TF之LSTM/GRU：基于tensorflow框架对boston房价数据集分别利用LSTM、GRU算法(batch_size调优对比)实现房价回归预测案例
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基于tensorflow框架对boston房价数据集分别利用LSTM、GRU算法(batch_size调优对比)实现房价回归预测案例
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TF之LSTM/GRU：基于tensorflow框架对boston房价数据集分别利用LSTM、GRU算法(batch_size调优对比)实现房价回归预测案例
TF之LSTM/GRU：基于tensorflow框架对boston房价数据集分别利用LSTM、GRU算法(batch_size调优对比)实现房价回归预测案例实现代码
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基于tensorflow框架对boston房价数据集分别利用LSTM、GRU算法(batch_size调优对比)实现房价回归预测案例
# 1、定义数据集
CRIM ZN INDUS CHAS NOX ... TAX PTRATIO B LSTAT target
0 0.00632 18.0 2.31 0.0 0.538 ... 296.0 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0.0 0.469 ... 242.0 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0.0 0.469 ... 242.0 17.8 392.83 4.03 34.7
3 0.03237 0.0 2.18 0.0 0.458 ... 222.0 18.7 394.63 2.94 33.4
4 0.06905 0.0 2.18 0.0 0.458 ... 222.0 18.7 396.90 5.33 36.2
.. ... ... ... ... ... ... ... ... ... ... ...
501 0.06263 0.0 11.93 0.0 0.573 ... 273.0 21.0 391.99 9.67 22.4
502 0.04527 0.0 11.93 0.0 0.573 ... 273.0 21.0 396.90 9.08 20.6
503 0.06076 0.0 11.93 0.0 0.573 ... 273.0 21.0 396.90 5.64 23.9
504 0.10959 0.0 11.93 0.0 0.573 ... 273.0 21.0 393.45 6.48 22.0
505 0.04741 0.0 11.93 0.0 0.573 ... 273.0 21.0 396.90 7.88 11.9
[506 rows x 14 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 14 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 CRIM 506 non-null float64
1 ZN 506 non-null float64
2 INDUS 506 non-null float64
3 CHAS 506 non-null float64
4 NOX 506 non-null float64
5 RM 506 non-null float64
6 AGE 506 non-null float64
7 DIS 506 non-null float64
8 RAD 506 non-null float64
9 TAX 506 non-null float64
10 PTRATIO 506 non-null float64
11 B 506 non-null float64
12 LSTAT 506 non-null float64
13 target 506 non-null float64
dtypes: float64(14)
memory usage: 55.5 KB# 2、数据预处理
# 2.1、分离特征和标签
# 3、模型训练与推理
# 3.1、切分数据集
# 3.2、数据再处理
# 将输入数据重塑为3D张量（样本数、时间步长、特征数）
<class 'numpy.ndarray'> (455, 1, 13) X_train
[[[6.04700e-02 0.00000e+00 2.46000e+00 ... 1.78000e+01 3.87110e+02
1.31500e+01]]
[[6.29760e-01 0.00000e+00 8.14000e+00 ... 2.10000e+01 3.96900e+02
8.26000e+00]]
[[7.99248e+00 0.00000e+00 1.81000e+01 ... 2.02000e+01 3.96900e+02
2.45600e+01]]
...
[[3.51140e-01 0.00000e+00 7.38000e+00 ... 1.96000e+01 3.96900e+02
7.70000e+00]]
[[9.18702e+00 0.00000e+00 1.81000e+01 ... 2.02000e+01 3.96900e+02
2.36000e+01]]
[[4.55587e+00 0.00000e+00 1.81000e+01 ... 2.02000e+01 3.54700e+02
7.12000e+00]]]
# 3.3、模型建立和训练
Epoch 1/800
15/15 [==============================] - 2s 22ms/step - loss: 367.5489 - val_loss: 189.7880
Epoch 2/800
15/15 [==============================] - 0s 3ms/step - loss: 95.0314 - val_loss: 133.7617
Epoch 3/800
15/15 [==============================] - 0s 3ms/step - loss: 63.8908 - val_loss: 110.7545
Epoch 4/800
15/15 [==============================] - 0s 3ms/step - loss: 54.9615 - val_loss: 108.7314
Epoch 5/800
15/15 [==============================] - 0s 3ms/step - loss: 53.5053 - val_loss: 104.1971
Epoch 6/800
15/15 [==============================] - 0s 3ms/step - loss: 50.4742 - val_loss: 111.0977
Epoch 7/800
15/15 [==============================] - 0s 4ms/step - loss: 46.4744 - val_loss: 100.7286
Epoch 8/800
15/15 [==============================] - 0s 3ms/step - loss: 46.6553 - val_loss: 99.4326
Epoch 9/800
15/15 [==============================] - 0s 3ms/step - loss: 48.1464 - val_loss: 96.9524
Epoch 10/800
15/15 [==============================] - 0s 3ms/step - loss: 46.4484 - val_loss: 96.3056
