Keras入门——(4)长短期记忆网络LSTM(一)
参考:
https://blog.csdn.net/zwqjoy/article/details/80493341
https://blog.csdn.net/u012735708/article/details/82769711
执行代码:
- # Naive LSTM to learn three-char window to one-char mapping
- import numpy
- from keras.models import Sequential
- from keras.layers import Dense
- from keras.layers import LSTM
- from keras.utils import np_utils
- # fix random seed for reproducibility
- numpy.random.seed(7)
- # define the raw dataset
- alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- # create mapping of characters to integers (0-25) and the reverse
- char_to_int = dict((c, i) for i, c in enumerate(alphabet))
- int_to_char = dict((i, c) for i, c in enumerate(alphabet))
- # prepare the dataset of input to output pairs encoded as integers
- seq_length = 3
- dataX = []
- dataY = []
- for i in range(0, len(alphabet) - seq_length, 1):
- seq_in = alphabet[i:i + seq_length]
- seq_out = alphabet[i + seq_length]
- dataX.append([char_to_int[char] for char in seq_in])
- dataY.append(char_to_int[seq_out])
- print(seq_in, '->', seq_out)
- # reshape X to be [samples, time steps, features]
- X = numpy.reshape(dataX, (len(dataX), 1, seq_length))
- # normalize
- X = X / float(len(alphabet))
- # one hot encode the output variable
- y = np_utils.to_categorical(dataY)
- # create and fit the model
- model = Sequential()
- model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2])))
- model.add(Dense(y.shape[1], activation='softmax'))
- model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
- model.fit(X, y, epochs=500, batch_size=1, verbose=2)
- # summarize performance of the model
- scores = model.evaluate(X, y, verbose=0)
- print("Model Accuracy: %.2f%%" % (scores[1]*100))
- # demonstrate some model predictions
- for pattern in dataX:
- x = numpy.reshape(pattern, (1, 1, len(pattern)))
- x = x / float(len(alphabet))
- prediction = model.predict(x, verbose=0)
- index = numpy.argmax(prediction)
- result = int_to_char[index]
- seq_in = [int_to_char[value] for value in pattern]
- print(seq_in, "->", result)
返回信息:
- Using TensorFlow backend.
- ABC -> D
- BCD -> E
- CDE -> F
- DEF -> G
- EFG -> H
- FGH -> I
- GHI -> J
- HIJ -> K
- IJK -> L
- JKL -> M
- KLM -> N
- LMN -> O
- MNO -> P
- NOP -> Q
- OPQ -> R
- PQR -> S
- QRS -> T
- RST -> U
- STU -> V
- TUV -> W
- UVW -> X
- VWX -> Y
- WXY -> Z
- WARNING:tensorflow:From D:\ProgramData\Anaconda2\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
- Instructions for updating:
- Colocations handled automatically by placer.
- WARNING:tensorflow:From D:\ProgramData\Anaconda2\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
- Instructions for updating:
- Use tf.cast instead.
