# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1284399746 14400
# Node ID b422cbaddc52470a83da184e820c9f42ef7df01c
# Parent  153cf820a97503bfc3d539fe9b9f738edf1e4370
v2planning - minor edits to use_cases

diff -r 153cf820a975 -r b422cbaddc52 doc/v2_planning/use_cases.txt
--- a/doc/v2_planning/use_cases.txt	Mon Sep 13 13:41:53 2010 -0400
+++ b/doc/v2_planning/use_cases.txt	Mon Sep 13 13:42:26 2010 -0400
@@ -66,6 +66,7 @@
             classification_accuracy(
                 examples=MNIST.validation_dataset,
                 function=as_classifier('learner_obj'))),
+
         step_fn = vm_lambda(('learner_obj',),
             sgd_step_fn(
                 parameters = vm_getattr('learner_obj', 'params'),
@@ -113,7 +114,7 @@
             initial_model=alloc_model('param1', 'param2'),
             burnin=100,
             score_fn = vm_lambda(('learner_obj',),
-                graph=classification_error(
+                classification_error(
                     function=as_classifier('learner_obj'),
                     dataset=MNIST.subset(validation_set))),
             step_fn = vm_lambda(('learner_obj',),
@@ -145,7 +146,7 @@
 extending the symbolic program, and calling the extended function.
 
     vm.call(
-        [pylearn.min(model.weights) for model in trained_models], 
+        [pylearn.min(pylearn_getattr(model, 'weights')) for model in trained_models], 
         param1=1, param2=2)
 
 If this is run after the previous calls: