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keras中的batchnormalization

时间: 2023-05-01 09:04:52 浏览: 109

Batch Normalization

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.

Keras中的BatchNormalization是一种用于深度学习模型的标准化方法，可以帮助加速模型的训练和提高其精度。它通过对每个批次数据的均值和方差进行计算来实现标准化，从而使得每个神经元中的输入数据尽可能地接近均值和方差为1的标准正态分布。这样可以有效减少梯度消失和梯度爆炸等问题，并且让神经网络更具有泛化能力。

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