This is the code used in the article Flexible, Non-parametric Modeling Using Regularized Neural Networks, available at https://arxiv.org/abs/2012.11369 and https://link.springer.com/content/pdf/10.1007/s00180-021-01190-4.pdf. All code is run using Python 2.7.15 and TensorFlow 1.10.1.

Figures and Tables in Section 3.1:

$ cd legendre

Figure 2a:

$ Rscript make_graph.R

Figure 2b:

$ python plot_legendre.py

Figure 2c

$ bash lbda_sweep.sh
$ python plot_lbda_sweep.py #with correct log file on line 7

Table 1

$ bash var_imp.sh
$ python var_imp_tab.py #with correct log file on lines 5, 6

Table 2

$ bash fcts.sh
$ python fcts_tab.py #with correct log file on line 5

Figures and Tables in Section 3.3:

$ cd black_smoke

Table 4:

$ bash lbda_sweep.sh #with correct model on line 33 in lbda_sweep.py
$ python plot_lbda_sweep.py #with correct log file on line 6

$ bash fcts.sh #with correct model on line 33 in fcts.py
$ python fcts_tab.py #with correct log file on line 5

Figure 3:

$ python plot_synth_bs.py #with correct model on lines 34 and 37

Figure 4:

$ python plot_synth_bs_lasso.py #with correct model on lines 34 and 37