1D Global max pooling

This block performs exactly the same operation as the 1D Max pooling block except that the pool size is the size of the entire input of the block, i.e., it computes a single max value for all the incoming data.

The 1D Global max pooling block takes a vector and computes the max value of all values for each of the input channels. The output is thus a tensor of size is 1 x 1 x (input channels).

1D Global max pooling
Figure 1. A 1D Global max pooling.

Use global max pooling blocks as an alternative to the Flattening block after the last pooling block of your convolutional neural network. Using 1D Global max 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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