update test for travis

theano/tensor/nnet/bn.py

@@ -32,7 +32,7 @@ def batch_normalization(inputs, gamma, beta, mean, std):
     to a set of activations. As no intermediate representations are stored for the
     back-propagation, this implementation lower the memory usage, however,
     it is 5-10% slower than a naive theano implementation, as it redo
-    some foward computations for the backprop.
+    some forward computations for the backprop.
     Work also on GPU
 
     Parameters

theano/tensor/nnet/tests/test_bn.py

@@ -40,7 +40,8 @@ def test_bn():
     res = f(X, G, B)
     res_ref = f_ref(X, G, B)
     utt.assert_allclose(res_ref, res)
-    utt.verify_grad(batch_normalization, [X, G, B, X.mean(axis=0, keepdims=True), X.std(axis=0, keepdims=True)])
+    utt.verify_grad(batch_normalization, [X, G, B,
+                                          X.mean(axis=0)[numpy.newaxis], X.std(axis=0)[numpy.newaxis]])
 
 
 def test_bn_feature_maps():