## 导入所需的包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
tf.reset_default_graph()
plt.rcParams['font.sans-serif'] = 'SimHei' ##设置字体为SimHei显示中文
plt.rcParams['axes.unicode_minus'] = False ##设置正常显示符号
## 导入所需数据
df = pd.read_csv('美元-人民币.csv',encoding='gbk',engine='python')
df['时间'] = pd.to_datetime(df['时间'],format='%Y/%m/%d')
df = df.sort_values(by='时间')
df.head()
## 用折线图展示数据
plt.figure(figsize=(12,8))
plt.title('1999年1月1日到8月21日最高价数据曲线')
plt.plot(df['时间'],df['高'])
plt.show()
### 提取测试数据
data = df.loc[:,['时间','高']]
## 标准化数据
data['高'] = (data['高']-np.mean(data['高']))/np.std(data['高'])
data['高(预)'] = data['高'].shift(-1)
data = data.iloc[:data.shape[0]-1]
data.columns = ['time','x','y']
data.head()
#获取最高价序列
data=np.array(df['高'])
normalize_data=(data-np.mean(data))/np.std(data) #标准化
normalize_data=normalize_data[:,np.newaxis] #增加维度
#———————————————形成训练集——————————————————
#设置常量
time_step=20 #时间步
rnn_unit=10 #hidden layer units
batch_size=60 #每一批次训练多少个样例
input_size=1 #输入层维度
output_size=1 #输出层维度
lr=0.0006 #学习率
train_x,train_y=[],[] #训练集
for i in range(len(normalize_data)-time_step-1):
x=normalize_data[i:i+time_step]
y=normalize_data[i+1:i+time_step+1]
train_x.append(x.tolist())
train_y.append(y.tolist())
test_x = train_x[len(train_x)-31:len(train_x)-1]
test_y = train_y[len(train_y)-31:len(train_y)-1]
X=tf.placeholder(tf.float32, [None,time_step,input_size]) #每批次输入网络的tensor
Y=tf.placeholder(tf.float32, [None,time_step,output_size]) #每批次tensor对应的标签
#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,1]))
}
biases={
'in':tf.Variable(tf.constant(0.1,shape=[rnn_unit,])),
'out':tf.Variable(tf.constant(0.1,shape=[1,]))
}
def lstm(batch): #参数:输入网络批次数目
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
cell=tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
init_state=cell.zero_state(batch,dtype=tf.float32)
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
def train_lstm():
global batch_size
pred,_=lstm(batch_size)
#损失函数
loss=tf.reduce_mean(tf.square(tf.reshape(pred,[-1])-tf.reshape(Y, [-1])))
train_op=tf.train.AdamOptimizer(lr).minimize(loss)
saver=tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#重复训练100次
for i in range(100):
step=0
start=0
end=start+batch_size
while(end
start+=batch_size
end=start+batch_size
#每10步保存一次参数
if step%10==0:
print(i,step,loss_)
print("保存模型:",saver.save(sess,'.\stock.model'))
step+=1
def prediction():
pred,_=lstm(1) #预测时只输入[1,time_step,input_size]的测试数据
saver=tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
#参数恢复
module_file = tf.train.latest_checkpoint('./')
saver.restore(sess, module_file)
#取训练集最后一行为测试样本。shape=[1,time_step,input_size]
prev_seq=train_x[-31]
predict=[]
#得到之后100个预测结果
for i in range(100):
next_seq=sess.run(pred,feed_dict={X:[prev_seq]})
predict.append(next_seq[-1])
#每次得到最后一个时间步的预测结果,与之前的数据加在一起,形成新的测试样本
prev_seq=np.vstack((prev_seq[1:],next_seq[-1]))
#以折线图表示结果
plt.figure()
plt.plot(list(range(len(normalize_data))), normalize_data, color='b')
plt.plot(list(range(len(normalize_data), len(normalize_data) + len(predict))), predict, color='r')
plt.show()
with tf.variable_scope('train'):
train_lstm()
with tf.variable_scope('train',reuse=True):
prediction()