Merge pull request #2030 from Tanjay94/As_Op

doc/tutorial/extending_theano.txt

@@ -415,6 +415,95 @@ efficiency over the basic solution that is asked here, the two operations would
 have to be jointly optimized explicitly in the code.)
 
 
+as_op
+=====
+
+- Decorator that converts a function into a basic Theano op 
+    that will call the supplied function as its implementation.
+- Takes an optional infer_shape parameter that should be a
+    callable with this signature:
+
+           def infer_shape(node, input_shapes):
+            ...
+            return output_shapes
+
+- `input_shapes` and `output_shapes` are lists of tuples that
+    represent the shape of the corresponding inputs/outputs.
+
+.. note::
+
+    This should not be used when performance is a concern since 
+    the very basic nature of the resulting Op may interfere with
+    certain graph optimizations.
+
+.. note::
+
+    Returns FromFunctionOp(fn, itypes, otypes, infer_shape)
+
+
+FromfunctionOp
+==============
+
+- Build a basic Theano Op around a function.
+
+.. note::
+
+    Since the resulting Op is very basic and is missing most 
+    of the optional functionalities, some optimizations may not 
+    apply.
+
+    If you want to help, you can supply an infer_shape function 
+    that computes the shapes of the output given the shapes of 
+    the inputs.
+
+    Also the gradient is undefined in the resulting op and 
+    Theano will raise an error if you attempt to get the 
+    gradient of a graph containing this op.
+
+
+Op Example
+==========
+
+.. code-block:: python
+
+    import theano
+    import numpy
+    from theano.compile.ops import as_op
+    from theano.compile.ops import FromFunctionOp
+
+    def infer_shape_numpy_dot(node, input_shapes):
+        ashp, bshp = input_shapes
+        return [ashp[:-1] + bshp[-1:]]
+
+    @as_op(itypes=[theano.tensor.fmatrix, theano.tensor.fmatrix],
+          otypes=[theano.tensor.fmatrix], infer_shape=infer_shape_numpy_dot)
+
+    def numpy_dot(a, b):
+       return numpy.dot(a, b)
+
+You can try it as follows:
+
+.. code-block:: python
+
+    x = theano.tensor.fmatrix()
+    y = theano.tensor.fmatrix()
+    f = function([x, y], numpy_dot(x, y))
+    inp1 = numpy.random.rand(5, 4)
+    inp2 = numpy.random.rand(4, 7)
+    out = f(inp1, inp2)
+
+
+Exercise
+========
+
+Run the code of the *numpy_dot* example above.
+
+Modify and execute to compute: numpy.add and numpy.subtract.
+
+Modify and execute the example to return two outputs: x + y
+    and x - y.
+
+
 Random numbers in tests
 =======================