def setUp(self):
self.layer = FullyConnectedLayer(num_nodes=3, weight_scale=1)
self.layer.set_input_shape((4,))
self.layer._weights = np.array([[1, 0.5, -1], [1, 0.5, 1], [1, 0.5, -1], [1, 0.5, 1]], dtype=np.float64)
class TestFullyConnectedLayer(unittest.TestCase):
def setUp(self):
self.layer = FullyConnectedLayer(num_nodes=3, weight_scale=1)
self.layer.set_input_shape((4,))
self.layer._weights = np.array([[1, 0.5, -1], [1, 0.5, 1], [1, 0.5, -1], [1, 0.5, 1]], dtype=np.float64)
def test_forward_prop(self):
input = np.array([-3, 14, -5, 6], dtype=np.float64)
expected_output = np.array([12, 6, 28], dtype=np.float64)
output = self.layer.forward_prop(input)
numpy.testing.assert_array_equal(output, expected_output)
def test_back_prop(self):
input = np.array([1, 1, 1, 1], dtype=np.float64)
self.layer.forward_prop(input)
out_grad = np.array([1, 1, 1], dtype=np.float64)
expected_in_grad = np.array([0.5, 2.5, 0.5, 2.5], dtype=np.float64)
in_grad = self.layer.back_prop(out_grad)
numpy.testing.assert_array_equal(in_grad, expected_in_grad)
def test_get_output_shape(self):
self.assertEqual(self.layer.get_output_shape(), (3,))
def test_update_parameters(self):
self.layer._weights = np.ones((4, 3), dtype=np.float64)
input = np.array([1, 1, 1, 1], dtype=np.float64)
expected_output = np.array([4, 4, 4], dtype=np.float64)
output = self.layer.forward_prop(input)
numpy.testing.assert_array_equal(output, expected_output)
out_grad = np.array([1, 1, 1], dtype=np.float64)
self.layer.back_prop(out_grad)
self.layer.update_parameters(0.1)
expected_output = np.array([3.5, 3.5, 3.5], dtype=np.float64)
output = self.layer.forward_prop(input)
numpy.testing.assert_array_equal(output, expected_output)
