Table 1.

The Network architecture: Dimensions of all of the intermediate layers of the convolutional neural network. The first column contains the input layer names. The second column displays the size of the input feature map. The middle column describes the type of filter applied followed by a column describing the filter size if applicable. The final column displays the name of the output layer, which serves as the input for the next layer. Residual layers contain two feature maps per layer

Input layer	Input layer dimensions	Filter type	Filter size	Output layer
Input	64 × 64 × 3	Convolutional	3 × 3 × 8	Hidden layer 1
Hidden layer 1	32 × 32 × 8	Residual	3 × 3 × 16	Hidden layer 2/3
Hidden layer 2/3	16 × 16 × 16	Residual	3 × 3 × 32	Hidden layer 4/5
Hidden layer 4/5	8 × 8 × 32	Residual	3 × 3 × 32	Hidden layer 5/6
Hidden layer 5/6	8 × 8 × 32	Residual	3 × 3 × 64	Hidden layer 6/7
Hidden layer 6/7	4 × 4 × 64	Residual	3 × 3 × 64	Hidden layer 8/9
Hidden layer 8/9	4 × 4 × 64	Residual	3 × 3 × 64	Hidden layer 10/11
Hidden layer 10/11	4 × 4 × 64	Linear	× 16	Hidden layer 12
Hidden layer 12	1 × 16	Linear	16 × 8	Hidden layer 13
Hidden layer 13	1 × 8	Softmax	8 × 4	Classification