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Table 1 Architecture used in the discriminator and generator networks. We used a batch size of \(m = 16\) samples. The neural network has 32 million trainable parameters. Parameters for our Wasserstein-1 distance implementation are shown in brackets

From: Fast cosmic web simulations with generative adversarial networks

Layer

Operation

Output

Dimension

Discriminator

X

  

m × 256 × 256 × 1

\(h_{0}\)

conv

LeakyRelu–BatchNorm

m × 128 × 128 × 64

\(h_{1}\)

conv

LeakyRelu–BatchNorm

m × 64 × 64 × 128

\(h_{2}\)

conv

LeakyRelu–BatchNorm

m × 32 × 32 × 256

\(h_{3}\)

conv

LeakyRelu–BatchNorm

m × 16 × 16 × 512

\(h_{4}\)

linear

sigmoid (identity)

m × 1

Generator

z

  

m × 200 (m × 100)

\(h_{0}\)

linear

Relu–BatchNorm

m × 16 × 16 × 512

\(h_{1}\)

deconv

Relu–BatchNorm

m × 32 × 32 × 256

\(h_{2}\)

deconv

Relu–BatchNorm

m × 64 × 64 × 128

\(h_{3}\)

deconv

Relu–BatchNorm

m × 128 × 128 × 64

\(h_{4}\)

deconv

tanh

m × 256 × 256 × 1