def test_init_state(self):
params = np.zeros((1, ))
optimizer_def = optim.Adadelta(learning_rate=0.1,
rho=0.9,
eps=1e-6,
weight_decay=0.1)
state = optimizer_def.init_state(params)
expected_hyper_params = _AdadeltaHyperParams(0.1, 0.9, 1e-6, 0.1)
self.assertEqual(optimizer_def.hyper_params, expected_hyper_params)
expected_state = optim.OptimizerState(
0, _AdadeltaParamState(np.zeros((1, )), np.zeros((1, ))))
self.assertEqual(state, expected_state)
def test_apply_gradient(self):
optimizer_def = optim.Adadelta(learning_rate=0.1,
rho=0.9,
eps=1e-6,
weight_decay=0.1)
params = np.array([1.])
state = optim.OptimizerState(
1, _AdadeltaParamState(np.zeros((1, )), np.zeros((1, ))))
grads = np.array([1.])
new_param, new_state = optimizer_def.apply_gradient(
optimizer_def.hyper_params, params, state, grads)
expected_new_state = optim.OptimizerState(
2, _AdadeltaParamState(np.array([0.1]), np.array([9.999902e-7])))
expected_new_params = np.array([0.9896838])
np.testing.assert_allclose(new_param, expected_new_params)
self.assertEqual(new_state, expected_new_state)