Er inside the generator network. Table 2. Output size in the layer within the generator network. Layer Layer Size Size Layer Layer Input Input 256 256 . ……. . … . ……. . … FC FC 4096 4096 Upsample 4 4 Upsample Reshape Reshape two two 21024 1024 Scale four four Scale Upsample 0 0 Upsample four four four 12 512 Upsample 5 five Upsample Scale 0 0 Scale four 4 four 12 512 Scale five five Scale Upsample 1 1 Upsample 8 eight 8 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 will likely be able to differentiate the generated, reconstructed, and realThe discriminator might be able to differentiate the generated, reconstructed, and istic images as a great deal as possible. Consequently, the score for the original image need to be as realistic photos as substantially as you can. Consequently, the score for the original image must higher as Dicaprylyl carbonate custom synthesis possible, as well as the scores for the generated and reconstructed images must be as be as higher as possible, plus the scores for the generated and reconstructed photos really should low low as possible. Its structure is similar on the from the encoder, that the final two FCs be asas achievable. Its structure is equivalent to that to that encoder, except 9 of 19 that the final except Cephapirin (sodium) site having a using a size of generated at the end and replaced with FC having 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 correct false, that is utilised to enhance the image generation capability with the The outputtrue or or false, which can be usedto boost the image generation capability of thenetwork, making the generated image much more like the particulars are shown in network, making the generated image extra like the actual image.the real image. The facts are shown in Figure 6 and related shown in are shown in Table 3. Figure 6 and associated parameters areparametersTable 3.Figure six. Discriminator network.Figure 6. Discriminator network. Table 3. Output size of the layer within the discriminator network.yer ze yer zeInput 128 128 3 …… ……Conv 128 128 16 Downsample 3 8 eight Scale 0 128 128 16 Scale 4 8 8 Downsample 0 64 64 32 ReducemeanScale 1 64 64 32 Scale_fcDownsample 1 32 32 64 FCAgriculture 2021, 11,9 ofFigure six. Discriminator network.Table 3. Output size with the layer inside the discriminator network. Conv Scale 0 Downsample 0 Scale 1 DownsampleLayer Size Layer Layer Size Size LayerSizeInputTable 3. Output size of your layer in the discriminator network.128 128 three 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 8 256 4 Reducemean256 Scale_fc 256 FC …… eight 8 256 eight eight 256 256 2563.two.3. Components of Stage 2 Stage two is often a VAE network consisting of the encoder (E) and decoder (D), which is used Stage two distribution of consisting of your encoder (E) and also the latent which can be used to study the is often a VAE network hidden space in stage 1 given that decoder (D),variables occupy the to find out the distribution of hidden space in stage 1 because the latent variables occupy the whole latent space dimension. Each the encoder (E) and decoder (D) are composed of a whole 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 of your model is really a latent fully connected layer. The structur.
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