parameters for training

experiments/meta_task_exp.py

@@ -41,7 +41,7 @@ from functionalities_test import test_for_fixpoints, FixTypes
 
 WORKER = 10 if not debug else 2
 BATCHSIZE = 500 if not debug else 50
-EPOCH = 400 if not debug else 3
+EPOCH = 200
 VALIDATION_FRQ = 5 if not debug else 1
 SELF_TRAIN_FRQ = 1 if not debug else 1
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -194,15 +194,16 @@ def flat_for_store(parameters):
 if __name__ == '__main__':
 
     self_train = True
-    training = False
+    training = True
     plotting = True
     particle_analysis = True
     as_sparse_network_test = True
+    self_train_alpha = 100
 
     data_path = Path('data')
     data_path.mkdir(exist_ok=True, parents=True)
 
-    run_path = Path('output') / 'mn_st_NoRes'
+    run_path = Path('output') / 'mn_st_200_8_alpha_100'
     model_path = run_path / '0000_trained_model.zip'
     df_store_path = run_path / 'train_store.csv'
     weight_store_path = run_path / 'weight_store.csv'
@@ -216,7 +217,7 @@ if __name__ == '__main__':
         d = DataLoader(dataset, batch_size=BATCHSIZE, shuffle=True, drop_last=True, num_workers=WORKER)
 
         interface = np.prod(dataset[0][0].shape)
-        metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=False).to(DEVICE)
+        metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=True).to(DEVICE)
         meta_weight_count = sum(p.numel() for p in next(metanet.particles).parameters())
 
         loss_fn = nn.CrossEntropyLoss()
@@ -236,7 +237,7 @@ if __name__ == '__main__':
                 if self_train and is_self_train_epoch:
                     # Zero your gradients for every batch!
                     optimizer.zero_grad()
-                    self_train_loss = metanet.combined_self_train()
+                    self_train_loss = metanet.combined_self_train() * self_train_alpha
                     self_train_loss.backward()
                     # Adjust learning weights
                     optimizer.step()

network.py

@@ -296,7 +296,7 @@ class MetaCell(nn.Module):
         self.name = name
         self.interface = interface
         self.weight_interface = 5
-        self.net_hidden_size = 4
+        self.net_hidden_size = 8
         self.net_ouput_size = 1
         self.meta_weight_list = nn.ModuleList()
         self.meta_weight_list.extend(