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首页Python进行GRU和LSTM
加载包 import numpy as np import pandas as pd import math #Sequential多个网络层的线性堆叠;Dense隐含层 from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import SimpleRNN from keras.layers import GRU from sklearn.preprocessing import MinMaxScaler from s
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Python进行进行GRU和和LSTM
加载包加载包
import numpy as np
import pandas as pd
import math
#Sequential多个网络层的线性堆叠;Dense隐含层
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import SimpleRNN
from keras.layers import GRU
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error
数据定义数据定义
def create_dataset(dataset, look_back):
dataX, dataY = [], [] for i in range(len(dataset)-look_back):
a = dataset[i:(i+look_back), 0] #append向列表的尾部增加元素
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
数据分割数据分割
# fix random seed for reproducibility
#seed用于指定随机数生成时所用算法开始的整数值
np.random.seed(7)
# load the dataset/下载数据集
#usecols获取数据的列,如果取前4列,则usecols=(0,1,2,3)
#print(XXXX)
dataframe = pd.read_csv(r'333.csv' \
, usecols=[0], engine='python')
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset/标准化数据集
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets/分割训练集与测试集
#train_size = int(len(dataset) * 0.84)
vsize = 200;
train_size = 800;
#print(train_size )
# test_size = len(dataset) - train_size
look_back =10
train, test = dataset[0:train_size-1,:], dataset[train_size-look_back-1:train_size+vsize-1,:] # dataset detail/具体分割后数据集
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
#predX, predY = create_dataset(pred, look_back)
# reshape input to be [samples, feature_num, features]
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
建模预测建模预测
#predX = np.reshape(predX,(predX.shape[0], 1, predX.shape[1]))
#print(trainX ))
# start_cr_a_fit_net = time.time()
# create and fit the LSTM network/创建并拟合LSTM网络
model = Sequential()
model.add(GRU(15, input_shape=(None,look_back)))
#model.add(SimpleRNN(18, input_shape=(None,look_back)))
#model.add(GRU(XXXX[1], input_shape=(None,look_back)))
model.add(Dense(1))
model.summary()
#model.compile(loss='mean_squared_error', optimizer='adam')
model.compile(loss='mean_squared_error', optimizer='sgd')
model.fit(trainX, trainY, epochs=331, batch_size=1, verbose=0)
# end_cr_a_fit_net = time.time()-start_cr_a_fit_net
# print('Running time of creating and fitting the LSTM network: %.2f Seconds' % (end_cr_a_fit_net))
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