diff --git a/7_network_train.py b/7_network_train.py
new file mode 100644
index 0000000..4fc0534
--- /dev/null
+++ b/7_network_train.py
@@ -0,0 +1,86 @@
+ 
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+from torchvision.utils import make_grid, save_image
+import time
+import pandas as pd
+from torchsummary import summary
+from Network.MyDataset import MyDataset
+from utils.train_val_lr import train, validate, adjust_learning_rate
+from Network.Model import Model
+from config import Network_train_Config as cfg
+from config import Path_Config as pcfg
+
+# File paths
+data_file = pcfg.dataset_path
+label_file = pcfg.labelset_path
+impulse_field_file = pcfg.field_impulse
+impulse_sim_file = pcfg.sim_impulse
+
+if __name__ == '__main__':
+    BATCH_SIZE = cfg.BATCH_SIZE
+    LR = cfg.LR  # Initial learning rate
+    EPOCHS = cfg.EPOCHS
+    val_loss_min = cfg.val_loss_min  # Initial high value for validation loss tracking
+    
+    # Model initialization
+    model = Model(
+        inplanes=2,
+        outplanes=1,
+        layers=cfg.network_layers,
+        cycle_weight=getattr(cfg, "cycle_weight", 1.0),  
+        cycle_loss='mse'  
+    ).cuda()
+    optimizer = torch.optim.Adam(model.parameters(), lr=LR)
+    loss_func = nn.MSELoss(reduction='mean')
+    
+    # Create a CSV file to log training progress
+    df = pd.DataFrame(columns=['epoch', 'train Loss', 'val Loss', 'learning rate'])
+    df.to_csv(pcfg.train_val_loss, index=False)
+    
+    # Load dataset
+    dataset = MyDataset(data_file, label_file, impulse_field_file, impulse_sim_file, mode='train', check=False)
+    train_size = int(len(dataset) * cfg.dataset_Proportion)
+    val_size = len(dataset) - train_size
+    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
+    
+    # Create data loaders
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=cfg.num_workers, pin_memory=True)
+    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=cfg.num_workers, pin_memory=True)
+    
+    # TensorBoard writer
+    writer = SummaryWriter(r'Log')
+    
+    print('Dataset loaded successfully.')
+    
+    for epoch in range(EPOCHS):
+        epoch_start_time = time.time()
+        print(f'Starting epoch {epoch}')
+        
+        # Adjust learning rate
+        LR = adjust_learning_rate(optimizer, epoch, LR)
+        
+        # Training and validation
+        train_loss = train(train_loader, model, loss_func, optimizer, epoch)
+        val_loss = validate(val_loader, model, loss_func)
+        
+        print(f'Validation Loss: {val_loss:.6f}')
+        
+        # Save best model
+        if val_loss < val_loss_min:
+            torch.save(model.state_dict(), pcfg.BEST_MODEL_PATH)
+            val_loss_min = val_loss
+        
+        # Periodically save model
+        if epoch % cfg.save_period == 0:
+            torch.save(model.state_dict(), pcfg.LATEST_MODEL_PATH)
+        
+        # Log training details
+        log_data = pd.DataFrame([[epoch, train_loss, val_loss, LR]])
+        log_data.to_csv(pcfg.train_val_loss, mode='a', header=False, index=False)
+        
+        print(f'Epoch {epoch} completed in {time.time() - epoch_start_time:.2f} seconds.')
+
+