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. 2022 Sep 17;17(4):985–1008. doi: 10.1007/s11571-022-09879-y

Table 1.

Hyperparameters considered in proposed CES-CNN

Block # Layer Output shape Image size Parameters
Block-1 (Conv-1) Convolution2D (3×3)@64 (n,n,64) (128,128,64) ((3×3)+1)×64 = 640
Batch Normalization (n,n,64) (128,128,64) 4×64=256
Activation Relu (n,n,64) (128,128,64) 0
Maxpooling2D (2×2) (n1,n1,64) n1=n/2 (64,64,64) 0
Dropout (n1,n1,64) (64,64,64) 0
Block-2 (Conv-2) Convolution2D (3×3)@128 (n1,n1,128) (64,64,128) ((3×3×64)+1) ×128=73876
Batch Normalization (n1,n1,128) (64,64,128) 4×128=512
Activation Relu (n1,n1,128) (64,64,128) 0
Maxpooling2D (2×2) (n2,n2,128) n2=n1/2 (32,32,128) 0
Dropout (n2,n2,128) (32,32,128) 0
Block-3 (Conv-3) Convolution2D (3×3)@256 (n2,n2,256) (32,32,256) ((3×3×128)+1) ×256=295168
Batch Normalization (n2,n2,256) (32,32,256) 4×256=1024
Activation Relu (n2,n2,128) (32,32,256) 0
Maxpooling2D (2×2) (n3,n3,256) n3=n2/2 (16,16,256) 0
Dropout (n3,n3,256) (16,16,256) 0
Block-4 (Conv-4) Convolution2D (3×3)@512 (n3,n3,512) (16,16,512) ((3×3×256)+1) ×512=1180160
Batch Normalization (n3,n3,256) (16,16,512) 4×512=2048
Activation Relu (n3,n3,256) (16,16,512) 0
Maxpooling2D (2×2) (n4,n4,512) n4=n3/2 (8,8,512) 0
Dropout (n4,n4,256) (8,8,512) 0
Block-5 (Conv-5) Convolution2D (3×3)@512 (n4,n4,512) (8,8,512) ((3×3×512)+1) ×512=2359808
Batch Normalization (n4,n4,512) (8,8,512) 4×512=2048
Activation Relu (n4,n4,512) (8,8,512) 0
Maxpooling2D (2×2) (n5,n5,512) n5=n4/2 (4,4,512) 0
Dropout (n5,n5,512) (4,4,512) 0
Flatten (1,n5×n5×512 (1,8192) 0
Dense (1256) (1,256) (8192+1)×256 = 2097408
Batch Normalization (1,256) (1,256) 512
activation Relu (1,256) (1,256) 0
Dropout (1,256) (1,256) 0
Dense (1,512) (1,512) (256+1)×512 = 131584
Batch Normalization (1,512) (1,512) 1024
activation Relu (1,512) (1,512) 0
Dropout (1,512) (1,512) 0
Dense (1,7) (1,7) (512+1)×7 = 3591
Total parameters for image of size (128×128) 9481607