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Table 7 Layer structure of the LeNet-5 “deep learning” CNN employed in this investigation. Sizes refer to pixels for layers 1–7, variables for layers 8–10

From: Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania

Layers Type Parameters
   Image
1 Input 3-tensor (size 1 × 28 × 28)
2 Convolution 3-tensor (size 10 × 25 × 25)
3 Ramp 3-tensor (size 10 × 25 × 25)
4 Pooling 3-tensor (size 10 × 12 × 12)
5 Convolution 3-tensor (size 20 × 9 × 9)
6 Ramp 3-tensor (size 20 × 9 × 9)
7 Pooling 3-tensor (size 20 × 4 × 4)
8 Flatten Vector (size 320)
9 Linear Vector (size 2)
10 Output Vector (size 2)