1. rawtext_BySpaceConnect: ALICE'S ADVENTURES IN WONDERLAND  Lewis Carroll  THE MILLENNIUM FULCRUM EDITION 3.0  CHAPTER I. Down the Rabbit-Hole  Alice was beginning to get very tired of sitting by her sister on the bank, and of having nothing to do: once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, 'and what is the use of a book,' thought Alice 'without pictures or conversations?'  So she was considering in her own mind (as well as she could, for the hot day 
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3. rawtext_BySpace: ALICE'S ADVENTURES IN WONDERLAND Lewis Carroll THE MILLENNIUM FULCRUM EDITION 3.0 CHAPTER I Down the Rabbit Hole Alice was beginning to get very tired of sitting by her sister on the bank and of having nothing to do once or twice she had peeped into the book her sister was reading but it had no pictures or conversations in it and what is the use of a book thought Alice without pictures or conversations So she was considering in her own mind as well as she could for the hot day made her feel very
4. words_num: 26694
5. vocab_num: 3063
6. dataX： 26594 100 [[19, 18, 238, 547, 278, 84, 469, 294, 160, 133, 16, 74, 227, 125, 2713, 393, 223, 31, 2932, 769, 2773, 1456, 2905, 2770, 2006, 2500, 862, 1569, 2495, 2019, 2713, 733, 660, 2006, 1543, 1988, 2773, 1144, 2020, 2035, 2841, 2434, 1513, 2091, 1663, 2713, 810, 1569, 2495, 2932, 2258, 856, 1675, 1513, 1977, 2111, 2035, 1006, 1640, 1675, 660, 2960, 1673, 2713, 2886, 2006, 594, 810, 2741, 31, 3004, 2111, 2035, 1006, 440, 2434, 2932, 996, 1640, 1569, 2051, 1897, 701, 2954, 701, 2434, 1012, 1402, 2713, 1603, 1083, 1847, 1569, 1328, 2905, 2513, 660, 2637, 2969, 2713], [18, 238, 547, 278, 84, 469, 294, 160, 133, 16, 74, 227, 125, 2713, 393, 223, 31, 2932, 769, 2773, 1456, 2905, 2770, 2006, 2500, 862, 1569, 2495, 2019, 2713, 733, 660, 2006, 1543, 1988, 2773, 1144, 2020, 2035, 2841, 2434, 1513, 2091, 1663, 2713, 810, 1569, 2495, 2932, 2258, 856, 1675, 1513, 1977, 2111, 2035, 1006, 1640, 1675, 660, 2960, 1673, 2713, 2886, 2006, 594, 810, 2741, 31, 3004, 2111, 2035, 1006, 440, 2434, 2932, 996, 1640, 1569, 2051, 1897, 701, 2954, 701, 2434, 1012, 1402, 2713, 1603, 1083, 1847, 1569, 1328, 2905, 2513, 660, 2637, 2969, 2713, 2144], [238, 547, 278, 84, 469, 294, 160, 133, 16, 74, 227, 125, 2713, 393, 223, 31, 2932, 769, 2773, 1456, 2905, 2770, 2006, 2500, 862, 1569, 2495, 2019, 2713, 733, 660, 2006, 1543, 1988, 2773, 1144, 2020, 2035, 2841, 2434, 1513, 2091, 1663, 2713, 810, 1569, 2495, 2932, 2258, 856, 1675, 1513, 1977, 2111, 2035, 1006, 1640, 1675, 660, 2960, 1673, 2713, 2886, 2006, 594, 810, 2741, 31, 3004, 2111, 2035, 1006, 440, 2434, 2932, 996, 1640, 1569, 2051, 1897, 701, 2954, 701, 2434, 1012, 1402, 2713, 1603, 1083, 1847, 1569, 1328, 2905, 2513, 660, 2637, 2969, 2713, 2144, 2006], [547, 278, 84, 469, 294, 160, 133, 16, 74, 227, 125, 2713, 393, 223, 31, 2932, 769, 2773, 1456, 2905, 2770, 2006, 2500, 862, 1569, 2495, 2019, 2713, 733, 660, 2006, 1543, 1988, 2773, 1144, 2020, 2035, 2841, 2434, 1513, 2091, 1663, 2713, 810, 1569, 2495, 2932, 2258, 856, 1675, 1513, 1977, 2111, 2035, 1006, 1640, 1675, 660, 2960, 1673, 2713, 2886, 2006, 594, 810, 2741, 31, 3004, 2111, 2035, 1006, 440, 2434, 2932, 996, 1640, 1569, 2051, 1897, 701, 2954, 701, 2434, 1012, 1402, 2713, 1603, 1083, 1847, 1569, 1328, 2905, 2513, 660, 2637, 2969, 2713, 2144, 2006, 1851], [278, 84, 469, 294, 160, 133, 16, 74, 227, 125, 2713, 393, 223, 31, 2932, 769, 2773, 1456, 2905, 2770, 2006, 2500, 862, 1569, 2495, 2019, 2713, 733, 660, 2006, 1543, 1988, 2773, 1144, 2020, 2035, 2841, 2434, 1513, 2091, 1663, 2713, 810, 1569, 2495, 2932, 2258, 856, 1675, 1513, 1977, 2111, 2035, 1006, 1640, 1675, 660, 2960, 1673, 2713, 2886, 2006, 594, 810, 2741, 31, 3004, 2111, 2035, 1006, 440, 2434, 2932, 996, 1640, 1569, 2051, 1897, 701, 2954, 701, 2434, 1012, 1402, 2713, 1603, 1083, 1847, 1569, 1328, 2905, 2513, 660, 2637, 2969, 2713, 2144, 2006, 1851, 594]]
7. dataY： 26594 [2144, 2006, 1851, 594, 1074]
8. Total patterns: 26594
9. X_train.shape (26594, 100, 1)
10. Y_train.shape (26594, 3063)
11. _________________________________________________________________
12. Layer (type)                 Output Shape              Param #   
13. =================================================================
14. lstm_1 (LSTM)                (None, 256)               264192
15. _________________________________________________________________
16. dropout_1 (Dropout)          (None, 256)               0
17. _________________________________________________________________
18. dense_1 (Dense)              (None, 3063)              787191
19. =================================================================
20. Total params: 1,051,383
21. Trainable params: 1,051,383
22. Non-trainable params: 0
23. _________________________________________________________________
24. LSTM_Model 
25. None
26. 
