run を固定して比較する

import wandb
import random
import math

def train_model(learning_rate, batch_size, run_name, tags=None):
    """Simulate training a model with given hyperparameters."""
    config = {
        "learning_rate": learning_rate,
        "batch_size": batch_size,
        "optimizer": "adam",
        "architecture": "resnet50"
    }
    
    with wandb.init(
      # ご自身のチーム名とプロジェクト名に置き換えてください
        project="hyperparameter-tuning",
        entity="<team>",
        name=run_name,
        config=config,
        tags=tags or []
    ) as run:
        # トレーニングループをシミュレート
        for epoch in range(50):
            # メトリクスをシミュレート
            accuracy = 0.6 + 0.3 * (1 - math.exp(-learning_rate * epoch / 10))
            loss = 1.0 * math.exp(-learning_rate * epoch / 10)
            
            run.log({
                "epoch": epoch,
                "accuracy": accuracy,
                "loss": loss
            })

# 標準設定でベースライン run を作成
train_model(
    learning_rate=0.001,
    batch_size=64,
    run_name="baseline-config",
    tags=["baseline", "production"]
)

# 異なる学習率で実験
train_model(
    learning_rate=0.003,
    batch_size=64,
    run_name="lr-experiment-0.003",
    tags=["experiment"]
)

train_model(
    learning_rate=0.0001,
    batch_size=64,
    run_name="lr-experiment-0.0001",
    tags=["experiment"]
)