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