首页nn.BatchNorm1d(256)

nn.BatchNorm1d(256)

时间: 2024-04-05 17:20:02 浏览: 121

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(256)` 是 PyTorch 中的一种函数，用于创建一个 `BatchNorm1d` 层，对输入数据进行批标准化。其中，参数 `256` 表示输入的特征维度为 256，即输入数据的形状为 `(batch_size, 256)`。`BatchNorm1d` 层可以有效地加速神经网络的训练，并且有助于防止梯度消失或梯度爆炸等问题。

阅读全文