Keras入门——长短期记忆网络LSTM

Posted 2022-04-14 ratels

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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：

技术图片

   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