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

以你之姓@ 2022-12-30 03:48 171阅读 0赞

**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][https_download.csdn.net_download_qq_41185868_13767751]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

Using TensorFlow backend.
    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'.
      _np_qint8 = np.dtype([("qint8", np.int8, 1)])
    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'.
      _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
    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'.
      _np_qint16 = np.dtype([("qint16", np.int16, 1)])
    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'.
      _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
    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'.
      _np_qint32 = np.dtype([("qint32", np.int32, 1)])
    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'.
      np_resource = np.dtype([("resource", np.ubyte, 1)])
    [nltk_data] Error loading punkt: <urlopen error [Errno 11004]
    [nltk_data]     getaddrinfo failed>
    raw_text[:10] : alice's ad
    Total Characters: 144413
    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']
    Total Vocab: 45
    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!'"]
    lengths (1625,) [420 289 140 ... 636 553   7]
    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}
    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'}
    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]]
    dataY： 144313 [1, 38, 26, 23, 1]
    Total patterns: 144313
    X_train.shape (144313, 100, 1)
    Y_train.shape (144313, 45)
    Init data，after read_out, chars: 
     144313 alice's adventures in wonderland
    
    lewis carroll
    
    tge millennium fulcrum edition 3.0
    
    cgapter i. down
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    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`.
      LSTM_Model.fit(X_train[:train_index], Y_train[:train_index], nb_epoch=10, batch_size=64, callbacks=callbacks_list)
    lstm_1 (LSTM)                (None, 256)               264192    
    _________________________________________________________________
    dropout_1 (Dropout)          (None, 256)               0         
    _________________________________________________________________
    dense_1 (Dense)              (None, 45)                11565     
    =================================================================
    Total params: 275,757
    Trainable params: 275,757
    Non-trainable params: 0
    _________________________________________________________________
    LSTM_Model 
     None
    Epoch 1/10
    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
    
      64/1000 [>.............................] - ETA: 29s - loss: 3.8086
     128/1000 [==>...........................] - ETA: 15s - loss: 3.7953
     192/1000 [====>.........................] - ETA: 11s - loss: 3.7823
     256/1000 [======>.......................] - ETA: 8s - loss: 3.7692 
     320/1000 [========>.....................] - ETA: 7s - loss: 3.7552
     384/1000 [==========>...................] - ETA: 5s - loss: 3.7372
     448/1000 [============>.................] - ETA: 4s - loss: 3.7026
     512/1000 [==============>...............] - ETA: 4s - loss: 3.6552
     576/1000 [================>.............] - ETA: 3s - loss: 3.5955
     640/1000 [==================>...........] - ETA: 2s - loss: 3.5678
     704/1000 [====================>.........] - ETA: 2s - loss: 3.5116
     768/1000 [======================>.......] - ETA: 1s - loss: 3.4778
     832/1000 [=======================>......] - ETA: 1s - loss: 3.4441
     896/1000 [=========================>....] - ETA: 0s - loss: 3.4278
     960/1000 [===========================>..] - ETA: 0s - loss: 3.4092
    1000/1000 [==============================] - 7s 7ms/step - loss: 3.3925
    
    Epoch 00001: loss improved from inf to 3.39249, saving model to hdf5/weights-improvement-01-3.3925.hdf5
    Epoch 2/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 3.1429
     128/1000 [==>...........................] - ETA: 4s - loss: 3.1370
     192/1000 [====>.........................] - ETA: 3s - loss: 3.1034
     256/1000 [======>.......................] - ETA: 3s - loss: 3.1038
     320/1000 [========>.....................] - ETA: 3s - loss: 3.0962
     384/1000 [==========>...................] - ETA: 2s - loss: 3.1055
     448/1000 [============>.................] - ETA: 2s - loss: 3.0986
     512/1000 [==============>...............] - ETA: 2s - loss: 3.0628
     576/1000 [================>.............] - ETA: 2s - loss: 3.0452
     640/1000 [==================>...........] - ETA: 1s - loss: 3.0571
     704/1000 [====================>.........] - ETA: 1s - loss: 3.0684
     768/1000 [======================>.......] - ETA: 1s - loss: 3.0606
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0596
     896/1000 [=========================>....] - ETA: 0s - loss: 3.0529
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0484
    1000/1000 [==============================] - 5s 5ms/step - loss: 3.0371
    
