added way to track random seeds for reproducibility

helperfns.py

@@ -342,6 +342,10 @@ def set_defaults(params):
     params['d'] = len(params['widths'])  # d must be calculated like this
 
     # defaults related to initialization of parameters
+    if 'seed' not in params:
+        random_seed = np.random.randint(2 ** 30)
+        print("setting default: choosing random seed of %d and saving to params" % random_seed)
+        params['seed'] = random_seed
     if 'dist_weights' not in params:
         print("setting default: distribution for weights on main net is tn (truncated normal)")
         params['dist_weights'] = 'tn'

training.py

@@ -304,6 +304,8 @@ def main_exp(params):
     if not os.path.exists(params['folder_name']):
         os.makedirs(params['folder_name'])
 
+    tf.set_random_seed(params['seed'])
+    np.random.seed(params['seed'])
     # data is num_steps x num_examples x n but load flattened version (matrix instead of tensor)
     data_val = np.loadtxt(('./data/%s_val_x.csv' % (params['data_name'])), delimiter=',', dtype=np.float64)
     try_net(data_val, params)