다음은 앞에서 생성한 모델 네트워크의 구조를 보여 줍니다(디코더만 보여 줍니다).
Decoder(
(decoder_lin): Sequential(
(0): Linear(in_features=4, out_features=128, bias=True)
(1): ReLU(inplace=True)
(2): Linear(in_features=128, out_features=288, bias=True)
(3): ReLU(inplace=True)
)
(unflatten): Unflatten(dim=1, unflattened_size=(32, 3, 3))
(decoder_conv): Sequential(
(0): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): ConvTranspose2d(16, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(4): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=True)
(6): ConvTranspose2d(8, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
)
)