DL之LSTM:基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(基于keras)对word实现预测daiding-阿里云开发者社区

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DL之LSTM:基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(基于keras)对word实现预测daiding

简介: DL之LSTM:基于《wonderland爱丽丝梦游仙境记》小说数据集利用LSTM算法(基于keras)对word实现预测daiding
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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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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

words_num: 26694

vocab_num: 3063

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]]

dataY: 26594 [2144, 2006, 1851, 594, 1074]

Total patterns: 26594

X_train.shape (26594, 100, 1)

Y_train.shape (26594, 3063)

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

lstm_1 (LSTM)                (None, 256)               264192    

_________________________________________________________________

dropout_1 (Dropout)          (None, 256)               0        

_________________________________________________________________

dense_1 (Dense)              (None, 3063)              787191    

=================================================================

Total params: 1,051,383

Trainable params: 1,051,383

Non-trainable params: 0

_________________________________________________________________

LSTM_Model

None

……

Epoch 00005: loss improved from 6.26403 to 6.26198, saving model to hdf5/word-weights-improvement-05-6.2620.hdf5

Epoch 6/10

 128/26594 [..............................] - ETA: 2:09 - loss: 6.8378

 256/26594 [..............................] - ETA: 2:06 - loss: 6.4136

 384/26594 [..............................] - ETA: 2:01 - loss: 6.3299

 512/26594 [..............................] - ETA: 1:57 - loss: 6.4469

 640/26594 [..............................] - ETA: 1:57 - loss: 6.4133

……

Epoch 00008: loss improved from 6.25725 to 6.25487, saving model to hdf5/word-weights-improvement-08-6.2549.hdf5

Epoch 9/10

 128/26594 [..............................] - ETA: 1:57 - loss: 6.2336

 256/26594 [..............................] - ETA: 2:02 - loss: 6.1897

 384/26594 [..............................] - ETA: 2:04 - loss: 6.3229

 512/26594 [..............................] - ETA: 2:01 - loss: 6.3550

 640/26594 [..............................] - ETA: 2:02 - loss: 6.3279

 768/26594 [..............................] - ETA: 2:05 - loss: 6.2614

 896/26594 [>.............................] - ETA: 2:06 - loss: 6.2433

1024/26594 [>.............................] - ETA: 2:07 - loss: 6.2477

……

25216/26594 [===========================>..] - ETA: 6s - loss: 6.2456

25344/26594 [===========================>..] - ETA: 6s - loss: 6.2469

25472/26594 [===========================>..] - ETA: 5s - loss: 6.2477

25600/26594 [===========================>..] - ETA: 4s - loss: 6.2486

25728/26594 [============================>.] - ETA: 4s - loss: 6.2480

25856/26594 [============================>.] - ETA: 3s - loss: 6.2483

25984/26594 [============================>.] - ETA: 2s - loss: 6.2487

26112/26594 [============================>.] - ETA: 2s - loss: 6.2485

26240/26594 [============================>.] - ETA: 1s - loss: 6.2483

26368/26594 [============================>.] - ETA: 1s - loss: 6.2482

26496/26594 [============================>.] - ETA: 0s - loss: 6.2485

26594/26594 [==============================] - 129s 5ms/step - loss: 6.2499

Epoch 00009: loss improved from 6.25487 to 6.24987, saving model to hdf5/word-weights-improvement-09-6.2499.hdf5

Epoch 10/10

 128/26594 [..............................] - ETA: 1:56 - loss: 6.4864

 256/26594 [..............................] - ETA: 2:04 - loss: 6.2577

 384/26594 [..............................] - ETA: 2:07 - loss: 6.2857

 512/26594 [..............................] - ETA: 2:10 - loss: 6.3230

……

25856/26594 [============================>.] - ETA: 3s - loss: 6.2426

25984/26594 [============================>.] - ETA: 3s - loss: 6.2447

26112/26594 [============================>.] - ETA: 2s - loss: 6.2446

26240/26594 [============================>.] - ETA: 1s - loss: 6.2449

26368/26594 [============================>.] - ETA: 1s - loss: 6.2467

26496/26594 [============================>.] - ETA: 0s - loss: 6.2461

26594/26594 [==============================] - 135s 5ms/step - loss: 6.2465

Epoch 00010: loss improved from 6.24987 to 6.24646, saving model to hdf5/word-weights-improvement-10-6.2465.hdf5

LSTM_Pre_word.shape:

(3, 3063)

LSTM_Model,Seed:

" 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 "

199 100

Generated Sequence:

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

Done.


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())


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