diff --git a/tinygrad/optim.py b/tinygrad/optim.py
index f1ee044fbd..5887767932 100644
--- a/tinygrad/optim.py
+++ b/tinygrad/optim.py
@@ -7,9 +7,6 @@ class Optimizer:
   def __init__(self, params):
     self.params = [x for x in params if x.requires_grad == True]
 
-  def num(self, x):
-    return Tensor([x], gpu=self.params[0].gpu, requires_grad=False)
-
   def zero_grad(self):
     for param in self.params:
       param.grad = None
@@ -17,7 +14,7 @@ class Optimizer:
 class SGD(Optimizer):
   def __init__(self, params, lr=0.001):
     super(SGD, self).__init__(params)
-    self.lr = self.num(lr)
+    self.lr = lr
 
   def step(self):
     for t in self.params:
@@ -26,27 +23,27 @@ class SGD(Optimizer):
 class RMSprop(Optimizer):
   def __init__(self, params, lr=0.001, decay=0.9, eps=1e-8):
     super(RMSprop, self).__init__(params)
-    self.lr, self.decay, self.eps, self.one, self.two = [self.num(x) for x in [lr, decay, eps, 1, 2]]
+    self.lr, self.decay, self.eps = Tensor([lr], gpu=self.params[0].gpu, requires_grad=False), decay, eps
 
     self.v = [Tensor(np.zeros(t.shape, dtype=np.float32), gpu=params[0].gpu, requires_grad=False) for t in self.params]
 
   def step(self):
     for i, t in enumerate(self.params):
-      self.v[i] = self.decay * self.v[i] + (self.one - self.decay) * t.grad.pow(self.two)
+      self.v[i] = self.decay * self.v[i] + (1.0 - self.decay) * t.grad.pow(2.0)
       t -= self.lr.div(self.v[i].sqrt() + self.eps) * t.grad
 
 class Adam(Optimizer):
   def __init__(self, params, lr=0.001, b1=0.9, b2=0.999, eps=1e-8):
     super(Adam, self).__init__(params)
-    self.lr, self.b1, self.b2, self.eps, self.t, self.one, self.two = [self.num(x) for x in [lr, b1, b2, eps, 0, 1, 2]]
+    self.lr, self.b1, self.b2, self.eps, self.t = lr, b1, b2, eps, Tensor([0.0], gpu=self.params[0].gpu, requires_grad=False)
 
     self.m = [Tensor(np.zeros(t.shape, dtype=np.float32), gpu=params[0].gpu, requires_grad=False) for t in self.params]
     self.v = [Tensor(np.zeros(t.shape, dtype=np.float32), gpu=params[0].gpu, requires_grad=False) for t in self.params]
 
   def step(self):
-    self.t = self.t + self.one
-    a = self.lr * (self.one - self.b2.pow(self.t)).sqrt().div(self.one - self.b1.pow(self.t))
+    self.t = self.t + 1.0
+    a = self.lr * ((1.0 - self.b2**self.t)**0.5).div(1.0 - self.b1**self.t)
     for i,t in enumerate(self.params):
-      self.m[i] = self.b1 * self.m[i] + (self.one - self.b1) * t.grad
-      self.v[i] = self.b2 * self.v[i] + (self.one - self.b2) * t.grad.pow(self.two)
+      self.m[i] = self.b1 * self.m[i] + (1.0 - self.b1) * t.grad
+      self.v[i] = self.b2 * self.v[i] + (1.0 - self.b2) * t.grad.pow(2.0)
       t -= a * self.m[i].div(self.v[i].sqrt() + self.eps)