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config_bak.py
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config_bak.py
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import argparse
import numpy as np
import os
import random
def get_params():
''' Get parameters from command line '''
parser = argparse.ArgumentParser()
# settings
parser.add_argument("--dataset", type=str, default='cora', help="Dataset string")# 'cora', 'citeseer', 'pubmed'
parser.add_argument('--id', type=str, default='default_id', help='id to store in database') #
parser.add_argument('--device', type=int, default=0,help='device to use') #
parser.add_argument('--setting', type=str, default="description of hyper-parameters.") #
parser.add_argument('--task_type', type=str, default='semi')
parser.add_argument('--early_stop', type=int, default= 300, help='early_stop')
parser.add_argument('--dtype', type=str, default='float32') #
parser.add_argument('--seed',type=int, default=123, help='seed')
parser.add_argument('--record',type=bool, default=False, help='write to database, for tuning.')
# shared parameters
parser.add_argument('--epochs', type=int, default=300, help='Number of epochs to train.')
parser.add_argument('--dropout',type=float, default=0.1, help='dropout rate (1 - keep probability).')
parser.add_argument('--weight_decay',type=float, default=5e-4, help='Weight for L2 loss on embedding matrix.')
parser.add_argument('--hiddens', type=str, default='256')
parser.add_argument("--lr", type=float, default=0.01,help='initial learning rate.')
parser.add_argument('--act', type=str, default='relu', help='activation funciton') #
parser.add_argument('--initializer', default='glorot')
# for dropedge
parser.add_argument('--dropedge',type=float, default=0.3, help='dropedge rate (1 - keep probability).')
# for PTDNet
parser.add_argument('--init_temperature', type=float, default=1.5)
parser.add_argument('--temperature_decay', type=float, default=0.999)
parser.add_argument('--denoise_hidden_1', type=int, default=128)
parser.add_argument('--denoise_hidden_2', type=int, default=0)
# parser.add_argument('--denoise_bias', type=float, default=1.0,help='initial ratio of edges')
parser.add_argument('--gamma', type=float, default=-0.1)
parser.add_argument('--zeta', type=float, default=5.0)
parser.add_argument('--lambda1', type=float, default=0.0, help='Weight for L0 loss on laplacian matrix.')
parser.add_argument('--lambda3', type=float, default=0., help='Weight for nuclear loss')
parser.add_argument('--k_svd', type=int, default=1)
args, _ = parser.parse_known_args()
return args
args = get_params()
params = vars(args)
SVD_PI = True
devices = ['0','1','-1']
real_device = args.device%len(devices)
os.environ["CUDA_VISIBLE_DEVICES"] = devices[real_device]
import tensorflow as tf
config = tf.compat.v1.ConfigProto(
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8)
config.gpu_options.allow_growth = True
# tf.enable_eager_execution(config=config)
seed = args.seed
random.seed(args.seed)
np.random.seed(seed)
# tf.set_random_seed(seed)
dtype = tf.float32
if args.dtype=='float64':
dtype = tf.float64
eps = 1e-7
#
# import mysql.connector
# mydb = mysql.connector.connect(
# host="104.39.196.38", #"",
# user="dul262",
# passwd="dul262dgx1"
# )
# mycursor = mydb.cursor()
# args.hiddens ='256'
# for i in range(1-1):
# args.hiddens+='-256'
# print(args.hiddens)