DL之LSTM：基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(层加深，基于keras)对单个character字符预测

cqyd
cqyd 2022-09-19 11:43:36 49394
分类专栏： 资讯

DL之LSTM：基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(层加深，基于keras)对单个character字符预测

目录

基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(层加深，基于keras)对单个character字符预测

设计思路

输出结果

核心代码

基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(层加深，基于keras)对单个character字符预测

设计思路

数据集下载：https://download.csdn.net/download/qq_41185868/13767751

输出结果

  1. Using TensorFlow backend.
  2. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  3.   _np_qint8 = np.dtype([("qint8", np.int8, 1)])
  4. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  5.   _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
  6. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  7.   _np_qint16 = np.dtype([("qint16", np.int16, 1)])
  8. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  9.   _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
  10. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  11.   _np_qint32 = np.dtype([("qint32", np.int32, 1)])
  12. F:\Program Files\Python\Python36\lib\site-packages\tensorflow\python\framework\dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  13.   np_resource = np.dtype([("resource", np.ubyte, 1)])
  14. [nltk_data] Error loading punkt: <urlopen error [Errno 11004]
  15. [nltk_data]     getaddrinfo failed>
  16. raw_text[:10] : alice's ad
  17. Total Characters: 144413
  18. chars ['\n', ' ', '!', '"', "'", '(', ')', '*', ',', '-', '.', '0', '3', ':', ';', '?', '[', ']', '_', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
  19. Total Vocab: 45
  20. sentences 1625 ["alice's adventures in wonderland\n\nlewis carroll\n\nthe millennium fulcrum edition 3.0\n\nchapter i. down the rabbit-hole\n\nalice was beginning to get very tired of sitting by her sister on the\nbank, and of having nothing to do: once or twice she had peeped into the\nbook her sister was reading, but it had no pictures or conversations in\nit, 'and what is the use of a book,' thought alice 'without pictures or\nconversations?'", 'so she was considering in her own mind (as well as she could, for the\nhot day made her feel very sleepy and stupid), whether the pleasure\nof making a daisy-chain would be worth the trouble of getting up and\npicking the daisies, when suddenly a white rabbit with pink eyes ran\nclose by her.', "there was nothing so very remarkable in that; nor did alice think it so\nvery much out of the way to hear the rabbit say to itself, 'oh dear!", 'oh dear!', "i shall be late!'"]
  21. lengths (1625,) [420 289 140 ... 636 553   7]
  22. CharMapInt_dict 45 {'\n': 0, ' ': 1, '!': 2, '"': 3, "'": 4, '(': 5, ')': 6, '*': 7, ',': 8, '-': 9, '.': 10, '0': 11, '3': 12, ':': 13, ';': 14, '?': 15, '[': 16, ']': 17, '_': 18, 'a': 19, 'b': 20, 'c': 21, 'd': 22, 'e': 23, 'f': 24, 'g': 25, 'h': 26, 'i': 27, 'j': 28, 'k': 29, 'l': 30, 'm': 31, 'n': 32, 'o': 33, 'p': 34, 'q': 35, 'r': 36, 's': 37, 't': 38, 'u': 39, 'v': 40, 'w': 41, 'x': 42, 'y': 43, 'z': 44}