Epoch 11/800
15/15 [==============================] - 0s 3ms/step - loss: 41.9167 - val_loss: 92.1237
Epoch 12/800
15/15 [==============================] - 0s 3ms/step - loss: 40.4515 - val_loss: 89.9320
Epoch 13/800
15/15 [==============================] - 0s 3ms/step - loss: 46.7765 - val_loss: 91.3324
Epoch 14/800
15/15 [==============================] - 0s 3ms/step - loss: 45.2451 - val_loss: 83.1068
Epoch 15/800
15/15 [==============================] - 0s 4ms/step - loss: 44.0281 - val_loss: 77.3420
Epoch 16/800
15/15 [==============================] - 0s 3ms/step - loss: 42.0810 - val_loss: 85.3165
Epoch 17/800
15/15 [==============================] - 0s 4ms/step - loss: 37.4590 - val_loss: 70.4207
……
Epoch 757/800
15/15 [==============================] - 0s 3ms/step - loss: 4.9589 - val_loss: 37.6601
Epoch 758/800
15/15 [==============================] - 0s 3ms/step - loss: 4.6070 - val_loss: 36.7595
Epoch 759/800
15/15 [==============================] - 0s 3ms/step - loss: 5.8827 - val_loss: 41.7672
Epoch 760/800
15/15 [==============================] - 0s 3ms/step - loss: 5.3787 - val_loss: 42.0669
Epoch 761/800
15/15 [==============================] - 0s 3ms/step - loss: 5.2201 - val_loss: 47.2067
Epoch 762/800
15/15 [==============================] - 0s 3ms/step - loss: 4.5653 - val_loss: 46.1523
Epoch 763/800
15/15 [==============================] - 0s 3ms/step - loss: 5.7319 - val_loss: 43.6643
Epoch 764/800
15/15 [==============================] - 0s 3ms/step - loss: 4.3259 - val_loss: 41.5630
Epoch 765/800
15/15 [==============================] - 0s 3ms/step - loss: 4.2562 - val_loss: 40.0810
Epoch 766/800
15/15 [==============================] - 0s 3ms/step - loss: 5.4430 - val_loss: 37.8770
Epoch 767/800
15/15 [==============================] - 0s 3ms/step - loss: 5.2480 - val_loss: 46.7311
Epoch 768/800
15/15 [==============================] - 0s 3ms/step - loss: 4.7596 - val_loss: 40.3361
Epoch 769/800
15/15 [==============================] - 0s 3ms/step - loss: 4.8908 - val_loss: 42.5085
Epoch 770/800
15/15 [==============================] - 0s 3ms/step - loss: 4.7232 - val_loss: 39.5460
Epoch 771/800
15/15 [==============================] - 0s 3ms/step - loss: 4.6950 - val_loss: 41.4992
Epoch 772/800
15/15 [==============================] - 0s 3ms/step - loss: 4.9918 - val_loss: 42.5983
Epoch 773/800
15/15 [==============================] - 0s 3ms/step - loss: 5.0848 - val_loss: 50.5700
Epoch 774/800
15/15 [==============================] - 0s 3ms/step - loss: 7.3065 - val_loss: 30.6110
Epoch 775/800
15/15 [==============================] - 0s 3ms/step - loss: 8.4268 - val_loss: 42.6159
Epoch 776/800
15/15 [==============================] - 0s 3ms/step - loss: 9.8120 - val_loss: 45.9334
Epoch 777/800
15/15 [==============================] - 0s 3ms/step - loss: 5.8191 - val_loss: 47.6144
Epoch 778/800
15/15 [==============================] - 0s 3ms/step - loss: 7.1561 - val_loss: 50.9774
Epoch 779/800
15/15 [==============================] - 0s 3ms/step - loss: 5.4377 - val_loss: 32.8492
Epoch 780/800
15/15 [==============================] - 0s 3ms/step - loss: 6.8226 - val_loss: 25.2842
Epoch 781/800
15/15 [==============================] - 0s 4ms/step - loss: 6.6671 - val_loss: 32.6189
Epoch 782/800
15/15 [==============================] - 0s 3ms/step - loss: 5.4168 - val_loss: 48.3716
Epoch 783/800
15/15 [==============================] - 0s 3ms/step - loss: 6.1797 - val_loss: 26.0899
Epoch 784/800
