rewrite momentum optimizer

the original version wasn't very useful as it would typically diverge.

onn/optimizer.py

@@ -26,26 +26,25 @@ def filter_gradients(accum, grads, param):
 
 class Momentum(Optimizer):
     def __init__(self, lr=0.01, mu=0.9, nesterov=False):
-        self.mu = _f(mu)  # momentum
+        self.mu = _f(mu)
         self.nesterov = bool(nesterov)
 
         super().__init__(lr)
 
     def reset(self):
-        self.Vprev = None
+        self.accum = None
 
         super().reset()
 
     def compute(self, dW, W):
-        if self.Vprev is None:
-            self.Vprev = np.copy(dW)
+        if self.accum is None:
+            self.accum = np.zeros_like(dW)
 
-        V = self.mu * self.Vprev - self.lr * dW
-        self.Vprev[:] = V
+        self.accum[:] = self.accum * self.mu + dW
         if self.nesterov:
-            return self.mu * V - self.lr * dW
-
-        return V
+            return -self.lr * (self.accum * self.mu + dW)
+        else:
+            return -self.lr * self.accum
 
 
 class Adadelta(Optimizer):