- Epoch 1/500
- - 8s - loss: 3.2651 - acc: 0.0000e+00
- Epoch 2/500
- - 0s - loss: 3.2527 - acc: 0.0435
- Epoch 3/500
- - 0s - loss: 3.2462 - acc: 0.0435
- Epoch 4/500
- - 0s - loss: 3.2402 - acc: 0.0000e+00
- Epoch 5/500
- - 0s - loss: 3.2339 - acc: 0.0435
- Epoch 6/500
- - 0s - loss: 3.2274 - acc: 0.0435
- Epoch 7/500
- - 0s - loss: 3.2209 - acc: 0.0435
- Epoch 8/500
- - 0s - loss: 3.2142 - acc: 0.0000e+00
- Epoch 9/500
- - 0s - loss: 3.2067 - acc: 0.0435
- Epoch 10/500
- - 0s - loss: 3.1993 - acc: 0.0435
- Epoch 11/500
- - 0s - loss: 3.1918 - acc: 0.0435
- Epoch 12/500
- - 0s - loss: 3.1839 - acc: 0.0000e+00
- Epoch 13/500
- - 0s - loss: 3.1756 - acc: 0.0435
- Epoch 14/500
- - 0s - loss: 3.1674 - acc: 0.0435
- Epoch 15/500
- - 0s - loss: 3.1586 - acc: 0.0000e+00
- Epoch 16/500
- - 0s - loss: 3.1498 - acc: 0.0435
- Epoch 17/500
- - 0s - loss: 3.1418 - acc: 0.0000e+00
- Epoch 18/500
- - 0s - loss: 3.1340 - acc: 0.0000e+00
- Epoch 19/500
- - 0s - loss: 3.1245 - acc: 0.0435
- Epoch 20/500
- - 0s - loss: 3.1167 - acc: 0.0435
- Epoch 21/500
- - 0s - loss: 3.1096 - acc: 0.0435
- Epoch 22/500
- - 0s - loss: 3.1018 - acc: 0.0435
- Epoch 23/500
- - 0s - loss: 3.0935 - acc: 0.0435
- Epoch 24/500
- - 0s - loss: 3.0857 - acc: 0.0435
- Epoch 25/500
- - 0s - loss: 3.0788 - acc: 0.0435
- Epoch 26/500
- - 0s - loss: 3.0721 - acc: 0.0435
- Epoch 27/500
- - 0s - loss: 3.0647 - acc: 0.0435
- Epoch 28/500
- - 0s - loss: 3.0584 - acc: 0.0435
- Epoch 29/500
- - 0s - loss: 3.0530 - acc: 0.0435
- Epoch 30/500
- - 0s - loss: 3.0449 - acc: 0.0435
- Epoch 31/500
- - 0s - loss: 3.0398 - acc: 0.0435
- Epoch 32/500
- - 0s - loss: 3.0328 - acc: 0.0870
- Epoch 33/500
- - 0s - loss: 3.0257 - acc: 0.0870
- Epoch 34/500
- - 0s - loss: 3.0200 - acc: 0.0870
- Epoch 35/500
- - 0s - loss: 3.0132 - acc: 0.0870
- Epoch 36/500
- - 0s - loss: 3.0077 - acc: 0.0870
- Epoch 37/500
- - 0s - loss: 2.9992 - acc: 0.0870
- Epoch 38/500
- - 0s - loss: 2.9946 - acc: 0.0870
- Epoch 39/500
- - 0s - loss: 2.9855 - acc: 0.0870
- Epoch 40/500
- - 0s - loss: 2.9790 - acc: 0.0870
- Epoch 41/500
- - 0s - loss: 2.9725 - acc: 0.0870
- Epoch 42/500
- - 0s - loss: 2.9655 - acc: 0.0870
- Epoch 43/500
- - 0s - loss: 2.9576 - acc: 0.0870
- Epoch 44/500
- - 0s - loss: 2.9501 - acc: 0.0870
- Epoch 45/500
- - 0s - loss: 2.9420 - acc: 0.0870
- Epoch 46/500
- - 0s - loss: 2.9353 - acc: 0.0870
- Epoch 47/500
- - 0s - loss: 2.9271 - acc: 0.0870
- Epoch 48/500
- - 0s - loss: 2.9193 - acc: 0.0870
- Epoch 49/500
- - 0s - loss: 2.9104 - acc: 0.0870
- Epoch 50/500
- - 0s - loss: 2.9012 - acc: 0.0870
- Epoch 51/500
- - 0s - loss: 2.8931 - acc: 0.0870
- Epoch 52/500
- - 0s - loss: 2.8841 - acc: 0.0870
- Epoch 53/500
- - 0s - loss: 2.8759 - acc: 0.0870
- Epoch 54/500
- - 0s - loss: 2.8653 - acc: 0.0870
