Pytorch模型的超参数优化 [英] Hyperparameter optimization for Pytorch model

问题描述

为Pytorch模型执行超参数优化的最佳方法是什么?实施例如随机搜索自己?使用Skicit Learn?还是我不知道的其他事情?

What is the best way to perform hyperparameter optimization for a Pytorch model? Implement e.g. Random Search myself? Use Skicit Learn? Or is there anything else I am not aware of?

推荐答案

许多研究人员使用 RayTune .这是一个可扩展的超参数调整框架，专门用于深度学习.您可以在任何深度学习框架(下面的两行代码)中轻松使用它，并且它提供了大多数最新算法，包括HyperBand，基于人口的训练，贝叶斯优化和BOHB.

Many researchers use RayTune. It's a scalable hyperparameter tuning framework, specifically for deep learning. You can easily use it with any deep learning framework (2 lines of code below), and it provides most state-of-the-art algorithms, including HyperBand, Population-based Training, Bayesian Optimization, and BOHB.

import torch.optim as optim
from ray import tune
from ray.tune.examples.mnist_pytorch import get_data_loaders, ConvNet, train, test


def train_mnist(config):
    train_loader, test_loader = get_data_loaders()
    model = ConvNet()
    optimizer = optim.SGD(model.parameters(), lr=config["lr"])
    for i in range(10):
        train(model, optimizer, train_loader)
        acc = test(model, test_loader)
        tune.track.log(mean_accuracy=acc)


analysis = tune.run(
    train_mnist, config={"lr": tune.grid_search([0.001, 0.01, 0.1])})

print("Best config: ", analysis.get_best_config(metric="mean_accuracy"))

# Get a dataframe for analyzing trial results.
df = analysis.dataframe()

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