nn.BatchNorm1d(hidden_features)
时间: 2023-11-18 09:03:19 浏览: 27
The BatchNorm1d layer in PyTorch is used for normalization of inputs in a neural network. It is a type of layer that is commonly used in deep learning models to improve the overall performance of the network.
The BatchNorm1d layer takes as input a 1-dimensional tensor of shape (batch_size, hidden_features) and normalizes the values in the tensor across the batch dimension. This means that it normalizes the values for each feature across all samples in the batch.
The input tensor is normalized using the mean and variance of the batch. The normalization is performed using the following formula:
normalized_input = (input - mean) / sqrt(variance + eps)
where eps is a small constant added to the variance to prevent division by zero.
The BatchNorm1d layer also has learnable parameters: a scaling parameter (gamma) and a bias parameter (beta). These parameters are learned during training and are used to adjust the normalized input. The scaling parameter is used to scale the normalized input, while the bias parameter is used to shift the normalized input.
Overall, the BatchNorm1d layer helps to improve the training of deep neural networks by reducing the effect of covariate shift and improving the stability of the gradients during backpropagation.