PyTorch implementation of “Monge-Ampère Flow for Generative Modeling” arXiv:1809.10188

How to run the code

Density estimation of MNIST

python density_estimation.py -dataset MNIST -hdim 1024 -Nsteps 100 -train -cuda 7

Variational free energy of Ising

python variational_free_energy.py -L 16 -fe_exact -2.3159198563359373 -train -cuda 7 -hdim 512 -Nsteps 50 -Batchsize 64 -symmetrize

Plots in the paper

• MNIST NLL

python paper/plot_nll.py -outname nll.pdf 

• Gaussianization MNIST

python density_estimation.py -hdim 1024 -Nsteps 100 -epsilon 0.1 -checkpoint data/learn_mnist/Simple_MLP_hdim1024_Batchsize100_lr0.001_Nsteps100_epsilon0.1/epoch-1.chkp -show 

• Direct sample Ising

python variational_free_energy.py -hdim 512 -Nsteps 50 -checkpoint data/learn_ot/ising_L16_d2_T2.269185314213022_symmetrize_Simple_MLP_hdim512_Batchsize64_lr0.001_delta0.0_Nsteps50_epsilon0.1/epoch-1.chkp -show  -L 16  -symmetrize 

Reference: Exact Ising free energy density at critical temperature on $L\times L$ lattices (For details see Appendix B of the paper)

| $L$ | periodic | open | | -- | ------------------ | ------------------ | | 4 | -2.33604476445 | -1.9470001244979966 | | 8 | -2.3227349295609376 | -2.1909718508291 | | 16 | -2.3159198563359373 | -2.272901214087426 | | 32 | -2.3140498159960936 | -2.2993352217736573 | | 64 | -2.3135805785878905| -2.3080749864821253 |