Er inside the generator network. Table two. Output size with the layer in the generator network. Layer Layer Size Size Layer Layer Input Input 256 256 . ……. . … . ……. . … FC FC 4096 4096 Upsample 4 4 Upsample Reshape Reshape 2 two 21024 1024 Scale four 4 Scale Upsample 0 0 Upsample four four 4 12 512 Upsample 5 five Upsample Scale 0 0 Scale four four 4 12 512 Scale five 5 Scale Upsample 1 1 Upsample 8 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 are going to be in a position to differentiate the generated, reconstructed, and realThe discriminator is going to be able to differentiate the generated, reconstructed, and istic pictures as a great deal as you can. Therefore, the score for the original image should be as realistic pictures as a great deal as you possibly can. Consequently, the score for the original image should high as you can, plus the scores for the generated and reconstructed pictures should be as be as high as you can, along with the scores for the generated and reconstructed images ought to low low as you can. Its structure is comparable in the on the encoder, that the final two FCs be asas achievable. Its structure is related to that to that encoder, except 9 of 19 that the final except with a with a size of generated at 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 having a size of 1. output is is correct false, which can be used to enhance the image generation ability of the The outputtrue or or false, that is usedto boost the image generation capacity of thenetwork, creating the generated image more like the specifics are shown in network, making the generated image more like the genuine image.the true image. The 1-Methylpyrrolidine site particulars are shown in Figure six and connected shown in are shown in Table 3. Figure 6 and associated Fluzoparib Description parameters areparametersTable 3.Figure six. Discriminator network.Figure 6. Discriminator network. Table three. Output size of your layer within the discriminator network.yer ze yer zeInput 128 128 three …… ……Conv 128 128 16 Downsample 3 8 8 Scale 0 128 128 16 Scale 4 eight 8 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 inside the discriminator network. Conv Scale 0 Downsample 0 Scale 1 DownsampleLayer Size Layer Layer Size Size LayerSizeInputTable 3. Output size from 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 three Scale 4 Reducemean Scale_fc FC 128 128 3 128 128 16 128 128 16 64 64 32 64 64 32 32 32 64 eight three 1 ……. . . . . . Downsample 256 Scale8 8 256 4 Reducemean256 Scale_fc 256 FC …… eight eight 256 eight 8 256 256 2563.2.3. Components of Stage 2 Stage two is a VAE network consisting from the encoder (E) and decoder (D), that is used Stage 2 distribution of consisting in the encoder (E) and the latent which is used to find out the is a VAE network hidden space in stage 1 due to the fact decoder (D),variables occupy the to understand the distribution of hidden space in stage 1 since the latent variables occupy the entire latent space dimension. Each the encoder (E) and decoder (D) are composed of a entire latent space dimension. Both the encoder (E) and decoder (D) are composed of a totally connected layer. The structure is shown in Figure 7. The input on the model is really a latent fully connected layer. The structur.
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