- Epoch 55/500
- - 0s - loss: 2.8574 - acc: 0.0870
- Epoch 56/500
- - 0s - loss: 2.8467 - acc: 0.0870
- Epoch 57/500
- - 0s - loss: 2.8372 - acc: 0.0870
- Epoch 58/500
- - 0s - loss: 2.8272 - acc: 0.0870
- Epoch 59/500
- - 0s - loss: 2.8180 - acc: 0.0870
- Epoch 60/500
- - 0s - loss: 2.8074 - acc: 0.0870
- Epoch 61/500
- - 0s - loss: 2.7979 - acc: 0.0870
- Epoch 62/500
- - 0s - loss: 2.7865 - acc: 0.1304
- Epoch 63/500
- - 0s - loss: 2.7778 - acc: 0.1304
- Epoch 64/500
- - 0s - loss: 2.7675 - acc: 0.1304
- Epoch 65/500
- - 0s - loss: 2.7577 - acc: 0.0870
- Epoch 66/500
- - 0s - loss: 2.7471 - acc: 0.0870
- Epoch 67/500
- - 0s - loss: 2.7384 - acc: 0.0870
- Epoch 68/500
- - 0s - loss: 2.7288 - acc: 0.0870
- Epoch 69/500
- - 0s - loss: 2.7165 - acc: 0.0870
- Epoch 70/500
- - 0s - loss: 2.7084 - acc: 0.0870
- Epoch 71/500
- - 0s - loss: 2.6975 - acc: 0.0870
- Epoch 72/500
- - 0s - loss: 2.6891 - acc: 0.0870
- Epoch 73/500
- - 0s - loss: 2.6801 - acc: 0.0870
- Epoch 74/500
- - 0s - loss: 2.6708 - acc: 0.0870
- Epoch 75/500
- - 0s - loss: 2.6624 - acc: 0.0870
- Epoch 76/500
- - 0s - loss: 2.6537 - acc: 0.0870
- Epoch 77/500
- - 0s - loss: 2.6471 - acc: 0.0870
- Epoch 78/500
- - 0s - loss: 2.6378 - acc: 0.1304
- Epoch 79/500
- - 0s - loss: 2.6304 - acc: 0.1304
- Epoch 80/500
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- Epoch 81/500
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- Epoch 82/500
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- Epoch 83/500
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- Epoch 84/500
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- Epoch 85/500
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- Epoch 86/500
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- Epoch 87/500
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- Epoch 88/500
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- Epoch 89/500
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- Epoch 90/500
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- Epoch 91/500
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- Epoch 92/500
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- Epoch 93/500
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- Epoch 94/500
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- Epoch 95/500
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- Epoch 96/500
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- Epoch 97/500
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- Epoch 98/500
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- Epoch 99/500
- - 0s - loss: 2.4984 - acc: 0.1304
- Epoch 100/500
- - 0s - loss: 2.4939 - acc: 0.1304
- Epoch 101/500
- - 0s - loss: 2.4886 - acc: 0.1304
- Epoch 102/500
