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Table 1 The network architectures of NetG

From: Generative adversarial network-based rogue device identification using differential constellation trace figure

Layer Output channels Output dimension Kernel, stride, padding Parameters Activation
Inputx 1 [64,64]
Convolution-2D 64 [32,32] [4,4], 2, 1 1024 LeakyReLU
Convolution-2D 128 [16,16] [4,4], 2, 1 131,072
BatchNorm-2D 128 [16,16] 256 LeakyReLU
Convolution-2D 256 [8,8] [4,4], 2, 1 524,288 -
BatchNorm-2D 256 [8,8] 512 LeakyReLU
Convolution-2D 100 [5,5] [4,4], 1, 0 409,600 Sigmoid
Outputz 100 [5,5]
Convolution transpose-2D 256 [8,8] [4,4], 1, 0 409,600
BatchNorm-2D 256 [8,8] 512 ReLU
Convolution transpose-2D 128 [16,16] [4,4], 2, 1 524,288
BatchNorm-2D 128 [16,16] 256 ReLU
Convolution transpose-2D 64 [32,32] [4,4], 2, 1 131,072
BatchNorm-2D 64 [32,32] 128 ReLU
Convolution transpose-2D 1 [64,64] [4,4], 2, 1 1024 Tanh
Output\(x'\) 1 [64,64]
Convolution-2D 64 [32,32] [4,4], 2, 1 1024 LeakyReLU
Convolution-2D 128 [16,16] [4,4], 2, 1 131,072
BatchNorm-2D 128 [16,16] 256 LeakyReLU
Convolution-2D 256 [8,8] [4,4], 2, 1 524,288
BatchNorm-2D 256 [8,8] 512 LeakyReLU
Convolution-2D 100 [5,5] [4,4], 1, 0 409,600
Output\(z'\) 100 [5,5]