  23. IntMapChar_dict 45 {0: '\n', 1: ' ', 2: '!', 3: '"', 4: "'", 5: '(', 6: ')', 7: '*', 8: ',', 9: '-', 10: '.', 11: '0', 12: '3', 13: ':', 14: ';', 15: '?', 16: '[', 17: ']', 18: '_', 19: 'a', 20: 'b', 21: 'c', 22: 'd', 23: 'e', 24: 'f', 25: 'g', 26: 'h', 27: 'i', 28: 'j', 29: 'k', 30: 'l', 31: 'm', 32: 'n', 33: 'o', 34: 'p', 35: 'q', 36: 'r', 37: 's', 38: 't', 39: 'u', 40: 'v', 41: 'w', 42: 'x', 43: 'y', 44: 'z'}
  24. dataX： 144313 100 [[19, 30, 27, 21, 23, 4, 37, 1, 19, 22, 40, 23, 32, 38, 39, 36, 23, 37, 1, 27, 32, 1, 41, 33, 32, 22, 23, 36, 30, 19, 32, 22, 0, 0, 30, 23, 41, 27, 37, 1, 21, 19, 36, 36, 33, 30, 30, 0, 0, 38, 26, 23, 1, 31, 27, 30, 30, 23, 32, 32, 27, 39, 31, 1, 24, 39, 30, 21, 36, 39, 31, 1, 23, 22, 27, 38, 27, 33, 32, 1, 12, 10, 11, 0, 0, 21, 26, 19, 34, 38, 23, 36, 1, 27, 10, 1, 22, 33, 41, 32], [30, 27, 21, 23, 4, 37, 1, 19, 22, 40, 23, 32, 38, 39, 36, 23, 37, 1, 27, 32, 1, 41, 33, 32, 22, 23, 36, 30, 19, 32, 22, 0, 0, 30, 23, 41, 27, 37, 1, 21, 19, 36, 36, 33, 30, 30, 0, 0, 38, 26, 23, 1, 31, 27, 30, 30, 23, 32, 32, 27, 39, 31, 1, 24, 39, 30, 21, 36, 39, 31, 1, 23, 22, 27, 38, 27, 33, 32, 1, 12, 10, 11, 0, 0, 21, 26, 19, 34, 38, 23, 36, 1, 27, 10, 1, 22, 33, 41, 32, 1], [27, 21, 23, 4, 37, 1, 19, 22, 40, 23, 32, 38, 39, 36, 23, 37, 1, 27, 32, 1, 41, 33, 32, 22, 23, 36, 30, 19, 32, 22, 0, 0, 30, 23, 41, 27, 37, 1, 21, 19, 36, 36, 33, 30, 30, 0, 0, 38, 26, 23, 1, 31, 27, 30, 30, 23, 32, 32, 27, 39, 31, 1, 24, 39, 30, 21, 36, 39, 31, 1, 23, 22, 27, 38, 27, 33, 32, 1, 12, 10, 11, 0, 0, 21, 26, 19, 34, 38, 23, 36, 1, 27, 10, 1, 22, 33, 41, 32, 1, 38], [21, 23, 4, 37, 1, 19, 22, 40, 23, 32, 38, 39, 36, 23, 37, 1, 27, 32, 1, 41, 33, 32, 22, 23, 36, 30, 19, 32, 22, 0, 0, 30, 23, 41, 27, 37, 1, 21, 19, 36, 36, 33, 30, 30, 0, 0, 38, 26, 23, 1, 31, 27, 30, 30, 23, 32, 32, 27, 39, 31, 1, 24, 39, 30, 21, 36, 39, 31, 1, 23, 22, 27, 38, 27, 33, 32, 1, 12, 10, 11, 0, 0, 21, 26, 19, 34, 38, 23, 36, 1, 27, 10, 1, 22, 33, 41, 32, 1, 38, 26], [23, 4, 37, 1, 19, 22, 40, 23, 32, 38, 39, 36, 23, 37, 1, 27, 32, 1, 41, 33, 32, 22, 23, 36, 30, 19, 32, 22, 0, 0, 30, 23, 41, 27, 37, 1, 21, 19, 36, 36, 33, 30, 30, 0, 0, 38, 26, 23, 1, 31, 27, 30, 30, 23, 32, 32, 27, 39, 31, 1, 24, 39, 30, 21, 36, 39, 31, 1, 23, 22, 27, 38, 27, 33, 32, 1, 12, 10, 11, 0, 0, 21, 26, 19, 34, 38, 23, 36, 1, 27, 10, 1, 22, 33, 41, 32, 1, 38, 26, 23]]
  25. dataY： 144313 [1, 38, 26, 23, 1]
  26. Total patterns: 144313
  27. X_train.shape (144313, 100, 1)
  28. Y_train.shape (144313, 45)
  29. Init data，after read_out, chars: 
  30.  144313 alice's adventures in wonderland
  32. lewis carroll
  34. tge millennium fulcrum edition 3.0
  36. cgapter i. down
  37. _________________________________________________________________
  38. Layer (type)                 Output Shape              Param    
  39. =================================================================
  40. F:\File_Jupyter\实用代码\NeuralNetwork（神经网络）\CharacterLanguageLSTM.py:135: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
  41.   LSTM_Model.fit(X_train[:train_index], Y_train[:train_index], nb_epoch=10, batch_size=64, callbacks=callbacks_list)
  42. lstm_1 (LSTM)                (None, 256)               264192    
  43. _________________________________________________________________
  44. dropout_1 (Dropout)          (None, 256)               0         
  45. _________________________________________________________________
