14. 使用tensorflow2中keras框架搭建LSTM神经网络模型进行时间序列预测

使用tensorflow2中keras框架搭建LSTM神经网络模型进行时间序列预测

Step1：导入包
Step2：设置GPU的使用率可按需增长
Step3：读取数据
Step4：设置参数
Step5：构建数据集

切分数据
创建数据集

Step6：模型构建及训练

构建LSMT神经网络模型
训练模型并保存
画图查看模型学习情况

Step7：模型检验

训练检验
画图检验

Step8：进行预测

设置训练集
进行预测
画图展示

完整代码

Step1：导入包

import numpy as np
import  pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import os
import tensorflow as tf
from tensorflow import keras

Step2：设置GPU的使用率可按需增长

# set GPU
tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
print(len(gpus))
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(logical_gpus))

Step3：读取数据

# read data-sensor.csv
dataframe = pd.read_csv('data-sensor.csv')
pd_value = dataframe.values

Step4：设置参数

look_back = 4
features = 28

look_back：一匹数据所含的数据个数
features：每个数据所拥有的特征数

Step5：构建数据集

切分数据

# ========= split dataset ===================
train_size = int(len(pd_value) * 0.8)
trainlist = pd_value[:train_size]
testlist = pd_value[train_size:]

创建数据集

# ========= numpy train ===========
def create_dataset(dataset, look_back):
#这里的look_back与timestep相同
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back)]
        dataX.append(a)
        dataY.append(dataset[i + look_back])
    return numpy.array(dataX),numpy.array(dataY)
#训练数据太少 look_back并不能过大
trainX,trainY  = create_dataset(trainlist,look_back)
testX,testY = create_dataset(testlist,look_back)

# ========== set dataset ======================
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], features))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1] , features))

Step6：模型构建及训练

构建LSMT神经网络模型

# create and fit the LSTM network
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(64, activation='relu', return_sequences=True, input_shape=(look_back, features)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(32, activation='relu'))
model.add(tf.keras.layers.Dense(features))
model.compile(metrics=['accuracy'], loss='mean_squared_error', optimizer='adam')

model.summary()

训练模型并保存

history = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=15, verbose=1).history
model.save("lstm-model.h5")

画图查看模型学习情况

plt.plot(history['loss'], linewidth=2, label='Train')
plt.plot(history['val_loss'], linewidth=2, label='Test')
plt.legend(loc='upper right')
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
#plt.ylim(ymin=0.70,ymax=1)
plt.show()

Step7：模型检验

训练检验

trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

画图检验

训练集情况

plt.plot(trainY[:100,1])
plt.plot(trainPredict[:100,1])
plt.show()

测试集情况

plt.plot(testY[:100,1])
plt.plot(testPredict[:100,1])
plt.plot()

Step8：进行预测

设置训练集

# set predict_data
predict_begin = 1
predict_num = 100
predict_result = np.zeros((predict_num+look_back,features),dtype=float)
for i in range(look_back):
    predict_result[i] = testX[-predict_begin:][0,i]

预测后100个数据情况，从最后一个数据开始进行时间序列预测。构建预测结果变量predict_result

进行预测

# predict
for i in range(predict_num):
    begin_data = np.reshape(predict_result[i:i+look_back,], (predict_begin, look_back, features))
    predict_data = model.predict(begin_data) 
    predict_result[look_back+i] = predict_data
    buff = predict_result[i+1:i+look_back]
    predict_call_back = np.append(buff,predict_data,axis=0)

构建滚动预测数据，每次取四行数据进行预测。
预测后，将原预测前的三行数据与预测后的结果进行拼接，组成四行数据进行下一轮的预测。

画图展示

# show plot
plt.plot(predict_result[-predict_num:,5])
plt.plot()

完整代码

import numpy as np
import  pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
import os
import tensorflow as tf
from tensorflow import keras

# set GPU
tf.debugging.set_log_device_placement(True)
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
print(len(gpus))
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(logical_gpus))

# read data-sensor.csv
dataframe = pd.read_csv('data-sensor.csv')
pd_value = dataframe.values

# ========= split dataset ===================
train_size = int(len(pd_value) * 0.8)
trainlist = pd_value[:train_size]
testlist = pd_value[train_size:]

look_back = 4
features = 28
step_out = 1

# ========= numpy train ===========
def create_dataset(dataset, look_back):
#这里的look_back与timestep相同
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back)]
        dataX.append(a)
        dataY.append(dataset[i + look_back])
    return numpy.array(dataX),numpy.array(dataY)
#训练数据太少 look_back并不能过大
trainX,trainY  = create_dataset(trainlist,look_back)
testX,testY = create_dataset(testlist,look_back)

# ========== set dataset ======================
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], features))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1] , features))

# create and fit the LSTM network
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(64, activation='relu', return_sequences=True, input_shape=(look_back, features)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.LSTM(32, activation='relu'))
model.add(tf.keras.layers.Dense(features))
#model.compile(optimizer='adam', loss='mse')
model.compile(metrics=['accuracy'], loss='mean_squared_error', optimizer='adam')

model.summary()

history = model.fit(trainX, trainY, validation_data=(testX, testY),epochs=15, verbose=1).history
model.save("lstm-model.h5")

plt.plot(history['loss'], linewidth=2, label='Train')
plt.plot(history['val_loss'], linewidth=2, label='Test')
plt.legend(loc='upper right')
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
#plt.ylim(ymin=0.70,ymax=1)
plt.show()

trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

plt.plot(trainY[:100,1])
plt.plot(trainPredict[:100,1])
plt.show()

plt.plot(testY[:100,1])
plt.plot(testPredict[:100,1])
plt.plot()

# set predict_data
predict_begin = 1
predict_num = 100
predict_result = np.zeros((predict_num+look_back,features),dtype=float)
for i in range(look_back):
    predict_result[i] = testX[-predict_begin:][0,i]

# predict
for i in range(predict_num):
    begin_data = np.reshape(predict_result[i:i+look_back,], (predict_begin, look_back, features))
    predict_data = model.predict(begin_data) 
    predict_result[look_back+i] = predict_data
    buff = predict_result[i+1:i+look_back]
    predict_call_back = np.append(buff,predict_data,axis=0)

# show plot
plt.plot(predict_result[-predict_num:,5])
plt.plot()