示例#1
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def get_trainer(trainer_name, net, ds, batchlearning):
    if trainer_name == "bp":
        return BackpropTrainer(net,
                               ds,
                               batchlearning=batchlearning,
                               verbose=True)
    elif trainer_name == "dl":
        return DeepBeliefTrainer(net, ds)
示例#2
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    net.addConnection(FullConnection(bias, h1))
    net.addConnection(FullConnection(bias, h2))
    net.addConnection(FullConnection(bias, out))

    net.sortModules()
    return net


if __name__ == "__main__":

    import GwData
    data = GwData.GwData()
    xs = get_binary_data(data)
    ys = data.labels_for("50")

    sdataset = SupervisedDataSet(xs.shape[1], 1)
    udataset = UnsupervisedDataSet(xs.shape[1])
    for i, x in enumerate(xs):
        sdataset.addSample(x, ys[i])
        udataset.addSample(x)

    epochs = 100
    layerDims = [xs.shape[1], 300, 100, 2]

    #net = buildNetwork(*layerDims)
    net = custom_build_network(layerDims)

    trainer = DeepBeliefTrainer(net, dataset=udataset)
    #trainer = DeepBeliefTrainer(net, dataset=sdataset)
    trainer.trainEpochs(epochs)
示例#3
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tf = open('novelty_plant.txt','r')

first = True

for line in tf.readlines():
	if (not first):
		data = [float(x) for x in line.strip().split('\t') if x != '']
		#    indata =  tuple(data[:6])
		#    outdata = tuple(data[6:])
		ds.addSample(data)
	first = False

n = buildNetwork(ds.dim,8,8,1,recurrent=True)
t = DeepBeliefTrainer(n,ds, epochs=50)
t.trainEpochs(1)
t.testOnData(ds, verbose= True)

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

for input, target in ds:
    print(input, target)
    
#net = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer)#1000
# net = buildNetwork(2, 6, 1, bias=True) # 3000
net = buildNetwork(2, 3, 1, bias=True)
示例#4
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文件: dbn-mnist.py 项目: HKou/pybrain
__author__ = 'Justin S Bayer, [email protected]'
__version__ = '$Id$'


import scipy

from pybrain.datasets import UnsupervisedDataSet
from pybrain.unsupervised.trainers.deepbelief import DeepBeliefTrainer
from pybrain.tools.shortcuts import buildNetwork

from pybrainexamples.datasets.mnist import makeMnistDataSets


net = buildNetwork(784, 500, 500, 2000, bias=True)
train, test = makeMnistDataSets('/Users/bayerj/Desktop/MNIST/')

trainer = DeepBeliefTrainer(net, train)
trainer.train()

print "RBM Phase finished. Now backprop."
softmaxer = SoftmaxLayer(10)
con = FullConnection(net.outmodules[0], softmaxer)
net.addModule(softmaxer)
net.outmodules = [softmaxer]

trainer = BackpropTrainer(trainer, ds)
for i in xrange(sys.maxint):
    error = trainer.train()
    print "%i: %.2f" % (i, error)
示例#5
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#! /usr/bin/env python2.5
# -*- coding: utf-8 -*-

# Miniscule deep belief net example 

__author__ = 'Justin S Bayer, [email protected]'
__version__ = '$Id$'


from pybrain.datasets import UnsupervisedDataSet
from pybrain.unsupervised.trainers.deepbelief import DeepBeliefTrainer
from pybrain.tools.shortcuts import buildNetwork


ds = UnsupervisedDataSet(6)
ds.addSample([0, 1] * 3)
ds.addSample([1, 0] * 3)

net = buildNetwork(6, 2, 2, 2, bias=True)
params = net.params.copy()

trainer = DeepBeliefTrainer(net, ds)

trainer.train()

print params == net.params
    
    net.addConnection(FullConnection(bias, h1))
    net.addConnection(FullConnection(bias, h2))
    net.addConnection(FullConnection(bias, out))
    
    
    net.sortModules()
    return net
    
if __name__ == "__main__":
    
    import GwData
    data = GwData.GwData()
    xs = get_binary_data(data)
    ys = data.labels_for("50")
    
    sdataset = SupervisedDataSet(xs.shape[1], 1)
    udataset = UnsupervisedDataSet(xs.shape[1])
    for i,x in enumerate(xs):
        sdataset.addSample(x, ys[i])
        udataset.addSample(x)
    
    epochs = 100
    layerDims = [xs.shape[1], 300, 100, 2]    
    
    #net = buildNetwork(*layerDims)
    net = custom_build_network(layerDims)

    trainer = DeepBeliefTrainer(net, dataset=udataset)
    #trainer = DeepBeliefTrainer(net, dataset=sdataset)
    trainer.trainEpochs(epochs)