  46. dense_1 (Dense)              (None, 45)                11565     
  47. =================================================================
  48. Total params: 275,757
  49. Trainable params: 275,757
  50. Non-trainable params: 0
  51. _________________________________________________________________
  52. LSTM_Model 
  53.  None
  54. Epoch 1/10
  55. 2020-12-23 23:42:07.919094: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
  57.   64/1000 [>.............................] - ETA: 29s - loss: 3.8086
  58.  128/1000 [==>...........................] - ETA: 15s - loss: 3.7953
  59.  192/1000 [====>.........................] - ETA: 11s - loss: 3.7823
  60.  256/1000 [======>.......................] - ETA: 8s - loss: 3.7692 
  61.  320/1000 [========>.....................] - ETA: 7s - loss: 3.7552
  62.  384/1000 [==========>...................] - ETA: 5s - loss: 3.7372
  63.  448/1000 [============>.................] - ETA: 4s - loss: 3.7026
  64.  512/1000 [==============>...............] - ETA: 4s - loss: 3.6552
  65.  576/1000 [================>.............] - ETA: 3s - loss: 3.5955
  66.  640/1000 [==================>...........] - ETA: 2s - loss: 3.5678
  67.  704/1000 [====================>.........] - ETA: 2s - loss: 3.5116
  68.  768/1000 [======================>.......] - ETA: 1s - loss: 3.4778
  69.  832/1000 [=======================>......] - ETA: 1s - loss: 3.4441
  70.  896/1000 [=========================>....] - ETA: 0s - loss: 3.4278
  71.  960/1000 [===========================>..] - ETA: 0s - loss: 3.4092
  72. 1000/1000 [==============================] - 7s 7ms/step - loss: 3.3925
  74. Epoch 00001: loss improved from inf to 3.39249, saving model to hdf5/weights-improvement-01-3.3925.hdf5
  75. Epoch 2/10
  77.   64/1000 [>.............................] - ETA: 4s - loss: 3.1429
  78.  128/1000 [==>...........................] - ETA: 4s - loss: 3.1370
  79.  192/1000 [====>.........................] - ETA: 3s - loss: 3.1034
  80.  256/1000 [======>.......................] - ETA: 3s - loss: 3.1038
  81.  320/1000 [========>.....................] - ETA: 3s - loss: 3.0962
  82.  384/1000 [==========>...................] - ETA: 2s - loss: 3.1055
  83.  448/1000 [============>.................] - ETA: 2s - loss: 3.0986
  84.  512/1000 [==============>...............] - ETA: 2s - loss: 3.0628
  85.  576/1000 [================>.............] - ETA: 2s - loss: 3.0452
  86.  640/1000 [==================>...........] - ETA: 1s - loss: 3.0571
  87.  704/1000 [====================>.........] - ETA: 1s - loss: 3.0684
  88.  768/1000 [======================>.......] - ETA: 1s - loss: 3.0606
  89.  832/1000 [=======================>......] - ETA: 0s - loss: 3.0596
  90.  896/1000 [=========================>....] - ETA: 0s - loss: 3.0529
  91.  960/1000 [===========================>..] - ETA: 0s - loss: 3.0484
  92. 1000/1000 [==============================] - 5s 5ms/step - loss: 3.0371
  94. Epoch 00002: loss improved from 3.39249 to 3.03705, saving model to hdf5/weights-improvement-02-3.0371.hdf5
  95. Epoch 3/10
  97.   64/1000 [>.............................] - ETA: 4s - loss: 3.1671
  98.  128/1000 [==>...........................] - ETA: 4s - loss: 3.0008
