keras 多层lstm_keras搭建多层LSTM时间序列预测模型

######################导入库##########################

import os

os.environ['KERAS_BACKEND']='tensorflow'

import keras

import tensorflow as tf

from keras import optimizers#优化器

from keras.models import Sequential, load_model

from keras.layers import Dense, LSTM

from keras.layers import Dropout

1层LSTM

def lstm_single(x,y,hidden_cell,epoch,batch_size,verbose):

#verbose=0不显示训练进度，verbose=2显示训练进度和每个epoch所需时间

model = Sequential()

model.add(LSTM(hidden_cell, input_shape=(x.shape[1],1)))

model.add(Dense(1))

adam=optimizers.Adam(learning_rate=0.01)

pile(loss='mean_squared_error', optimizer=adam)

model.fit(x,y, epochs=epoch, batch_size=batch_size, verbose=verbose)

#loss=model.history.history['loss']#记录模型模型的损失函数值

return model

4层LSTM

def lstm_4layer(x,y,hidden_cell,epoch,batch_size,verbose):

model = Sequential()

model.add(LSTM(units=hidden_cell,return_sequences=True,input_shape=(x.shape[1], 1)))

model.add(Dropout(0.2))#可有可无，

model.add(LSTM(units=hidden_cell,return_sequences=True))

model.add(Dropout(0.2))

model.add(LSTM(units=hidden_cell,return_sequences=True))

model.add(Dropout(0.2))

model.add(LSTM(units=hidden_cell))

model.add(Dropout(0.2))

model.add(Dense(units=1))

adam=optimizers.Adam(learning_rate=0.01)

pile(loss='mean_squared_error', optimizer=adam)

#上面两行可以用下面这行代替，但是下面这行不能修改学习率

#pile(optimizer='adam',loss='mean_squared_error')

model.fit(x,y,epochs=epoch,batch_size=batch_size,verbose=verbose)

return model

参数初始化

'''变量初始化'''

import numpy as np

x_train=np.random.random(size=(300,5,1))

y_train=np.random.random(300)

x_test=np.random.random(size=(100,5,1))

y_test=np.random.random(100)

hidden_cell,epoch,batch_size,verbose=5,100,1,0

#hidden_cell是隐层节点数，epoch是最大训练次数，batch_size我说不清，只知batch_size越大训练越快，但是精度下降，verbose=0不会显示训练过程，=1或者=2会显示训练过程batch_size和verbose有默认值

模型训练

x=x_train;y=y_train

hidden_cell,epoch,batch_size,verbose=5,100,1,0

model=lstm_4layer((x,y,hidden_cell,epoch,batch_size,verbose))

绘制模型训练过程损失值变化情况

plt.plot(model.history.history['loss'])

plt.xlabel('epochs')

plt.ylabel('loss')

使用训练好的模型预测

ypre=model.predict(x_test,y_test)

标签：hidden,verbose,keras,batch,多层,add,LSTM,model,size

来源： /HYQ666888999/article/details/111721386