chickadee » nanograd » make-conv2d-layer

Creates a 2-D convolutional layer. Unless weight-values is given, weights start as uniform random values scaled by sqrt(2 / fan-in) (He scaling).

in-channels

number of input channels

out-channels

number of output channels

kernel-size

size of the square kernel

stride

convolution stride (default 1)

padding

zero padding (default 0)

activation

activation object

dtype

'f32 or 'f64

weight-values

optional SRFI-4 vector of initial weights, laid out as (out-channels, in-channels, kernel-size, kernel-size)

bias-values

optional SRFI-4 vector of initial biases

name

layer name

Input shapes:

• 3-D: (C_in, H, W) - single image
• 4-D: (N, C_in, H, W) - batch of images

Output shapes:

• 3-D: (C_out, H_out, W_out)
• 4-D: (N, C_out, H_out, W_out)

(define conv (make-conv2d-layer 3 32 3
                                stride: 1
                                padding: 1
                                activation: (make-relu)))

; Single image
(define img (make-tensor32 img-data '(3 32 32)))
(define features (forward conv img))  ; Shape: (32, 32, 32)

; Batch of images
(define batch (make-tensor32 batch-data '(16 3 32 32)))
(define batch-features (forward conv batch))  ; Shape: (16, 32, 32, 32)