# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 4 The Proposed Methodology
### Practical DCNN Architecture Selection and Pruning Strategy for Optimal Node Selection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Original Deep Convolutional Network**} & \multicolumn{3}{c}{**Pruned Deep Convolutional Network**} \\ \hline
**Layers** & **Output Shape** & **Parameters** & **Layers** & **Output Shape** & **Parameters** \\ \hline Input & 224, 224, 3 & 0 & Input & 224, 224, 3 & 0 \\ (Conv2D) 64 & 224, 224, 64 & 1792 & (Conv2D) 32 & 224, 224, 32 & 896 \\ Batch Normalization & 224, 224, 64 & 256 & Batch Normalization & 224, 224, 32 & 128 \\ Max Pooling (2 \(\times\) 2) & 112, 112, 64 & 0 & Max Pooling (2 \(\times\) 2) & 112, 112, 32 & 0 \\ (Conv2D) 128 & 112, 112, 128 & 73,856 & (Conv2D) 64 & 112, 112, 64 & 18,496 \\ Batch Normalization & 112, 112, 128 & 512 & Batch Normalization & 112, 112, 64 & 256 \\ (Conv2D) 128 & 112, 112, 128 & 147,584 & (Conv2D) 64 & 112, 112, 64 & 36,928 \\ Batch Normalization & 112, 112, 128 & 512 & Batch Normalization & 112, 112, 64 & 256 \\ Max Pooling (2 \(\times\) 2) & 56, 56, 128 & 0 & Max Pooling (2 \(\times\) 2) & 56, 56, 64 & 0 \\ (Conv2D) 256 & 56, 56, 256 & 295,168 & (Conv2D) 128 & 56, 56, 128 & 73,856 \\ Batch Normalization & 56, 56, 256 & 1024 & Batch Normalization & 56, 56, 128 & 512 \\ (Conv2D) 256 & 56, 56, 256 & 590,080 & (Conv2D) 128 & 56, 56, 128 & 147,584 \\ Batch Normalization & 56, 56, 256 & 1024 & Batch Normalization & 56, 56, 128 & 512 \\ Max Pooling (2 \(\times\) 2) & 56, 56, 256 & 0 & Max Pooling (2 \(\times\) 2) & 28, 28, 128 & 0 \\ (Conv2D) 512 & 28, 28, 512 & 1,180,160 & (Conv2D) 256 & 28, 28, 256 & 295,168 \\ Batch Normalization & 28, 28, 512 & 2048 & Batch Normalization & 28, 28, 256 & 1024 \\ (Conv2D) 512 & 28, 28, 512 & 2,359,808 & (Conv2D) 256 & 28, 28, 256 & 590,080 \\ Batch Normalization & 28, 28, 512 & 2048 & Batch Normalization & 28, 28, 256 & 1024 \\ Max Pooling (2 \(\times\) 2) & 14, 14, 512 & 0 & Max Pooling (2 \(\times\) 2) & 14, 14, 256 & 0 \\ (Conv2D) 512 & 14, 14, 512 & 2,359,808 & (Conv2D) 256 & 14, 14, 256 & 590,080 \\ Batch Normalization & 14, 14, 512 & 2048 & Batch Normalization & 14, 14, 256 & 1024 \\ (Conv2D) 512 & 14, 14, 512 & 2,359,808 & (Conv2D) 256 & 14, 14, 256 & 590,080 \\ Batch Normalization & 14, 14, 512 & 2048 & Batch Normalization & 14, 14, 256 & 1024 \\ Max Pooling (2 \(\times\) 2) & 7, 7, 512 & 0 & Max Pooling (2 \(\times\) 2) & 7, 7, 256 & 0 \\ Flatten & 25,088 & 0 & Flatten & 12,544 & 0 \\ (Fully Connected) 512 & 512 & 12,845,568 & (Fully Connected) 384 & 384 & 4,817,280 \\ Batch Normalization & 512 & 2048 & Batch Normalization & 384 & 1536 \\ (Fully Connected) 512 & 512 & 262,656 & (Fully Connected) 384 & 384 & 147,840 \\ Batch Normalization & 512 & 2048 & Batch Normalization & 384 & 1536 \\ (Fully Connected) 512 & 29 & 14,877 & (Fully Connected) 29 & 29 & 11,165 \\ Output Softmax & 29 & 0 & Output Softmax & 29 & 0 \\ \hline Total Parameters & 22,506,781 & Total Parameters & 7,328,285 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters in the proposed deep convolutional network and pruned deep convolutional network.