- - 0s - loss: 2.4820 - acc: 0.1304
- Epoch 103/500
- - 0s - loss: 2.4761 - acc: 0.1739
- Epoch 104/500
- - 0s - loss: 2.4696 - acc: 0.1739
- Epoch 105/500
- - 0s - loss: 2.4660 - acc: 0.1304
- Epoch 106/500
- - 0s - loss: 2.4610 - acc: 0.1304
- Epoch 107/500
- - 0s - loss: 2.4551 - acc: 0.1304
- Epoch 108/500
- - 0s - loss: 2.4498 - acc: 0.1304
- Epoch 109/500
- - 0s - loss: 2.4431 - acc: 0.1304
- Epoch 110/500
- - 0s - loss: 2.4387 - acc: 0.1739
- Epoch 111/500
- - 0s - loss: 2.4333 - acc: 0.1304
- Epoch 112/500
- - 0s - loss: 2.4270 - acc: 0.1304
- Epoch 113/500
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- Epoch 114/500
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- Epoch 115/500
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- Epoch 116/500
- - 0s - loss: 2.4078 - acc: 0.1739
- Epoch 117/500
- - 0s - loss: 2.4023 - acc: 0.1739
- Epoch 118/500
- - 0s - loss: 2.3974 - acc: 0.1304
- Epoch 119/500
- - 0s - loss: 2.3921 - acc: 0.2174
- Epoch 120/500
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- Epoch 121/500
- - 0s - loss: 2.3831 - acc: 0.1304
- Epoch 122/500
- - 0s - loss: 2.3777 - acc: 0.1739
- Epoch 123/500
- - 0s - loss: 2.3728 - acc: 0.2174
- Epoch 124/500
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- Epoch 125/500
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- Epoch 126/500
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- Epoch 127/500
- - 0s - loss: 2.3532 - acc: 0.1739
- Epoch 128/500
- - 0s - loss: 2.3482 - acc: 0.1739
- Epoch 129/500
- - 0s - loss: 2.3463 - acc: 0.2174
- Epoch 130/500
- - 0s - loss: 2.3414 - acc: 0.2174
- Epoch 131/500
- - 0s - loss: 2.3363 - acc: 0.2174
- Epoch 132/500
- - 0s - loss: 2.3322 - acc: 0.1739
- Epoch 133/500
- - 0s - loss: 2.3270 - acc: 0.2174
- Epoch 134/500
- - 0s - loss: 2.3238 - acc: 0.2174
- Epoch 135/500
- - 0s - loss: 2.3194 - acc: 0.2174
- Epoch 136/500
- - 0s - loss: 2.3152 - acc: 0.2174
- Epoch 137/500
- - 0s - loss: 2.3090 - acc: 0.2174
- Epoch 138/500
- - 0s - loss: 2.3051 - acc: 0.2174
- Epoch 139/500
- - 0s - loss: 2.3028 - acc: 0.2174
- Epoch 140/500
- - 0s - loss: 2.2952 - acc: 0.2174
- Epoch 141/500
- - 0s - loss: 2.2936 - acc: 0.2174
- Epoch 142/500
- - 0s - loss: 2.2890 - acc: 0.1739
- Epoch 143/500
- - 0s - loss: 2.2830 - acc: 0.1739
- Epoch 144/500
- - 0s - loss: 2.2797 - acc: 0.2174
- Epoch 145/500
- - 0s - loss: 2.2757 - acc: 0.2174
- Epoch 146/500
- - 0s - loss: 2.2710 - acc: 0.2174
- Epoch 147/500
- - 0s - loss: 2.2676 - acc: 0.2174
- Epoch 148/500
- - 0s - loss: 2.2635 - acc: 0.1739
- Epoch 149/500
- - 0s - loss: 2.2603 - acc: 0.2174
- Epoch 150/500
- - 0s - loss: 2.2570 - acc: 0.2174
- Epoch 151/500
- - 0s - loss: 2.2524 - acc: 0.2174
- Epoch 152/500
- - 0s - loss: 2.2483 - acc: 0.1739
