_

core/leras/optimizers/RMSprop.py

@@ -4,16 +4,16 @@
 tf = nn.tf
 
 class RMSprop(nn.OptimizerBase):
-    def __init__(self, lr=0.001, rho=0.9, lr_dropout=1.0, clipnorm=0.0, name=None, **kwargs):
+    def __init__(self, lr=0.001, rho=0.9, lr_dropout=1.0, lr_cos=0, clipnorm=0.0, name=None, **kwargs):
         super().__init__(name=name)
 
         if name is None:
             raise ValueError('name must be defined.')
 
         self.lr_dropout = lr_dropout
+        self.lr_cos = lr_cos
         self.lr = lr
         self.rho = rho
-
         self.clipnorm = clipnorm
 
         with tf.device('/CPU:0') :
@@ -58,6 +58,8 @@ def get_update_op(self, grads_vars):
             new_a = self.rho * a + (1. - self.rho) * tf.square(g)
 
             lr = tf.constant(self.lr, g.dtype)
+            if self.lr_cos != 0:
+                lr *= (tf.cos(  tf.cast(self.iterations, g.dtype) * (2*3.1415926535/ float(self.lr_cos) )  ) + 1.0) / 2.0
 
             v_diff = - lr * g / (tf.sqrt(new_a) + np.finfo( g.dtype.as_numpy_dtype ).resolution  )
             if self.lr_dropout != 1.0: