RNN第二周-LSTM-火灾温度预测

一句话介绍LSTM，它是RNN的进阶版，如果说RNN的最大限度是理解一句话，那么LSTM的最大限度则是理解一段话

任务说明：数据集中提供了火灾温度（Tem1）、一氧化碳浓度（CO 1）、烟雾浓度（Soot 1）随着时间变化数据，我们需要根据这些数据对未来某一时刻的火灾温度做出预测

1、导入数据

采用了panda函数

panda常用函数链接：pandas常用函数 - 简书 (jianshu.com)

import tensorflow as tf
import pandas     as pd
import numpy      as np

gpus = tf.config.list_physical_devices("GPU")
if gpus:
    tf.config.experimental.set_memory_growth(gpus[0], True)  #设置GPU显存用量按需使用
    tf.config.set_visible_devices([gpus[0]],"GPU")
print(gpus)

df_1 = pd.read_csv("D:/DeepLearning/woodpine2.csv")

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

2、数据可视化

Seaborn是matplotlib库的扩展，主要专注于统计学的分析

import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['savefig.dpi'] = 500 #图片像素
plt.rcParams['figure.dpi']  = 500 #分辨率

fig, ax =plt.subplots(1,3,constrained_layout=True, figsize=(14, 3))

sns.lineplot(data=df_1["Tem1"], ax=ax[0])
sns.lineplot(data=df_1["CO 1"], ax=ax[1])
sns.lineplot(data=df_1["Soot 1"], ax=ax[2])
plt.show()

 3、构建数据集

dataFrame = df_1.iloc[:,1:]
dataFrame

	Tem1	CO 1	Soot 1
0	25.0	0.000000	0.000000
1	25.0	0.000000	0.000000
2	25.0	0.000000	0.000000
3	25.0	0.000000	0.000000
4	25.0	0.000000	0.000000
...	...	...	...
5943	295.0	0.000077	0.000496
5944	294.0	0.000077	0.000494
5945	292.0	0.000077	0.000491
5946	291.0	0.000076	0.000489
5947	290.0	0.000076	0.000487

5948 rows × 3 columns

设置X,Y

width_X = 8
width_y = 1

取前8个时间段的Tem1、CO 1、Soot 1为X，第9个时间段的Tem1为y。

X = []
y = []

in_start = 0

for _, _ in df_1.iterrows():
    in_end  = in_start + width_X
    out_end = in_end   + width_y
    
    if out_end < len(dataFrame):
        X_ = np.array(dataFrame.iloc[in_start:in_end , ])
        X_ = X_.reshape((len(X_)*3))
        y_ = np.array(dataFrame.iloc[in_end  :out_end, 0])

        X.append(X_)
        y.append(y_)
    
    in_start += 1

X = np.array(X)
y = np.array(y)

X.shape, y.shape

((5939, 24), (5939, 1))

归一化

from sklearn.preprocessing import MinMaxScaler

#将数据归一化，范围是0到1
sc       = MinMaxScaler(feature_range=(0, 1))
X_scaled = sc.fit_transform(X)
X_scaled.shape

(5939, 24)

X_scaled = X_scaled.reshape(len(X_scaled),width_X,3)
X_scaled.shape

(5939, 8, 3)

划分数据集

取5000之前的数据为训练集，5000之后的为验证集

X_train = np.array(X_scaled[:5000]).astype('float64')
y_train = np.array(y[:5000]).astype('float64')

X_test  = np.array(X_scaled[5000:]).astype('float64')
y_test  = np.array(y[5000:]).astype('float64')

X_train.shape

(5000, 8, 3)

4、构建模型

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,LSTM,Bidirectional
from tensorflow.keras        import Input

# 多层 LSTM
model_lstm = Sequential()
model_lstm.add(LSTM(units=64, activation='relu', return_sequences=True,
               input_shape=(X_train.shape[1], 3)))
model_lstm.add(LSTM(units=64, activation='relu'))

model_lstm.add(Dense(width_y))

WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.

5、模型训练与编译

 只观测loss数值，不观测准确率，所以删去metrics选项
model_lstm.compile(optimizer=tf.keras.optimizers.Adam(1e-3),
                   loss='mean_squared_error')  # 损失函数用均方误差

X_train.shape, y_train.shape

((5000, 8, 3), (5000, 1))

history_lstm = model_lstm.fit(X_train, y_train, 
                         batch_size=64, 
                         epochs=40, 
                         validation_data=(X_test, y_test),
                         validation_freq=1)

