small fix found while reviewing.

theano/gpuarray/dnn.py

@@ -2574,7 +2574,7 @@ def dnn_batch_normalization_train(inputs, gamma, beta, mode='per-activation',
     if epsilon < 1e-5:
         raise ValueError("epsilon must be at least 1e-5, got %f" % epsilon)
 
-    running_averages = (running_var is not None and running_var is not None)
+    running_averages = (running_mean is not None and running_var is not None)
 
     if ndim < 4:
         inputs = theano.tensor.shape_padright(inputs, 4 - ndim)

theano/gpuarray/tests/test_dnn.py

@@ -1439,7 +1439,8 @@ def test_dnn_batchnorm_train():
                                          mode=mode_with_gpu)
             f_ref = theano.function([x, scale, bias, running_mean, running_var, dy],
                                     [out_ref, x_mean_ref, x_invstd_ref,
-                                     out_running_mean_ref, out_running_var_ref] + grads_ref)
+                                     out_running_mean_ref, out_running_var_ref] + grads_ref,
+                                    mode=mode_without_gpu)
             # check if the abstract Ops have been replaced
             assert any([isinstance(n.op, dnn.GpuDnnBatchNorm) for n
                         in f_abstract.maker.fgraph.toposort()])