def dense_update(self, var, grad):
return xdl.ps_dense_apply_momentum_op(learning_rate=self._lr,
momentum=self._momentum,
grad=grad,
var_name=var.name,
var_type=var.vtype,
use_nesterov=self._use_nesterov)
def test_all(self):
var = xdl.Variable(name="w",
dtype=DataType.float,
shape=[4],
initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_dense_apply_momentum_op(learning_rate=np.array(
0.5, dtype=np.float),
momentum=np.array(0.9,
dtype=np.float),
grad=np.array([1, 2, 3, 4],
dtype=np.float32),
var_name="w",
var_type="index",
use_nesterov=False)
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0.5, 0, -0.5, -1],
dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([-0.45, -1.9, -3.35, -4.8],
dtype=np.float32)).all())