Merge pull request #805 from lamblin/fix_py24

theano/sparse/tests/test_basic.py

@@ -1468,13 +1468,13 @@ class SpSumTester(utt.InferShapeTester):
                 variable, data = sparse_random_inputs(format,
                                                       shape=(10, 10))
 
-                z = theano.sparse.sp_sum(*variable, axis=axis)
+                z = theano.sparse.sp_sum(variable[0], axis=axis)
                 if axis == None:
                     assert z.type.broadcastable == ()
                 else:
                     assert z.type.broadcastable == (False, )
 
-                f = theano.function(variable, self.op(*variable, axis=axis))
+                f = theano.function(variable, self.op(variable[0], axis=axis))
                 tested = f(*data)
                 expected = data[0].todense().sum(axis).ravel()
                 assert numpy.allclose(tested, expected)
@@ -1485,7 +1485,7 @@ class SpSumTester(utt.InferShapeTester):
                 variable, data = sparse_random_inputs(format,
                                                       shape=(10, 10))
                 self._compile_and_check(variable,
-                                        [self.op(*variable, axis=axis)],
+                                        [self.op(variable[0], axis=axis)],
                                         data,
                                         self.op_class)
 

theano/tensor/tests/test_elemwise.py

@@ -8,7 +8,7 @@ import numpy
 from numpy.testing import dec
 
 import theano
-from theano.gof.python25 import all
+from theano.gof.python25 import all, any
 from theano.gof import Variable, Op
 from theano import gof, scalar, config
 
@@ -673,7 +673,7 @@ class T_mean_dtype(unittest.TestCase):
                     if sum_dtype in tensor.discrete_dtypes:
                         assert mean_var.dtype == 'float64', (mean_var.dtype, sum_dtype)
                     else:
-                        assert mean_var.dtype == sum_dtype, (mean_var.dtype, output_dtype)
+                        assert mean_var.dtype == sum_dtype, (mean_var.dtype, sum_dtype)
 
                     # Check that we can take the gradient, when implemented
                     try: