1,150,150),(0,1))
        self.data.addSample((1,1,199,142),(0,1))
        self.data.addSample((150,120,43,12),(1,0))
        self.data.addSample((198,123,54,65),(1,0))

        return self.data


    def training(self,d):
        """
        Builds a network ,trains and returns it
#.........这里部分代码省略.........

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.........

 def __init__(self, **args):
     FeedForwardNetwork.__init__(self, **args)

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

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

 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

ds.addSample((1, 1), (0,))

for input, target in ds:
    print(input, target)
    
#define layers and connections
inLayer = LinearLayer(2)
hiddenLayerOne = SigmoidLayer(4, "one")
hiddenLayerTwo = SigmoidLayer(4, "two")
outLayer = LinearLayer(1)
inToHiddenOne = FullConnection(inLayer, hiddenLayerOne)
hiddenOneToTwo = FullConnection(hiddenLayerOne, hiddenLayerTwo)
hiddenTwoToOut = FullConnection(hiddenLayerTwo, outLayer)

#wire the layers and connections to a net
net = FeedForwardNetwork()
net.addInputModule(inLayer)
net.addModule(hiddenLayerOne)
net.addModule(hiddenLayerTwo)
net.addOutputModule(outLayer)
net.addConnection(inToHiddenOne)
net.addConnection(hiddenOneToTwo)
net.addConnection(hiddenTwoToOut)
net.sortModules()

print(net)

trainer = BackpropTrainer(net, ds)

for i in range(20):
    for j in range(1000):               

    bias_to_out = FullConnection(biasUnit, outLayer)

    tosave = [ inLayer, hiddenLayer, outLayer, biasUnit, in_to_hidden, hidden_to_out, bias_to_hidden, bias_to_out ];

    return tosave


if (len(sys.argv) <= 3):
    saved = buildNet()
else:
    saved = pickle.load(open(sys.argv[3], "rb"));

pickle.dump( saved, open( "pablosemptynet.p", "wb" ) )


net = FeedForwardNetwork(name='mynet');

net.addInputModule(saved[0])
net.addModule(saved[1])
net.addOutputModule(saved[2])
net.addModule(saved[3])
net.addConnection(saved[4])
net.addConnection(saved[5])
net.addConnection(saved[6])
net.addConnection(saved[7])

net.sortModules()

trainer = BackpropTrainer(net, None, learningrate=lrate, verbose=False, batchlearning=True, weightdecay=wdecay)                                        
stressErrors=list();
phonemeErrors=list();

class Network:
    "NETwhisperer neural network"
        
    def phoneme_to_layer(self, phoneme):
        return self.phonemes_to_layers[phoneme]

    def layer_to_phoneme(self, layer):
        def cos_to_input(item):
            phoneme, phoneme_layer = item
            return _cos(layer,phoneme_layer)
        # minimum angle should be maximum cos    
        return max(self.phonemes_to_layers.iteritems(), key=cos_to_input)[0]    

    def __init__(self, window_size, window_middle, n_hidden_neurons):
        self.window_size = window_size
        self.window_middle = window_middle
        self.n_hidden_neurons = n_hidden_neurons
        self.n_trainings = 0
        self.training_errors = []
        self._init_layers()
        self._generate_pybrain_network()
        
    def _init_layers(self):
        # one neuron for each window/letter combination
        self.letter_neuron_names = list(product(range(self.window_size), corpus.all_letters))
        # one neuron for each phoneme trait
        self.phoneme_trait_neuron_names = list(corpus.all_phoneme_traits)
        # neuron counts
        self.n_input_neurons = len(self.letter_neuron_names)
        self.n_output_neurons = len(self.phoneme_trait_neuron_names)        
        # mapping from (pos, letter) to input neuron index
        self.letters_to_neurons = dict({(pos_and_letter, index) for index, pos_and_letter in enumerate(self.letter_neuron_names)})
        # mapping from trait to neuron
        self.traits_to_neurons = dict({(trait, index) for index, trait in enumerate(self.phoneme_trait_neuron_names)})
        # mapping from phoneme to layer
        self.phonemes_to_layers = {}
        for (phoneme, traits) in corpus.phoneme_traits.iteritems():
            layer = zeros(self.n_output_neurons)
            for trait in traits:
                index = self.traits_to_neurons[trait]
                layer[index] = 1
            self.phonemes_to_layers[phoneme] = layer
            
    def _generate_pybrain_network(self):
        # make network
        self._pybrain_network = FeedForwardNetwork()
        # make layers
        self._in_layer = LinearLayer(self.n_input_neurons, name='in')
        self._hidden_layer = SigmoidLayer(self.n_hidden_neurons, name='hidden')
        self._out_layer = LinearLayer(self.n_output_neurons, name='out')
        self._bias_neuron = BiasUnit(name='bias')
        # make connections between layers
        self._in_hidden_connection = FullConnection(self._in_layer, self._hidden_layer)
        self._hidden_out_connection = FullConnection(self._hidden_layer, self._out_layer)
        self._bias_hidden_connection = FullConnection(self._bias_neuron, self._hidden_layer)
        self._bias_out_connection = FullConnection(self._bias_neuron, self._out_layer)
        # add modules to network
        self._pybrain_network.addInputModule(self._in_layer)
        self._pybrain_network.addModule(self._hidden_layer)
        self._pybrain_network.addOutputModule(self._out_layer)
        self._pybrain_network.addModule(self._bias_neuron)
        # add connections to network
        for c in (self._in_hidden_connection, self._hidden_out_connection, self._bias_hidden_connection, self._bias_out_connection):
            self._pybrain_network.addConnection(c)
        # initialize network with added modules/connections
        self._pybrain_network.sortModules()

    def windowIter(self, letters):
        assert type(letters) == str
        padding_before = ' ' * self.window_middle
        padding_after = ' ' * (self.window_size - self.window_middle - 1)
        padded_letters = padding_before + letters + padding_after
        # for each letter in the sample
        for l_num in range(len(letters)):
            letters_window = padded_letters[l_num:l_num+self.window_size]
            yield letters_window    

    def generateSamples(self, letters, phonemes):
        assert len(letters) == len(phonemes)
        for (letters_window, current_phoneme) in izip(self.windowIter(letters), phonemes):
            yield self.letters_to_layer(letters_window), self.phoneme_to_layer(current_phoneme)

    def letters_to_layer(self, letters):
        assert len(letters) == self.window_size
        # start with empty layer
        layer = zeros(self.n_input_neurons)
        # loop through letters and activate each neuron
        for (pos, letter) in enumerate(letters):
            index = self.letters_to_neurons[(pos, letter)]
            layer[index] = 1
        return layer
        
    def train(self, training_set, n_epochs=1, callback=None):
        # build dataset
        dataset = DataSet(self.n_input_neurons, self.n_output_neurons)
        for (ltr,ph) in training_set:
            for sample in self.generateSamples(ltr,ph):
                dataset.addSample(*sample)
        # build trainer
        trainer = Trainer(self._pybrain_network, dataset, 0.01, 1.0, 0.9)
#.........这里部分代码省略.........

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")