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PrincetonUniversity · Oct 29, 2023 · 251d124 · 251d124
1 parent 6b72ef2
commit 251d124
Showing 1 changed file with 32 additions and 11 deletions.
diff --git a/psyneulink/library/compositions/autodiffcomposition.py b/psyneulink/library/compositions/autodiffcomposition.py
@@ -664,9 +664,15 @@ def create_pathway(node)->list:
             # target_mechs = [ProcessingMechanism(default_variable = np.zeros_like(mech.value),
             #                                     name= 'TARGET for ' + mech.name)
             #                 for mech in output_mechs if mech not in self.target_output_map.values()]
-            # MODIFIED 10/27/23 NEW:
+            # # MODIFIED 10/27/23 NEW:
+            # target_mechs = [ProcessingMechanism(default_variable = np.array([np.zeros_like(value)
+            #                                                                  for value in output_mechs[0].value],
+            #                                                                 dtype=object),
+            #                                     name= 'TARGET for ' + mech.name)
+            #                 for mech in output_mechs if mech not in self.target_output_map.values()]
+            # # MODIFIED 10/27/23 NEWER:
             target_mechs = [ProcessingMechanism(default_variable = np.array([np.zeros_like(value)
-                                                                             for value in output_mechs[0].value],
+                                                                             for value in mech.value],
                                                                             dtype=object),
                                                 name= 'TARGET for ' + mech.name)
                             for mech in output_mechs if mech not in self.target_output_map.values()]
@@ -784,15 +790,30 @@ def autodiff_training(self, inputs, targets, context=None, scheduler=None):
                 input = inputs[component]
             curr_tensor_inputs[component] = torch.tensor(input, device=self.device).double()
         for component in targets.keys():
-            # # MODIFIED 10/27/23 OLD:
+            # FIX: 10/28/23:  BRANCH ON WHETHER TARGET ARRAY IS REGULAR OR RAGGED?
+            # # MODIFIED 10/27/23 OLD: WORKS FOR TESTS BUT NOT SCRATCH PAD BECAUSE IT FORCES A RAGGED ARRAY INTO A TENSOR
             # curr_tensor_targets[self.target_output_map[component]] = [torch.tensor(target, device=self.device).double()
             #                                                           for target in targets[component]]
-            # MODIFIED 10/27/23 NEW:
+            # # # MODIFIED 10/27/23 NEW:
+            # terminal_node = self.target_output_map[component]
+            # curr_tensor_targets[terminal_node] = [torch.tensor(target_port_input,
+            #                                                    device=self.device).double()
+            #                                       for target in targets[component] for
+            #                                       target_port_input in target]
+            # # # MODIFIED 10/27/23 NEWER:  WORKS FOR SCRATCH PAD BUT NOT TESTS BECAUSE IT REDUCES ONE DIMENSION TOO FAR
+            # #                             AND PRODUCES BAD RESULT FOR TESTS BECAUSE IT ADDS A DIMENSION TO THE TENSORS
+            # terminal_node = self.target_output_map[component]
+            # curr_tensor_targets[terminal_node] = [torch.tensor([target_port_input],
+            #                                                    device=self.device).double()
+            #                                       for target in targets[component] for
+            #                                       target_port_input in target]
+            # # # MODIFIED 10/27/23 NEWEST:
             terminal_node = self.target_output_map[component]
-            curr_tensor_targets[terminal_node] = [torch.tensor(target_port_input,
-                                                               device=self.device).double()
-                                                  for target in targets[component] for
-                                                  target_port_input in target]
+            curr_tensor_target = []
+            for target in targets[component]:
+                for target_port_input in target:
+                    curr_tensor_target.append(torch.tensor(target_port_input, device=self.device).double())
+            curr_tensor_targets[terminal_node] = curr_tensor_target
             # MODIFIED 10/27/23 END
 
         # do forward computation on current inputs
@@ -805,9 +826,9 @@ def autodiff_training(self, inputs, targets, context=None, scheduler=None):
             # new_loss = self.loss(curr_tensor_outputs[component][0],
             #                      curr_tensor_targets[component][0])
             # MODIFIED 10/27/23 NEW:
-            # FIX: 10/26/23 - SHOULD HANDLE MULTIPLE OUTPUT PORTS curr_tensor_outputs[component][idx] as per below
+            new_loss = 0
             for i in range(len(curr_tensor_outputs[component])):
-                new_loss = self.loss(curr_tensor_outputs[component][i],
+                new_loss += self.loss(curr_tensor_outputs[component][i],
                                      curr_tensor_targets[component][i])
             # MODIFIED 10/27/23 END
             tracked_loss += new_loss
@@ -863,7 +884,7 @@ def _infer_output_nodes(self, nodes: dict):
         ---------
         A dict mapping TARGET Nodes -> target values
         """
-        return {node:value for node,value in nodes.items() if node in self.target_output_map}
+        return {node:value for node, value in nodes.items() if node in self.target_output_map}
 
     def _infer_input_nodes(self, nodes: dict):
         """Remove TARGET Nodes, and return dict with values of INPUT Nodes for single trial