Global average pooling 1D

This block performs exactly the same operation as the 1D Average pooling block except that the pool size is the size of the entire input of the block, i.e., it computes a single average value for each of the input channels (the second dimension).


The 1D Global average pooling block takes a 2-dimensional tensor tensor of size (input size) x (input channels) and computes the maximum of all the (input size) values for each of the (input channels).

If your input has only one dimension, you can use a reshape block with a Target shape of (input size, 1) to make it compatible with the 1D Global max pooling block.


The output is a 1-dimensional tensor of size (input channels).

1D Global average pooling
Figure 1. A 1D Global average pooling.

When to use global average pooling 1D

Use global average pooling blocks as an alternative to the Flattening block after the last pooling block of your convolutional neural network. Using 1D Global average pooling block can replace the fully connected blocks of your CNN.

For more information, see Section 3.2 of Min Lin, Qiang Chen, Shuicheng Yan. Network In Network.

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