  99.  192/1000 [====>.........................] - ETA: 4s - loss: 3.0159
  100.  256/1000 [======>.......................] - ETA: 4s - loss: 3.0019
  101.  320/1000 [========>.....................] - ETA: 3s - loss: 3.0056
  102.  384/1000 [==========>...................] - ETA: 3s - loss: 3.0156
  103.  448/1000 [============>.................] - ETA: 2s - loss: 3.0392
  104.  512/1000 [==============>...............] - ETA: 2s - loss: 3.0243
  105.  576/1000 [================>.............] - ETA: 2s - loss: 3.0226
  106.  640/1000 [==================>...........] - ETA: 1s - loss: 3.0162
  107.  704/1000 [====================>.........] - ETA: 1s - loss: 3.0238
  108.  768/1000 [======================>.......] - ETA: 1s - loss: 3.0195
  109.  832/1000 [=======================>......] - ETA: 0s - loss: 3.0286
  110.  896/1000 [=========================>....] - ETA: 0s - loss: 3.0272
  111.  960/1000 [===========================>..] - ETA: 0s - loss: 3.0214
  112. 1000/1000 [==============================] - 6s 6ms/step - loss: 3.0225
  114. Epoch 00003: loss improved from 3.03705 to 3.02249, saving model to hdf5/weights-improvement-03-3.0225.hdf5
  115. Epoch 4/10
  117.   64/1000 [>.............................] - ETA: 5s - loss: 2.7843
  118.  128/1000 [==>...........................] - ETA: 5s - loss: 2.8997
  119.  192/1000 [====>.........................] - ETA: 4s - loss: 2.9975
  120.  256/1000 [======>.......................] - ETA: 4s - loss: 3.0150
  121.  320/1000 [========>.....................] - ETA: 3s - loss: 3.0025
  122.  384/1000 [==========>...................] - ETA: 3s - loss: 3.0442
  123.  448/1000 [============>.................] - ETA: 3s - loss: 3.0494
  124.  512/1000 [==============>...............] - ETA: 2s - loss: 3.0398
  125.  576/1000 [================>.............] - ETA: 2s - loss: 3.0170
  126.  640/1000 [==================>...........] - ETA: 2s - loss: 3.0421
  127.  704/1000 [====================>.........] - ETA: 1s - loss: 3.0366
  128.  768/1000 [======================>.......] - ETA: 1s - loss: 3.0339
  129.  832/1000 [=======================>......] - ETA: 0s - loss: 3.0316
  130.  896/1000 [=========================>....] - ETA: 0s - loss: 3.0361
  131.  960/1000 [===========================>..] - ETA: 0s - loss: 3.0326
  132. 1000/1000 [==============================] - 6s 6ms/step - loss: 3.0352
  134. Epoch 00004: loss did not improve from 3.02249
  135. Epoch 5/10
  137.   64/1000 [>.............................] - ETA: 4s - loss: 2.8958
  138.  128/1000 [==>...........................] - ETA: 4s - loss: 2.9239
  139.  192/1000 [====>.........................] - ETA: 4s - loss: 2.9044
  140.  256/1000 [======>.......................] - ETA: 4s - loss: 2.9417
  141.  320/1000 [========>.....................] - ETA: 3s - loss: 2.9674
  142.  384/1000 [==========>...................] - ETA: 3s - loss: 2.9646
  143.  448/1000 [============>.................] - ETA: 3s - loss: 2.9629
  144.  512/1000 [==============>...............] - ETA: 2s - loss: 2.9707
  145.  576/1000 [================>.............] - ETA: 2s - loss: 2.9699
  146.  640/1000 [==================>...........] - ETA: 1s - loss: 2.9594
  147.  704/1000 [====================>.........] - ETA: 1s - loss: 2.9830
