refactor: reorganize project structure by moving core modules and update import paths in API server

scripts/training/train_ndvi_lstm_gru.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+# coding: utf-8
+import os
+import json
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from core.ndvi_data_loader import NDVITimeSeriesDataset
+
+print("=" * 70)
+print("🚀 Training LSTM/GRU Time Series model for NDVI (REAL DATA & GPU)")
+print("=" * 70)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"[SYSTEM] Device: {device.type.upper()}")
+
+dataset = NDVITimeSeriesDataset(sequence_length=3, spatial=False)
+dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
+
+class LSTMModel(nn.Module):
+    def __init__(self, input_size=1, hidden_size=32, num_layers=2):
+        super().__init__()
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = nn.Linear(hidden_size, 1)
+        
+    def forward(self, x):
+        out, _ = self.lstm(x)
+        out = self.fc(out[:, -1, :]) # Take last time step
+        return out
+
+model = LSTMModel().to(device)
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+print("\n[TRAIN] Bắt đầu Training...")
+model.train()
+total_loss = 0
+for epoch in range(50):
+    for inputs, targets in dataloader:
+        inputs, targets = inputs.to(device), targets.to(device)
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        total_loss += loss.item()
+
+avg_loss = total_loss / (len(dataloader) * 50)
+print(f"✅ Training completed! Avg MSE Loss (RMSE): {avg_loss**0.5:.4f}")
+
+model_dir = "ndvi_forecast_model"
+model_path = os.path.join(model_dir, "ndvi_lstm_real.pth")
+torch.save(model.state_dict(), model_path)
+print(f"\n[SAVE] Model saved to {model_path}")
+
+with open(os.path.join(model_dir, "ndvi_lstm_real_info.json"), "w") as f:
+    json.dump({
+        "model_type": "LSTM Time Series (Real Data & GPU)",
+        "target": "NDVI", "epoch": 50,
+        "rmse": float(avg_loss**0.5), "mae": float(avg_loss)
+    }, f, indent=2)
+print("[SAVE] Model info saved.")