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chore: 调整部分参数

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unanmed 2026-02-12 23:50:08 +08:00
parent a07d2cf960
commit becf625bdb
1 changed files with 6 additions and 5 deletions
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11
ginka/train_vae.py
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@ -58,6 +58,7 @@ LATENT_DIM = 48
KL_BETA = 0.1
SELF_GATE = 0.5
GATE_EPOCH = 5
VAL_BATCH_DIVIDER = 1
device = torch.device(
"cuda:1" if torch.cuda.is_available()
@ -92,12 +93,12 @@ def train():
dataset = GinkaRNNDataset(args.train, device)
dataset_val = GinkaRNNDataset(args.validate, device)
dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // 64, shuffle=True)
dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // VAL_BATCH_DIVIDER, shuffle=True)
optimizer_ginka = optim.AdamW(vae.parameters(), lr=2e-4, weight_decay=1e-4)
# 自定义调度器允许在 self_prob 提高时重置调度器信息并提高学习率以适应学习
scheduler_ginka = VAEScheduler(
optimizer_ginka, factor=0.9, increase_factor=1.1, patience=10, max_lr=2e-4, min_lr=1e-6
optimizer_ginka, factor=0.9, increase_factor=2, patience=10, max_lr=2e-4, min_lr=1e-6
)
criterion = VAELoss()
@ -166,11 +167,11 @@ def train():
# 先使用训练集的损失值，因为过拟合比较严重，后续再想办法
if avg_loss < SELF_GATE:
gate_epochs += 1
prob_epochs += 1
if gate_epochs >= GATE_EPOCH and self_prob < 1:
if prob_epochs >= GATE_EPOCH and self_prob < 1:
self_prob += 0.01
gate_epochs = 0
prob_epochs = 0
scheduler_ginka.step(avg_loss, self_prob)