def test_FANN_converges_on_and_problem():
fc = FullConnection(2, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
and_ = load_and()
theta = np.array([-0.1, 0.1])
for i in range(100):
g = nn.calculate_gradient(theta, and_.data, and_.target)
theta -= g * 1
error = nn.calculate_error(theta, and_.data, and_.target)
assert_less(error, 0.2)
def test_FANN_converges_on_and_problem():
fc = FullConnection(2, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
and_ = load_and()
theta = np.array([-0.1, 0.1])
for i in range(100):
g = nn.calculate_gradient(theta, and_.data, and_.target)
theta -= g * 1
error = nn.calculate_error(theta, and_.data, and_.target)
assert_less(error, 0.2)
def test_load_and_wellformed() :
and_problem = load_and()
assert_dataset_wellformed(and_problem)