chickadee » nanograd » global-avg-pool2d

global-avg-pool2d inputprocedure

Averages over the spatial dimensions.

  • 3-D: (C, H, W) -> (C)
  • 4-D: (N, C, H, W) -> (N, C)

The gradient is distributed uniformly over the spatial positions of each channel.

;; Batch of feature maps
(define batch-features (make-tensor32 (make-f32vector (* 32 128 8 8)) '(32 128 8 8)))
(define batch-pooled (global-avg-pool2d batch-features))  ; Shape: (32, 128)

;; Classification head
(define logits (forward fc-layer batch-pooled))  ; Shape: (32, num_classes)

Global average pooling can replace a large fully connected layer at the end of a CNN, with far fewer parameters.