  148.  768/1000 [======================>.......] - ETA: 1s - loss: 2.9773
  149.  832/1000 [=======================>......] - ETA: 0s - loss: 2.9774
  150.  896/1000 [=========================>....] - ETA: 0s - loss: 2.9891
  151.  960/1000 [===========================>..] - ETA: 0s - loss: 3.0070
  152. 1000/1000 [==============================] - 5s 5ms/step - loss: 3.0120
  154. Epoch 00005: loss improved from 3.02249 to 3.01205, saving model to hdf5/weights-improvement-05-3.0120.hdf5
  155. Epoch 6/10
  157.   64/1000 [>.............................] - ETA: 4s - loss: 3.0241
  158.  128/1000 [==>...........................] - ETA: 4s - loss: 3.0463
  159.  192/1000 [====>.........................] - ETA: 3s - loss: 3.0364
  160.  256/1000 [======>.......................] - ETA: 3s - loss: 2.9712
  161.  320/1000 [========>.....................] - ETA: 3s - loss: 2.9840
  162.  384/1000 [==========>...................] - ETA: 3s - loss: 2.9887
  163.  448/1000 [============>.................] - ETA: 2s - loss: 2.9785
  164.  512/1000 [==============>...............] - ETA: 2s - loss: 2.9852
  165.  576/1000 [================>.............] - ETA: 2s - loss: 2.9893
  166.  640/1000 [==================>...........] - ETA: 1s - loss: 2.9931
  167.  704/1000 [====================>.........] - ETA: 1s - loss: 2.9790
  168.  768/1000 [======================>.......] - ETA: 1s - loss: 2.9962
  169.  832/1000 [=======================>......] - ETA: 0s - loss: 3.0166
  170.  896/1000 [=========================>....] - ETA: 0s - loss: 3.0213
  171.  960/1000 [===========================>..] - ETA: 0s - loss: 3.0143
  172. 1000/1000 [==============================] - 5s 5ms/step - loss: 3.0070
  174. Epoch 00006: loss improved from 3.01205 to 3.00701, saving model to hdf5/weights-improvement-06-3.0070.hdf5
  175. Epoch 7/10
  177.   64/1000 [>.............................] - ETA: 5s - loss: 3.0738
  178.  128/1000 [==>...........................] - ETA: 5s - loss: 3.0309
  179.  192/1000 [====>.........................] - ETA: 4s - loss: 2.9733
  180.  256/1000 [======>.......................] - ETA: 4s - loss: 2.9728
  181.  320/1000 [========>.....................] - ETA: 4s - loss: 2.9422
  182.  384/1000 [==========>...................] - ETA: 3s - loss: 2.9496
  183.  448/1000 [============>.................] - ETA: 3s - loss: 2.9548
  184.  512/1000 [==============>...............] - ETA: 3s - loss: 2.9635
  185.  576/1000 [================>.............] - ETA: 2s - loss: 2.9614
  186.  640/1000 [==================>...........] - ETA: 2s - loss: 2.9537
  187.  704/1000 [====================>.........] - ETA: 1s - loss: 2.9454
  188.  768/1000 [======================>.......] - ETA: 1s - loss: 2.9649
  189.  832/1000 [=======================>......] - ETA: 1s - loss: 2.9814
  190.  896/1000 [=========================>....] - ETA: 0s - loss: 2.9955
  191.  960/1000 [===========================>..] - ETA: 0s - loss: 2.9948
  192. 1000/1000 [==============================] - 6s 6ms/step - loss: 2.9903
  194. Epoch 00007: loss improved from 3.00701 to 2.99027, saving model to hdf5/weights-improvement-07-2.9903.hdf5
  195. Epoch 8/10

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