参考:

https://blog.csdn.net/u012735708/article/details/82769711

https://zybuluo.com/hanbingtao/note/581764

http://blog.sina.com.cn/s/blog_afc8730e0102xup1.html

https://blog.csdn.net/qq_30638831/article/details/80060045

执行代码:

import pandas as pd
from datetime import datetime
from matplotlib import pyplot
from sklearn.preprocessing import LabelEncoder,MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from numpy import concatenate
from math import sqrt

# load data
def parse(x):
    return datetime.strptime(x, '%Y %m %d %H')

def read_raw():
    dataset = pd.read_csv('C:/Users/cf_pc/Documents/jupyter/data/PRSA_data_2010.1.1-2014.12.31.csv',  parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
    dataset.drop('No', axis=1, inplace=True)
    # manually specify column names
    dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
    dataset.index.name = 'date'
    # mark all NA values with 0
    dataset['pollution'].fillna(0, inplace=True)
    # drop the first 24 hours
    dataset = dataset[24:]
    # summarize first 5 rows
    print(dataset.head(5))
    # save to file
    dataset.to_csv('C:/Users/cf_pc/Documents/jupyter/data/pollution.csv')

def drow_pollution():
    dataset = pd.read_csv('C:/Users/cf_pc/Documents/jupyter/data/pollution.csv', header=0, index_col=0)
    values = dataset.values
    # specify columns to plot
    groups = [0, 1, 2, 3, 5, 6, 7]
    i = 1
    # plot each column
    pyplot.figure(figsize=(10,10))
    for group in groups:
        pyplot.subplot(len(groups), 1, i)
        pyplot.plot(values[:, group])
        pyplot.title(dataset.columns[group], y=0.5, loc='right')
        i += 1
    pyplot.show()

def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
     # convert series to supervised learning
        n_vars = 1 if type(data) is list else data.shape[1]
        df = pd.DataFrame(data)
        cols, names = list(), list()
        # input sequence (t-n, ... t-1)
        for i in range(n_in, 0, -1):
            cols.append(df.shift(i))
            names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
        # forecast sequence (t, t+1, ... t+n)
        for i in range(0, n_out):
            cols.append(df.shift(-i))
            if i == 0:
                names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
            else:
                names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
        # put it all together
        agg = pd.concat(cols, axis=1)
        agg.columns = names
        # drop rows with NaN values
        if dropnan:
            agg.dropna(inplace=True)
        return agg

def cs_to_sl():
    # load dataset
    dataset = pd.read_csv('C:/Users/cf_pc/Documents/jupyter/data/pollution.csv', header=0, index_col=0)
    values = dataset.values
    # integer encode direction
    encoder = LabelEncoder()
    values[:,4] = encoder.fit_transform(values[:,4])
    # ensure all data is float
    values = values.astype('float32')
    # normalize features
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled = scaler.fit_transform(values)
    # frame as supervised learning
    reframed = series_to_supervised(scaled, 1, 1)
    # drop columns we don't want to predict
    reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
    print(reframed.head())
    return reframed,scaler

def train_test(reframed):
    # split into train and test sets
    values = reframed.values
    n_train_hours = 365 * 24
    train = values[:n_train_hours, :]
    test = values[n_train_hours:, :]
    # split into input and outputs
    train_X, train_y = train[:, :-1], train[:, -1]
    test_X, test_y = test[:, :-1], test[:, -1]
    # reshape input to be 3D [samples, timesteps, features]
    train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
    test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
    print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
    return train_X,train_y,test_X,test_y

def fit_network(train_X,train_y,test_X,test_y,scaler):
    model = Sequential()
    model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
    model.add(Dense(1))
    model.compile(loss='mae', optimizer='adam')
    # fit network
    history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
    # plot history
    pyplot.plot(history.history['loss'], label='train')
    pyplot.plot(history.history['val_loss'], label='test')
    pyplot.legend()
    pyplot.show()
    # make a prediction
    yhat = model.predict(test_X)
    test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
    # invert scaling for forecast
    inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
    inv_yhat = scaler.inverse_transform(inv_yhat)
    inv_yhat = inv_yhat[:,0]
    # invert scaling for actual
    inv_y = scaler.inverse_transform(test_X)
    inv_y = inv_y[:,0]
    # calculate RMSE
    rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
    print('Test RMSE: %.3f' % rmse)

if __name__ == '__main__':
    drow_pollution()
    reframed,scaler = cs_to_sl()
    train_X,train_y,test_X,test_y = train_test(reframed)
    fit_network(train_X,train_y,test_X,test_y,scaler)

