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Simulations, Data Analysis and Algorithms

Computational Astrophysics and Cosmology Cover Image

Table 1 Generator network architecture: layer types, activations, output shapes (channels × height × width) and number of trainable parameters for each layer. TransposedConv have strides =2

From: CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

  Activ. Output shape Params.
Latent 64
Dense 512 × 16 × 16 8.5M
BatchNorm ReLU 512 × 16 × 16 1024
TransposedConv 5 × 5 256 × 32 × 32 3.3M
BatchNorm ReLU 256 × 32 × 32 512
TransposedConv 5 × 5 128 × 64 × 64 819K
BatchNorm ReLU 128 × 64 × 64 256
TransposedConv 5 × 5 64 × 128 × 128 205K
BatchNorm ReLU 64 × 128 × 128 128
TransposedConv 5 × 5 Tanh 1 × 256 × 256 1601
Total trainable parameters 12.3M
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