#！/usr/bin/env python
# encoding: utf-8
'''
@author: 真梦行路
@file: tf_lstm2.py
@time: 2018/8/13 11:27
'''
###引入第三方模块###
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import pandas as pd
###读取数据###
df=pd.read_csv(os.getcwd()+'\\data\\dataset_2.csv')
data=df.iloc[:,2:10].values
###定义设置LSTM常量###
rnn_unit=20 #隐层单元的数量
input_size=6 #输入矩阵维度
output_size=2 #输出矩阵维度
lr=0.0006 #学习率
time_step=20 #设置时间步长
###制作带时间步长的训练集###
def get_train_data(batch_size=60,time_step=15,train_begin=0,train_end=5800):
batch_index=[]
data_train=data[train_begin:train_end]
normalized_train_data=(data_train-np.mean(data_train,axis=0))/np.std(data_train,axis=0) #标准化
train_x,train_y=[],[] #训练集
for i in range(len(normalized_train_data)-time_step):
if i % batch_size==0:
batch_index.append(i)
x=normalized_train_data[i:i+time_step,:6]
y=normalized_train_data[i:i+time_step,6:]
train_x.append(x.tolist())
train_y.append(y.tolist())
batch_index.append((len(normalized_train_data)-time_step))
return batch_index,train_x,train_y
###制作带时间步长的测试集###
def get_test_data(time_step=15,test_begin=5800):
data_test=data[test_begin:]
mean=np.mean(data_test,axis=0)
std=np.std(data_test,axis=0)
normalized_test_data=(data_test-mean)/std #标准化
size=(len(normalized_test_data)+time_step-1)//time_step #有size个sample
test_x,test_y=[],[]
for i in range(size-1):
x=normalized_test_data[i*time_step:(i+1)*time_step,:6]
y=normalized_test_data[i*time_step:(i+1)*time_step,6:]
test_x.append(x.tolist())
test_y.extend(y)
test_x.append((normalized_test_data[(i+1)*time_step:,:6]).tolist())
test_y.extend((normalized_test_data[(i+1)*time_step:,6:]).tolist())
return mean,std,test_x,test_y
#——————————————————定义LSTM网络权重和偏置——————————————————
#输入层、输出层权重、偏置
weights={
'in':tf.Variable(tf.random_normal([input_size,rnn_unit])),
'out':tf.Variable(tf.random_normal([rnn_unit,2]))
}
biases={
'in':tf.Variable(tf.constant(0.1,shape=[rnn_unit,])),
'out':tf.Variable(tf.constant(0.1,shape=[2,]))
}
#——————————————————定义LSTM网络——————————————————
def lstm(X):
batch_size=tf.shape(X)[0]
time_step=tf.shape(X)[1]
w_in=weights['in']
b_in=biases['in']
input=tf.reshape(X,[-1,input_size]) #需要将tensor转成2维进行计算，计算后的结果作为隐藏层的输入
input_rnn=tf.matmul(input,w_in)+b_in
input_rnn=tf.reshape(input_rnn,[-1,time_step,rnn_unit]) #将tensor转成3维，作为lstm cell的输入
cell1=tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
cell2=tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
cell=tf.nn.rnn_cell.MultiRNNCell(cells=[cell1,cell2])
init_state=cell.zero_state(batch_size,dtype=tf.float32)
with tf.variable_scope('scope', reuse=tf.AUTO_REUSE):
output_rnn,final_states=tf.nn.dynamic_rnn(cell, input_rnn,initial_state=init_state, dtype=tf.float32) #output_rnn是记录lstm每个输出节点的结果，final_states是最后一个cell的结果
output=tf.reshape(output_rnn,[-1,rnn_unit]) #作为输出层的输入
w_out=weights['out']
b_out=biases['out']
pred=tf.matmul(output,w_out)+b_out
return pred,final_states
#——————————————————LSTM模型训练——————————————————
def train_lstm(batch_size=60,time_step=15,train_begin=2000,train_end=5800):
X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])
Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])
batch_index,train_x,train_y=get_train_data(batch_size,time_step,train_begin,train_end)
pred,_=lstm(X)
###损失函数###
loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1,2])-tf.reshape(Y, [-1,2])))
train_op=tf.train.AdamOptimizer(lr).minimize(loss)
saver=tf.train.Saver(tf.global_variables(),max_to_keep=4)#只保留最后4次的模型参数
# module_file = tf.train.latest_checkpoint(os.getcwd()+'\\data\\module')
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# saver.restore(sess, module_file)
#重复训练10000次
for i in range(1001):
for step in range(len(batch_index)-1):
_,loss_=sess.run([train_op,loss],feed_dict={X:train_x[batch_index[step]:batch_index[step+1]],Y:train_y[batch_index[step]:batch_index[step+1]]})
print(i,loss_)
if i % 10==0:
print("保存模型：",saver.save(sess,os.getcwd()+'\\data\\module\\stock2.mode2',global_step=i))
#————————————————LSTM模型预测————————————————————
def prediction(time_step=15):
X=tf.placeholder(tf.float32, shape=[None,time_step,input_size])#创建输入流图
Y=tf.placeholder(tf.float32, shape=[None,time_step,output_size])#创建输出流图
mean,std,test_x,test_y=get_test_data(time_step)
pred,_=lstm(X)
saver=tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
###参数恢复，调用已经训练好的模型###
module_file = tf.train.latest_checkpoint(os.getcwd()+'\\data\\module')
saver.restore(sess, module_file)
test_predict=[]
for step in range(len(test_x)-1):
prob=sess.run(pred,feed_dict={X:[test_x[step]]})
predict=prob.reshape(-1,1)
test_predict.extend(predict)
###循环输出其中一个预测变量###
predict_test=[]
for i in range(len(test_predict)):
if i%2==0:
predict_test.append(test_predict[i])
###循环输出测试原始数据###
y_test=[]
test_y=np.array(test_y).reshape(-1,1)
for i in range(len(test_y)):
if i%2==0:
y_test.append(test_y[i])
###数据反归一化###
test_y=np.array(y_test)*std[6]+mean[6]
test_predict=np.array(predict_test)*std[6]+mean[6]
acc=np.average(np.abs(test_predict-test_y[:len(test_predict)])/test_y[:len(test_predict)]) #偏差
print(acc)
#以折线图表示结果
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()
train_lstm()
prediction()
- 1
- 2
前往页