返回信息:

   var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  var5(t-1)  var6(t-1)  \
1   0.129779   0.352941   0.245902   0.527273   0.666667   0.002290
2   0.148893   0.367647   0.245902   0.527273   0.666667   0.003811
3   0.159960   0.426471   0.229508   0.545454   0.666667   0.005332
4   0.182093   0.485294   0.229508   0.563637   0.666667   0.008391
5   0.138833   0.485294   0.229508   0.563637   0.666667   0.009912   

   var7(t-1)  var8(t-1)   var1(t)
1   0.000000        0.0  0.148893
2   0.000000        0.0  0.159960
3   0.000000        0.0  0.182093
4   0.037037        0.0  0.138833
5   0.074074        0.0  0.109658
(8760, 1, 8) (8760,) (35039, 1, 8) (35039,)
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From C:\3rd\Anaconda2\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 8760 samples, validate on 35039 samples
Epoch 1/50
 - 2s - loss: 0.0578 - val_loss: 0.0562
Epoch 2/50
 - 1s - loss: 0.0413 - val_loss: 0.0563
Epoch 3/50
 - 1s - loss: 0.0254 - val_loss: 0.0454
Epoch 4/50
 - 1s - loss: 0.0179 - val_loss: 0.0388
Epoch 5/50
 - 1s - loss: 0.0158 - val_loss: 0.0237
Epoch 6/50
 - 1s - loss: 0.0149 - val_loss: 0.0175
Epoch 7/50
 - 1s - loss: 0.0148 - val_loss: 0.0163
Epoch 8/50
 - 1s - loss: 0.0147 - val_loss: 0.0160
Epoch 9/50
 - 1s - loss: 0.0148 - val_loss: 0.0155
Epoch 10/50
 - 1s - loss: 0.0147 - val_loss: 0.0151
Epoch 11/50
 - 1s - loss: 0.0146 - val_loss: 0.0148
Epoch 12/50
 - 1s - loss: 0.0147 - val_loss: 0.0145
Epoch 13/50
 - 1s - loss: 0.0146 - val_loss: 0.0143
Epoch 14/50
 - 1s - loss: 0.0146 - val_loss: 0.0143
Epoch 15/50
 - 1s - loss: 0.0145 - val_loss: 0.0141
Epoch 16/50
 - 1s - loss: 0.0145 - val_loss: 0.0144
Epoch 17/50
 - 1s - loss: 0.0147 - val_loss: 0.0140
Epoch 18/50
 - 1s - loss: 0.0145 - val_loss: 0.0140
Epoch 19/50
 - 1s - loss: 0.0145 - val_loss: 0.0138
Epoch 20/50
 - 1s - loss: 0.0145 - val_loss: 0.0138
Epoch 21/50
 - 1s - loss: 0.0144 - val_loss: 0.0138
Epoch 22/50
 - 1s - loss: 0.0145 - val_loss: 0.0138
Epoch 23/50
 - 1s - loss: 0.0146 - val_loss: 0.0137
Epoch 24/50
 - 1s - loss: 0.0144 - val_loss: 0.0137
Epoch 25/50
 - 1s - loss: 0.0144 - val_loss: 0.0137
Epoch 26/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 27/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 28/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 29/50
 - 1s - loss: 0.0145 - val_loss: 0.0137
Epoch 30/50
 - 1s - loss: 0.0145 - val_loss: 0.0136
Epoch 31/50
 - 1s - loss: 0.0144 - val_loss: 0.0137
Epoch 32/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 33/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 34/50
 - 1s - loss: 0.0145 - val_loss: 0.0136
Epoch 35/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 36/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 37/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 38/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 39/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 40/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 41/50
 - 1s - loss: 0.0143 - val_loss: 0.0135
Epoch 42/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 43/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 44/50
 - 1s - loss: 0.0144 - val_loss: 0.0135
Epoch 45/50
 - 1s - loss: 0.0144 - val_loss: 0.0137
Epoch 46/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 47/50
 - 1s - loss: 0.0143 - val_loss: 0.0135
Epoch 48/50
 - 1s - loss: 0.0144 - val_loss: 0.0136
Epoch 49/50
 - 1s - loss: 0.0143 - val_loss: 0.0135
Epoch 50/50
 - 1s - loss: 0.0144 - val_loss: 0.0134

Test RMSE: 4.401

参考:

https://www.cnblogs.com/tianrunzhi/p/7825671.html

https://www.cnblogs.com/king-lps/p/7846414.html

https://www.cnblogs.com/datablog/p/6127000.html

https://www.cnblogs.com/charlotte77/p/5622325.html

https://www.cnblogs.com/bawu/p/7701810.html

Keras入门——(6)长短期记忆网络LSTM(三)的更多相关文章

  1. 如何预测股票分析--长短期记忆网络(LSTM)

    在上一篇中,我们回顾了先知的方法,但是在这个案例中表现也不是特别突出,今天介绍的是著名的l s t m算法,在时间序列中解决了传统r n n算法梯度消失问题的的它这一次还会有令人杰出的表现吗? 长短期 ...

