Skip to main content
. 2019 Oct 29;19:206. doi: 10.1186/s12911-019-0946-1

Table 2.

Residual-Network Architecture: Input Dimension & Number of Kernels

Layer/Model Residual 1–1 Residual 1–2 Residual 1–3 Residual 1–4 Residual 1–5 Residual 1–6
Convolution (1, 1000, 64)a (1, 1000, 32) (1, 1000, 16) (1, 1000, 8) (1, 1000, 4) (1, 1000, 2)
Pooling (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Residual Block (1, 125, 256) (1, 125, 128) (1, 125, 64) (1, 125, 32) (1, 125, 16) (1, 125, 8)
Residual Block (1, 125, 256) (1, 125, 128) (1, 125, 64) (1, 125, 32) (1, 125, 16) (1, 125, 8)
Residual Block (1, 63, 512) (1, 63, 256) (1, 63, 128) (1, 63, 64) (1, 63, 32) (1, 63, 16)
Residual Block (1, 63, 512) (1, 63, 256) (1, 63, 128) (1, 63, 64) (1, 63, 32) (1, 63, 16)
Pooling (1, 1, 512) (1, 1, 256) (1, 1, 128) (1, 1, 64) (1, 1, 32) (1, 1, 16)
Output (2) (2) (2) (2) (2) (2)
Layer/Model Residual 2–1 Residual 2–2 Residual 2–3 Residual 2–4 Residual 2–5 Residual 2–6
Convolution (1, 1000, 64) (1, 1000, 32) (1, 1000, 16) (1, 1000, 8) (1, 1000, 4) (1, 1000, 2)
Pooling (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Residual Block (1, 125, 256) (1, 125, 128) (1, 125, 64) (1, 125, 32) (1, 125, 16) (1, 125, 8)
Residual Block (1, 125, 256) (1, 125, 128) (1, 125, 64) (1, 125, 32) (1, 125, 16) (1, 125, 8)
Pooling (1, 1, 256) (1, 1, 128) (1, 1, 64) (1, 1, 32) (1, 1, 16) (1, 1, 8)
Output (2) (2) (2) (2) (2) (2)
Layer/Model Residual 3–1 Residual 3–2 Residual 3–3 Residual 3–4 Residual 3–5 Residual 3–6
Convolution (1, 1000, 64) (1, 1000, 32) (1, 1000, 16) (1, 1000, 8) (1, 1000, 4) (1, 1000, 2)
Pooling (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Residual Block (1, 250, 128) (1, 250, 64) (1, 250, 32) (1, 250, 16) (1, 250, 8) (1, 250, 4)
Pooling (1, 1, 128) (1, 1, 64) (1, 1, 32) (1, 1, 16) (1, 1, 8) (1, 1, 4)
Output (2) (2) (2) (2) (2) (2)
Layer/Model Residual 4–1 Residual 4–2 Residual 4–3 Residual 4–4 Residual 4–5 Residual 4–6
Convolution (1, 1000, 64) (1, 1000, 32) (1, 1000, 16) (1, 1000, 8) (1, 1000, 4) (1, 1000, 2)
Pooling (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Residual Block (1, 500, 64) (1, 500, 32) (1, 500, 16) (1, 500, 8) (1, 500, 4) (1, 500, 2)
Pooling (1, 1, 64) (1, 1, 32) (1, 1, 16) (1, 1, 8) (1, 1, 4) (1, 1, 2)
Output (2) (2) (2) (2) (2) (2)

a(1, 1000, 64), Input Dimension 1, Input Dimension 2, Number of Kernels