def test_backward3(self): x_data = np.random.rand(2, 3) f = lambda x: F.sum(x, axis=1) check_backward(f, x_data, testcase=self)
def test_backward3(self): x_data = np.random.rand(10, 20, 20) f = lambda x: F.sum_to(x, (10, )) check_backward(f, x_data, testcase=self)
def test_backward(self): x_data = np.random.rand(10) f = lambda x: F.sum(x) check_backward(f, x_data, testcase=self)
def test_backward3(self): x_data = np.random.rand(10, 20, 20) f = lambda x: F.sum(x, axis=(0, 2), keepdims=True) check_backward(f, x_data, testcase=self)
def test_backward(self): x_data = np.random.rand(1) check_backward(F.tanh, x_data, testcase=self)
def test_backward2(self): x_data = np.random.rand(10,10) y_grad = np.ones_like(x_data) check_backward(F.tanh, x_data, y_grad, testcase=self)
def test_backward_prob2(self): f = Normal(np.array(0.0), np.array(1.0)).prob x_data = np.random.rand(10) check_backward(f, x_data, testcase=self, verbose=True)
def test_backward(self): x_data = np.random.rand(5, 5, 5) check_backward(F.relu, x_data, testcase=self)
def test_backward4(self): x_data = np.random.rand(5, 5, 5) * 100 f = lambda x: F.max(x, axis=None, keepdims=True) check_backward(f, x_data, testcase=self)
def test_backward2(self): x_data = np.random.rand(5, 5) * 100 check_backward(F.max, x_data, testcase=self)
def test_backward(self): x_data = np.random.rand(10) * 100 f = lambda x: F.max(x, keepdims=True) check_backward(f, x_data, testcase=self)