PEP8 and adressed comments.

theano/scan_module/scan_checkpoint.py

 import theano
-import theano.tensor as T
+
 
 def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
                           non_sequences=[], name="checkpointscan_fn",
                           n_steps=None, save_every_N=10):
-
     """
-    Current assumptions : 
+    Current assumptions :
     - Every sequence has the same length
     - If n_steps is specified, it has the same value as the length of any sequence
     - The value of "save_every_N" divides the number of steps the Scan will
@@ -14,8 +13,8 @@ def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
     - Only singly-recurrent and non-recurrent outputs are used.
       No multiple recurrences.
     - Only the last timestep of any output will ever be used.
+
     """
-    
     # Standardize the format of input arguments
     if not isinstance(sequences, list):
         sequences = [sequences]
@@ -23,7 +22,7 @@ def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
         outputs_info = [outputs_info]
     if not isinstance(non_sequences, list):
         non_sequences = [non_sequences]
-    
+
     # Determine how many steps the original scan would run
     if n_steps is None:
         n_steps = sequences[0].shape[0]
@@ -31,14 +30,14 @@ def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
         n_steps = n_steps
 
     # Compute the number of steps of the inner and of the outer scan
-    o_n_steps = n_steps / save_every_N
+    o_n_steps = theano.tensor.cast(n_steps / save_every_N, 'int64')
     i_n_steps = save_every_N
 
     # Establish the input variables of the outer scan
     o_sequences = [s.reshape([s.shape[0] / save_every_N, save_every_N] +
-                             [s.shape[i] for i in range(1, s.ndim)], s.ndim + 1) for s in sequences]
+                             [s.shape[i] for i in range(1, s.ndim)],
+                             s.ndim + 1) for s in sequences]
     new_nitsots = [i for i in outputs_info if i is None]
-    new_sitsots = [i for i in outputs_info if i is not None]
     o_nonsequences = non_sequences + [i_n_steps]
 
     def outer_step(*args):
@@ -47,14 +46,12 @@ def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
         i_sequences = list(args[:len(o_sequences)])
         i_prev_outputs = list(args[len(o_sequences):-len(o_nonsequences)])
         i_non_sequences = list(args[-len(o_nonsequences):])
-        
-        # Assemble the correct outputs_info list for the inner_scan
-        i_outputs_info = []
+        i_outputs_infos = i_prev_outputs + [None, ] * len(new_nitsots)
 
         # Call the user-provided function with the proper arguments
         results, updates = theano.scan(fn=fn,
                                        sequences=i_sequences,
-                                       outputs_info=i_prev_outputs + [None,] * len(new_nitsots),
+                                       outputs_info=i_outputs_infos,
                                        non_sequences=i_non_sequences[:-1],
                                        name=name + "_inner",
                                        n_steps=i_non_sequences[-1])
@@ -65,18 +62,14 @@ def scan_with_checkpoints(fn, sequences=[], outputs_info=None,
         if not isinstance(results, list):
             return results[-1], updates
         else:
-            return [r[-1] for r in results], updates       
+            return [r[-1] for r in results], updates
 
     results, updates = theano.scan(fn=outer_step,
-                                  sequences=o_sequences,
-                                  outputs_info=outputs_info,
-                                  non_sequences=o_nonsequences,
-                                  name=name + "_outer",
-                                  n_steps=o_n_steps, allow_gc=True)
-                                  
+                                   sequences=o_sequences,
+                                   outputs_info=outputs_info,
+                                   non_sequences=o_nonsequences,
+                                   name=name + "_outer",
+                                   n_steps=o_n_steps, allow_gc=True)
+
     # Keep only the last timestep of every output but keep all the updates
-    return results, updates # TODO is it a bug?
-    if not isinstance(results, list):
-        return results[-1:], updates
-    else:
-        return [r[-1:] for r in results], updates
+    return results, updates

theano/scan_module/tests/test_scan_checkpoint.py

@@ -13,11 +13,11 @@ def example1(checkpoint=True):
     # Symbolic description of the result
     if checkpoint:
         result, updates = theano.scan_with_checkpoints(
-                                    fn=lambda prior_result, A: prior_result * A,
-                                    outputs_info=T.ones_like(A),
-                                    non_sequences=A,
-                                    n_steps=k,
-                                    save_every_N=20)
+            fn=lambda prior_result, A: prior_result * A,
+            outputs_info=T.ones_like(A),
+            non_sequences=A,
+            n_steps=k,
+            save_every_N=20)
     else:
         result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A,
                                       outputs_info=T.ones_like(A),
@@ -31,13 +31,13 @@ def example1(checkpoint=True):
 
     # compiled function that returns A**k
     start_compile = time.time()
-    power = theano.function(inputs=[A,k], outputs=result, updates=updates)
+    power = theano.function(inputs=[A, k], outputs=result, updates=updates)
     time_compile = time.time() - start_compile
 
     start_exec = time.time()
     out = power(range(10), 100)
     time_exec = time.time() - start_exec
-    
+
     if checkpoint:
         print("Example 1 with checkpoints")
     else:
@@ -45,7 +45,7 @@ def example1(checkpoint=True):
     print("Compile time:", time_compile)
     print("Exec time:", time_exec)
     print("Output:", out)
-    
+
 
 def example2(checkpoint=True):
 
@@ -57,26 +57,26 @@ def example2(checkpoint=True):
     seq = T.arange(up_to)
 
     outputs_info = T.as_tensor_variable(numpy.asarray(0, seq.dtype))
-    
+
     if checkpoint:
         scan_result, scan_updates = theano.scan_with_checkpoints(
-                                                fn=accumulate_by_adding,
-                                                outputs_info=outputs_info,
-                                                sequences=seq,
-                                                save_every_N=10)
+            fn=accumulate_by_adding,
+            outputs_info=outputs_info,
+            sequences=seq,
+            save_every_N=10)
     else:
         scan_result, scan_updates = theano.scan(fn=accumulate_by_adding,
                                                 outputs_info=outputs_info,
                                                 sequences=seq)
-        
-    start_compile = time.time()    
+
+    start_compile = time.time()
     triangular_sequence = theano.function(inputs=[up_to], outputs=scan_result)
     time_compile = time.time() - start_compile
-    
+
     start_exec = time.time()
     out = triangular_sequence(100)[-1]
     time_exec = time.time() - start_exec
-    
+
     if checkpoint:
         print("Example 2 with checkpoints")
     else:
@@ -92,4 +92,4 @@ def test_scan_checkpoint():
     print("----")
     example2(False)
     example2(True)
-    print("----")
+    print("----")
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