def test_apply_gradient(self):
optimizer_def = optim.Adagrad(learning_rate=0.1, eps=0.01)
params = np.array([1.])
state = optim.OptimizerState(1, _AdagradParamState(np.array([0.1])))
grads = np.array([4.])
new_params, new_state = optimizer_def.apply_gradient(
optimizer_def.hyper_params, params, state, grads)
expected_new_state = optim.OptimizerState(
2, _AdagradParamState(np.array([16.1])))
expected_new_params = np.array([0.9005588])
np.testing.assert_allclose(new_params, expected_new_params)
self.assertEqual(new_state, expected_new_state)
def test_init_state(self):
params = onp.zeros((1,))
optimizer_def = optim.Adagrad(learning_rate=0.1, eps=0.01)
state = optimizer_def.init_state(params)
expected_hyper_params = _AdagradHyperParams(0.1, 0.01)
self.assertEqual(optimizer_def.hyper_params, expected_hyper_params)
expected_state = optim.OptimizerState(
0, _AdagradParamState(onp.zeros((1,))))
self.assertEqual(state, expected_state)
