commented version of scan; I took out the updates ( which could have resulted in many issues)

theano/sandbox/test_scan.py

@@ -95,7 +95,7 @@ class T_Scan(unittest.TestCase):
     # non sequence arguments
     def test_1(self):
       def f_pow2(x_tm1):
-        return (2*x_tm1, {})
+        return 2*x_tm1
     
       s = theano.tensor.dscalar()
       n_steps = theano.tensor.dscalar()
@@ -109,7 +109,7 @@ class T_Scan(unittest.TestCase):
     # vectors, weights are scalars
     def test_2(self):
         def f_rnn(u_t,x_tm1,W_in, W):
-            return (u_t*W_in+x_tm1*W, {})
+            return u_t*W_in+x_tm1*W
     
         u    = theano.tensor.dvector()
         x0   = theano.tensor.dscalar()
@@ -134,7 +134,7 @@ class T_Scan(unittest.TestCase):
         W    = theano.shared(1., name ='w')
     
         def f_rnn_shared(u_t,x_tm1):
-            return (u_t*W_in+x_tm1*W, {})
+            return u_t*W_in+x_tm1*W
     
         Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0,[])
 
@@ -177,7 +177,7 @@ class T_Scan(unittest.TestCase):
         assert( compareArrays(x,v_x)) 
         assert( compareArrays(y,v_y))
 
-
+    '''
     # basic ESN using updates 
     def test_5(self): 
         W_in = theano.shared(numpy.array([1.,1.]), name='win')
@@ -225,7 +225,7 @@ class T_Scan(unittest.TestCase):
         out   = f6(v_u, v_y0)
         
         assert( compareArrays(out, v_out))
-
+    '''
     # simple rnn, one input, one state, weights for each; input/state are 
     # vectors, weights are scalars; using shared variables and past 
     # taps (sequences and outputs)
@@ -237,7 +237,7 @@ class T_Scan(unittest.TestCase):
         W    = theano.shared(1., name ='w')
     
         def f_rnn_shared(u_tm2, x_tm1, x_tm2):
-            return (u_tm2*W_in+x_tm1*W+x_tm2, {})
+            return u_tm2*W_in+x_tm1*W+x_tm2
     
         Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0, [], \
                  sequences_taps = {0:[-2]}, outputs_taps = {0:[-1,-2]})
@@ -259,7 +259,7 @@ class T_Scan(unittest.TestCase):
         W    = theano.shared(1., name ='w')
     
         def f_rnn_shared(u_tm2,u_tp2, x_tm1, x_tm2):
-            return ((u_tm2+u_tp2)*W_in+x_tm1*W+x_tm2, {})
+            return (u_tm2+u_tp2)*W_in+x_tm1*W+x_tm2
     
         Y = theano.sandbox.scan.scan(f_rnn_shared, u,x0, [], \
                  sequences_taps = {0:[-2,2]}, outputs_taps = {0:[-1,-2]})