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nn.BatchNorm1d(120)

时间: 2024-04-05 21:23:33 浏览: 25

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.

BatchNorm1d是什么？ BatchNorm1d是一个用于深度学习神经网络中的层，主要用于对输入数据进行标准化处理，以提高训练速度和模型效果。BatchNorm1d可以使输入数据在特性维度上的均值为0，方差为1，从而防止梯度消失和梯度爆炸现象的发生，提高模型训练的稳定性和收敛速度。这种方法还可以提高模型的泛化能力，降低过拟合的风险。

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