keras实现简单lstm_四十二.长短期记忆网络(LSTM)过程和keras实现股票预测

一.概述

传统循环网络RNN可以通过记忆体实现短期记忆进行连续数据的预测，但是，当连续数据的序列边长时，会使展开时间步过长，在反向传播更新参数的过程中，梯度要按时间步连续相乘，会导致梯度消失或者梯度爆炸。

LSTM是RNN的变体，通过门结构，有效的解决了梯度爆炸或者梯度消失问题。

LSTM在RNN的基础上引入了三个门结构和记录长期记忆的细胞态以及归纳出新知识的候选态。

二.LSTM结构

1.短期记忆

短期记忆即为RNN中的记忆体，在LSTM中，它的通过输出门

和经过tanh函数的长期记忆的哈达玛积得到：

2.细胞态(长期记忆)

长期记忆记录了当前时刻的历史信息：

其中，

为上一时刻的长期记忆，

为遗忘门，

为输入门，

为候选状态，表示在本时间段归纳出的新知识：

3.输入门、遗忘门、输出门

它们三个都是当前时刻的输入特征

和上个时刻的短期记忆

的函数。

遗忘门通过sigmod函数，将上一层隐藏状态

和本层输入

映射到[0,1]，表示上一层的内部状态

需要遗忘多少信息，公式为下：

输入门

控制当前候选状态

有多少信息需要保存。

输出门

控制当前时刻的内部状态

有多少信息传递给隐藏信息

。

三.LSTM过程

1.先利用上一时刻的隐藏状态

和当前输入计算出三个门和候选状态：

2.结合遗忘门

和输入门

更新长期记忆：

3.结合输出门和内部状态更新隐藏状态：

4.反向传播，利用梯度下降等优化方法更新参数矩阵和偏置。

四.keras+LSTM实现股票预测

导入依赖包

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from tensorflow.keras.layers import Dense,Dropout,LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_absolute_error,mean_squared_error

读取数据

maotai = pd.read_csv('./SH600519.csv')
training_set = maotai.iloc[0:2126,2:3].values
test_set = maotai.iloc[2126:,2:3].values
print(training_set.shape,test_set.shape)
输出：
(2126, 1) (300, 1)

归一化

sc = MinMaxScaler(feature_range=(0,1))
training_set = sc.fit_transform(training_set)
test_set = sc.fit_transform(test_set)

划分训练数据和测试数据

x_train,y_train,x_test,y_test=[],[],[],[]
for i in range(60,len(training_set)):x_train.append(training_set[i-60:i,0])y_train.append(training_set[i,0])
np.random.seed(7)
np.random.shuffle(x_train)
np.random.seed(7)
np.random.shuffle(y_train)
tf.random.set_seed(7)
x_train,y_train = np.array(x_train),np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], 60, 1))
for i in range(60, len(test_set)):x_test.append(test_set[i - 60:i, 0])y_test.append(test_set[i, 0])
x_test, y_test = np.array(x_test), np.array(y_test)
x_test = np.reshape(x_test, (x_test.shape[0], 60, 1))

搭建网络

model = tf.keras.Sequential([LSTM(80,return_sequences=True),Dropout(0.2),LSTM(100),Dropout(0.2),Dense(1)
])

配置网络

model.compile(optimizer=tf.keras.optimizers.Adam(0.001),loss='mean_squared_error')

开始训练

history = model.fit(x_train, y_train, batch_size=64, epochs=50, validation_data=(x_test, y_test), validation_freq=1)

