Table 1 Autoencoder architecture summary.
From: Machine learning enhanced evaluation of semiconductor quantum dots
Layer | Output shape | Parameter | |
|---|---|---|---|
Residual unit | Kernel 3, stride 3, padding 1 | \(4 \times 341\) | 248 |
Residual unit | Kernel 3, stride 3, padding 1 | \(16 \times 113\) | 3344 |
Residual unit | Kernel 3, stride 3, padding 1 | \(32 \times 37\) | 13,216 |
Residual unit | Kernel 3, stride 3, padding 1 | \(64 \times 12\) | 52,032 |
Flatten | \(1 \times 768\) | – | |
Linear feed-forward | \(1 \times 16 = \dim \,\Xi \) | 12,304 | |
Linear feed-forward | \(1 \times 4608\) | 78,336 | |
Reshape | \(128 \times 36\) | – | |
Transposed conv. layer | Kernel 7, stride 3 | \(64 \times 112\) | 57,664 |
Transposed conv. layer | Kernel 6, stride 3 | \(32 \times 339\) | 12,448 |
Transposed conv. layer | Kernel 6, stride 3 | \(16 \times {1020}\) | 3152 |
Transposed conv. layer | Kernel 3, stride 1, padding 1 | \(8 \times {1020}\) | 424 |
Transposed conv. layer | Kernel 3, stride 1, padding 1 | \(4 \times {1020}\) | 116 |
Transposed conv. layer | Kernel 5, stride 1 | \(1 \times {1024} = \dim \,{\mathscr {U}}\) | 29 |