Gradient descent batch size 在machine learning中 256

Batch size refers to the number of training examples used in one iteration of gradient descent. In the context of machine learning, a batch size of 256 means that 256 training examples are used to update the parameters of the model in one iteration of gradient descent. This is often referred to as "mini-batch gradient descent" because it uses a smaller subset of the training data compared to using the entire dataset (batch gradient descent) or using just one training example at a time (stochastic gradient descent). The choice of batch size can affect the convergence rate and generalization performance of the model. A larger batch size can lead to faster convergence but may result in a loss of generalization power, while a smaller batch size can provide better generalization but may require more iterations to converge