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)
