

def test_adagrad(backend_default):
    ada = Adagrad()
    param = np.random.rand(200, 128)
    param2 = copy.deepcopy(param)
    grad = (0.01 * np.random.rand(200, 128))
    grad2 = (grad / 128.0)
    states = [(0.01 * np.random.rand(200, 128))]
    states2 = [copy.deepcopy(grad[0])]
    states2[0][:] = (states2[0] + np.square(grad2))
    denom = np.sqrt((states2[0] + ada.epsilon))
    param2[:] -= ((grad2 * ada.learning_rate) / denom)
    param_list = [((wrap(param), wrap(grad)), [wrap(states[0])])]
    compare_tensors(ada, param_list, param2, tol=1e-07)

dataset/ETHPy150Open NervanaSystems/neon/tests/test_optimizer.py test_adagrad/VarMisuse@89/101 `states`->`grad`