reverted the many copies scan used to do since it introduced bugs and the…

theano/scan.py

@@ -377,7 +377,6 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
         seqs = [sequences]
     else:
         seqs = sequences
-
     if not (type(outputs_info) in (list,tuple)):
         outs_info = [outputs_info]
     else:
@@ -633,29 +632,10 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     # remove shared variables from the non sequences list
     # such that we can compile the function ( the user has the option to add them when 
     # writing scan, because in some situations this might make the code more readable)
-    # Also duplicate the list of non sequences arguments to contain copies of the 
-    # non-shared inputs ( this fixes the case when one of this inputs has a default 
-    # update attached to it that belongs to some shared random stream )
-    #
-    # Note : In that case, scan assumes that you do not want to draw new numbers at 
-    #        every call ( you would have made the internal function do that explicitly 
-    #        if you wanted to) but rather to use that initial draw as a matrix of values
-    new_non_seqs = []
     notshared_other_args = []
-    notshared_other_args_copies = []
     for non_seq in non_seqs:
         if not isinstance(non_seq, SharedVariable):
-            if n_fixed_steps not in [-1,1]:
-                non_seq_copy = non_seq.type()
-                if non_seq.name : 
-                    non_seq_copy.name = non_seq.name + '_copy'
-            else:
-                non_seq_copy = non_seq
             notshared_other_args += [non_seq]
-            notshared_other_args_copies += [non_seq_copy]
-            new_non_seqs += [non_seq_copy]
-        else:
-            new_non_seqs += [non_seq]
 
     # add only the not shared variables to the arguments of the dummy
     # function [ a function should not get shared variables as input ]
@@ -663,10 +643,10 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
     for arg in args:
         if not isinstance(arg, SharedVariable):
             dummy_args += [arg]
-    dummy_args += notshared_other_args_copies
+    dummy_args += notshared_other_args
     # arguments for the lambda expression that gives us the output
     # of the inner function
-    args += new_non_seqs
+    args += non_seqs
 
     # when we apply the lambda expression we get a mixture of update rules
     # and outputs that needs to be separated
@@ -776,9 +756,8 @@ def scan(fn, sequences=[], outputs_info=[], non_sequences=[],
             # or defult behaviour ( like always add the extra outputs at the end !?)
             # But I did not bother implementing this, I leave it to the user to clearly
             # express what he/she wants to do
-            raise ValueError('There has been a terrible mistake in our input arguments'
-                    ' and scan is totally lost. Make sure that you indicate for every '
-                    ' output what taps you want to use, or None, if you do not want to '
+            raise ValueError('Scan is totally lost. Make sure that you indicate for each'
+                    ' output what taps you want to use, or None, if you do not want to'
                     ' use any !')
     inner_fn_inputs=[input.variable for input in \
         dummy_f.maker.expanded_inputs[:dummy_notshared_ins+dummy_notshared_init_outs]]

theano/tests/test_scan.py

@@ -975,6 +975,34 @@ class T_Scan(unittest.TestCase):
         print f([2,3])
         assert numpy.allclose(f([2,3]) , 5)
 
+    def test_computing_gradient(self):
+        x1 = theano.tensor.scalar()
+        x2 = theano.shared(numpy.array([1,2,3,4,5]))
+        K = x2*x1
+
+        out,updates = theano.scan(lambda i,v: theano.tensor.grad(K[i], v), 
+                sequences = theano.tensor.arange(K.shape[0]), non_sequences=x1)
+        f = theano.function([x1], out)
+
+        print f(3.)
+        assert numpy.all( f(3.) != 0. )
+
+    '''
+    def test_shared_updates(self):
+        X = theano.shared( numpy.array( [[1,2,3],[4,5,6]])) 
+
+        out,updates = theano.scan( lambda :{X: X+1}, outputs_info = [], non_sequences= [], 
+                sequences = [], n_steps = 10)
+
+        f = theano.function([],[], updates = updates)
+
+        f()
+
+        print X.value
+    '''
+
+
+
 if __name__ == '__main__':
     unittest.main()