27. 
28. ……
29. 
30. Epoch 00005: loss improved from 6.26403 to 6.26198, saving model to hdf5/word-weights-improvement-05-6.2620.hdf5
31. Epoch 6/10
32. 
33. 128/26594 [..............................] - ETA: 2:09 - loss: 6.8378
34. 256/26594 [..............................] - ETA: 2:06 - loss: 6.4136
35. 384/26594 [..............................] - ETA: 2:01 - loss: 6.3299
36. 512/26594 [..............................] - ETA: 1:57 - loss: 6.4469
37. 640/26594 [..............................] - ETA: 1:57 - loss: 6.4133
38. 
39. ……
40. 
41. Epoch 00008: loss improved from 6.25725 to 6.25487, saving model to hdf5/word-weights-improvement-08-6.2549.hdf5
42. Epoch 9/10
43. 
44. 128/26594 [..............................] - ETA: 1:57 - loss: 6.2336
45. 256/26594 [..............................] - ETA: 2:02 - loss: 6.1897
46. 384/26594 [..............................] - ETA: 2:04 - loss: 6.3229
47. 512/26594 [..............................] - ETA: 2:01 - loss: 6.3550
48. 640/26594 [..............................] - ETA: 2:02 - loss: 6.3279
49. 768/26594 [..............................] - ETA: 2:05 - loss: 6.2614
50. 896/26594 [>.............................] - ETA: 2:06 - loss: 6.2433
51. 1024/26594 [>.............................] - ETA: 2:07 - loss: 6.2477
52. ……
53. 
54. 25216/26594 [===========================>..] - ETA: 6s - loss: 6.2456
55. 25344/26594 [===========================>..] - ETA: 6s - loss: 6.2469
56. 25472/26594 [===========================>..] - ETA: 5s - loss: 6.2477
57. 25600/26594 [===========================>..] - ETA: 4s - loss: 6.2486
58. 25728/26594 [============================>.] - ETA: 4s - loss: 6.2480
59. 25856/26594 [============================>.] - ETA: 3s - loss: 6.2483
60. 25984/26594 [============================>.] - ETA: 2s - loss: 6.2487
61. 26112/26594 [============================>.] - ETA: 2s - loss: 6.2485
62. 26240/26594 [============================>.] - ETA: 1s - loss: 6.2483
63. 26368/26594 [============================>.] - ETA: 1s - loss: 6.2482
64. 26496/26594 [============================>.] - ETA: 0s - loss: 6.2485
65. 26594/26594 [==============================] - 129s 5ms/step - loss: 6.2499
66. 
67. Epoch 00009: loss improved from 6.25487 to 6.24987, saving model to hdf5/word-weights-improvement-09-6.2499.hdf5
68. Epoch 10/10
69. 
70. 128/26594 [..............................] - ETA: 1:56 - loss: 6.4864
71. 256/26594 [..............................] - ETA: 2:04 - loss: 6.2577
72. 384/26594 [..............................] - ETA: 2:07 - loss: 6.2857
73. 512/26594 [..............................] - ETA: 2:10 - loss: 6.3230
74. ……
75. 
76. 25856/26594 [============================>.] - ETA: 3s - loss: 6.2426
77. 25984/26594 [============================>.] - ETA: 3s - loss: 6.2447
78. 26112/26594 [============================>.] - ETA: 2s - loss: 6.2446
79. 26240/26594 [============================>.] - ETA: 1s - loss: 6.2449
80. 26368/26594 [============================>.] - ETA: 1s - loss: 6.2467
81. 26496/26594 [============================>.] - ETA: 0s - loss: 6.2461
82. 26594/26594 [==============================] - 135s 5ms/step - loss: 6.2465
83. 
84. 
85. 
86. Epoch 00010: loss improved from 6.24987 to 6.24646, saving model to hdf5/word-weights-improvement-10-6.2465.hdf5
87. LSTM_Pre_word.shape: 
88.  (3, 3063)
89. 
90. 
91. 
92. 
93. 
94. LSTM_Model，Seed:
95. " cheerfully he seems to grin How neatly spread his claws And welcome little fishes in With gently smiling jaws I'm sure those are not the right words said poor Alice and her eyes filled with tears again as she went on I must be Mabel after all and I shall have to go and live in that poky little house and have next to no toys to play with and oh ever so many lessons to learn No I've made up my mind about it if I'm Mabel I'll stay down here It'll be no use their putting their heads "
96. 199 100
97. 
98.  Generated Sequence:
99. the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the
100. 
101.  Done.
102. 
103. 
104.

1. LSTM_Model = Sequential()
2. LSTM_Model.add(LSTM(256, input_shape=(X_train.shape[1], X_train.shape[2])))
3. LSTM_Model.add(Dropout(0.2))
4. LSTM_Model.add(Dense(Y_train.shape[1], activation='softmax'))
5. LSTM_Model.compile(loss='categorical_crossentropy', optimizer='adam')
6. print('LSTM_Model \n',LSTM_Model.summary())