import pprint
import json
import os

def get_neural_net_configuration():

	with open(os.path.join("./datasets","config.json"),'r') as coni:
		nn_params = json.load(coni)

	return nn_params

	#nn_params = {}
	#nn_params['sampling_frequency'] = 44100
	##Number of hidden dimensions.
	##For best results, this should be >= freq_space_dims, but most consumer GPUs can't handle large sizes
	#nn_params['hidden_dimension_size'] = 64



	#nn_params['num_iters'] = 20
	#nn_params['epochs_per_iter'] = 2


	#nn_params['work_folder'] = './datasets'
	##The weights filename for saving/loading trained models
	#nn_params['model_basename'] = './datasets/YourMusicLibraryNPWeights'
	##The model filename for the training data
	#nn_params['model_file'] = './datasets/YourMusicLibraryNP'
	##The dataset directory
	#nn_params['dataset_directory'] = './datasets/YourMusicLibrary/'
	#return nn_params

