global average pooling
时间: 2023-09-28 19:09:16 浏览: 45
Global average pooling (GAP) is a pooling operation in deep learning that calculates the average value of each feature map in a convolutional neural network (CNN), resulting in a single value per feature map. It is commonly used as a replacement for fully connected layers in CNNs to reduce the number of parameters and computational complexity of the network.
GAP is applied globally across the spatial dimensions of the feature maps, which means that it reduces the spatial dimensions to a single value. For example, if the feature map has dimensions of 7x7x256, applying GAP will result in a single value for each of the 256 feature maps. This can be useful for classification tasks where the output of the network needs to be a fixed number of values.