# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1243988403 14400
# Node ID 88839ba37b97322db7f9cfc4724ea8dc09b46d97
# Parent  b4aa46f856c1bc47d9b4cb0d653df4547211e400
added methods kwarg to sgd module

diff -r b4aa46f856c1 -r 88839ba37b97 pylearn/algorithms/sgd.py
--- a/pylearn/algorithms/sgd.py	Tue Jun 02 20:19:30 2009 -0400
+++ b/pylearn/algorithms/sgd.py	Tue Jun 02 20:20:03 2009 -0400
@@ -4,10 +4,16 @@
 import theano
 
 class StochasticGradientDescent(theano.Module):
-    """Fixed stepsize gradient descent"""
+    """Fixed stepsize gradient descent
+
+    Methods for gradient descent are:
+    - step(arg_vals) which returns None and updates the params
+    - step_cost(arg_vals) which returns the cost value, and updates the params
+    
+    """
     def __init__(self, args, cost, params, 
                  gradients=None, stepsize=None, 
-                 updates=None, auxout=None):
+                 updates=None, auxout=None, methods=True):
         """
         :param stepsize: the step to take in (negative) gradient direction
         :type stepsize: None, scalar value, or scalar TensorVariable
@@ -15,8 +21,9 @@
         :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
-        :type auxout: auxiliary outputs, list containing output symbols to 
+        :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?
         """
         super(StochasticGradientDescent, self).__init__()
         self.stepsize_init = None
@@ -38,13 +45,19 @@
         if updates is not None:
             self._updates.update(updates)
 
-        auxout = auxout if auxout else []
-        self.step = theano.Method(
-                args, auxout,
-                updates=self._updates)
-        self.step_cost = theano.Method(
-                args, [cost]+auxout,
-                updates=self._updates)
+        if methods:
+            if auxout is None:
+                self.step = theano.Method(args, [], updates=self._updates)
+                self.step_cost = theano.Method(args, cost, updates=self._updates)
+            else:
+                # step cost always returns a list if auxout
+                self.step = theano.Method(
+                        args, [] + auxout,
+                        updates=self._updates)
+                self.step_cost = theano.Method(
+                        args, [cost]+auxout,
+                        updates=self._updates)
+
 
     updates = property(lambda self: self._updates.copy())