YAO-optimizer/model/model_tensorflow.py

import tensorflow as tf
import numpy as np


class Model:
	def __init__(self, config):
		self.config = config

		self.placeholders()
		self.net()
		self.operate()

	def placeholders(self):
		self.X = tf.placeholder(tf.float32, [None, self.config.time_step, self.config.input_size])
		self.Y = tf.placeholder(tf.float32, [None, self.config.time_step, self.config.output_size])

	def net(self):
		def dropout_cell():
			basicLstm = tf.nn.rnn_cell.LSTMCell(self.config.hidden_size)
			dropoutLstm = tf.nn.rnn_cell.DropoutWrapper(basicLstm, output_keep_prob=1 - self.config.dropout_rate)
			return dropoutLstm

		cell = tf.nn.rnn_cell.MultiRNNCell([dropout_cell() for _ in range(self.config.lstm_layers)])

		output_rnn, _ = tf.nn.dynamic_rnn(cell=cell, inputs=self.X, dtype=tf.float32)

		# shape of output_rnn is: [batch_size, time_step, hidden_size]
		self.pred = tf.layers.dense(inputs=output_rnn, units=self.config.output_size)

	def operate(self):
		self.loss = tf.reduce_mean(tf.square(tf.reshape(self.pred, [-1]) - tf.reshape(self.Y, [-1])))
		self.optim = tf.train.AdamOptimizer(self.config.learning_rate).minimize(self.loss)
		self.saver = tf.train.Saver(tf.global_variables())


def train(config, train_X, train_Y, valid_X, valid_Y):
	with tf.variable_scope("stock_predict"):
		model = Model(config)

	train_len = len(train_X)
	valid_len = len(valid_X)
	with tf.Session() as sess:
		sess.run(tf.global_variables_initializer())

		valid_loss_min = float("inf")
		bad_epoch = 0
		for epoch in range(config.epoch):
			print("Epoch {}/{}".format(epoch, config.epoch))

			train_loss_array = []
			for step in range(train_len // config.batch_size):
				feed_dict = {model.X: train_X[step * config.batch_size: (step + 1) * config.batch_size],
				             model.Y: train_Y[step * config.batch_size: (step + 1) * config.batch_size]}
				train_loss, _ = sess.run([model.loss, model.optim], feed_dict=feed_dict)
				train_loss_array.append(train_loss)


			valid_loss_array = []
			for step in range(valid_len // config.batch_size):
				feed_dict = {model.X: valid_X[step * config.batch_size: (step + 1) * config.batch_size],
				             model.Y: valid_Y[step * config.batch_size: (step + 1) * config.batch_size]}
				valid_loss = sess.run(model.loss, feed_dict=feed_dict)
				valid_loss_array.append(valid_loss)

			valid_loss_cur = np.mean(valid_loss_array)
			print("The train loss is {:.4f}. ".format(np.mean(train_loss_array)),
			      "The valid loss is {:.4f}.".format(valid_loss_cur))

			if valid_loss_cur < valid_loss_min:
				valid_loss_min = valid_loss_cur
				bad_epoch = 0
				path = model.saver.save(sess, config.model_save_path + config.model_name)
				print(path)
			else:
				bad_epoch += 1
				if bad_epoch >= config.patience:
					print(" The training stops early in epoch {}".format(epoch))
					break
		return model


def predict(config, test_X, model):
	config.dropout_rate = 0.1

	#with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
	#	model = Model(config)

	test_len = len(test_X)
	with tf.Session() as sess:
		#module_file = tf.train.latest_checkpoint(config.model_save_path)
		#model.saver.restore(sess, module_file)

		result = np.zeros((test_len * config.time_step, config.output_size))
		for step in range(test_len):
			feed_dict = {model.X: test_X[step: (step + 1)]}
			test_pred = sess.run(model.pred, feed_dict=feed_dict)
			result[step * config.time_step: (step + 1) * config.time_step] = test_pred[0, :, :]
	return result