- Epoch 153/500
- - 0s - loss: 2.2437 - acc: 0.2174
- Epoch 154/500
- - 0s - loss: 2.2409 - acc: 0.2174
- Epoch 155/500
- - 0s - loss: 2.2361 - acc: 0.1739
- Epoch 156/500
- - 0s - loss: 2.2345 - acc: 0.2174
- Epoch 157/500
- - 0s - loss: 2.2296 - acc: 0.2174
- Epoch 158/500
- - 0s - loss: 2.2252 - acc: 0.2174
- Epoch 159/500
- - 0s - loss: 2.2219 - acc: 0.2174
- Epoch 160/500
- - 0s - loss: 2.2190 - acc: 0.2174
- Epoch 161/500
- - 0s - loss: 2.2161 - acc: 0.2609
- Epoch 162/500
- - 0s - loss: 2.2119 - acc: 0.2609
- Epoch 163/500
- - 0s - loss: 2.2065 - acc: 0.2609
- Epoch 164/500
- - 0s - loss: 2.2046 - acc: 0.2609
- Epoch 165/500
- - 0s - loss: 2.2011 - acc: 0.2609
- Epoch 166/500
- - 0s - loss: 2.1987 - acc: 0.3043
- Epoch 167/500
- - 0s - loss: 2.1948 - acc: 0.2174
- Epoch 168/500
- - 0s - loss: 2.1914 - acc: 0.3043
- Epoch 169/500
- - 0s - loss: 2.1882 - acc: 0.2609
- Epoch 170/500
- - 0s - loss: 2.1863 - acc: 0.2609
- Epoch 171/500
- - 0s - loss: 2.1808 - acc: 0.2174
- Epoch 172/500
- - 0s - loss: 2.1779 - acc: 0.3478
- Epoch 173/500
- - 0s - loss: 2.1744 - acc: 0.3478
- Epoch 174/500
- - 0s - loss: 2.1736 - acc: 0.3478
- Epoch 175/500
- - 0s - loss: 2.1686 - acc: 0.3478
- Epoch 176/500
- - 0s - loss: 2.1652 - acc: 0.3043
- Epoch 177/500
- - 0s - loss: 2.1617 - acc: 0.2609
- Epoch 178/500
- - 0s - loss: 2.1613 - acc: 0.2609
- Epoch 179/500
- - 0s - loss: 2.1553 - acc: 0.3478
- Epoch 180/500
- - 0s - loss: 2.1534 - acc: 0.2609
- Epoch 181/500
- - 0s - loss: 2.1511 - acc: 0.2609
- Epoch 182/500
- - 0s - loss: 2.1477 - acc: 0.3043
- Epoch 183/500
- - 0s - loss: 2.1445 - acc: 0.2609
- Epoch 184/500
- - 0s - loss: 2.1416 - acc: 0.3913
- Epoch 185/500
- - 0s - loss: 2.1383 - acc: 0.3478
- Epoch 186/500
- - 0s - loss: 2.1366 - acc: 0.3478
- Epoch 187/500
- - 0s - loss: 2.1328 - acc: 0.3043
- Epoch 188/500
- - 0s - loss: 2.1317 - acc: 0.3043
- Epoch 189/500
- - 0s - loss: 2.1284 - acc: 0.3478
- Epoch 190/500
- - 0s - loss: 2.1242 - acc: 0.3478
- Epoch 191/500
- - 0s - loss: 2.1225 - acc: 0.3043
- Epoch 192/500
- - 0s - loss: 2.1178 - acc: 0.3043
- Epoch 193/500
- - 0s - loss: 2.1171 - acc: 0.2609
- Epoch 194/500
- - 0s - loss: 2.1141 - acc: 0.2609
- Epoch 195/500
- - 0s - loss: 2.1108 - acc: 0.3043
- Epoch 196/500
- - 0s - loss: 2.1100 - acc: 0.3478
- Epoch 197/500
- - 0s - loss: 2.1051 - acc: 0.3043
- Epoch 198/500
- - 0s - loss: 2.1025 - acc: 0.3478
- Epoch 199/500
- - 0s - loss: 2.1005 - acc: 0.3478
- Epoch 200/500
- - 0s - loss: 2.0982 - acc: 0.3478
- Epoch 201/500
- - 0s - loss: 2.0951 - acc: 0.3478
- Epoch 202/500
- - 0s - loss: 2.0926 - acc: 0.3043
- Epoch 203/500
- - 0s - loss: 2.0919 - acc: 0.3043
- Epoch 204/500
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- Epoch 205/500
