def test_FANN_multilayer_gradient_single_sample():
fc0 = FullConnection(4, 2)
fc1 = FullConnection(2, 1)
sig0 = SigmoidLayer(2)
sig1 = SigmoidLayer(1)
nn = FANN([fc0, sig0, fc1, sig1])
theta = np.random.randn(nn.get_param_dim())
for x, t in zip(X, T):
grad_c = nn.calculate_gradient(theta, x, t)
grad_e = approx_fprime(theta, nn.calculate_error, 1e-8, x, t)
assert_almost_equal(grad_c, grad_e)
def test_FANN_error_single_sample():
fc = FullConnection(4, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
for x, t, e in zip(X, T, E):
assert_equal(nn.calculate_error(theta, x, t), 0)
assert_equal(nn.calculate_error(theta, x, 0), e)
def test_FANN_feed_forward_single_sample():
fc = FullConnection(4, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
for x, t in zip(X, T):
t = np.atleast_2d(t)
assert_equal(nn.forward_pass(theta, x), t)
def test_FANN_gradient_multisample():
fc = FullConnection(4, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
theta = np.random.randn(nn.get_param_dim())
grad_c = nn.calculate_gradient(theta, X, T)
grad_e = approx_fprime(theta, nn.calculate_error, 1e-8, X, T)
assert_almost_equal(grad_c, grad_e)
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_FullConnection_forward_pass_multi_sample():
fc = FullConnection(4, 1)
assert_equal(fc.forward_pass(theta, X), T)
def test_FullConnection_forward_pass_single_samples():
fc = FullConnection(4, 1)
for x, t in zip(X, T):
t = np.atleast_2d(t)
assert_equal(fc.forward_pass(theta, x), t)
def test_FullConnection_dimensions():
fc = FullConnection(5, 7)
assert_equal(fc.input_dim, 5)
assert_equal(fc.output_dim, 7)
assert_equal(fc.get_param_dim(), 5*7)
def test_FANN_error_multisample():
fc = FullConnection(4, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
assert_equal(nn.calculate_error(theta, X, T), 0.0)
assert_equal(nn.calculate_error(theta, X, np.zeros_like(T)), np.sum(E))
def test_FANN_feed_forward_multisample():
fc = FullConnection(4, 1)
sig = SigmoidLayer(1)
nn = FANN([fc, sig])
assert_equal(nn.forward_pass(theta, X), T)
def test_FANN_dimensions():
fc = FullConnection(5, 1)
nn = FANN([fc])
assert_equal(nn.input_size, 5)
assert_equal(nn.output_size, 1)
def test_FullConnection_forward_pass_multi_sample():
fc = FullConnection(4, 1)
assert_equal(fc.forward_pass(theta, X), T)
def test_FullConnection_forward_pass_single_samples():
fc = FullConnection(4, 1)
for x, t in zip(X, T):
t = np.atleast_2d(t)
assert_equal(fc.forward_pass(theta, x), t)
def test_FullConnection_dimensions():
fc = FullConnection(5, 7)
assert_equal(fc.input_dim, 5)
assert_equal(fc.output_dim, 7)
assert_equal(fc.get_param_dim(), 5 * 7)
