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Simulations, Data Analysis and Algorithms

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Table 2 Discriminator network architecture: layer types, activations, output shapes (channels × height × width) and number of trainable parameters for each layer. All convolutional layers have stride =2. LeakyReLU’s leakines =0.2

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

  Activ. Output shape Params.
Input map 1 × 256 × 256
Conv 5 × 5 LReLU 64 × 128 × 128 1664
Conv 5 × 5 128 × 64 × 64 205K
BatchNorm LReLU 128 × 64 × 64 256
Conv 5 × 5 256 × 32 × 32 819K
BatchNorm LReLU 256 × 32 × 32 512
Conv 5 × 5 512 × 16 × 16 3.3M
BatchNorm LReLU 512 × 16 × 16 1024
Linear Sigmoid 1 131K
Total trainable parameters 4.4M
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