layer normalization
时间: 2023-10-17 22:06:14 浏览: 49
Layer normalization is a technique used in deep learning to normalize the activations of hidden layers of a neural network. It is similar to batch normalization, but instead of normalizing over the batch dimension, it normalizes over the feature dimension. This means that each element in a feature vector is normalized independently, rather than being normalized with respect to the other elements in the same batch.
Layer normalization helps to stabilize the training process and improve the generalization performance of the model. It is particularly useful for recurrent neural networks, where the input distribution can change over time, and for models with deep architectures, where gradients can become unstable.
The basic idea behind layer normalization is to subtract the mean and divide by the standard deviation of each feature vector. This is done independently for each element in the feature vector. The resulting normalized values are then scaled and shifted using learnable parameters, called gamma and beta, respectively.
Layer normalization has been shown to improve the performance of various types of neural networks, including feedforward networks, recurrent networks, and convolutional networks. It is now a standard technique used in many deep learning applications.
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