15/15 [==============================] - 0s 3ms/step - loss: 7.9516 - val_loss: 23.3273
Epoch 785/800
15/15 [==============================] - 0s 3ms/step - loss: 7.4502 - val_loss: 29.4346
Epoch 786/800
15/15 [==============================] - 0s 3ms/step - loss: 6.0257 - val_loss: 40.4242
Epoch 787/800
15/15 [==============================] - 0s 3ms/step - loss: 5.0951 - val_loss: 49.2547
Epoch 788/800
15/15 [==============================] - 0s 3ms/step - loss: 4.8296 - val_loss: 37.7677
Epoch 789/800
15/15 [==============================] - 0s 3ms/step - loss: 6.2584 - val_loss: 40.6493
Epoch 790/800
15/15 [==============================] - 0s 4ms/step - loss: 6.6211 - val_loss: 31.1713
Epoch 791/800
15/15 [==============================] - 0s 4ms/step - loss: 5.2383 - val_loss: 55.5448
Epoch 792/800
15/15 [==============================] - 0s 3ms/step - loss: 5.4096 - val_loss: 43.4175
Epoch 793/800
15/15 [==============================] - 0s 3ms/step - loss: 5.0846 - val_loss: 39.2757
Epoch 794/800
15/15 [==============================] - 0s 4ms/step - loss: 4.5122 - val_loss: 41.4339
Epoch 795/800
15/15 [==============================] - 0s 3ms/step - loss: 4.8337 - val_loss: 47.6556
Epoch 796/800
15/15 [==============================] - 0s 3ms/step - loss: 5.1507 - val_loss: 44.6231
Epoch 797/800
15/15 [==============================] - 0s 3ms/step - loss: 5.2973 - val_loss: 44.1294
Epoch 798/800
15/15 [==============================] - 0s 3ms/step - loss: 4.5970 - val_loss: 46.2830
Epoch 799/800
15/15 [==============================] - 0s 3ms/step - loss: 4.4145 - val_loss: 45.5006
Epoch 800/800
15/15 [==============================] - 0s 4ms/step - loss: 4.1411 - val_loss: 46.0986# 3.4、模型训练
测试集均方误差: 46.099
boston_val_MAE: 3.6606673203262625
boston_val_MSE： 46.098603197697834
boston_val_RMSE： 6.7895952160418105
boston_val_R2: 0.5967941831772676# 绘制训练集和测试集的loss曲线
# 3.5、模型评估
boston_LSTM_8_val_MAE: 3.642160939235313
boston_LSTM_8_val_MSE： 45.455696025386075
boston_LSTM_8_val_RMSE： 6.742083952709732
boston_LSTM_8_val_R2: 0.6024174319000424
model_name： boston_LSTM_8_0.6024
boston_GRU_8_val_MAE: 2.9867677613800647
boston_GRU_8_val_MSE： 27.82258275867772
boston_GRU_8_val_RMSE： 5.2747116280113095
boston_GRU_8_val_R2: 0.7566471339875444
model_name： boston_GRU_8_0.7566
boston_LSTM_16_val_MAE: 3.33460968615962
boston_LSTM_16_val_MSE： 31.442621036483846
boston_LSTM_16_val_RMSE： 5.607372025867719
boston_LSTM_16_val_R2: 0.7249841249268865
model_name： boston_LSTM_16_0.7250
boston_GRU_16_val_MAE: 3.244604744630701
boston_GRU_16_val_MSE： 31.916550943083372
boston_GRU_16_val_RMSE： 5.649473510255922
boston_GRU_16_val_R2: 0.7208388519283171
model_name： boston_GRU_16_0.7208
boston_LSTM_32_val_MAE: 3.289968232547536
boston_LSTM_32_val_MSE： 32.27946842476036
boston_LSTM_32_val_RMSE： 5.6815023035074415
boston_LSTM_32_val_R2: 0.7176645596615586
model_name： boston_LSTM_32_0.7177
boston_GRU_32_val_MAE: 3.6742190005732516
boston_GRU_32_val_MSE： 38.03228366757621
boston_GRU_32_val_RMSE： 6.167031998261093
boston_GRU_32_val_R2: 0.6673470140504223
model_name： boston_GRU_32_0.6673
boston_LSTM_64_val_MAE: 3.2653301463407627
boston_LSTM_64_val_MSE： 31.565284368449653
boston_LSTM_64_val_RMSE： 5.618299063635688
boston_LSTM_64_val_R2: 0.7239112384286261
model_name： boston_LSTM_64_0.7239
boston_GRU_64_val_MAE: 3.738212989358341
boston_GRU_64_val_MSE： 35.85519371644912
boston_GRU_64_val_RMSE： 5.987920650480358
boston_GRU_64_val_R2: 0.6863891383481191
model_name： boston_GRU_64_0.6864
# 绘制预测结果
