network test

sparse_net.py

@@ -270,20 +270,26 @@ def test_sparse_net():
 def test_sparse_net_sef_train():
     net = SparseNetwork(30, 5, 6, 10)
     optimizer = torch.optim.SGD(net.parameters(), lr=0.008, momentum=0.9)
-    epochs = 120
+    optimizer_dict = {
+        key: torch.optim.SGD(layer.parameters(), lr=0.008, momentum=0.9) for key, layer in enumerate(net.sparselayers)
+                      }
+    epochs = 1000
+    loss_fn = torch.nn.MSELoss(reduction="sum")
 
     for _ in trange(epochs):
-        optimizer.zero_grad()
+        for layer, optim in zip(net.sparselayers, optimizer_dict.values()):
-        loss = net.combined_self_train()
+            optim.zero_grad()
-
+            x, target_data = layer.get_self_train_inputs_and_targets()
-        loss.backward(retain_graph=True)
+            output = layer(x)
-        optimizer.step()
+            loss = loss_fn(output, target_data)
+            loss.backward()
+            optim.step()
 
     # is each of the networks self-replicating?
     counter = defaultdict(lambda: 0)
     id_functions = functionalities_test.test_for_fixpoints(counter, list(net.particles))
     counter = dict(counter)
-    print(f"identity_fn after {epochs+1} self-train epochs: {counter}")
+    print(f"identity_fn after {epochs} self-train epochs: {counter}")
 
 
 def test_manual_for_loop():
@@ -307,7 +313,7 @@ def test_manual_for_loop():
 
 
 if __name__ == '__main__':
-    # test_sparse_layer()
+    test_sparse_layer()
     test_sparse_net_sef_train()
     # test_sparse_net()
     # for comparison