def test_FANN_converges_on_vote_problem():
fc = FullConnectionWithBias(9, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
vote = generate_majority_vote()
theta = np.zeros((10, ))
for i in range(500):
g = nn.calculate_gradient(theta, vote.data, vote.target)
theta -= g * 1
error = nn.calculate_error(theta, vote.data, vote.target)
assert_less(error, 0.2)
def test_FANN_converges_on_vote_problem():
fc = FullConnectionWithBias(9, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
vote = generate_majority_vote()
theta = np.zeros((10,))
for i in range(500):
g = nn.calculate_gradient(theta, vote.data, vote.target)
theta -= g * 1
error = nn.calculate_error(theta, vote.data, vote.target)
assert_less(error, 0.2)
def test_generate_majority_vote_wellformed():
vote_problem = generate_majority_vote()
assert_dataset_wellformed(vote_problem)
