def main(_):
check_dir()
print_config()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
run_option = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=run_option) as sess:
rnn = RNN(config=FLAGS, sess=sess)
rnn.build_model()
if FLAGS.is_training:
rnn.train_model()
if FLAGS.is_testing:
rnn.test_model()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 27 20:32:01 2018
@author: phien
"""
import Utils
from DataCreator import DataGeneratorSeq
from RNN import RNN
dataset = Utils.load_data(
"~/AI_AnhThieu/ai/data/GoogleTrace/data_resource_usage_5Minutes_6176858948.csv"
)
data, min_data, max_data = Utils.normailize_data(dataset)
# print(data[:10])
data_gen = DataGeneratorSeq(data)
X, Y = data_gen.create_batch_data(input_size=1, num_steps=3, batch_size=64)
X_train, X_test = Utils.split_data(X, ratio_train=0.8)
Y_train, Y_test = Utils.split_data(Y, ratio_train=0.8)
# print(X_train.shape[0])
# print(X_train.shape)
# lol
X_test = X_test.reshape(-1, 3, 1)
Y_test = Y_test.reshape(-1, 1)
rnn = RNN(input_size=1, num_steps=3, lstm_size=8, num_layers=1, num_epoch=10)
rnn.build_model(X_train, Y_train, X_test, Y_test, min_data, max_data)
