首页epoch、iteration、batchsize

epoch、iteration、batchsize

时间: 2023-12-01 12:03:34 浏览: 58

炼丹技巧 | BERT的下接结构调参

前情回顾我们之前介绍了 BERT的原理与应用 BERT与其他预训练模型 BERT四大下游任务现在我们基于（2019BDCI互联网金融新实体发现 | 思路与代码框架分享（单模第一，综合第二））代码实践来介绍一下BERT如何调参才能更加充分训练，使得到的模型性能更好。具体代码链接： https://link.zhihu.com/?target=https://github.com/ChileWang0228/Deep-Learning-With-Python/tree/master/chapter8 1.Epoch epoch：训练模型的迭代次数。我们主要看损失是否收敛在一个稳定值，若

Epoch refers to the number of times a dataset is passed through a neural network during training. One epoch means that the entire dataset has been presented to the network once. Iteration refers to the number of times the model is trained on a single batch of data during one epoch. For example, if the batch size is 32 and there are 1000 samples in the training set, each epoch will have 1000/32 = 31.25 iterations. Batch size refers to the number of training samples presented to the network at once. Larger batch sizes can lead to faster training times and smoother convergence, but may also require more memory and computing power. Smaller batch sizes can help the model generalize better but may take longer to train.

阅读全文