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. 2021 Aug 3;21(15):5239. doi: 10.3390/s21155239

Figure 3.

Figure 3

CNN network architecture for this study. It is made up of two convolution layers (C1 and C3), two pooling layers (D2 and D4), and two fully connected layers (F5 and F6). C1, the first convolution layer, filters the 28 × 28 input number image with 32 kernels of 5 × 5 size; C3, the second convolution layer, filters the down-sampled 12 × 12 × 32 feature maps with 64 kernels of 5 × 5 × 32 size. Both of the convolution layers use a unit stride, and at the output of each, a ReLU nonlinear function is used. At layers D2 and D4, down-sampling is performed with 2 × 2 non-overlapping max pooling. For the two final fully-connected layers, F5 and F6, they respectively have 1024 and 10 neurons.