Epoch 1/40
79/79 [==============================] - 5s 23ms/step - loss: 10543.9570 - val_loss: 6043.4136
Epoch 2/40
79/79 [==============================] - 2s 21ms/step - loss: 129.8679 - val_loss: 709.7237
Epoch 3/40
79/79 [==============================] - 1s 19ms/step - loss: 13.4282 - val_loss: 282.2633
Epoch 4/40
79/79 [==============================] - 1s 19ms/step - loss: 8.2278 - val_loss: 254.7455
Epoch 5/40
79/79 [==============================] - 1s 19ms/step - loss: 8.1414 - val_loss: 235.5970
Epoch 6/40
79/79 [==============================] - 1s 19ms/step - loss: 8.5222 - val_loss: 192.8408
Epoch 7/40
79/79 [==============================] - 2s 21ms/step - loss: 8.5886 - val_loss: 192.3040
Epoch 8/40
79/79 [==============================] - 2s 20ms/step - loss: 7.9806 - val_loss: 273.1663
Epoch 9/40
79/79 [==============================] - 2s 19ms/step - loss: 7.7559 - val_loss: 210.4082
Epoch 10/40
79/79 [==============================] - 2s 19ms/step - loss: 7.7049 - val_loss: 219.2136
Epoch 11/40
79/79 [==============================] - 2s 20ms/step - loss: 8.3173 - val_loss: 178.7746
Epoch 12/40
79/79 [==============================] - 1s 19ms/step - loss: 7.5541 - val_loss: 244.2220
Epoch 13/40
79/79 [==============================] - 2s 19ms/step - loss: 7.7218 - val_loss: 216.0784
Epoch 14/40
79/79 [==============================] - 2s 20ms/step - loss: 7.5529 - val_loss: 208.1548
Epoch 15/40
79/79 [==============================] - 1s 19ms/step - loss: 8.5332 - val_loss: 202.2611
Epoch 16/40
79/79 [==============================] - 2s 19ms/step - loss: 7.2165 - val_loss: 200.9129
Epoch 17/40
79/79 [==============================] - 2s 20ms/step - loss: 7.6918 - val_loss: 164.5995
Epoch 18/40
79/79 [==============================] - 2s 19ms/step - loss: 8.3463 - val_loss: 191.1199
Epoch 19/40
79/79 [==============================] - 2s 20ms/step - loss: 7.1454 - val_loss: 352.0036
Epoch 20/40
79/79 [==============================] - 1s 19ms/step - loss: 8.5322 - val_loss: 182.7811
Epoch 21/40
79/79 [==============================] - 2s 19ms/step - loss: 7.5082 - val_loss: 157.3525
Epoch 22/40
79/79 [==============================] - 2s 19ms/step - loss: 7.6907 - val_loss: 212.4112
Epoch 23/40
79/79 [==============================] - 2s 19ms/step - loss: 7.2731 - val_loss: 237.3845
Epoch 24/40
79/79 [==============================] - 2s 20ms/step - loss: 8.4894 - val_loss: 129.4779
Epoch 25/40
79/79 [==============================] - 2s 19ms/step - loss: 8.6488 - val_loss: 135.0774
Epoch 26/40
79/79 [==============================] - 1s 19ms/step - loss: 8.7399 - val_loss: 199.4531
Epoch 27/40
79/79 [==============================] - 1s 18ms/step - loss: 7.4957 - val_loss: 196.7446
Epoch 28/40
79/79 [==============================] - 1s 18ms/step - loss: 6.7455 - val_loss: 136.7112
Epoch 29/40
79/79 [==============================] - 2s 22ms/step - loss: 6.8989 - val_loss: 273.9738
Epoch 30/40
79/79 [==============================] - 2s 20ms/step - loss: 10.6817 - val_loss: 197.4949
Epoch 31/40
79/79 [==============================] - 2s 20ms/step - loss: 6.5239 - val_loss: 317.7299
Epoch 32/40
79/79 [==============================] - 2s 20ms/step - loss: 8.6730 - val_loss: 136.9751
Epoch 33/40
79/79 [==============================] - 2s 19ms/step - loss: 6.9990 - val_loss: 156.7759
Epoch 34/40
79/79 [==============================] - 2s 19ms/step - loss: 6.9430 - val_loss: 157.6123
Epoch 35/40
79/79 [==============================] - 1s 19ms/step - loss: 6.8603 - val_loss: 94.2304
Epoch 36/40
79/79 [==============================] - 2s 19ms/step - loss: 8.4595 - val_loss: 149.4800
Epoch 37/40
79/79 [==============================] - 2s 19ms/step - loss: 7.1759 - val_loss: 171.2727
Epoch 38/40
79/79 [==============================] - 2s 20ms/step - loss: 8.0675 - val_loss: 99.9099
Epoch 39/40
79/79 [==============================] - 2s 19ms/step - loss: 6.3221 - val_loss: 83.2262
Epoch 40/40
79/79 [==============================] - 2s 19ms/step - loss: 6.7495 - val_loss: 133.3407

6、loss图

# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号

plt.figure(figsize=(5, 3),dpi=120)

plt.plot(history_lstm.history['loss']    , label='LSTM Training Loss')
plt.plot(history_lstm.history['val_loss'], label='LSTM Validation Loss')

plt.title('Training and Validation Loss')
plt.legend()
plt.show()

预测

predicted_y_lstm = model_lstm.predict(X_test)                        # 测试集输入模型进行预测

y_test_one = [i[0] for i in y_test]
predicted_y_lstm_one = [i[0] for i in predicted_y_lstm]

plt.figure(figsize=(5, 3),dpi=120)
# 画出真实数据和预测数据的对比曲线
plt.plot(y_test_one[:1000], color='red', label='真实值')
plt.plot(predicted_y_lstm_one[:1000], color='blue', label='预测值')

plt.title('Title')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
plt.show()

30/30 [==============================] - 0s 5ms/step

 

from sklearn import metrics
"""
RMSE ：均方根误差  ----->  对均方误差开方
R2   ：决定系数，可以简单理解为反映模型拟合优度的重要的统计量
"""
RMSE_lstm  = metrics.mean_squared_error(predicted_y_lstm, y_test)**0.5
R2_lstm    = metrics.r2_score(predicted_y_lstm, y_test)

print('均方根误差: %.5f' % RMSE_lstm)
print('R2: %.5f' % R2_lstm)

均方根误差: 11.54733
R2: 0.73235