    Epoch 00002: loss improved from 3.39249 to 3.03705, saving model to hdf5/weights-improvement-02-3.0371.hdf5
    Epoch 3/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 3.1671
     128/1000 [==>...........................] - ETA: 4s - loss: 3.0008
     192/1000 [====>.........................] - ETA: 4s - loss: 3.0159
     256/1000 [======>.......................] - ETA: 4s - loss: 3.0019
     320/1000 [========>.....................] - ETA: 3s - loss: 3.0056
     384/1000 [==========>...................] - ETA: 3s - loss: 3.0156
     448/1000 [============>.................] - ETA: 2s - loss: 3.0392
     512/1000 [==============>...............] - ETA: 2s - loss: 3.0243
     576/1000 [================>.............] - ETA: 2s - loss: 3.0226
     640/1000 [==================>...........] - ETA: 1s - loss: 3.0162
     704/1000 [====================>.........] - ETA: 1s - loss: 3.0238
     768/1000 [======================>.......] - ETA: 1s - loss: 3.0195
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0286
     896/1000 [=========================>....] - ETA: 0s - loss: 3.0272
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0214
    1000/1000 [==============================] - 6s 6ms/step - loss: 3.0225
    
    Epoch 00003: loss improved from 3.03705 to 3.02249, saving model to hdf5/weights-improvement-03-3.0225.hdf5
    Epoch 4/10
    
      64/1000 [>.............................] - ETA: 5s - loss: 2.7843
     128/1000 [==>...........................] - ETA: 5s - loss: 2.8997
     192/1000 [====>.........................] - ETA: 4s - loss: 2.9975
     256/1000 [======>.......................] - ETA: 4s - loss: 3.0150
     320/1000 [========>.....................] - ETA: 3s - loss: 3.0025
     384/1000 [==========>...................] - ETA: 3s - loss: 3.0442
     448/1000 [============>.................] - ETA: 3s - loss: 3.0494
     512/1000 [==============>...............] - ETA: 2s - loss: 3.0398
     576/1000 [================>.............] - ETA: 2s - loss: 3.0170
     640/1000 [==================>...........] - ETA: 2s - loss: 3.0421
     704/1000 [====================>.........] - ETA: 1s - loss: 3.0366
     768/1000 [======================>.......] - ETA: 1s - loss: 3.0339
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0316
     896/1000 [=========================>....] - ETA: 0s - loss: 3.0361
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0326
    1000/1000 [==============================] - 6s 6ms/step - loss: 3.0352
    
    Epoch 00004: loss did not improve from 3.02249
    Epoch 5/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 2.8958
     128/1000 [==>...........................] - ETA: 4s - loss: 2.9239
     192/1000 [====>.........................] - ETA: 4s - loss: 2.9044
     256/1000 [======>.......................] - ETA: 4s - loss: 2.9417
     320/1000 [========>.....................] - ETA: 3s - loss: 2.9674
     384/1000 [==========>...................] - ETA: 3s - loss: 2.9646
     448/1000 [============>.................] - ETA: 3s - loss: 2.9629
     512/1000 [==============>...............] - ETA: 2s - loss: 2.9707
     576/1000 [================>.............] - ETA: 2s - loss: 2.9699
     640/1000 [==================>...........] - ETA: 1s - loss: 2.9594
     704/1000 [====================>.........] - ETA: 1s - loss: 2.9830
     768/1000 [======================>.......] - ETA: 1s - loss: 2.9773
     832/1000 [=======================>......] - ETA: 0s - loss: 2.9774
     896/1000 [=========================>....] - ETA: 0s - loss: 2.9891
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0070
    1000/1000 [==============================] - 5s 5ms/step - loss: 3.0120
    
    Epoch 00005: loss improved from 3.02249 to 3.01205, saving model to hdf5/weights-improvement-05-3.0120.hdf5
    Epoch 6/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 3.0241
     128/1000 [==>...........................] - ETA: 4s - loss: 3.0463
     192/1000 [====>.........................] - ETA: 3s - loss: 3.0364
     256/1000 [======>.......................] - ETA: 3s - loss: 2.9712
     320/1000 [========>.....................] - ETA: 3s - loss: 2.9840
     384/1000 [==========>...................] - ETA: 3s - loss: 2.9887
     448/1000 [============>.................] - ETA: 2s - loss: 2.9785
     512/1000 [==============>...............] - ETA: 2s - loss: 2.9852
     576/1000 [================>.............] - ETA: 2s - loss: 2.9893
     640/1000 [==================>...........] - ETA: 1s - loss: 2.9931
     704/1000 [====================>.........] - ETA: 1s - loss: 2.9790
     768/1000 [======================>.......] - ETA: 1s - loss: 2.9962
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0166
     896/1000 [=========================>....] - ETA: 0s - loss: 3.0213
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0143
    1000/1000 [==============================] - 5s 5ms/step - loss: 3.0070
    
    Epoch 00006: loss improved from 3.01205 to 3.00701, saving model to hdf5/weights-improvement-06-3.0070.hdf5
    Epoch 7/10
    
