Merge pull request #8 from nouiz/abergeron-op_decorator

doc/library/compile/index.txt

@@ -16,6 +16,7 @@
     shared
     function
     io
+    ops
     mode
     module
     debugmode

doc/library/compile/ops.txt

+==================================================
+:mod:`ops` --  Some Common Ops and extra Ops stuff
+==================================================
+
+.. automodule:: theano.compile.ops
+    :members:

theano/compile/ops.py

-"""This file contain auxiliary Ops, used during the compilation phase."""
+"""This file contain auxiliary Ops, used during the compilation phase
+and Ops building class (:class:`FromFunctionOp`) and decorator
+(:func:`as_op`) that help make new Ops more rapidly.
+
+"""
 import copy
 import warnings
 

theano/tests/test_op_decorator.py → theano/compile/tests/test_ops.py

 """
  Tests for the Op decorator
 """
+import numpy as np
 
-import unittest
-from theano.tests  import unittest_tools as utt
+from theano.tests import unittest_tools as utt
 from theano import function
 import theano
 from theano import tensor
 from theano.tensor import dmatrix, dvector
-import numpy as np 
 from numpy import allclose
 from theano.compile import as_op
 
-class OpDecoratorTests(unittest.TestCase): 
+
+class OpDecoratorTests(utt.InferShapeTester):
     def test_1arg(self):
         x = dmatrix('x')
-        
+
         @as_op(dmatrix, dvector)
-        def diag(x): 
+        def diag(x):
             return np.diag(x)
 
         fn = function([x], diag(x))
-        r = fn([[1.5, 5],[2, 2]])
+        r = fn([[1.5, 5], [2, 2]])
         r0 = np.array([1.5, 2])
 
         assert allclose(r, r0), (r, r0)
 
     def test_2arg(self):
         x = dmatrix('x')
-        x.tag.test_value=np.zeros((2,2))
+        x.tag.test_value = np.zeros((2, 2))
         y = dvector('y')
-        y.tag.test_value=[0,0]
-        
+        y.tag.test_value = [0, 0]
+
         @as_op([dmatrix, dvector], dvector)
-        def diag_mult(x, y): 
-            return np.diag(x) * y 
+        def diag_mult(x, y):
+            return np.diag(x) * y
 
         fn = function([x, y], diag_mult(x, y))
-        r = fn([[1.5, 5],[2, 2]], [1, 100])
+        r = fn([[1.5, 5], [2, 2]], [1, 100])
         r0 = np.array([1.5, 200])
 
         assert allclose(r, r0), (r, r0)
 
     def test_infer_shape(self):
         x = dmatrix('x')
-        x.tag.test_value=np.zeros((2,2))
+        x.tag.test_value = np.zeros((2, 2))
         y = dvector('y')
-        y.tag.test_value=[0,0]
-        
+        y.tag.test_value = [0, 0]
+
         def infer_shape(node, shapes):
-            x,y = shapes
+            x, y = shapes
             return [y]
 
         @as_op([dmatrix, dvector], dvector, infer_shape)
-        def diag_mult(x, y): 
-            return np.diag(x) * y 
-
-        fn = function([x, y], diag_mult(x, y).shape)
-        r = fn([[1.5, 5],[2, 2]], [1, 100])
-        r0 = (2,)
-
-        assert allclose(r, r0), (r, r0)
+        def diag_mult(x, y):
+            return np.diag(x) * y
 
+        self._compile_and_check([x, y], [diag_mult(x, y)],
+                                [[[1.5, 5], [2, 2]], [1, 100]],
+                                diag_mult.__class__, warn=False)