nn.BatchNorm1d(hidden_features)
时间: 2023-11-21 14:05:21 浏览: 35
Batch normalization is a technique used in deep learning to improve the training of neural networks. It works by normalizing the activations of each layer by adjusting and scaling the inputs to a mean of zero and variance of one. This helps to alleviate the problem of internal covariate shift, which occurs when the distribution of the activations of one layer changes as the parameters of the previous layer are updated during training.
The nn.BatchNorm1d(hidden_features) function in PyTorch is a batch normalization layer specifically designed for 1D input data, such as the output of a fully connected layer. The "hidden_features" parameter specifies the number of features (i.e. neurons) in the preceding layer that the batch normalization should be applied to.
By applying batch normalization to the hidden layer, the activations are adjusted in a way that makes the optimization of the network more stable and efficient. This can lead to faster training times and improved generalization performance.