# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1281546965 14400
# Node ID 0181459b53a129701a0ec29c4be910595e884824
# Parent  fdd648c7c58309353a29be9beeb21367b4b875cf# Parent  a75bf0aca18f9ae34c29c02723d17512bfb812b8
merge

diff -r a75bf0aca18f -r 0181459b53a1 pylearn/shared/layers/sgd.py
--- a/pylearn/shared/layers/sgd.py	Mon Jun 21 15:47:30 2010 -0400
+++ b/pylearn/shared/layers/sgd.py	Wed Aug 11 13:16:05 2010 -0400
@@ -20,27 +20,17 @@
         """
         :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 updates: dict Variable -> Variable
-        :param auxout: auxiliary outputs, list containing output symbols to 
-                      compute at the same time as cost (for efficiency)
-        :param methods: Should this module define the step and step_cost methods?
         """
         if len(inputs) != len(gradients):
             raise ValueError('inputs list and gradients list must have same len')
 
         self.inputs = inputs
-        self.params = params
-        self.updates = updates = []
-        self.outputs = outputs = []
-
-        for i, g in zip(inputs, gradients):
-            o = i - stepsize * g
-            outputs.append(o)
-            if hasattr(i, 'value'): # this is true for shared variables, false for most things.
-                updates.append((i, o))
+        self.gradients = gradients
+        self.params = params # contains either nothing or the learning rate
+        self.outputs = [i - stepsize*g for (i,g) in zip(inputs, gradients)]
+        self.updates = [(input, self.outputs[i])
+            for (i,input) in enumerate(self.inputs)
+            if hasattr(input, 'value')] # true for shared variables
 
     @classmethod
     def new(cls, inputs, cost, stepsize, dtype=None):