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- Epoch 206/500
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- Epoch 207/500
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- Epoch 208/500
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- Epoch 209/500
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- Epoch 210/500
- - 0s - loss: 2.0723 - acc: 0.2609
- Epoch 211/500
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- Epoch 212/500
- - 0s - loss: 2.0690 - acc: 0.3043
- Epoch 213/500
- - 0s - loss: 2.0663 - acc: 0.3478
- Epoch 214/500
- - 0s - loss: 2.0632 - acc: 0.3913
- Epoch 215/500
- - 0s - loss: 2.0628 - acc: 0.3478
- Epoch 216/500
- - 0s - loss: 2.0603 - acc: 0.3478
- Epoch 217/500
- - 0s - loss: 2.0567 - acc: 0.3913
- Epoch 218/500
- - 0s - loss: 2.0559 - acc: 0.3913
- Epoch 219/500
- - 0s - loss: 2.0509 - acc: 0.3913
- Epoch 220/500
- - 0s - loss: 2.0499 - acc: 0.3043
- Epoch 221/500
- - 0s - loss: 2.0482 - acc: 0.3478
- Epoch 222/500
- - 0s - loss: 2.0439 - acc: 0.3478
- Epoch 223/500
- - 0s - loss: 2.0427 - acc: 0.3913
- Epoch 224/500
- - 0s - loss: 2.0404 - acc: 0.4348
- Epoch 225/500
- - 0s - loss: 2.0393 - acc: 0.3913
- Epoch 226/500
- - 0s - loss: 2.0379 - acc: 0.4348
- Epoch 227/500
- - 0s - loss: 2.0360 - acc: 0.4348
- Epoch 228/500
- - 0s - loss: 2.0330 - acc: 0.4348
- Epoch 229/500
- - 0s - loss: 2.0307 - acc: 0.4348
- Epoch 230/500
- - 0s - loss: 2.0269 - acc: 0.4783
- Epoch 231/500
- - 0s - loss: 2.0251 - acc: 0.3913
- Epoch 232/500
- - 0s - loss: 2.0234 - acc: 0.4783
- Epoch 233/500
- - 0s - loss: 2.0222 - acc: 0.4348
- Epoch 234/500
- - 0s - loss: 2.0190 - acc: 0.4783
- Epoch 235/500
- - 0s - loss: 2.0175 - acc: 0.5652
- Epoch 236/500
- - 0s - loss: 2.0161 - acc: 0.4783
- Epoch 237/500
- - 0s - loss: 2.0133 - acc: 0.4348
- Epoch 238/500
- - 0s - loss: 2.0097 - acc: 0.4348
- Epoch 239/500
- - 0s - loss: 2.0094 - acc: 0.3913
- Epoch 240/500
- - 0s - loss: 2.0077 - acc: 0.4783
- Epoch 241/500
- - 0s - loss: 2.0048 - acc: 0.4348
- Epoch 242/500
- - 0s - loss: 2.0028 - acc: 0.4348
- Epoch 243/500
- - 0s - loss: 2.0002 - acc: 0.4348
- Epoch 244/500
- - 0s - loss: 1.9974 - acc: 0.4348
- Epoch 245/500
- - 0s - loss: 1.9958 - acc: 0.4783
- Epoch 246/500
- - 0s - loss: 1.9956 - acc: 0.4348
- Epoch 247/500
- - 0s - loss: 1.9929 - acc: 0.4783
- Epoch 248/500
- - 0s - loss: 1.9916 - acc: 0.4783
- Epoch 249/500
- - 0s - loss: 1.9888 - acc: 0.5652
- Epoch 250/500
- - 0s - loss: 1.9895 - acc: 0.5217
- Epoch 251/500
- - 0s - loss: 1.9838 - acc: 0.4348
- Epoch 252/500
- - 0s - loss: 1.9840 - acc: 0.4348
- Epoch 253/500
- - 0s - loss: 1.9814 - acc: 0.5652
- Epoch 254/500
- - 0s - loss: 1.9812 - acc: 0.4783
- Epoch 255/500
- - 0s - loss: 1.9768 - acc: 0.5217
- Epoch 256/500
- - 0s - loss: 1.9759 - acc: 0.4348
