神经网络调节参数

最新推荐文章于 2024-08-11 10:57:12 发布

原创最新推荐文章于 2024-08-11 10:57:12 发布 · 1.2k 阅读

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本博客探讨了在神经网络训练过程中调整关键超参数的重要性，包括隐藏层数、学习率、训练轮数和正则化强度，以改善模型性能。通过实验，解释了如何通过调整这些参数来减少损失函数的线性下降趋势，增加训练和验证准确性的差距，以及提高模型容量。

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cs231a: assignment2

What's wrong? Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.

Turing。Turing the hyperparameters and developing intuition for how they affect the final performance is a large part of Neural Networks. So you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, number of training epochs, and regularization strength. You might also consider tuning the momentum and learning rate decay parameters.

PCA to reduce dimensionality, adding dropout, or adding features to the solver.