Merge pull request #2416 from nouiz/doc

doc/introduction.txt

@@ -195,11 +195,13 @@ Here is the state of that vision as of December 3th, 2013 (after Theano release
   * How to have `DebugMode` check it? Right now, DebugMode checks the computation non-lazily.
 
 * SIMD parallelism on the CPU comes from the compiler.
-* Multi-core parallelism is only supported by Conv2d(not by default).
+* Multi-core parallelism support limited.
   If the external BLAS implementation supports it,
-  there are also, gemm, gemv and ger that are parallelized.
+  many dot are parallelized via gemm, gemv and ger.
+  Also, element-wise operation are supported. See :ref:`tut_multi_cores`.
+
 * No multi-node support.
-* Many, but not all NumPy functions/aliases are implemented.
+* Most, but not all NumPy functions/aliases are implemented.
   * https://github.com/Theano/Theano/issues/1080
 * Wrapping an existing Python function in easy and documented.
 * We know how to separate the shared variable memory

doc/library/index.txt

@@ -43,7 +43,7 @@ There are also some top-level imports that you might find more convenient:
 
 .. function:: shared(...)
     
-    Alias for :func:`shared.shared`
+    Alias for :func:`theano.compile.sharedvalue.shared`
 
 .. class:: Param
 

doc/tutorial/examples.txt

@@ -276,6 +276,15 @@ expression that evaluates to a tensor of same shape and dtype.
 
 .. _using_random_numbers:
 
+
+.. note::
+
+    Theano shared variable broadcast pattern default to False for each
+    dimensions. Shared variable size can change over time, so we can't
+    use the shape to find the broadcastable pattern. If you want a
+    different pattern, just pass it as a parameter
+    ``theano.shared(..., broadcastable=(True, False))``
+
 Using Random Numbers
 ====================
 

theano/tensor/tests/test_extra_ops.py

@@ -286,6 +286,20 @@ class SqueezeTester(utt.InferShapeTester):
 
             utt.verify_grad(self.op, [data])
 
+    def test_var_interface(self):
+        # same as test_op, but use a_theano_var.squeeze.
+        for shape, broadcast in zip(self.shape_list, self.broadcast_list):
+            data = numpy.random.random(size=shape).astype(theano.config.floatX)
+            variable = tensor.TensorType(theano.config.floatX, broadcast)()
+
+            f = theano.function([variable], variable.squeeze())
+
+            expected = numpy.squeeze(data)
+            tested = f(data)
+
+            assert tested.shape == expected.shape
+            assert numpy.allclose(tested, expected)
+
 
 class TestRepeatOp(utt.InferShapeTester):
     def _possible_axis(self, ndim):

theano/tensor/var.py

@@ -585,7 +585,19 @@ class _tensor_py_operators:
 
     def choose(self, a, choices, out=None, mode='raise'):
         """Construct an array from an index array and a set of arrays to choose from."""
-        return theano.tensor.basic.choose(self, a, choices, out=None, mode='raise')
+        return theano.tensor.basic.choose(self, a, choices, out=None,
+                                          mode='raise')
+
+    def squeeze(self):
+        """Remove broadcastable dimensions from
+        the shape of an array.
+
+        It returns the input array, but with the
+        broadcastable dimensions removed. This is
+        always `x` itself or a view into `x`.
+        """
+        return theano.tensor.extra_ops.squeeze(self)
+
 
 class TensorVariable(_tensor_py_operators, Variable):
     """Subclass to add the tensor operators to the basic `Variable` class."""