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_backward3(self):
self.x = fake_data((2, 3, 5, 5))
nm_x_grad = numerical_gradient(self.layer, self.x, self.x)
self.layer.forward(self.x)
y = np.ones((2, 3, 2, 2))
x_grad = self.layer.backward(y)
self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward4(self):
h = 5
layer = ConvLayer(2, 5, h)
x = fake_data((2, 2, 8, 8))
layer.W = fake_data((5, 2, h, h))
layer.b = fake_data(layer.b.shape)
y = layer.forward(x)
x_grad = layer.backward(np.ones_like(y))
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_backward4(self):
x = np.array([[[[0, 0, 0, 0], [0, 0, 1, 1], [2, 0, 0, 3],
[2, 0, 3, 0]]]]).astype('float64')
nm_x_grad = numerical_gradient(self.layer, x, x)
out = self.layer.forward(x)
y = np.ones((1, 1, 2, 2))
x_grad = self.layer.backward(y)
self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward2(self):
nm_x_grad = numerical_gradient(self.layer, self.x, self.x)
self.layer.forward(self.x)
y = np.ones((1, 3, 2, 2))
x_grad = self.layer.backward(y)
print("x", x_grad)
print("expected", nm_x_grad)
self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward1(self):
layer = ConvLayer(1, 1, 3)
x = fake_data((1, 1, 8, 8))
layer.W = fake_data((1, 1, 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)
# note that this does not check the gradients of the padded elements
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_backward5(self):
h = 5
layer = ConvLayer(2, 5, h)
x = fake_data((2, 2, 8, 8))
layer.W = fake_data((5, 2, h, h))
layer.b = fake_data(layer.b.shape)
y = layer.forward(x)
y_grad = fake_data(y.shape)
x_grad = layer.backward(y_grad)
nm_x_grad = numerical_gradient(layer, x, x, y_grad)
nm_w_grad = numerical_gradient(layer, x, layer.W, y_grad)
nm_b_grad = numerical_gradient(layer, x, layer.b, y_grad)
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))
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))
def test_backward1(self):
layer = FlattenLayer()
x = fake_data((1, 2, 3, 3))
y = layer.forward(x)
x_grad = layer.backward(np.ones_like(y))
# do numerical gradients
nm_x_grad = numerical_gradient(layer, x, x)
print(x_grad)
print(nm_x_grad)
self.assertTrue(np.allclose(nm_x_grad, x_grad))