      64/1000 [>.............................] - ETA: 5s - loss: 3.0738
     128/1000 [==>...........................] - ETA: 5s - loss: 3.0309
     192/1000 [====>.........................] - ETA: 4s - loss: 2.9733
     256/1000 [======>.......................] - ETA: 4s - loss: 2.9728
     320/1000 [========>.....................] - ETA: 4s - loss: 2.9422
     384/1000 [==========>...................] - ETA: 3s - loss: 2.9496
     448/1000 [============>.................] - ETA: 3s - loss: 2.9548
     512/1000 [==============>...............] - ETA: 3s - loss: 2.9635
     576/1000 [================>.............] - ETA: 2s - loss: 2.9614
     640/1000 [==================>...........] - ETA: 2s - loss: 2.9537
     704/1000 [====================>.........] - ETA: 1s - loss: 2.9454
     768/1000 [======================>.......] - ETA: 1s - loss: 2.9649
     832/1000 [=======================>......] - ETA: 1s - loss: 2.9814
     896/1000 [=========================>....] - ETA: 0s - loss: 2.9955
     960/1000 [===========================>..] - ETA: 0s - loss: 2.9948
    1000/1000 [==============================] - 6s 6ms/step - loss: 2.9903
    
    Epoch 00007: loss improved from 3.00701 to 2.99027, saving model to hdf5/weights-improvement-07-2.9903.hdf5
    Epoch 8/10
    
      64/1000 [>.............................] - ETA: 5s - loss: 2.9248
     128/1000 [==>...........................] - ETA: 4s - loss: 2.9293
     192/1000 [====>.........................] - ETA: 4s - loss: 2.9820
     256/1000 [======>.......................] - ETA: 4s - loss: 3.0261
     320/1000 [========>.....................] - ETA: 3s - loss: 2.9989
     384/1000 [==========>...................] - ETA: 3s - loss: 3.0101
     448/1000 [============>.................] - ETA: 3s - loss: 3.0050
     512/1000 [==============>...............] - ETA: 2s - loss: 3.0155
     576/1000 [================>.............] - ETA: 2s - loss: 3.0414
     640/1000 [==================>...........] - ETA: 2s - loss: 3.0180
     704/1000 [====================>.........] - ETA: 1s - loss: 3.0295
     768/1000 [======================>.......] - ETA: 1s - loss: 2.9996
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0151
     896/1000 [=========================>....] - ETA: 0s - loss: 3.0201
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0063
    1000/1000 [==============================] - 6s 6ms/step - loss: 3.0064
    
    Epoch 00008: loss did not improve from 2.99027
    Epoch 9/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 2.8417
     128/1000 [==>...........................] - ETA: 4s - loss: 2.9652
     192/1000 [====>.........................] - ETA: 4s - loss: 2.9907
     256/1000 [======>.......................] - ETA: 3s - loss: 3.0133
     320/1000 [========>.....................] - ETA: 3s - loss: 3.0092
     384/1000 [==========>...................] - ETA: 3s - loss: 3.0139
     448/1000 [============>.................] - ETA: 2s - loss: 3.0453
     512/1000 [==============>...............] - ETA: 2s - loss: 3.0481
     576/1000 [================>.............] - ETA: 2s - loss: 3.0434
     640/1000 [==================>...........] - ETA: 1s - loss: 3.0158
     704/1000 [====================>.........] - ETA: 1s - loss: 3.0141
     768/1000 [======================>.......] - ETA: 1s - loss: 3.0203
     832/1000 [=======================>......] - ETA: 0s - loss: 3.0068
     896/1000 [=========================>....] - ETA: 0s - loss: 2.9980
     960/1000 [===========================>..] - ETA: 0s - loss: 3.0016
    1000/1000 [==============================] - 5s 5ms/step - loss: 2.9944
    
    Epoch 00009: loss did not improve from 2.99027
    Epoch 10/10
    
      64/1000 [>.............................] - ETA: 4s - loss: 3.0100
     128/1000 [==>...........................] - ETA: 4s - loss: 3.0620
     192/1000 [====>.........................] - ETA: 4s - loss: 3.0169
     256/1000 [======>.......................] - ETA: 3s - loss: 3.0289
     320/1000 [========>.....................] - ETA: 3s - loss: 3.0060
     384/1000 [==========>...................] - ETA: 3s - loss: 2.9940
     448/1000 [============>.................] - ETA: 2s - loss: 2.9823
     512/1000 [==============>...............] - ETA: 2s - loss: 2.9686
     576/1000 [================>.............] - ETA: 2s - loss: 2.9699
     640/1000 [==================>...........] - ETA: 1s - loss: 2.9710
     704/1000 [====================>.........] - ETA: 1s - loss: 2.9625
     768/1000 [======================>.......] - ETA: 1s - loss: 2.9748
     832/1000 [=======================>......] - ETA: 0s - loss: 2.9794
     896/1000 [=========================>....] - ETA: 0s - loss: 2.9788
     960/1000 [===========================>..] - ETA: 0s - loss: 2.9802
    1000/1000 [==============================] - 5s 5ms/step - loss: 2.9963
    
    Epoch 00010: loss did not improve from 2.99027
    LSTM_Pre_word.shape: 
     (3, 45)
    after LSTM read_out, chars: 
     3 ["\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n '\n\n!!\n\n\n\n !\n\n! ' \n\n\n\n\n", "\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n '\n\n!!\n\n\n\n !\n\n! ' \n\n\n\n\n", '\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n "\n\n!!\n\n \n !\n\n! \' \n\n\n\n\n']
    LSTM_Model，Seed:
    " ent down its head to hide a smile: some of the other birds
    tittered audibly.
    
