--- a/pylearn/shared/layers/tests/test_kouh2008.py	Wed Mar 31 15:18:09 2010 -0400
+++ b/pylearn/shared/layers/tests/test_kouh2008.py	Thu Apr 01 11:53:09 2010 -0400
@@ -1,5 +1,6 @@
 import numpy
 import theano.compile.debugmode
+from theano.compile.debugmode import DebugMode
 from theano import tensor
 from theano.compile import pfunc
 from pylearn.shared.layers import LogisticRegression, Kouh2008
@@ -15,6 +16,9 @@
     assert layer.output.dtype =='float32'
 
 def run_w_random(bsize=10, n_iter=200, n_in = 1024, n_out = 100, n_terms=2, dtype='float64'):
+    if isinstance(theano.compile.mode.get_default_mode(),DebugMode):
+        n_iter=2
+        
     x = tensor.dmatrix()
     y = tensor.lvector()
     rng = numpy.random.RandomState(23455)
@@ -97,11 +101,16 @@
     test_A()
 
 def test_smaller():
-    assert run_w_random(n_in=10, n_out=8) < 6.1
+    rval = run_w_random(n_in=10, n_out=8)
+    if not isinstance(theano.compile.mode.get_default_mode(),DebugMode):
+        assert rval < 6.1
 
 def test_smaller32():
-    assert run_w_random(n_in=10, n_out=8, dtype='float32') < 6.1
+    rval = run_w_random(n_in=10, n_out=8, dtype='float32')
+    if not isinstance(theano.compile.mode.get_default_mode(),DebugMode):
+        assert rval < 6.1
 
 def test_big():
-    assert run_w_random() < 0.1
-
+    rval = run_w_random()
+    if not isinstance(theano.compile.mode.get_default_mode(),DebugMode):
+        assert rval < 0.1