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)