Merge pull request #6199 from abergeron/fix_buildbot

theano/compile/debugmode.py

@@ -28,6 +28,7 @@ from theano.compile.function_module import (
     std_fgraph)
 from theano.compile.mode import Mode, register_mode
 from theano.compile.ops import OutputGuard, _output_guard
+from theano.configparser import change_flags
 
 
 __docformat__ = "restructuredtext en"
@@ -2227,17 +2228,11 @@ class _Maker(FunctionMaker):  # inheritance buys a few helper functions
                 inputs, outputs, accept_inplace)
             fgraph.equivalence_tracker = equivalence_tracker
 
-            # optimize the fgraph
-            compute_test_value_orig = theano.config.compute_test_value
-            try:
-                theano.config.compute_test_value = \
-                    theano.config.compute_test_value_opt
+            with change_flags(compute_test_value=config.compute_test_value_opt):
                 optimizer(fgraph)
 
                 theano.compile.function_module.insert_deepcopy(
                     fgraph, inputs, list(chain(outputs, additional_outputs)))
-            finally:
-                theano.config.compute_test_value = compute_test_value_orig
 
             if i == 0:
                 fgraph0 = fgraph
@@ -2286,7 +2281,8 @@ class _Maker(FunctionMaker):  # inheritance buys a few helper functions
             fgraph.attach_feature(gof.DestroyHandler())
         for o in fgraph.outputs:
             try:
-                fgraph.replace_validate(o, _output_guard(o), reason='output_guard')
+                with change_flags(compute_test_value=config.compute_test_value_opt):
+                    fgraph.replace_validate(o, _output_guard(o), reason='output_guard')
                 raise Exception("Output variable %s required output_guard, "
                                 "how was this output left unprotected against "
                                 "destructive operations?" % o)

theano/tensor/nnet/tests/test_bn.py

@@ -293,7 +293,7 @@ def test_batch_normalization_train_grad_grad():
                 x_mean_val = np.random.randn(*param_shape).astype('float64')
                 x_invstd_val = np.random.randn(*param_shape).astype('float64')
 
-                utt.verify_grad(bn_grad_wrt_inputs_f, [x_val, dy_val, scale_val, x_mean_val, x_invstd_val])
+                utt.verify_grad(bn_grad_wrt_inputs_f, [x_val, dy_val, scale_val, x_mean_val, x_invstd_val], abs_tol=5e-4, rel_tol=5e-4)
                 utt.verify_grad(bn_grad_wrt_scale_f, [x_val, dy_val, scale_val, x_mean_val, x_invstd_val])
                 utt.verify_grad(bn_grad_wrt_bias_f, [x_val, dy_val, scale_val, x_mean_val, x_invstd_val])