    'what i was going to s "
                                                                                                                                                                                                            199 100
    
     Generated Sequence:
     
    
     Done.
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    lstm_2 (LSTM)                (None, 100, 256)          264192    
    _________________________________________________________________
    dropout_2 (Dropout)          (None, 100, 256)          0         
    _________________________________________________________________
    lstm_3 (LSTM)                (None, 64)                82176     
    _________________________________________________________________
    dropout_3 (Dropout)          (None, 64)                0         
    _________________________________________________________________
    dense_2 (Dense)              (None, 45)                2925      
    =================================================================
    Total params: 349,293
    Trainable params: 349,293
    Non-trainable params: 0
    _________________________________________________________________
    DeepLSTM_Model 
     None
    F:\File_Jupyter\实用代码\NeuralNetwork（神经网络）\CharacterLanguageLSTM.py:246: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
      DeepLSTM_Model.fit(X_train[:train_index], Y_train[:train_index], nb_epoch=2, batch_size=256, callbacks=callbacks_list)
    Epoch 1/2
    
     256/1000 [======>.......................] - ETA: 11s - loss: 3.8128
     512/1000 [==============>...............] - ETA: 5s - loss: 3.8058 
     768/1000 [======================>.......] - ETA: 2s - loss: 3.7976
    1000/1000 [==============================] - 10s 10ms/step - loss: 3.7883
    
    Epoch 00001: loss improved from inf to 3.78827, saving model to hdf5/weights-improvement-01-3.7883.hdf5
    Epoch 2/2
    
     256/1000 [======>.......................] - ETA: 5s - loss: 3.7167
     512/1000 [==============>...............] - ETA: 4s - loss: 3.6880
     768/1000 [======================>.......] - ETA: 1s - loss: 3.6622
    1000/1000 [==============================] - 8s 8ms/step - loss: 3.6151
    
    Epoch 00002: loss improved from 3.78827 to 3.61512, saving model to hdf5/weights-improvement-02-3.6151.hdf5
    DeepLSTM_Pre_word.shape: 
     (3, 45)
    after DeepLSTM read_out, chars: 
     3 ["\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n '\n\n!!\n\n\n\n !\n\n! ' \n\n\n\n\n", "\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n '\n\n!!\n\n\n\n !\n\n! ' \n\n\n\n\n", '\n,\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n!\n\n "\n\n!!\n\n \n !\n\n! \' \n\n\n\n\n']

## 核心代码 ##

LSTM_Model = Sequential()
    LSTM_Model.add(LSTM(256, input_shape=(X_train.shape[1], X_train.shape[2])))
    LSTM_Model.add(Dropout(0.2))
    LSTM_Model.add(Dense(Y_train.shape[1], activation='softmax'))
    LSTM_Model.compile(loss='categorical_crossentropy', optimizer='adam')
    print('LSTM_Model \n',LSTM_Model.summary())
    
    
    
    
    embedding_vector_length = 32
    LSTMWithE_Model = Sequential()
    LSTMWithE_Model.add(Embedding(chars_len, embedding_vector_length, input_length=seq_length))
    LSTMWithE_Model.add(LSTM(256))
    LSTMWithE_Model.add(Dropout(0.2))
    LSTMWithE_Model.add(Dense(Y_train.shape[1], activation='softmax'))
    LSTMWithE_Model.compile(loss='categorical_crossentropy', optimizer='adam')
    print (LSTMWithE_Model.summary())
    
    
    
    DeepLSTM_Model = Sequential()
    DeepLSTM_Model.add(LSTM(256, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))
    DeepLSTM_Model.add(Dropout(0.2))
    DeepLSTM_Model.add(LSTM(64))
    DeepLSTM_Model.add(Dropout(0.2))
    DeepLSTM_Model.add(Dense(Y_train.shape[1], activation='softmax'))
    DeepLSTM_Model.compile(loss='categorical_crossentropy', optimizer='adam')
    print('DeepLSTM_Model \n',DeepLSTM_Model.summary())

[https_download.csdn.net_download_qq_41185868_13767751]: https://download.csdn.net/download/qq_41185868/13767751
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