Refactor condition and use isinstance

theano/tensor/blas.py

@@ -1190,32 +1190,31 @@ def _beta_L_plus_alpha_M(beta, L, alpha, M, recurse_flip=True):
 
     # it also might be the case that there is a dimshuffle between the +
     # and the dot22. local_dot_to_dot22 in particular will put in such things.
-    if M.owner and isinstance(M.owner.op, T.DimShuffle):
+    if (M.owner and isinstance(M.owner.op, T.DimShuffle) and
+        M.owner.inputs[0].owner and
+        isinstance(M.owner.inputs[0].owner.op, Dot22)):
         MM = M.owner.inputs[0]
-        if tuple(M.owner.op.new_order) == (0,):
+        if M.owner.op.new_order == (0,):
             # it is making a column MM into a vector
-            if MM.owner and MM.owner.op == _dot22:
-                MMl, MMr = MM.owner.inputs
-                g = gemm_no_inplace(L.dimshuffle(0, 'x'),
-                        alpha, MMl, MMr, beta)
-                rval = [g.dimshuffle(0)]
-                return rval, MM
-        if tuple(M.owner.op.new_order) == (1,):
+            MMl, MMr = MM.owner.inputs
+            g = gemm_no_inplace(L.dimshuffle(0, 'x'),
+                                alpha, MMl, MMr, beta)
+            rval = [g.dimshuffle(0)]
+            return rval, MM
+        if M.owner.op.new_order == (1,):
             # it is making a row MM into a vector
-            if MM.owner and MM.owner.op == _dot22:
-                MMl, MMr = MM.owner.inputs
-                g = gemm_no_inplace(L.dimshuffle('x', 0),
-                        alpha, MMl, MMr, beta)
-                rval = [g.dimshuffle(1)]
-                return rval, MM
-        if tuple(M.owner.op.new_order) == ():
+            MMl, MMr = MM.owner.inputs
+            g = gemm_no_inplace(L.dimshuffle('x', 0),
+                                alpha, MMl, MMr, beta)
+            rval = [g.dimshuffle(1)]
+            return rval, MM
+        if len(M.owner.op.new_order) == 0:
             # it is making a row MM into a vector
-            if MM.owner and MM.owner.op == _dot22:
-                MMl, MMr = MM.owner.inputs
-                g = gemm_no_inplace(L.dimshuffle('x', 'x'),
-                        alpha, MMl, MMr, beta)
-                rval = [g.dimshuffle()]
-                return rval, MM
+            MMl, MMr = MM.owner.inputs
+            g = gemm_no_inplace(L.dimshuffle('x', 'x'),
+                                alpha, MMl, MMr, beta)
+            rval = [g.dimshuffle()]
+            return rval, MM
 
     # this is False'd out because of inadequate testing.
     # TODO see ticket #237