mini batch

mini batch是指从训练数据集中选择的较小的数据子集。在训练模型时，通常将整个训练数据集划分为多个mini batch。每个mini batch包含一定数量的训练样本，通常是2的幂次方，例如32、64或128。模型使用每个mini batch的样本来进行前向传播、计算损失和反向传播，然后根据这些样本的梯度更新模型的参数。使用mini batch的主要目的是减少计算开销和内存占用，并提高训练的效率。

相关问题

Minibatch size

Minibatch size refers to the number of examples or instances of data that are processed together in a single iteration or batch during training of a machine learning model. The size of the minibatch is typically smaller than the total size of the training dataset, and is chosen to balance the computational efficiency and the quality of the model's optimization. A larger minibatch size can lead to faster convergence, but may also require more memory and computational resources, while a smaller minibatch size may result in slower convergence and may require more training iterations to reach the same level of performance. The choice of minibatch size depends on the specific problem and the characteristics of the data and the model being trained.

minibatch discrimination

minibatch discrimination是一种在生成对抗网络（GAN）中使用的技术，旨在提高生成器的稳定性和多样性。它通过在判别器中引入批量信息，使得判别器能够同时考虑多个样本之间的距离，从而更好地区分真实样本和生成样本。具体地说，minibatch discrimination通过计算样本之间的距离来度量它们的相似性，并将这些距离信息添加到判别器的中间层表示中。这样，判别器就能够学习到更多关于生成样本之间的差异和多样性的信息，从而帮助生成器生成更多样化的样本。