Python Network.create方法代码示例

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import create [as 别名]
from network import Network
nn=Network.create([4, 1000, 1])

lamb=0.3
cost=1
alf = 0.2
xTrain = [[1, 2.3, 4.5, 5.3], [1.1, 1.3, 2.4, 2.4], [1.9, 1.7, 1.5, 1.3], [2.3, 2.9, 3.3, 4.9], [3, 5.2, 6.1, 8.2], [3.31, 2.9, 2.4, 1.5], [4.9, 5.7, 6.1, 6.3],
 [4.85, 5.0, 7.2, 8.1], [5.9, 5.3, 4.2, 3.3], [7.7, 5.4, 4.3, 3.9], [6.7, 5.3, 3.2, 1.4], [7.1, 8.6, 9.1, 9.9], [8.5, 7.4, 6.3, 4.1], [9.8, 5.3, 3.1, 2.9]]
yTrain = [[1], [1], [0], [1], [1], [0], [1],
 [1], [0], [0], [0], [1], [0], [0]]

xTest= [[0.4, 1.9, 2.5, 3.1], [1.51, 2.0, 2.4, 3.8], [2.6, 5.1, 6.2, 7.2], [3.23, 4.1, 4.3, 4.9], [7.1, 7.6, 8.2, 9.3],
 [5.78, 5.1, 4.5, 3.55], [6.33, 4.8, 3.4, 2.5], [7.67, 6.45, 5.8, 4.31], [8.22, 6.32, 5.87, 3.59], [9.1, 8.5, 7.7, 6.1]]
yTest = [[1], [1], [1], [1], [1],
 [0], [0], [0], [0], [0]]
                
while cost>0:
    cost=Network.costTotal(False, nn, xTrain, yTrain, lamb)
    costTest=Network.costTotal(False, nn, xTest, yTest, lamb)
    delta=Network.backpropagation(False, nn, xTrain, yTrain, lamb)
    nn['theta']=[nn['theta'][i]-alf*delta[i] for i in range(0,len(nn['theta']))]
    print('Train cost ', cost[0,0], 'Test cost ', costTest[0,0])
    print(Network.runAll(nn, xTest))

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import create [as 别名]
from scipy import optimize
from network import Network 

nt = Network()    
nn=nt.create([4, 1000, 1])

lamb=0.3
cost=1
alf = 0.2
xTrain = [
	[1, 2.3, 4.5, 5.3], 
	[1.1, 1.3, 2.4, 2.4], 
	[1.9, 1.7, 1.5, 1.3], 
	[2.3, 2.9, 3.3, 4.9], 
	[3, 5.2, 6.1, 8.2], 
	[3.31, 2.9, 2.4, 1.5], 
	[4.9, 5.7, 6.1, 6.3],
 	[4.85, 5.0, 7.2, 8.1], 
 	[5.9, 5.3, 4.2, 3.3], 
 	[7.7, 5.4, 4.3, 3.9], 
 	[6.7, 5.3, 3.2, 1.4], 
 	[7.1, 8.6, 9.1, 9.9], 
 	[8.5, 7.4, 6.3, 4.1], 
 	[9.8, 5.3, 3.1, 2.9],

 	[1.1, 3.5, 4.5, 7.6],
 	[2.1, 3.5, 5.5, 8.6],
 	[3.1, 5.5, 7.5, 9.6],
 	[0.1, 1.5, 2.5, 6.6],

 	[9.5, 8.1, 5.5, 3.6],

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import create [as 别名]
class FFnetApp(HasTraits):
    network = Instance(Network)
    data = Instance(TrainingData)
    training_data = Instance(TrainingData)
    testing_data = Instance(TrainingData)
    recall_data = Instance(TrainingData)
    dumper = Instance(Dumper)
    trainer = Instance(Trainer)
    shared = Instance(Shared)
    logs = Instance(Logger)
    plots = Instance(MPLPlots, transient=True)
    shell = PythonValue(Dict)
    mode = Enum('train', 'test', 'recall')
    algorithm = Enum('tnc') #, 'bfgs', 'cg')
    running = DelegatesTo('trainer')
    net = DelegatesTo('network')
    data_status = DelegatesTo('data',  prefix='status')
    selected = DelegatesTo('plots')

