In the reshape op, put the output broadcast flag correctly when we receive an…

theano/tensor/basic.py

@@ -3592,6 +3592,7 @@ class Reshape(Op):
                 shp_list = [shp_orig]
             for index in xrange(self.ndim):
                 y = shp_list[index]
+                y = as_tensor_variable(y)
                 # Try to see if we can infer that y has a constant value of 1.
                 # If so, that dimension should be broadcastable.
                 try:

theano/tensor/tests/test_basic.py

@@ -2808,14 +2808,14 @@ def test_reshape():
     c = reshape(b, as_tensor_variable(6), ndim=1)
     f = inplace_func([b], c)
     assert numpy.all(f(numpy.asarray([[0,1,2],[3,4,5]])) == numpy.asarray([0,1,2,3,4,5]))
-    print f.maker.env.toposort()
+    #print f.maker.env.toposort()
     #check that we remove the useless reshape
 
     #basic to 1 dim(with list)
     c = reshape(b, (as_tensor_variable(6),), ndim=1)
     f = inplace_func([b], c)
     assert numpy.all(f(numpy.asarray([[0,1,2],[3,4,5]])) == numpy.asarray([0,1,2,3,4,5]))
-    print f.maker.env.toposort()
+    #print f.maker.env.toposort()
     #check that we remove the useless reshape
 
     #basic to shape object of same ndim
@@ -2860,6 +2860,13 @@ def test_reshape():
         #assert numpy.all(f_sub(a_val,numpy.asarray([[0,1],[2,3],[4,5]]))==[2,3])#work in FAST_RUN, but fail on other!
         #assert numpy.all(f_sub(a_val,numpy.asarray([[0,1],[2,3],[4,5],[6,7]]))==[2,3])#work in FAST_RUN, but fail on other!
 
+    # test broadcast flag for constant value of 1
+    c = reshape(b, (b.shape[0],b.shape[1],1))
+    f = inplace_func([b], c)
+    assert numpy.all(f(numpy.asarray([[0,1,2],[3,4,5]])) == numpy.asarray([[[0],[1],[2]],[[3],[4],[5]]]))
+    assert f.maker.env.toposort()[-2].outputs[0].type.broadcastable==(False, False, True)
+
+
     assert numpy.all(f_sub(a_val,b_val)==[2,3])
 
 def test_make_column_matrix_broadcastable():