add weight decay in RMSProp optimizer

mindspore/nn/optim/rmsprop.py

@@ -18,12 +18,8 @@ from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore._checkparam import ParamValidator as validator
 import mindspore.common.dtype as mstype
-<<<<<<< HEAD
 from mindspore.common import Tensor
 from .optimizer import Optimizer, grad_scale, apply_decay
-=======
-from .optimizer import Optimizer, grad_scale
->>>>>>> add RMSProp optimizer
 
 rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
 centered_rmsprop_opt = C.MultitypeFuncGraph("rmsprop_opt")
@@ -123,12 +119,9 @@ class RMSProp(Optimizer):
         use_locking (bool): Enable a lock to protect the update of variable and accumlation tensors. Default: False.
         centered (bool): If True, gradients are normalized by the estimated variance of the gradient. Default: False
         loss_scale (float): A floating point value for the loss scale. Default: 1.0.
-<<<<<<< HEAD
         weight_decay (float): Weight decay (L2 penalty). Default: 0.0.
         decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default:
                                  lambda x: 'beta' not in x.name and 'gamma' not in x.name.
-=======
->>>>>>> add RMSProp optimizer
 
     Inputs:
         - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
@@ -139,20 +132,12 @@ class RMSProp(Optimizer):
     Examples:
         >>> net = Net()
         >>> loss = nn.SoftmaxCrossEntropyWithLogits()
-<<<<<<< HEAD
         >>> opt = nn.RMSProp(params=net.trainable_params(), learning_rate=lr)
         >>> model = Model(net, loss, opt)
     """
     def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
                  use_locking=False, centered=False, loss_scale=1.0, weight_decay=0.0,
                  decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
-=======
-        >>> opt = RMSProp(params=net.trainable_params(), learning_rate=lr)
-        >>> model = Model(net, loss, opt)
-    """
-    def __init__(self, params, learning_rate=0.1, decay=0.9, momentum=0.0, epsilon=1e-10,
-                 use_locking=False, centered=False, loss_scale=1.0):
->>>>>>> add RMSProp optimizer
         super(RMSProp, self).__init__(learning_rate, params)
 
         if isinstance(momentum, float) and momentum < 0.0:
@@ -209,4 +194,4 @@ class RMSProp(Optimizer):
         else:
             success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
                                                self.momentum), params, self.ms, self.moment, gradients)
-        return success
+        return success