How to train your neural network?（如何训练你的神经网络）

How To train a Neural Network:

原始文章地址

• Verify your implementation using a small batch of data while being mindful of potential issues.
• Conduct a sanity check by ensuring your initial loss is reasonable and confirming that you can attain 100% training accuracy on a minimal dataset portion.
• While training, keep an eye on the loss metrics such as training/validation accuracy. Additionally, monitor updates relative to parameter magnitudes (expecting ~1e-3) and first-layer weights in Convolutional Neural Networks.
• The two primary approaches recommended are Stochastic Gradient Descent with Nesterov Momentum or Adam optimization.
• Reduce your learning rate during training. Consider halving it after a fixed number of epochs or when validation accuracy plateaus.
• Conduct hyperparameter tuning using random search instead of grid search. Begin with broad ranges and short training periods before narrowing ranges and extending training duration.
• Formulate model ensembles to enhance performance.

如何训练神经网络

1. 基于少量数据集执行梯度验证，并避免潜在问题。
2. 为了确保模型的基本健壮性，请确认初始损失设置得当，并能在极小的数据比例下实现100%的训练准确度。
3. 在训练过程中实时跟踪损失值、评估模型性能（包括训练和验证准确率），如果观察到异常现象，请根据参数值的变化幅度（约为1e-3）进行调整，并在处理卷积神经网络时优先考虑第一层权重。
4. 推荐采用SGD配合Nesterov动量或Adam优化器。
5. 在训练阶段逐步降低学习率：例如，在固定的周期结束后或每当验证集准确率达到峰值时减半学习率。
6. 使用随机搜索而非网格搜索来优化超参数配置：将搜索范围设定为宽泛阶段（超参数范围较广，在每个周期内仅进行1-5次迭代）至精炼阶段（聚焦于关键参数，在每个周期内展开更多迭代）。
7. 将多个模型集成起来能够显著提升性能表现。