def train_xor(self, learning_rate=0.1):
"""Trains XOR with a neural net"""
X = np.array([[0.0, 0.0], [0, 1], [1, 0], [1, 1]])
y = np.array([0, 1, 1, 0])
net = PyBrainNN(learning_rate=learning_rate,
maxiterations=10000,
lam=0.0,
args=(2, 3, 1),
kwargs={
'fast': True,
'bias': True
})
net.fit(X, y)
return net, X, y
def test_load_save_model(self):
import tempfile
net, X, y = self.train_xor()
# save the model
tfile = tempfile.TemporaryFile()
net.save_model(tfile)
# load in the model
tfile.seek(0)
model = json.load(tfile)
netloaded = PyBrainNN.load_model(model)
self.assertTrue(np.allclose(net.predict(X), netloaded.predict(X)))