训练过程

Epoch 1/50
33/33 [==============================] - 4s 114ms/step - loss: 0.0135 - val_loss: 0.0110
Epoch 2/50
33/33 [==============================] - 3s 95ms/step - loss: 0.0013 - val_loss: 0.0049
Epoch 3/50
33/33 [==============================] - 3s 99ms/step - loss: 0.0011 - val_loss: 0.0051
Epoch 4/50
33/33 [==============================] - 3s 98ms/step - loss: 0.0013 - val_loss: 0.0057
Epoch 5/50
33/33 [==============================] - 3s 95ms/step - loss: 0.0011 - val_loss: 0.0047
Epoch 6/50
33/33 [==============================] - 3s 99ms/step - loss: 0.0011 - val_loss: 0.0046
Epoch 7/50
33/33 [==============================] - 3s 92ms/step - loss: 0.0011 - val_loss: 0.0046
Epoch 8/50
33/33 [==============================] - 3s 86ms/step - loss: 0.0010 - val_loss: 0.0049
Epoch 9/50
33/33 [==============================] - 3s 84ms/step - loss: 0.0010 - val_loss: 0.0051
Epoch 10/50
33/33 [==============================] - 3s 86ms/step - loss: 0.0010 - val_loss: 0.0051
Epoch 11/50
33/33 [==============================] - 3s 86ms/step - loss: 9.7592e-04 - val_loss: 0.0044
Epoch 12/50
33/33 [==============================] - 3s 87ms/step - loss: 9.6163e-04 - val_loss: 0.0043
Epoch 13/50
33/33 [==============================] - 3s 88ms/step - loss: 0.0011 - val_loss: 0.0041
Epoch 14/50
33/33 [==============================] - 3s 89ms/step - loss: 9.1143e-04 - val_loss: 0.0042
Epoch 15/50
33/33 [==============================] - 3s 89ms/step - loss: 0.0011 - val_loss: 0.0046
Epoch 16/50
33/33 [==============================] - 3s 89ms/step - loss: 8.8493e-04 - val_loss: 0.0040
Epoch 17/50
33/33 [==============================] - 3s 90ms/step - loss: 9.2448e-04 - val_loss: 0.0042
Epoch 18/50
33/33 [==============================] - 3s 91ms/step - loss: 8.7795e-04 - val_loss: 0.0038
Epoch 19/50
33/33 [==============================] - 3s 91ms/step - loss: 7.1217e-04 - val_loss: 0.0045
Epoch 20/50
33/33 [==============================] - 3s 91ms/step - loss: 0.0012 - val_loss: 0.0038
Epoch 21/50
33/33 [==============================] - 3s 93ms/step - loss: 8.5274e-04 - val_loss: 0.0037
Epoch 22/50
33/33 [==============================] - 3s 92ms/step - loss: 9.9773e-04 - val_loss: 0.0052
Epoch 23/50
33/33 [==============================] - 3s 93ms/step - loss: 9.0810e-04 - val_loss: 0.0046
Epoch 24/50
33/33 [==============================] - 3s 93ms/step - loss: 8.4353e-04 - val_loss: 0.0041
Epoch 25/50
33/33 [==============================] - 3s 95ms/step - loss: 8.7846e-04 - val_loss: 0.0037
Epoch 26/50
33/33 [==============================] - 3s 94ms/step - loss: 7.2408e-04 - val_loss: 0.0035
Epoch 27/50
33/33 [==============================] - 3s 95ms/step - loss: 7.8355e-04 - val_loss: 0.0059
Epoch 28/50
33/33 [==============================] - 3s 96ms/step - loss: 8.1942e-04 - val_loss: 0.0035
Epoch 29/50
33/33 [==============================] - 3s 96ms/step - loss: 7.7674e-04 - val_loss: 0.0033
Epoch 30/50
33/33 [==============================] - 3s 95ms/step - loss: 7.3867e-04 - val_loss: 0.0037
Epoch 31/50
33/33 [==============================] - 3s 97ms/step - loss: 7.2609e-04 - val_loss: 0.0033
Epoch 32/50
33/33 [==============================] - 3s 96ms/step - loss: 6.9374e-04 - val_loss: 0.0033
Epoch 33/50
33/33 [==============================] - 3s 96ms/step - loss: 6.3776e-04 - val_loss: 0.0050
Epoch 34/50
33/33 [==============================] - 3s 97ms/step - loss: 7.6443e-04 - val_loss: 0.0036
Epoch 35/50
33/33 [==============================] - 3s 98ms/step - loss: 7.9301e-04 - val_loss: 0.0032
Epoch 36/50
33/33 [==============================] - 3s 97ms/step - loss: 7.7646e-04 - val_loss: 0.0036
Epoch 37/50
33/33 [==============================] - 3s 99ms/step - loss: 8.3467e-04 - val_loss: 0.0033
Epoch 38/50
33/33 [==============================] - 3s 99ms/step - loss: 7.6392e-04 - val_loss: 0.0032
Epoch 39/50
33/33 [==============================] - 3s 99ms/step - loss: 6.3954e-04 - val_loss: 0.0047
Epoch 40/50
33/33 [==============================] - 3s 99ms/step - loss: 7.3498e-04 - val_loss: 0.0034
Epoch 41/50
33/33 [==============================] - 3s 99ms/step - loss: 5.8371e-04 - val_loss: 0.0031
Epoch 42/50
33/33 [==============================] - 3s 99ms/step - loss: 5.7156e-04 - val_loss: 0.0034
Epoch 43/50
33/33 [==============================] - 3s 100ms/step - loss: 6.2417e-04 - val_loss: 0.0030
Epoch 44/50
33/33 [==============================] - 3s 101ms/step - loss: 6.8761e-04 - val_loss: 0.0035
Epoch 45/50
33/33 [==============================] - 4s 108ms/step - loss: 6.7483e-04 - val_loss: 0.0031
Epoch 46/50
33/33 [==============================] - 4s 113ms/step - loss: 6.2236e-04 - val_loss: 0.0031
Epoch 47/50
33/33 [==============================] - 4s 115ms/step - loss: 6.4746e-04 - val_loss: 0.0034
Epoch 48/50
33/33 [==============================] - 4s 112ms/step - loss: 7.4622e-04 - val_loss: 0.0029
Epoch 49/50
33/33 [==============================] - 3s 101ms/step - loss: 6.8864e-04 - val_loss: 0.0028
Epoch 50/50
33/33 [==============================] - 3s 101ms/step - loss: 5.6762e-04 - val_loss: 0.0028

loss曲线

loss =  history.history['loss']
val_loss = history.history['val_loss']
plt.plot(loss,label='Training Loss')
plt.plot(val_loss,label='Validation Loss')
plt.legend()
plt.title('Loss')
plt.show()

预测结果与真实值比较

predict_price = model.predict(x_test)
predict_price = sc.inverse_transform(predict_price)
real_price = sc.inverse_transform(test_set[60:])
plt.plot(real_price, color='red', label='MaoTai Stock Price')
plt.plot(predict_price, color='blue', label='Predicted MaoTai Stock Price')
plt.title('MaoTai Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('MaoTai Stock Price')
plt.legend()
plt.show()

查看评价指标(均方误差和均方根差)

mse=mean_squared_error(predict_price,real_price)
mae = mean_absolute_error(predict_price,real_price)
print('mean_squared_error',mse)
print('mean_absolute_error',mae)
输出：
mean_squared_error 922.6493975725148
mean_absolute_error 23.789508666992194