- Epoch 257/500
- - 0s - loss: 1.9741 - acc: 0.4783
- Epoch 258/500
- - 0s - loss: 1.9703 - acc: 0.5652
- Epoch 259/500
- - 0s - loss: 1.9713 - acc: 0.4348
- Epoch 260/500
- - 0s - loss: 1.9653 - acc: 0.5217
- Epoch 261/500
- - 0s - loss: 1.9658 - acc: 0.5217
- Epoch 262/500
- - 0s - loss: 1.9624 - acc: 0.5652
- Epoch 263/500
- - 0s - loss: 1.9614 - acc: 0.5217
- Epoch 264/500
- - 0s - loss: 1.9632 - acc: 0.5217
- Epoch 265/500
- - 0s - loss: 1.9588 - acc: 0.5217
- Epoch 266/500
- - 0s - loss: 1.9556 - acc: 0.5217
- Epoch 267/500
- - 0s - loss: 1.9556 - acc: 0.5217
- Epoch 268/500
- - 0s - loss: 1.9511 - acc: 0.5217
- Epoch 269/500
- - 0s - loss: 1.9522 - acc: 0.5652
- Epoch 270/500
- - 0s - loss: 1.9502 - acc: 0.5652
- Epoch 271/500
- - 0s - loss: 1.9494 - acc: 0.5652
- Epoch 272/500
- - 0s - loss: 1.9450 - acc: 0.5652
- Epoch 273/500
- - 0s - loss: 1.9455 - acc: 0.5217
- Epoch 274/500
- - 0s - loss: 1.9446 - acc: 0.3913
- Epoch 275/500
- - 0s - loss: 1.9406 - acc: 0.4783
- Epoch 276/500
- - 0s - loss: 1.9392 - acc: 0.4783
- Epoch 277/500
- - 0s - loss: 1.9353 - acc: 0.5652
- Epoch 278/500
- - 0s - loss: 1.9356 - acc: 0.4348
- Epoch 279/500
- - 0s - loss: 1.9355 - acc: 0.6087
- Epoch 280/500
- - 0s - loss: 1.9345 - acc: 0.5652
- Epoch 281/500
- - 0s - loss: 1.9291 - acc: 0.6087
- Epoch 282/500
- - 0s - loss: 1.9311 - acc: 0.6087
- Epoch 283/500
- - 0s - loss: 1.9298 - acc: 0.4783
- Epoch 284/500
- - 0s - loss: 1.9264 - acc: 0.5217
- Epoch 285/500
- - 0s - loss: 1.9245 - acc: 0.6087
- Epoch 286/500
- - 0s - loss: 1.9233 - acc: 0.5652
- Epoch 287/500
- - 0s - loss: 1.9217 - acc: 0.4783
- Epoch 288/500
- - 0s - loss: 1.9193 - acc: 0.5217
- Epoch 289/500
- - 0s - loss: 1.9149 - acc: 0.5217
- Epoch 290/500
- - 0s - loss: 1.9153 - acc: 0.5217
- Epoch 291/500
- - 0s - loss: 1.9128 - acc: 0.6087
- Epoch 292/500
- - 0s - loss: 1.9112 - acc: 0.6957
- Epoch 293/500
- - 0s - loss: 1.9112 - acc: 0.6087
- Epoch 294/500
- - 0s - loss: 1.9095 - acc: 0.6087
- Epoch 295/500
- - 0s - loss: 1.9077 - acc: 0.5652
- Epoch 296/500
- - 0s - loss: 1.9059 - acc: 0.6087
- Epoch 297/500
- - 0s - loss: 1.9054 - acc: 0.6522
- Epoch 298/500
- - 0s - loss: 1.9045 - acc: 0.6087
- Epoch 299/500
- - 0s - loss: 1.9010 - acc: 0.6522
- Epoch 300/500
- - 0s - loss: 1.8994 - acc: 0.5217
- Epoch 301/500
- - 0s - loss: 1.8975 - acc: 0.4348
- Epoch 302/500
- - 0s - loss: 1.8957 - acc: 0.5652
- Epoch 303/500
- - 0s - loss: 1.8956 - acc: 0.6087
- Epoch 304/500
- - 0s - loss: 1.8962 - acc: 0.4783
- Epoch 305/500
- - 0s - loss: 1.8935 - acc: 0.5217
- Epoch 306/500
- - 0s - loss: 1.8892 - acc: 0.5652
- Epoch 307/500
- - 0s - loss: 1.8881 - acc: 0.6087
- Epoch 308/500
- - 0s - loss: 1.8867 - acc: 0.5652
- Epoch 309/500
- - 0s - loss: 1.8869 - acc: 0.5652
