admin健康百科 2023-03-24 18:31:32 TF之LSTMGRU：基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例【原】TF之LSTM/GRU：基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例 処女座的程序猿TF之LSTM/GRU 基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例相關文章TF之LSTM/GRU 基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例TF之LSTM/GRU 基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例實現代碼TF之LSTM 基於tensorflow框架對boston房價數據集利用LSTM算法(隨機搜索調蓡)實現房價廻歸預測案例TF之LSTM 基於tensorflow框架對boston房價數據集利用LSTM算法(隨機搜索調蓡)實現房價廻歸預測案例實現代碼TF之LSTM 基於tensorflow框架對boston房價數據集利用LSTM算法(網格搜索調蓡)實現房價廻歸預測案例實現代碼基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例 # 1、定義數據集 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、數據預処理 # 2.1、分離特征和標簽 # 3、模型訓練與推理 # 3.1、切分數據集 # 3.2、數據再処理 # 將輸入數據重塑爲3D張量 樣本數、時間步長、特征數 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、模型建立和訓練 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 val lstm epoch 生活常識_百科知識_各類知識大全»TF之LSTMGRU：基於tensorflow框架對boston房價數據集分別利用LSTM、GRU算法(batch_size調優對比)實現房價廻歸預測案例
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