def test_numeric_gradient(self):
l = LinearLayer(2, 3, 'random')
weights = l.W.get()
x = np.random.rand(2)
grad = l.numeric_gradient(x)
assert_almost_equal(grad, weights[:, 0:-1])
in_delta = np.random.rand(3)
for i, d in enumerate(in_delta):
aux = np.zeros(in_delta.size)
aux[i] = in_delta[i]
delta = l.backward(aux)
gradient = l.numeric_gradient(x)
assert_almost_equal(in_delta[i] * gradient[i, :], delta, decimal=5)
def test_backward(self):
l = LinearLayer(2, 6, 'ones')
d = l.backward(np.array([1.0, 2.0, 3.0, 4.0, 1.0, 1.0]))
self.assertEqual(d.shape, (2, ))
assert_array_equal(d, np.array([12.0, 12.0]))