Er in the generator network. Table 2. Output size from the layer within the generator network. Layer Layer Size Size Layer Layer Input Input 256 256 . ……. . … . ……. . … FC FC 4096 4096 Upsample four 4 Upsample Reshape Reshape 2 2 21024 1024 Scale 4 four Scale Upsample 0 0 Upsample four 4 4 12 512 Upsample five 5 Upsample Scale 0 0 Scale 4 four 4 12 512 Scale 5 five Scale Upsample 1 1 Upsample eight eight eight 56 256 Conv ConvSize Size64 64 32 64 64 64 64 32 64 64 128 128 16 128 128 128 128 16 128 128 128 128 128 128 ure 2021, 11, x FOR PEER REVIEWThe discriminator might be capable to differentiate the generated, reconstructed, and realThe discriminator are going to be able to differentiate the generated, reconstructed, and istic photos as substantially as possible. Hence, the score for the original image need to be as realistic photos as a lot as you can. As a result, the score for the original image must high as you can, and also the scores for the generated and reconstructed images need to be as be as high as possible, plus the scores for the generated and reconstructed photos really should low low as you can. Its structure is comparable in the with the encoder, that the final two FCs be asas feasible. Its structure is equivalent to that to that encoder, except 9 of 19 that the final except with a having a size of generated in the end and replaced with FC using a size of 1. The two FCssize of 256 are256 are generated in the finish and replaced with FC with a size of 1. output is is true false, that is utilized to improve the image generation capacity in the The outputtrue or or false, which can be usedto enhance the image generation ability of thenetwork, creating the generated image far more just like the details are shown in network, generating the generated image a lot more just like the genuine image.the genuine image. The information are shown in Figure six and connected shown in are shown in Table 3. Figure 6 and related parameters areparametersTable 3.Figure 6. Discriminator network.Figure 6. Discriminator network. Table three. Output size of the layer within the discriminator network.yer ze yer zeInput 128 128 3 …… ……Conv 128 128 16 Downsample three 8 8 Scale 0 128 128 16 Scale 4 8 eight Downsample 0 64 64 32 ReducemeanScale 1 64 64 32 Scale_fcDownsample 1 32 32 64 FCAgriculture 2021, 11,9 ofFigure 6. Discriminator network.Table three. Output size in the layer within the discriminator network. Conv Scale 0 Downsample 0 Scale 1 DownsampleLayer Size Layer Layer Size Size LayerSizeInputTable 3. Output size on the layer within the discriminator network.128 128 3 128 128 16 128 128 16 64 64 32 64 64 32 32 32 64 Input Conv Scale 0 Downsample 0 Scale 1 Downsample 1 … … Downsample 3 Scale four Reducemean Scale_fc FC 128 128 3 128 128 16 128 128 16 64 64 32 64 64 32 32 32 64 8 3 1 ……. . . . . . Downsample 256 Scale8 eight 256 4 Reducemean256 Scale_fc 256 FC …… eight eight 256 8 eight 256 256 2563.2.three. Components of Stage two Stage 2 is really a VAE network consisting of the encoder (E) and decoder (D), that is employed Stage two distribution of consisting from the encoder (E) along with the Succinic anhydride Biological Activity latent which can be applied to learn the is a VAE network hidden space in stage 1 given that decoder (D),variables occupy the to DBCO-Sulfo-NHS ester medchemexpress understand the distribution of hidden space in stage 1 because the latent variables occupy the entire latent space dimension. Both the encoder (E) and decoder (D) are composed of a entire latent space dimension. Each the encoder (E) and decoder (D) are composed of a completely connected layer. The structure is shown in Figure 7. The input from the model is often a latent fully connected layer. The structur.
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