diff --git a/core/leras/optimizers/RMSprop.py b/core/leras/optimizers/RMSprop.py
index 37cebe9..668fea3 100644
--- a/core/leras/optimizers/RMSprop.py
+++ b/core/leras/optimizers/RMSprop.py
@@ -10,20 +10,22 @@ class RMSprop(nn.OptimizerBase):
             raise ValueError('name must be defined.')
 
         self.lr_dropout = lr_dropout
+        self.lr = lr
+        self.rho = rho
+        self.epsilon = epsilon
+        
         self.clipnorm = clipnorm
 
         with tf.device('/CPU:0') :
             with tf.variable_scope(self.name):
-                self.lr = tf.Variable (lr, name="lr")
-                self.rho = tf.Variable (rho, name="rho")
-                self.epsilon = tf.Variable (epsilon, name="epsilon")
+                
                 self.iterations = tf.Variable(0, dtype=tf.int64, name='iters')
 
         self.accumulators_dict = {}
         self.lr_rnds_dict = {}
 
     def get_weights(self):
-        return [self.lr, self.rho, self.epsilon, self.iterations] + list(self.accumulators_dict.values())
+        return [self.iterations] + list(self.accumulators_dict.values())
 
     def initialize_variables(self, trainable_weights, vars_on_cpu=True, lr_dropout_on_cpu=False):
         # Initialize here all trainable variables used in training
@@ -53,13 +55,11 @@ class RMSprop(nn.OptimizerBase):
 
             a = self.accumulators_dict[ v.name ]
 
-            rho = tf.cast(self.rho, a.dtype)
-            new_a = rho * a + (1. - rho) * tf.square(g)
+            new_a = self.rho * a + (1. - self.rho) * tf.square(g)
 
-            lr = tf.cast(self.lr, a.dtype)
-            epsilon = tf.cast(self.epsilon, a.dtype)
+            lr = tf.constant(self.lr, g.dtype)
 
-            v_diff = - lr * g / (tf.sqrt(new_a) + epsilon)
+            v_diff = - lr * g / (tf.sqrt(new_a) + self.epsilon)
             if self.lr_dropout != 1.0:
                 lr_rnd = self.lr_rnds_dict[v.name]
                 v_diff *= lr_rnd