def test_add_numpy_numpy_backward(self):
x = np.random.randn(3, 3)
y = np.random.randn(3, 1)
f = lambda x, y: x + y
self.assertTrue(gradient_check(f, x, y))
def test_mul_ndarray_ndarray_backward3(self):
x = np.random.randn(3, 3)
y = np.random.randn(3, 1)
f = lambda x, y: x * y
self.assertTrue(gradient_check(f, x, y))
def test_change_sign_of_numpy_when_backward(self):
x = Variable(np.random.randn(5, 5))
f = lambda x: -x
self.assertTrue(gradient_check(f, x))
def test_add_variable_ndarray_backward2(self):
x = Variable(np.random.randn(3, 3))
y = np.random.randn(3, 1)
f = lambda x, y: x + y
self.assertTrue(gradient_check(f, x, y))
def test_pow_variable_backward2(self):
x = Variable(np.random.randn(5, 5))
f = lambda x: x**3
self.assertTrue(gradient_check(f, x))
def test_div_variable_ndarray_backward(self):
x0 = Variable(np.random.randn(3, 3))
x1 = np.random.randn(3, 3)
f = lambda x, y: x / y
self.assertTrue(gradient_check(f, x0, x1))
def test_sub_ndarray_variable_backward(self):
x0 = np.random.randn(3, 3)
x1 = Variable(np.random.randn(3, 3))
f = lambda x, y: x - y
self.assertTrue(gradient_check(f, x0, x1))
def test_square_variable_backward2(self):
x = Variable(np.random.randn(3))
self.assertTrue(gradient_check(F.square, x))
def test_exp_variable_backward1(self):
x = Variable(np.random.randn(3, 3))
self.assertTrue(gradient_check(F.exp, x))
