Use python any where we aren't working with ndarrays.

theano/compile/function.py

@@ -13,7 +13,6 @@ from six import string_types
 from theano.compile.io import In
 from theano.compile.function_module import orig_function
 from theano.compile.pfunc import pfunc
-import numpy as np
 import warnings
 from theano import compat
 
@@ -286,7 +285,7 @@ def function(inputs, outputs=None, mode=None, updates=None, givens=None,
                         "input.")
 
     # compute some features of the arguments:
-    uses_tuple = np.any([isinstance(i, (list, tuple)) for i in inputs])
+    uses_tuple = any([isinstance(i, (list, tuple)) for i in inputs])
     uses_updates = bool(updates)
     uses_givens = bool(givens)
 

theano/compile/function_module.py

@@ -836,9 +836,9 @@ class Function(object):
                              in args_share_memory[j]],
                             [self.input_storage[k].storage[0] for k
                              in args_share_memory[j]])
-                        if np.any([(var.type is i_var.type and
-                                  var.type.may_share_memory(val, i_val))
-                                  for (var, val) in group_j]):
+                        if any([(var.type is i_var.type and
+                                 var.type.may_share_memory(val, i_val))
+                                for (var, val) in group_j]):
 
                             is_aliased = True
                             args_share_memory[j].append(i)

theano/scan_module/scan_utils.py

@@ -948,7 +948,7 @@ def scan_can_remove_outs(op, out_idxs):
         added = False
         for pos, idx in enumerate(out_idxs):
             if (out_idxs_mask[pos] and
-                 np.any([x in required_inputs for x in out_ins[idx]])):
+                    any([x in required_inputs for x in out_ins[idx]])):
                 # This output is required ..
                 out_idxs_mask[pos] = 0
                 required_inputs += gof.graph.inputs([op.outputs[idx]])