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nn.BatchNorm1d

时间: 2024-04-05 09:13:57 浏览: 64

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.

`nn.BatchNorm1d` 是 PyTorch 中的一维 Batch Normalization 操作，它将输入规范化并进行缩放和平移，以使其在训练过程中更易于优化。一维 Batch Normalization 通常用于处理一维的时间序列数据或者一维的特征向量。在训练过程中，`nn.BatchNorm1d` 会计算每个 batch 的均值和方差，并使用它们来规范化输入。在测试时，`nn.BatchNorm1d` 使用在训练过程中累积的移动平均值和方差来规范化输入。

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