def test_forward4(self):
shape = (10, 20, 30)
axis = (0, 1)
x = Variable(np.random.rand(*shape))
y = F.max(x, axis=axis, keepdims=True)
expected = np.max(x.data, axis=axis, keepdims=True)
self.assertTrue(array_allclose(y.data, expected))
def test_forward2(self):
shape = (10, 20, 30)
axis = 1
x = Variable(np.random.rand(*shape))
y = F.max(x, axis=axis)
expected = np.max(x.data, axis=axis)
self.assertTrue(array_allclose(y.data, expected))
def test_forward1(self):
x = Variable(np.random.rand(10))
y = F.max(x)
expected = np.max(x.data)
self.assertTrue(array_allclose(y.data, expected))
def test_backward3(self):
x_data = np.random.rand(10, 20, 30) * 100
f = lambda x: F.max(x, axis=(1, 2))
self.assertTrue(gradient_check(f, x_data))
def test_backward1(self):
x_data = np.random.rand(10)
f = lambda x: F.max(x)
self.assertTrue(gradient_check(f, x_data))
def test_backward2(self):
x_data = np.random.rand(10, 10) * 100
f = lambda x: F.max(x, axis=1)
self.assertTrue(check_backward(f, x_data))
