--- a/pylearn/algorithms/sgd.py	Wed Apr 01 19:48:47 2009 -0400
+++ b/pylearn/algorithms/sgd.py	Thu Apr 02 15:08:22 2009 -0400
@@ -5,10 +5,14 @@
 
 class StochasticGradientDescent(theano.Module):
     """Fixed stepsize gradient descent"""
-    def __init__(self, args, cost, params, gradients=None, stepsize=None):
+    def __init__(self, args, cost, params, gradients=None, stepsize=None, updates=None):
         """
         :param stepsize: the step to take in (negative) gradient direction
         :type stepsize: None, scalar value, or scalar TensorVariable
+
+        :param updates: extra symbolic updates to make when evating either step or step_cost
+        (these override the gradients if necessary)
+        :type updatess: dict Variable -> Variable
         """
         super(StochasticGradientDescent, self).__init__()
         self.stepsize_init = None
@@ -26,14 +30,19 @@
         self.params = params
         self.gparams = theano.tensor.grad(cost, self.params) if gradients is None else gradients
 
-        self.updates = dict((p, p - self.stepsize * g) for p, g in zip(self.params, self.gparams))
+        self._updates = (dict((p, p - self.stepsize * g) for p, g in zip(self.params, self.gparams)))
+        if updates is not None:
+            self._updates.update(updates)
+
 
         self.step = theano.Method(
                 args, [],
-                updates=self.updates)
+                updates=self._updates)
         self.step_cost = theano.Method(
                 args, cost,
-                updates=self.updates)
+                updates=self._updates)
+
+    updates = property(lambda self: self._updates.copy())
 
     def _instance_initialize(self, obj):
         pass
@@ -43,6 +52,6 @@
     
     :returns: standard minimizer constructor f(args, cost, params, gradient=None)
     """
-    def f(args, cost, params, gradient=None):
-        return StochasticGradientDescent(args, cost, params, gradient, stepsize)
+    def f(args, cost, params, gradient=None, updates=None):
+        return StochasticGradientDescent(args, cost, params, gradient, stepsize, updates=updates)
     return f