settings.py

import tensorflow as tf
import numpy as np
flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_integer('hidden3', 64, 'Number of units in hidden layer 3.')
flags.DEFINE_integer('discriminator_out', 0, 'discriminator_out.')
flags.DEFINE_float('discriminator_learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_float('learning_rate', .5*0.001, 'Initial learning rate.')
flags.DEFINE_integer('hidden1', 32, 'Number of units in hidden layer 1.')
flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.')
flags.DEFINE_float('weight_decay', 0., 'Weight for L2 loss on embedding matrix.')
flags.DEFINE_float('dropout', 0., 'Dropout rate (1 - keep probability).')
flags.DEFINE_integer('features', 1, 'Whether to use features (1) or not (0).')
flags.DEFINE_integer('seed', 50, 'seed for fixing the results.')
flags.DEFINE_integer('iterations', 50, 'number of iterations.')

'''
We did not set any seed when we conducted the experiments described in the paper;
We set a seed here to steadily reveal better performance of ARGA
'''
seed = 7
np.random.seed(seed)
tf.set_random_seed(seed)

def get_settings_new(model):
        iterations = FLAGS.iterations
        re = {'iterations' : iterations,'model' : model}
        
        return re