首页 BatchNormalization(), # 加入BN层

BatchNormalization(), # 加入BN层

时间: 2024-05-27 14:08:21 浏览: 195

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）是深度学习中常用的一种技术，可以加速模型训练过程并提高模型的准确性。它通过对每一批数据进行归一化来缓解梯度消失问题，在一定程度上也有正则化的效果。在神经网络中，BatchNormalization可以被插入到卷积层或全连接层之后，将输出进行标准化处理。这样可以使得输入数据的分布更加稳定，有助于提高模型的鲁棒性和泛化能力。

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