def test_forward1(self):
layer = ConvLayer(1, 1, 3)
x = fake_data((1, 1, 3, 3))
layer.W = fake_data((1, 1, 3, 3))
layer.b = fake_data(layer.b.shape)
y = layer.forward(x)
should_be = np.array([[[[58., 100., 70.], [132., 204., 132.],
[70., 100., 58.]]]])
self.assertTrue(np.allclose(y, should_be))
def test_backward3_5(self):
layer = ConvLayer(5, 3, 3)
x = fake_data((2, 5, 3, 3))
layer.W = fake_data(layer.W.shape)
layer.b = fake_data(layer.b.shape)
y = layer.forward(x)
x_grad = layer.backward(np.ones_like(y))
# do numerical gradients
nm_x_grad = numerical_gradient(layer, x, x)
nm_w_grad = numerical_gradient(layer, x, layer.W)
nm_b_grad = numerical_gradient(layer, x, layer.b)
self.assertTrue(np.allclose(nm_x_grad, x_grad))
self.assertTrue(np.allclose(nm_w_grad, layer.W_grad))
self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))
def test_backward2(self):
layer = ConvLayer(2, 1, 3)
x = fake_data((1, 2, 4, 4))
layer.W = fake_data((1, 2, 3, 3))
layer.b = fake_data(layer.b.shape)
y = layer.forward(x)
x_grad = layer.backward(np.ones_like(y))
# do numerical gradients
nm_x_grad = numerical_gradient(layer, x, x)
nm_w_grad = numerical_gradient(layer, x, layer.W)
nm_b_grad = numerical_gradient(layer, x, layer.b)
self.assertTrue(np.allclose(nm_x_grad, x_grad))
#print("expected", nm_x_grad)
#print(x_grad)
self.assertTrue(np.allclose(nm_w_grad, layer.W_grad))
#print("expected2", nm_w_grad)
#print(layer.W_grad)
self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))