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Feasibility of 3*3 Convolution Kernels Replace 5*5 Convolution Kernels
| Assuming: feature_map = 28*28 | Convolution step = 1 | Padding = 0 |
|---|---|---|
| 1-Layer 5×5 convolutional kernel | 2-Layer 3×3 convolutional kernel | |
| Layer1: (28-5) / 1 + 1 = 24 | Layer1:(28-3) / 1 + 1 = 26 | |
| Output: Feature map = 24×24 | Layer2:(26-3) / 1 + 1 = 24 | |
| Output:Feature map = 24×24 |
Confusion matrix under binary classification
| Confusion Matrix | Predict | ||
|---|---|---|---|
| 0 | 1 | ||
| Real | 0 | a | b |
| 1 | c | d | |