from
pybrain.tools.shortcuts
import
buildNetwork
from
pybrain.structure
import
TanhLayer
from
pybrain.datasets
import
SupervisedDataSet
from
pybrain.supervised.trainers
import
BackpropTrainer
network
=
buildNetwork(
2
,
2
,
1
, bias
=
True
, hiddenclass
=
TanhLayer)
or_train
=
SupervisedDataSet(
2
,
1
)
or_test
=
SupervisedDataSet(
2
,
1
)
or_train.addSample((
0
,
0
), (
0
,))
or_train.addSample((
0
,
1
), (
1
,))
or_train.addSample((
1
,
0
), (
1
,))
or_train.addSample((
1
,
1
), (
1
,))
or_test.addSample((
0
,
0
), (
0
,))
or_test.addSample((
0
,
1
), (
1
,))
or_test.addSample((
1
,
0
), (
1
,))
or_test.addSample((
1
,
1
), (
1
,))
trainer
=
BackpropTrainer(network, or_train)
for
iteration
in
range
(
1000
):
trainer.train()
trainer.testOnData(dataset
=
or_test, verbose
=
True
)