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Simulations, Data Analysis and Algorithms

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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
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