神经网络模型建立步骤

最新推荐文章于 2025-06-18 16:38:30 发布

Liu Zhian

最新推荐文章于 2025-06-18 16:38:30 发布

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分类专栏：神经网络文章标签：模型建立框架

本文链接：https://blog.youkuaiyun.com/qq_37174526/article/details/83958850

在构建深度学习模型如CNN时，需要遵循一系列步骤：首先进行损失合理性检查，确保随机权重下损失合理；其次，执行梯度检查，验证反向传播的正确性，避免隐藏层过大；接着，在小规模数据上过拟合，以达到高训练准确率；然后，训练完整网络，选择合适的层结构；最后，调整超参数以优化模型性能。

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When establish a deep learning model like CNN, we should follow these steps below.

1.Sanity check your loss.

IF You use a softmax classifier, we expect the loss for random weights (with no regularization) to be about logC where C d/enotes the number of classes.

2. Gradient check

You should use a small set of training data or even a random dataset to make sure that the backward pass you implenments is correct. BY THE WAY, you not have to set the hidden layers’ dimension or the number of hidden layers too large.

3. Overfit a small dataset

In this step, you should randomly choose just a few training samples (say 100 or 200). Your basic model should have a high training accuracy and comparatively low validation accuracy.