README.md Update

experiments/meta_task_exp.py

@@ -198,12 +198,13 @@ if __name__ == '__main__':
     plotting = True
     particle_analysis = True
     as_sparse_network_test = True
-    self_train_alpha = 100
+    self_train_alpha = 1
+    batch_train_beta = 1
 
     data_path = Path('data')
     data_path.mkdir(exist_ok=True, parents=True)
 
-    run_path = Path('output') / 'mn_st_200_8_alpha_100'
+    run_path = Path('output') / 'mn_st_400_2_no_res'
     model_path = run_path / '0000_trained_model.zip'
     df_store_path = run_path / 'train_store.csv'
     weight_store_path = run_path / 'weight_store.csv'
@@ -217,7 +218,7 @@ 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=True).to(DEVICE)
+        metanet = MetaNet(interface, depth=5, width=6, out=10, residual_skip=False).to(DEVICE)
         meta_weight_count = sum(p.numel() for p in next(metanet.particles).parameters())
 
         loss_fn = nn.CrossEntropyLoss()
@@ -249,7 +250,7 @@ if __name__ == '__main__':
                 batch_x, batch_y = batch_x.to(DEVICE), batch_y.to(DEVICE)
                 y = metanet(batch_x)
                 # loss = loss_fn(y, batch_y.unsqueeze(-1).to(torch.float32))
-                loss = loss_fn(y, batch_y.to(torch.long))
+                loss = loss_fn(y, batch_y.to(torch.long)) * batch_train_beta
                 loss.backward()
 
                 # Adjust learning weights
@@ -312,7 +313,7 @@ if __name__ == '__main__':
         plot_training_result(df_store_path)
         if particle_analysis:
             plot_training_particle_types(df_store_path)
-    exit()
+
     if particle_analysis:
         model_path = next(run_path.glob(f'*e{EPOCH}.tp'))
         latest_model = torch.load(model_path, map_location=DEVICE).eval()