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BatchNormalization

时间: 2024-04-05 10:23:32 浏览: 27

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.

BatchNormalization（批量归一化）是一种用于深度学习神经网络的技术，能够加速神经网络的收敛速度并提高泛化能力。它通过对每个神经网络层的输入进行归一化，从而减少了内部协变量位移，使得神经网络更易于训练。BatchNormalization技术通常用于卷积神经网络和全连接神经网络中，并被广泛应用于图像分类、物体检测和语音识别等领域。

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