From 1a6f5b07aab65fac42714b5bd9734aeb9e315e01 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=88=98=E8=88=AA=E5=AE=87?= <3364451258@qq.com>
Date: Thu, 28 May 2026 11:37:43 +0800
Subject: [PATCH] =?UTF-8?q?fix:=20sqrt=20=E9=80=86=E9=A2=91=E7=8E=87?=
 =?UTF-8?q?=E6=9D=83=E9=87=8D=20+=20=E6=AF=8F=20epoch=20=E6=89=93=E5=8D=B0?=
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---
 src/models/train.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/models/train.py b/src/models/train.py
index 18f9744..b2adfc0 100644
--- a/src/models/train.py
+++ b/src/models/train.py
@@ -168,10 +168,10 @@ def train() -> HeatRiskPredictor:
     print(f"模型参数量: {sum(p.numel() for p in model.parameters()):,}")
 
     # -------------------- 损失、优化器、调度器 --------------------
-    # 基于训练集类别分布计算权重
+    # 基于训练集类别分布计算权重 (sqrt inverse freq, 温和加权)
     class_counts = np.bincount(y_train_np[:, 0])
     class_weights_tensor = torch.tensor(
-        1.0 / (class_counts + 1), dtype=torch.float32
+        1.0 / np.sqrt(class_counts + 1), dtype=torch.float32
     ).to(device)
     class_weights_tensor = class_weights_tensor / class_weights_tensor.sum() * 4
     focal_loss = FocalLoss(class_weight=class_weights_tensor)
@@ -256,7 +256,7 @@ def train() -> HeatRiskPredictor:
         scheduler.step(avg_val_loss)
 
         # ---- 打印进度 ----
-        if epoch % 5 == 0 or epoch == 1:
+        if True:  # print every epoch
             lr_now = optimizer.param_groups[0]["lr"]
             print(
                 f"Epoch {epoch:3d}/{MAX_EPOCHS} | "