How to run:

step 0: If at NERSC, load the tensorflow module: module load tensorflow/intel-head-MKL-DNN.

step 1: Change the hyper parameters in hyper_parameters_Cosmo.py (see the notations in the python script) if you want to

step 2: python CosmoNet_noFeed.py

output data: losses.txt: the loss as a function of epoch

loss_train.txt: the relative error for training data

loss_val.txt: the relative error for validation

loss_test.txt: the relative error for test data

test_batch_X.txt: the file to store the predicted and the ground true ([\Omega_m_true \Sigma_8_true \Omega_m_predicted \Omega_m_true])

best model information: best_validation.meta, best_validation.index, best_validation.data-00000-of-00001 

Note that the data path is hardcoded into hyper_parameters_Cosmo.py, pointing to a directory of data files (already converted, so no need to re-run the io_cosmo code).

All parameters you might want to adjust are set in hyper_parameters_Cosmo.py.