# HG changeset patch
# User Frederic Bastien <nouiz@nouiz.org>
# Date 1315843195 14400
# Node ID 7f166d01bf8e0bf3b24cf76a2d7bacb72397a9ac
# Parent  9ffe5d6faee3181bc465c8cf340f63a009795b06
Remove deprecation warning.

diff -r 9ffe5d6faee3 -r 7f166d01bf8e pylearn/algorithms/tests/test_mcRBM.py
--- a/pylearn/algorithms/tests/test_mcRBM.py	Mon Sep 12 11:47:23 2011 -0400
+++ b/pylearn/algorithms/tests/test_mcRBM.py	Mon Sep 12 11:59:55 2011 -0400
@@ -135,8 +135,8 @@
         last_epoch = epoch
 
         if as_unittest and epoch == 5:
-            U = rbm.U.value
-            W = rbm.W.value
+            U = rbm.U.get_value(borrow=True)
+            W = rbm.W.get_value(borrow=True)
             def allclose(a,b):
                 return numpy.allclose(a,b,rtol=1.01,atol=1e-3)
             print ""
@@ -168,25 +168,25 @@
 
             print 'saving samples', jj, 'epoch', jj/(epoch_size/batchsize)
 
-            print 'l2(U)', l2(rbm.U.value),
-            print 'l2(W)', l2(rbm.W.value),
+            print 'l2(U)', l2(rbm.U.get_value(borrow=True)),
+            print 'l2(W)', l2(rbm.W.get_value(borrow=True)),
             print 'l1_penalty',
             try:
-                print trainer.effective_l1_penalty.value
+                print trainer.effective_l1_penalty.get_value(borrow=True)
             except:
                 print trainer.effective_l1_penalty
 
-            print 'U min max', rbm.U.value.min(), rbm.U.value.max(),
-            print 'W min max', rbm.W.value.min(), rbm.W.value.max(),
-            print 'a min max', rbm.a.value.min(), rbm.a.value.max(),
-            print 'b min max', rbm.b.value.min(), rbm.b.value.max(),
-            print 'c min max', rbm.c.value.min(), rbm.c.value.max()
+            print 'U min max', rbm.U.get_value(borrow=True).min(), rbm.U.get_value(borrow=True).max(),
+            print 'W min max', rbm.W.get_value(borrow=True).min(), rbm.W.get_value(borrow=True).max(),
+            print 'a min max', rbm.a.get_value(borrow=True).min(), rbm.a.get_value(borrow=True).max(),
+            print 'b min max', rbm.b.get_value(borrow=True).min(), rbm.b.get_value(borrow=True).max(),
+            print 'c min max', rbm.c.get_value(borrow=True).min(), rbm.c.get_value(borrow=True).max()
 
             if persistent_chains:
-                print 'parts min', smplr.positions.value.min(),
-                print 'max',smplr.positions.value.max(),
-            print 'HMC step', smplr.stepsize.value,
-            print 'arate', smplr.avg_acceptance_rate.value
+                print 'parts min', smplr.positions.get_value(borrow=True).min(),
+                print 'max',smplr.positions.get_value(borrow=True).max(),
+            print 'HMC step', smplr.stepsize.get_value(borrow=True),
+            print 'arate', smplr.avg_acceptance_rate.get_value(borrow=True)
 
 
         l2_of_Ugrad = learn_fn(jj)
@@ -275,24 +275,24 @@
 
                     print 'saving samples', ii, 'epoch', i_epoch, i_batch
 
-                    print 'l2(U)', l2(rbm.U.value),
-                    print 'l2(W)', l2(rbm.W.value),
+                    print 'l2(U)', l2(rbm.U.get_value(borrow=True)),
+                    print 'l2(W)', l2(rbm.W.get_value(borrow=True)),
                     print 'l1_penalty',
                     try:
-                        print trainer.effective_l1_penalty.value
+                        print trainer.effective_l1_penalty.get_value(borrow=True)
                     except:
                         print trainer.effective_l1_penalty
 
-                    print 'U min max', rbm.U.value.min(), rbm.U.value.max(),
-                    print 'W min max', rbm.W.value.min(), rbm.W.value.max(),
-                    print 'a min max', rbm.a.value.min(), rbm.a.value.max(),
-                    print 'b min max', rbm.b.value.min(), rbm.b.value.max(),
-                    print 'c min max', rbm.c.value.min(), rbm.c.value.max()
+                    print 'U min max', rbm.U.get_value(borrow=True).min(), rbm.U.get_value(borrow=True).max(),
+                    print 'W min max', rbm.W.get_value(borrow=True).min(), rbm.W.get_value(borrow=True).max(),
+                    print 'a min max', rbm.a.get_value(borrow=True).min(), rbm.a.get_value(borrow=True).max(),
+                    print 'b min max', rbm.b.get_value(borrow=True).min(), rbm.b.get_value(borrow=True).max(),
+                    print 'c min max', rbm.c.get_value(borrow=True).min(), rbm.c.get_value(borrow=True).max()
 
-                    print 'HMC step', smplr.stepsize.value,
-                    print 'arate', smplr.avg_acceptance_rate.value
-                    print 'P min max', rbm.P.value.min(), rbm.P.value.max(),
-                    print 'P_lr', trainer.p_lr.value
+                    print 'HMC step', smplr.stepsize.get_value(borrow=True),
+                    print 'arate', smplr.avg_acceptance_rate.get_value(borrow=True)
+                    print 'P min max', rbm.P.get_value(borrow=True).min(), rbm.P.get_value(borrow=True).max(),
+                    print 'P_lr', trainer.p_lr.get_value(borrow=True)
                     print ''
                     print 'Saving rbm...'
                     cPickle.dump(rbm, open('mcRBM.rbm.%06i.pkl'%ii, 'w'), -1)