8971, 85688, 9467, 32830, 28689, 94845, 69840, 50883, 74177, 79585,
1055, 75631, 6825, 93188, 95426, 54514, 31467, 70597, 71149, 81994
]
seeds = [42]
counter = 0
args.log_db = args.name
print("log_db:", args.log_db)
avg_val = []
avg_test = []
for seed in seeds:
# set seed
args.seed = seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
set_gpu(args.gpu)
args.name = '{}_run_{}'.format(args.log_db, counter)
# start training the model
model = Trainer(args)
# val_acc, test_acc = model.run()
model.run_new()
# print('For seed {}\t Val Accuracy: {:.3f} \t Test Accuracy: {:.3f}\n'.format(seed, val_acc, test_acc))
# avg_val.append(val_acc)
# avg_test.append(test_acc)
counter += 1
# print('Val Accuracy: {:.3f} ± {:.3f} Test Accuracy: {:.3f} ± {:.3f}'.format(np.mean(avg_val), np.std(avg_val),
# np.mean(avg_test), np.std(avg_test)))
type=int,
help='Max length of the sentences in data.txt (default: 40)')
parser.add_argument(
'-maxdeplen',
dest="max_dep_len",
default=800,
type=int,
help='Max length of the dependency relations in data.txt (default: 800)'
)
args = parser.parse_args()
if not args.restore:
args.name = args.name + '_' + time.strftime(
"%d_%m_%Y") + '_' + time.strftime("%H:%M:%S")
tf.set_random_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
hp.set_gpu(args.gpu)
model = SynGCN(args)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
model.fit(sess)
print('Model Trained Successfully!!')
parser.add_argument("-pred_mode", dest="mode", choices=["entity", "temporal"], default="entity")
parser.add_argument(
"-granularity",
dest="granularity",
choices=["day", "year"],
default="year",
help="Day or year level granularity. Note: day only works for a single year span!",
)
args = parser.parse_args()
args.dataset = "data/" + args.data_type + "_" + args.version + "/train.txt"
args.entity2id = "data/" + args.data_type + "_" + args.version + "/entity2id.txt"
args.relation2id = "data/" + args.data_type + "_" + args.version + "/relation2id.txt"
args.test_data = "data/" + args.data_type + "_" + args.version + "/test.txt"
args.valid_data = "data/" + args.data_type + "_" + args.version + "/valid.txt"
args.triple2id = "data/" + args.data_type + "_" + args.version + "/triple2id.txt"
args.embed_dim = int(args.embed_init.split("_")[1])
tf.set_random_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
Helper.set_gpu(args.gpu)
model = HyTE(args)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print("enter fitting")
model.fit(sess)