subtle changes to scan op

theano/sandbox/scan.py

@@ -73,13 +73,13 @@ def scan(fn, sequences, initial_states, non_sequences, inplace_map={},
     for i in xrange(n_outs):
         if not outputs_taps.has_key(i):
             outputs_taps.update({i:[-1]})
-        # if output sequence is not actually used as input to the recursive 
-        # function
         elif outputs_taps[i] == []:
             outputs_taps.__delitem__(i)
         elif not(type(outputs_taps[i]) in (list,tuple)):
             outputs_taps[i] = [outputs_taps[i]]
 
+
+
     # create theano inputs for the recursive function  
     args = []
     for (i,seq) in enumerate(seqs):
@@ -89,7 +89,10 @@ def scan(fn, sequences, initial_states, non_sequences, inplace_map={},
     for (i,init_out) in enumerate(init_outs):
       if outputs_taps.has_key(i):
         for k in xrange(len(outputs_taps[i])):
-            args += [init_out[0].type() ]
+            if outputs_taps[i] == [-1]:
+                args += [init_out.type() ]
+            else:
+                args += [init_out[0].type() ]
 
     args += non_seqs
     t1,t2 = fn(*args)
@@ -313,6 +316,9 @@ class Scan(theano.Op):
         for i in xrange(self.n_seqs+1, \
                         self.n_seqs+self.n_outs+1):
           if self.outs_taps.has_key(i-self.n_seqs-1):
+            if self.outs_taps[i-self.n_seqs-1] == [-1]:
+                args[i] = numpy.array([args[i]])
+
             req_size = abs(min(self.outs_taps[i-self.n_seqs-1]))-1
             if args[i].shape[0] < req_size:
               warning(('Initial state for output %d has fewer values then '

theano/sandbox/test_scan.py

@@ -97,13 +97,13 @@ class T_Scan(unittest.TestCase):
       def f_pow2(x_tm1):
         return (2*x_tm1, {})
     
-      s = theano.tensor.dvector()
+      s = theano.tensor.dscalar()
       n_steps = theano.tensor.dscalar()
       Y = theano.sandbox.scan.scan(f_pow2, [],s, [],n_steps = n_steps)
     
       f1 = theano.function([s,n_steps], Y)
       
-      assert(compareArrays(f1([1],3), [2,4,8]))
+      assert(compareArrays(f1(1,3), [2,4,8]))
 
     # simple rnn, one input, one state, weights for each; input/state are 
     # vectors, weights are scalars
@@ -112,7 +112,7 @@ class T_Scan(unittest.TestCase):
             return (u_t*W_in+x_tm1*W, {})
     
         u    = theano.tensor.dvector()
-        x0   = theano.tensor.dvector()
+        x0   = theano.tensor.dscalar()
         W_in = theano.tensor.dscalar()
         W    = theano.tensor.dscalar()
 
@@ -120,7 +120,7 @@ class T_Scan(unittest.TestCase):
     
         f2    = theano.function([u,x0,W_in,W], Y)
         v_u   = numpy.array([1.,2.,3.,4.])
-        v_x0  = numpy.array([1])
+        v_x0  = numpy.array(1)
         v_out = numpy.array([1.1,1.3,1.6,2.])
         assert(compareArrays( f2(v_u,v_x0,.1,1), v_out   ) )
 
@@ -129,7 +129,7 @@ class T_Scan(unittest.TestCase):
     def test_3(self):
     
         u    = theano.tensor.dvector()
-        x0   = theano.tensor.dvector()
+        x0   = theano.tensor.dscalar()
         W_in = theano.shared(.1, name = 'w_in')
         W    = theano.shared(1., name ='w')
     
@@ -140,7 +140,7 @@ class T_Scan(unittest.TestCase):
 
         f3    = theano.function([u,x0], Y)
         v_u   = numpy.array([1.,2.,3.,4.])
-        v_x0  = numpy.array([1.])
+        v_x0  = numpy.array(1.)
         v_out = numpy.array([1.1,1.3,1.6,2.])
         assert(compareArrays(f3(v_u,v_x0),v_out))
 
@@ -155,8 +155,8 @@ class T_Scan(unittest.TestCase):
         W_in1 = theano.tensor.dmatrix('win')
         u1    = theano.tensor.dmatrix('u1')
         u2    = theano.tensor.dvector('u2')
-        x0    = theano.tensor.dmatrix('x0')
-        y0    = theano.tensor.dvector('y0')
+        x0    = theano.tensor.dvector('x0')
+        y0    = theano.tensor.dscalar('y0')
     
         def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
             return ({}, [theano.dot(u1_t,W_in1) + u2_t* W_in2 + \
@@ -167,8 +167,8 @@ class T_Scan(unittest.TestCase):
         f4     = theano.function([u1,u2,x0,y0,W_in1], Y)
         v_u1   = numpy.array([[1.,2.],[1.,2.],[1.,2.]])
         v_u2   = numpy.array([1.,2.,3.])
-        v_x0   = numpy.array([[0.,0.]])
-        v_y0   = numpy.array([1])
+        v_x0   = numpy.array([0.,0.])
+        v_y0   = numpy.array(1)
         v_Win1 = numpy.array([[1.,1.],[1.,1.]])
         v_x    = numpy.array([[4.,5.],[18.,16.],[58.,43.]])
         v_y    = numpy.array([0.,7.,25.])
@@ -186,7 +186,7 @@ class T_Scan(unittest.TestCase):
     
         u  = theano.tensor.dvector('u')
         x  = theano.shared(numpy.array([0.,0.]),'x')
-        y0 = theano.tensor.dvector('y0')
+        y0 = theano.tensor.dscalar('y0')
     
         def f_ESN(u_t):
             return ( theano.dot(x,W_out), \
@@ -196,7 +196,7 @@ class T_Scan(unittest.TestCase):
     
         f5    = theano.function([u,y0],Y)
         v_u   = numpy.array([1.,2.,3.])
-        v_y0  = numpy.array([0.])
+        v_y0  = numpy.array(0.)
         v_out  = numpy.array([0.,1.5,3.15])
         out = f5( v_u, v_y0 )
         assert( compareArrays(v_out, out))