    def __init__(self, **traits):
        super(FFnetApp, self).__init__(**traits)
        self.network = Network(app = self)
        self.training_data = TrainingData(app = self)
        self.testing_data = TrainingData(app = self)
        self.recall_data = TrainingData(app = self)
        self.data = self.training_data  # by default
        self.dumper = Dumper(app=self)
        self.trainer = TncTrainer(app = self) # default trainer
        self.shared = Shared()
        self.logs = Logger()
        self.plots = MPLPlots()
        self.logs.logger.info('Welcome! You are using ffnet-%s.' %ffnet_version)
        self.shell = {'app':self}

    def new(self):
        net = self.network.create()
        if net is not None:
            self.mode = 'train'
            self.data.normalize = True
            self._new_net_setup()

    def load(self):
        net = self.network.load()
        if net is not None:
            self.mode = 'recall'
            self._new_net_setup()

    def save_as(self):
        self.network.save_as()

    def export(self):
        self.network.export()

    def dump(self):
        self.dumper.configure_traits(kind='modal')

    def settings(self):
        if self.net:
            self._pmode = self.mode
            self.edit_traits(view='settings_view', kind='livemodal')

    def train_start(self):
        self.logs.logger.info('Training network: %s' %self.network.filename)
        self.trainer.train()

    def train_stop(self):
        self.trainer.running = False

    def reset(self):
        if self.net:
            self.net.randomweights()
            self.logs.logger.info('Weights has been randomized!')
        self.clear()

    def about(self):
        from about import about
        about.open()

    def donate(self):
        import webbrowser
        url = 'https://sourceforge.net/p/ffnet/donate'
        webbrowser.open(url)

    def cite(self):
        from pyface.api import information
        import os
        try:
            basedir = os.path.dirname(os.path.realpath(__file__)) + '/'
        except NameError:  #__file__ not defined if this is main script
            basedir = ''
        fname = basedir + 'data/cite.txt'
        citations = open(fname, 'r').read()
        msg = u'You are encouraged to cite in your papers one (or all) of the following:\n\n\n' + \
                unicode(citations, 'utf-8').replace(u'\ufeff', '')
        information(None, msg, title = "Citing ffnet/ffnetui")

    def clear(self):
        self.shared.populate() 
        self.plots.selected.replot()

#.........这里部分代码省略.........

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import create [as 别名]
from scipy import optimize
from network import Network 

nt = Network()    
nn=nt.create([1, 1000, 1])

lamb=0.3
cost=1
alf = 0.2
xTrain = [[0], [1], [1.9], [2], [3], [3.31], [4], [4.7], [5], [5.1], [6], [7], [8], [9]]
yTrain = [[0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [1], [1], [1], [1]]

xTest= [[0.4], [1.51], [2.6], [3.23], [4.87], [5.78], [6.334], [7.667], [8.22], [9.1]]
yTest = [[0], [0], [0], [0], [0], [1], [1], [1], [1], [1]]

theta = nt.unroll(nn['theta'])
print(nt.runAll(nn, xTest))
theta =  optimize.fmin_cg(nt.costTotal, fprime=nt.backpropagation,
                x0=theta, args=(nn, xTrain, yTrain, lamb), maxiter=200)
print(nt.runAll(nn, xTest))

# 需要导入模块: from network import Network [as 别名]
# 或者: from network.Network import create [as 别名]
y_train = list(map(lambda x: [x], y_train))

# y_train = y_train[:100]
# X_train = X_train[:100]

# http://rasbt.github.io/mlxtend/docs/data/mnist/
# def plot_digit(X, y, idx):
#     img = X[idx].reshape(28,28)
#     plt.imshow(img, cmap='Greys',  interpolation='nearest')
#     plt.title('true label: %d' % y[idx])
#     plt.show()

# plot_digit(X_train, y_train, 4)

nt = Network()
nn = nt.create([784, 100, 1])

lamb = 0.3
cost = 1
alf = 0.005

i = 0
results = []
while cost > 0:
    cost = nt.costTotal(False, nn, X_train, y_train, lamb)
    delta = nt.backpropagation(False, nn, X_train, y_train, lamb)
    nn["theta"] = [nn["theta"][i] - alf * delta[i] for i in range(0, len(nn["theta"]))]
    i = i + 1
    print("Train cost ", cost[0, 0], "Iteration ", i)
    results = nt.runAll(nn, X_test)
    print(results)