def test_ForwardAndRecurrentConnections_backprop_gradient_check():
frc = ForwardAndRecurrentConnection(1, 1)
theta = np.ones(frc.get_param_dim())
X = [[1.], [1.]]
Y = [[1.], [2.]]
T = np.array([[0.], [0.]])
out_error = [[-1.], [-2.]]
error, grad = frc.backprop(theta, X, Y, out_error)
f = lambda t : error_function(T - frc.forward_pass(t, X))
assert_almost_equal(approx_fprime(theta, f, 1e-8), grad)
def test_ForwardAndRecurrentConnections_backprop_gradient_check():
frc = ForwardAndRecurrentConnection(1, 1)
theta = np.ones(frc.get_param_dim())
X = [[1.], [1.]]
Y = [[1.], [2.]]
T = np.array([[0.], [0.]])
out_error = [[-1.], [-2.]]
error, grad = frc.backprop(theta, X, Y, out_error)
f = lambda t: error_function(T - frc.forward_pass(t, X))
assert_almost_equal(approx_fprime(theta, f, 1e-8), grad)
def test_ForwardAndRecurrentSigmoidConnections_backprop_random_example_gradient_check():
frc = ForwardAndRecurrentSigmoidConnection(4, 3)
theta = np.random.randn(frc.get_param_dim())
X = np.random.randn(10, 4)
Y = frc.forward_pass(theta, X)
T = np.zeros((10, 3))
out_error = (T - Y)
error, grad_c = frc.backprop(theta, X, Y, out_error)
f = lambda t : error_function(T - frc.forward_pass(t, X))
grad_e = approx_fprime(theta, f, 1e-8)
assert_allclose(grad_c, grad_e, rtol=1e-3, atol=1e-5)
def test_ForwardAndRecurrentSigmoidConnections_backprop_random_example_gradient_check(
):
frc = ForwardAndRecurrentSigmoidConnection(4, 3)
theta = np.random.randn(frc.get_param_dim())
X = np.random.randn(10, 4)
Y = frc.forward_pass(theta, X)
T = np.zeros((10, 3))
out_error = (T - Y)
error, grad_c = frc.backprop(theta, X, Y, out_error)
f = lambda t: error_function(T - frc.forward_pass(t, X))
grad_e = approx_fprime(theta, f, 1e-8)
assert_allclose(grad_c, grad_e, rtol=1e-3, atol=1e-5)
