def setUp(self):
self.conec = [(0, 3), (1, 3), (2, 3), \
(0, 4), (1, 4), (2, 4), (3, 4)]
self.net = ffnet(self.conec); self.net([1.,1.]) #try if net works
self.net.weights = array([1.]*7)
self.tnet = ffnet(self.conec)
self.tnet.weights = array([ 0.65527021, -1.12400619, 0.02066321, \
0.13930684, -0.40153965, 0.11965115, -1.00622429 ])
self.input = [[0.,0.], [0.,1.], [1.,0.], [1.,1.]]
self.target = [[1.], [0.], [0.], [1.]]
def main(): #Create Network getData() conec = imlgraph((1, 5, 3, 5, 1), biases=False) net = ffnet(conec) net.train_rprop(inputs, outputs) savenet(net, "test_net")
def create(self):
try:
conn = self.connectivity_type
arch = self.architecture.replace('-', ',')
biases = self.biases
conec = eval('%s((%s), biases=%s)' %(conn, arch, biases))
self.net = ffnet(conec)
self.net.name = self.architecture.replace(',', '-')
return self.net
except:
display_error("Network cannot be created!")
self.net = None
return None
def testDerivative2(self):
conec = [(1, 3), (2, 3), (0, 3), \
(1, 4), (0, 4), \
(3, 4), (4, 5), (0, 5),
(4, 6), (3, 6), (5, 6) ]
net = ffnet(conec)
y1n, y2n = net.derivative([1, 1])[0]
from scipy import derivative
def func1(x):
return net([x, 1])[0]
def func2(x):
return net([1, x])[0]
y1 = derivative(func1, 1, dx=0.001)
y2 = derivative(func2, 1, dx=0.001)
self.assertAlmostEqual(y1n, y1, 7)
self.assertAlmostEqual(y2n, y2, 7)
#def setup(self):
#if self.maxiter == 0:
#self.maxiter = max(100, 10*len(self.app.network.net.weights))
def stopper(self):
if self.app.shared.running.value == 0:
raise AssertionError
def training_process(self):
process = Process(target=self.app.network.net.train_cg,
args=(self.app.data.input_t, self.app.data.target_t),
kwargs={'maxiter': self.maxfun,
'disp': 0,
'callback': self.callback})
return process
traits_view = View(Item('maxfun', label='Maxiter'),
resizable=True)
if __name__ == "__main__":
from ffnet import *
net = ffnet(mlgraph((2,2,1)))
inp = [[0,0], [1,1], [1,0], [0,1]]
trg = [[1], [1], [0], [0]]
tnc = TncTrainer()
tnc.configure_traits()
#tnc.train(net, inp, trg, Logs())
def setUp(self):
conec = imlgraph( (5,5,5) )
self.net = ffnet(conec)
def testTestdata(self):
net = ffnet( mlgraph((1, 5, 1)) )
input = [1, 2., 5]
target = [2, 3, 5.]
net.train_tnc(input, target, maxfun = 10)
