proximal_ada_grad optimizer

mindspore/nn/optim/proximal_ada_grad.py

@@ -31,15 +31,13 @@ def _tensor_run_opt(opt, learning_rate, l1, l2, gradient, weight, accum):
     return success
 
 
-def _check_param_value(accum, learning_rate, l1, l2, use_locking, prim_name=None):
+def _check_param_value(accum, l1, l2, use_locking, prim_name=None):
     """Check inputs param."""
     validator.check_value_type("accum", accum, [float], prim_name)
-    validator.check_value_type("learning_rate", learning_rate, [float], prim_name)
     validator.check_value_type("l1", l1, [float], prim_name)
     validator.check_value_type("l2", l2, [float], prim_name)
     validator.check_value_type("use_locking", use_locking, [bool], prim_name)
     validator.check_number_range("accum", accum, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
-    validator.check_number_range("learning_rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
     validator.check_number_range("l1", l1, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
     validator.check_number_range("l2", l2, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
 
@@ -79,10 +77,10 @@ class ProximalAdagrad(Optimizer):
 
     def __init__(self, params, accum=0.1, learning_rate=0.001, l1=0.0, l2=0.0,
                  use_locking=False, loss_scale=1.0, weight_decay=0.0):
-        super(ProximalAdagrad, self).__init__(0.0, params, weight_decay, loss_scale)
+        super(ProximalAdagrad, self).__init__(learning_rate, params, weight_decay, loss_scale)
         if self.is_group:
             raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
-        _check_param_value(accum, learning_rate, l1, l2, use_locking, self.cls_name)
+        _check_param_value(accum, l1, l2, use_locking, self.cls_name)
         self.accum = self.parameters.clone(prefix="accum", init=accum)
         self.l1 = Tensor(l1, mstype.float32)
         self.l2 = Tensor(l2, mstype.float32)