def buildNet(indim, hidden, outdim=2, temperature=1., recurrent=True):
from pybrain import FullConnection, BiasUnit, TanhLayer, SoftmaxLayer, RecurrentNetwork, LinearLayer, LinearConnection, FeedForwardNetwork, SigmoidLayer
if recurrent:
net = RecurrentNetwork()
else:
net = FeedForwardNetwork()
net.addInputModule(LinearLayer(indim, name = 'i'))
net.addModule(TanhLayer(hidden, name = 'h'))
net.addModule(BiasUnit('bias'))
net.addModule(SigmoidLayer(outdim, name = 'unscaled'))
net.addOutputModule(SoftmaxLayer(outdim, name = 'o'))
net.addConnection(FullConnection(net['i'], net['h']))
net.addConnection(FullConnection(net['bias'], net['h']))
net.addConnection(FullConnection(net['bias'], net['unscaled']))
net.addConnection(FullConnection(net['h'], net['unscaled']))
lconn = LinearConnection(net['unscaled'], net['o'])
lconn._setParameters([1./temperature]*outdim)
# these are fixed.
lconn.paramdim = 0
net.addConnection(lconn)
if recurrent:
net.addRecurrentConnection(FullConnection(net['h'], net['h']))
net.sortModules()
print net
print 'number of parameters', net.paramdim
return net
def testBank():
D = readData()
print len(D), 'samples', D.indim, 'features'
from pybrain import LinearLayer, FullConnection, FeedForwardNetwork, BiasUnit, SigmoidLayer
net = FeedForwardNetwork()
net.addInputModule(LinearLayer(D.indim, name='in'))
net.addModule(BiasUnit(name='bias'))
net.addOutputModule(SigmoidLayer(1, name='out'))
net.addConnection(FullConnection(net['in'], net['out']))
net.addConnection(FullConnection(net['bias'], net['out']))
net.sortModules()
p = net.params
p *= 0.01
provider = ModuleWrapper(D, net, shuffling=False)
algo = SGD(
provider,
net.params.copy(), #callback=printy,
learning_rate=5.5e-5)
#algo = vSGDfd(provider, net.params.copy(), #callback=printy
# )
printy(algo, force=True)
algo.run(len(D))
printy(algo, force=True)
algo.run(len(D))
printy(algo, force=True)
algo.run(len(D))
printy(algo, force=True)
algo.run(len(D))
printy(algo, force=True)
algo.run(len(D))
printy(algo, force=True)
def constructPerceptron(name, numNeurons):
"""Возвращает необученную сеть
Аргументы:
name -- имя сети, строка
numNeurons -- число нейронов в каждом слое, список из целых чисел
"""
# Создаём сеть
net = FeedForwardNetwork(name)
# Создаём слои и добавляем их в сеть
prevLayer = None
newLayer = None
for i, val in enumerate(numNeurons):
# Если слой входной, он линейный
if (i == 0):
newLayer = LinearLayer(val, 'input')
net.addInputModule(newLayer)
prevLayer = newLayer
# Если слой выходной, он линейный
elif (i == len(numNeurons) - 1):
newLayer = LinearLayer(val, 'output')
net.addOutputModule(newLayer)
# Иначе - слой сигмоидный
else:
newLayer = SigmoidLayer(val, 'hidden_' + str(i))
net.addModule(newLayer)
# Если слой не входной, создаём связь между новым и предыдущим слоями
if (i > 0):
conn = FullConnection(prevLayer, newLayer, 'conn_' + str(i))
net.addConnection(conn)
prevLayer = newLayer
# Готовим сеть к активации, упорядочивая её внутреннюю структуру
net.sortModules()
# Готово
return net
def train(self):
"""Train neuron grid by training sample"""
self.net = FeedForwardNetwork()
inLayer = LinearLayer(self.input_neurons)
hiddenLayer = SigmoidLayer(self.hiden_neurons)
outLayer = LinearLayer(self.OUTPUT_NEURONS)
self.net.addInputModule(inLayer)
self.net.addModule(hiddenLayer)
self.net.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
self.net.addConnection(in_to_hidden)
self.net.addConnection(hidden_to_out)
self.net.sortModules()
ds = ClassificationDataSet(self.input_neurons,
self.OUTPUT_NEURONS,
nb_classes=3)
for i, coord in enumerate(self.X):
ds.addSample(coord, (self.y[i], ))
trainer = BackpropTrainer(self.net,
dataset=ds,
momentum=0.1,
verbose=True,
weightdecay=0.01)
if self.maxErr:
for i in range(self.maxEpochs):
if trainer.train() < self.maxErr:
print "Desired error reached"
break
else:
trainer.trainUntilConvergence(maxEpochs=self.maxEpochs)
print "Successfully finished"
from pybrain.supervised.trainers import BackpropTrainer
import matplotlib.pylab as plt
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')
data = pd.read_csv('AirPassengers.csv',
parse_dates=['Month'],
index_col='Month',
date_parser=dateparse)
ts = data['#Passengers']
# Make a new FFN object:
n = FeedForwardNetwork()
# Constructing the input, output and hidden layers:
inLayer = LinearLayer(3)
hiddenLayer = SigmoidLayer(4)
outLayer = LinearLayer(1)
# Adding layers to the network:
n.addInputModule(inLayer)
n.addModule(hiddenLayer)
n.addOutputModule(outLayer)
# determining how neurons should be connected:
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
# Adding connections to the network
n.addConnection(in_to_hidden)
n.addConnection(hidden_to_out)