def test_call_hooks_uninitialized_param(self):
target = UninitializedChain()
opt = optimizers.MomentumSGD()
opt.setup(target)
opt.add_hook(optimizer.GradientNoise(eta=1))
target(np.ones((4, 10), dtype=np.float32))
opt.call_hooks()
def check_gradient_noise(self):
w = self.target.param.data
g = self.target.param.grad
xp = cuda.get_array_module(w)
eta = 0.01
expect = w - g - xp.random.normal(
loc=0, scale=np.sqrt(
eta / np.power(1, 0.55)), size=g.shape).astype(np.float32)
opt = optimizers.SGD(lr=1)
opt.setup(self.target)
opt.add_hook(optimizer.GradientNoise(eta))
opt.update()
testing.assert_allclose(expect, w, rtol=0.4)
def check_gradient_noise(self):
w = self.target.param.data
g = self.target.param.grad
xp = cuda.get_array_module(w)
noise_value = xp.asarray(self.noise_value)
expect = w - g - noise_value
noise = mock.Mock(return_value=noise_value)
opt = optimizers.SGD(lr=1)
opt.setup(self.target)
hook = optimizer.GradientNoise(self.eta, noise_func=noise)
opt.add_hook(hook)
opt.update()
testing.assert_allclose(expect, w, rtol=0.4)
noise.assert_called_once_with(xp, (2, 3), np.float32, hook, opt)
