def first_hid_rec(value, good_data, bad_data):
build_network = buildNetwork(len(good_data[0]), value, 2,
bias=True,
hiddenclass=SigmoidLayer,
outclass=SoftmaxLayer,
recurrent=True)
trainer = BackpropTrainer(build_network,
get_supervised_data_set(good_data, bad_data))
result = trainer.trainUntilConvergence()
return result[0][-1]
def third_hidden_lvl(first_hidd, second_hidd, value, good_data, bad_data):
build_network = buildNetwork(len(good_data[0]),
first_hidd,
second_hidd,
value,
2,
bias=True,
hiddenclass=SigmoidLayer,
outclass=SoftmaxLayer)
trainer = BackpropTrainer(build_network,
get_supervised_data_set(good_data, bad_data))
result = trainer.trainUntilConvergence()
return result[0][-1]
def get_third_nn(value, good_data, bad_data):
build_network = FeedForwardNetwork()
inLayer = LinearLayer(len(good_data[0]))
hiddenLayer = SigmoidLayer(value)
outLayer = SigmoidLayer(1)
build_network.addInputModule(inLayer)
build_network.addModule(hiddenLayer)
build_network.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
in_to_out = FullConnection(inLayer, outLayer)
build_network.addConnection(in_to_hidden)
build_network.addConnection(hidden_to_out)
build_network.addConnection(in_to_out)
build_network.sortModules()
trainer = BackpropTrainer(build_network,
get_supervised_data_set(good_data, bad_data))
result = trainer.trainUntilConvergence()
return result[0][-1]
def hidden0_tst(good_data, n, bad_file):
net = buildNetwork(len(good_data[0]), n, 2)
trainer = BackpropTrainer(net,
get_supervised_data_set(good_data, bad_file))
# trainer.trainUntilConvergence()
return trainer