def main(_):
# pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
# run_config = tf.ConfigProto()
# run_config.gpu_options.allow_growth = True
# with tf.Session(config=run_config) as sess:
with tf.Session() as sess:
rnn_model = LSTM(
sess, "SP500"
# FLAGS.stock_count,
# lstm_size=FLAGS.lstm_size,
# num_layers=FLAGS.num_layers,
# num_steps=FLAGS.num_steps,
# input_size=FLAGS.input_size,
# embed_size=FLAGS.embed_size,
)
show_all_variables()
# stock_data_list = load_sp500(
# FLAGS.input_size,
# FLAGS.num_steps,
# k=FLAGS.stock_count,
# target_symbol=FLAGS.stock_symbol,
# )
rnn_model.build_graph()
rnn_model.train_lstm_graph("SP500")
def prediction():
label_column = 6
X = tf.placeholder(tf.float32,
shape=[None, CONFIG["time_step"], CONFIG['input_size']])
stock_dataset = StockDataSet(CONFIG, "SP500")
test_x = stock_dataset.test_x
test_y = stock_dataset.test_y
mean = stock_dataset.mean
std = stock_dataset.std
lstm = LSTM("SP500")
pred, _ = lstm.build_graph(X)
# ckpt = tf.train.get_checkpoint_state('models/stock_name:SP500_lr0.00_lr_decay0.990_lstm10_step1_input6_batch60_epoch50') # 通过检查点文件锁定最新的模型
# saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta') # 载入图结构，保存在.meta文件中
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
#参数恢复
model_file = tf.train.latest_checkpoint(
'models/stock_name:SP500_lr0.00_lr_decay0.990_lstm10_step20_input6_batch64_epoch50'
)
saver.restore(sess, model_file)
test_predict = []
for step in range(len(test_x) - 1):
prob = sess.run(pred, feed_dict={X: [test_x[step]]})
predict = prob.reshape((-1))
test_predict.extend(predict)
test_y = np.array(test_y) * std[label_column] + mean[label_column]
test_predict = np.array(
test_predict) * std[label_column] + mean[label_column]
acc = np.average(
np.abs(test_predict - test_y[:len(test_predict)]) /
test_y[:len(test_predict)]) #偏差
#以折线图表示结果
print(acc)
plt.figure()
plt.plot(list(range(len(test_predict))), test_predict, color='b')
plt.plot(list(range(len(test_y))), test_y, color='r')
plt.show()
