Merge pull request #1806 from nouiz/mixed

theano/__init__.py

@@ -61,7 +61,7 @@ from theano.compile import \
     Component, External, Member, Method, \
     Composite, ComponentList, ComponentDict, Module, \
     ProfileMode, ProfileStats, \
-    Param, shared
+    Param, shared, as_op
 
 from theano.misc.safe_asarray import _asarray
 

theano/sandbox/gpuarray/basic_ops.py

@@ -672,7 +672,7 @@ class GpuAlloc(HideC, Alloc):
                       #theano.sandbox.gpuarray.subtensor.GpuAdvancedIncSubtensor1,
                       theano.sandbox.gpuarray.blas.GpuGemm,
                       theano.sandbox.gpuarray.blas.GpuGemv,
-                      #theano.sandbox.gpuarray.blas.GpuGer, Not Yet implemented
+                      theano.sandbox.gpuarray.blas.GpuGer,
                   ))):
                 return False
             #If the clients is a transfer, we don't want to fold. We

theano/scan_module/tests/test_scan.py

@@ -3696,9 +3696,7 @@ class T_Scan(unittest.TestCase):
 
         cost = result_outer[-1]
         H = theano.gradient.hessian(cost, W_flat)
-        print >> sys.stderr, "."
         f = theano.function([W_flat], H)
-        print >> sys.stderr, "."
         f(numpy.ones((8,), dtype='float32'))
 
 

theano/tensor/tests/test_basic.py

@@ -4116,16 +4116,16 @@ class t_dot(unittest.TestCase):
                 return numpy.asarray([[1.3]], dtype=r.dtype)
             raise ValueError()
 
-        for dtype0 in ('float32', 'float64', 'complex64', 'complex128'):
-            for dtype1 in ('float32', 'float64', 'complex64', 'complex128'):
+        for dtype0 in ('float32', 'float64', 'complex64'):
+            for dtype1 in ('float32', 'complex64', 'complex128'):
                 for bc0 in ((True,), (False,), (True, True),
                             (True, False), (False, True),
                             (False, False)):
+                    x = TensorType(dtype=dtype0, broadcastable=bc0)()
                     for bc1 in ((True,), (False,), (True, True),
                                 (True, False), (False, True),
                                 (False, False)):
 
-                        x = TensorType(dtype=dtype0, broadcastable=bc0)()
                         y = TensorType(dtype=dtype1, broadcastable=bc1)()
                         z = dot(x, y)
                         t = TensorType(dtype=dtype0,