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
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
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
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()) 赞 (0)