  2. Keras入门——(7)长短期记忆网络LSTM(四)

    数据准备:http://www.manythings.org/anki/cmn-eng.zip 源代码:https://github.com/pjgao/seq2seq_keras 参考:https: ...

  3. Keras入门——(5)长短期记忆网络LSTM(二)

    参考: https://blog.csdn.net/zwqjoy/article/details/80493341 https://blog.csdn.net/u012735708/article/d ...

  4. Keras入门——(4)长短期记忆网络LSTM(一)

    参考: https://blog.csdn.net/zwqjoy/article/details/80493341 https://blog.csdn.net/u012735708/article/d ...

  5. LSTM - 长短期记忆网络

    循环神经网络(RNN) 人们不是每一秒都从头开始思考,就像你阅读本文时,不会从头去重新学习一个文字,人类的思维是有持续性的.传统的卷积神经网络没有记忆,不能解决这一个问题,循环神经网络(Recurre ...

  6. 递归神经网络之理解长短期记忆网络(LSTM NetWorks)(转载)

    递归神经网络 人类并不是每时每刻都从头开始思考.正如你阅读这篇文章的时候,你是在理解前面词语的基础上来理解每个词.你不会丢弃所有已知的信息而从头开始思考.你的思想具有持续性. 传统的神经网络不能做到这 ...

  7. 理解长短期记忆网络(LSTM NetWorks)

    转自:http://www.csdn.net/article/2015-11-25/2826323 原文链接:Understanding LSTM Networks(译者/刘翔宇 审校/赵屹华 责编/ ...

  8. LSTMs 长短期记忆网络系列

    RNN的长期依赖问题 什么是长期依赖? 长期依赖是指当前系统的状态,可能受很长时间之前系统状态的影响,是RNN中无法解决的一个问题. 如果从(1) “ 这块冰糖味道真?”来预测下一个词,是很容易得出“ ...

  9. LSTM(Long Short-Term Memory)长短期记忆网络

    1. 摘要 对于RNN解决了之前信息保存的问题,例如,对于阅读一篇文章,RNN网络可以借助前面提到的信息对当前的词进行判断和理解,这是传统的网络是不能做到的.但是,对于RNN网络存在长期依赖问题,比如 ...

随机推荐

  1. java基础数据类型和处理

    import com.alibaba.fastjson.JSONObject; import com.alibaba.fastjson.JSON; import java.io.*; import j ...

  2. 2.2.FastDFS-单机拆分版-存储器安装配置

    Centos610系列配置 我们在Centos610FastDFS单机模式-FastDFS安装 中已经完成了FastDFS的安装,接下来我们进行FastDFS存储器的安装. 1.找到FastDFS配置 ...

  3. DataGrid DataGridTextColumn 樣式

    <DataGridTextColumn.ElementStyle> <Style TargetType="TextBlock" > <Setter P ...

  4. set的使用-Hdu 2094

    产生冠军 Time Limit: 1000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)Total Submis ...

  5. 【SSM 验证码】登录验证码

    LoginController /** * 登陆方法 */ @ResponseBody @RequestMapping("login2") public Map<String ...

  6. mcast_block_source函数

    #include <errno.h> #include <sys/socket.h> #define SA struct sockaddr int mcast_block_so ...

  7. 「AT2381 [AGC015C] Nuske vs Phantom Thnook」

    题目大意 给出一个01矩阵,这个矩阵有一个特殊的性质: 对于任意两个 \(1\) 之间最多只有 \(1\) 条由 \(1\) 构成的路径.每次询问给出一个矩形范围,查询在这个范围内的联通快个数. 分析 ...

  8. Dart语言学习(十四) Dart泛型

    什么是泛型? 通俗理解:泛型就是解决 类 接口 方法的复用性.以及对不特定数据类型的支持(类型校验) 如下代码,只能返回string类型的数据 String getData(String value) ...

  9. Java通过反射实现实例化

    public static void main(String[] args) throws Exception { User user= (User) test(User.class); System ...

  10. DVWA靶机实战-文件上传漏洞(二)

    继续打靶机:当前靶机的安全级别:medium 第一步 上传一句话木马,这次没有之前那么顺利了,文件显示上传失败,被过滤. 点开右下角view source查看源码: 只允许上传image/jpeg格式 ...