- Epoch 310/500
- - 0s - loss: 1.8837 - acc: 0.6087
- Epoch 311/500
- - 0s - loss: 1.8825 - acc: 0.6522
- Epoch 312/500
- - 0s - loss: 1.8791 - acc: 0.5217
- Epoch 313/500
- - 0s - loss: 1.8790 - acc: 0.6087
- Epoch 314/500
- - 0s - loss: 1.8771 - acc: 0.6087
- Epoch 315/500
- - 0s - loss: 1.8766 - acc: 0.6087
- Epoch 316/500
- - 0s - loss: 1.8746 - acc: 0.5652
- Epoch 317/500
- - 0s - loss: 1.8720 - acc: 0.5652
- Epoch 318/500
- - 0s - loss: 1.8711 - acc: 0.6087
- Epoch 319/500
- - 0s - loss: 1.8699 - acc: 0.5652
- Epoch 320/500
- - 0s - loss: 1.8688 - acc: 0.4783
- Epoch 321/500
- - 0s - loss: 1.8674 - acc: 0.5652
- Epoch 322/500
- - 0s - loss: 1.8677 - acc: 0.5652
- Epoch 323/500
- - 0s - loss: 1.8627 - acc: 0.5217
- Epoch 324/500
- - 0s - loss: 1.8636 - acc: 0.6087
- Epoch 325/500
- - 0s - loss: 1.8623 - acc: 0.6522
- Epoch 326/500
- - 0s - loss: 1.8608 - acc: 0.5217
- Epoch 327/500
- - 0s - loss: 1.8619 - acc: 0.6522
- Epoch 328/500
- - 0s - loss: 1.8582 - acc: 0.6087
- Epoch 329/500
- - 0s - loss: 1.8554 - acc: 0.5652
- Epoch 330/500
- - 0s - loss: 1.8540 - acc: 0.6522
- Epoch 331/500
- - 0s - loss: 1.8567 - acc: 0.5652
- Epoch 332/500
- - 0s - loss: 1.8520 - acc: 0.5652
- Epoch 333/500
- - 0s - loss: 1.8515 - acc: 0.6522
- Epoch 334/500
- - 0s - loss: 1.8484 - acc: 0.6087
- Epoch 335/500
- - 0s - loss: 1.8498 - acc: 0.6087
- Epoch 336/500
- - 0s - loss: 1.8451 - acc: 0.6522
- Epoch 337/500
- - 0s - loss: 1.8434 - acc: 0.6522
- Epoch 338/500
- - 0s - loss: 1.8431 - acc: 0.5217
- Epoch 339/500
- - 0s - loss: 1.8418 - acc: 0.6087
- Epoch 340/500
- - 0s - loss: 1.8410 - acc: 0.5217
- Epoch 341/500
- - 0s - loss: 1.8395 - acc: 0.6522
- Epoch 342/500
- - 0s - loss: 1.8392 - acc: 0.6087
- Epoch 343/500
- - 0s - loss: 1.8362 - acc: 0.5652
- Epoch 344/500
- - 0s - loss: 1.8336 - acc: 0.6087
- Epoch 345/500
- - 0s - loss: 1.8320 - acc: 0.6087
- Epoch 346/500
- - 0s - loss: 1.8316 - acc: 0.6522
- Epoch 347/500
- - 0s - loss: 1.8325 - acc: 0.5652
- Epoch 348/500
- - 0s - loss: 1.8284 - acc: 0.5652
- Epoch 349/500
- - 0s - loss: 1.8278 - acc: 0.6087
- Epoch 350/500
- - 0s - loss: 1.8263 - acc: 0.6087
- Epoch 351/500
- - 0s - loss: 1.8234 - acc: 0.5217
- Epoch 352/500
- - 0s - loss: 1.8244 - acc: 0.6087
- Epoch 353/500
- - 0s - loss: 1.8224 - acc: 0.6522
- Epoch 354/500
- - 0s - loss: 1.8208 - acc: 0.6522
- Epoch 355/500
- - 0s - loss: 1.8225 - acc: 0.6522
- Epoch 356/500
- - 0s - loss: 1.8181 - acc: 0.6522
- Epoch 357/500
- - 0s - loss: 1.8170 - acc: 0.5217
- Epoch 358/500
- - 0s - loss: 1.8182 - acc: 0.6522
- Epoch 359/500
- - 0s - loss: 1.8146 - acc: 0.5652
- Epoch 360/500
- - 0s - loss: 1.8114 - acc: 0.6957
- Epoch 361/500
- - 0s - loss: 1.8111 - acc: 0.7391
- Epoch 362/500
- - 0s - loss: 1.8091 - acc: 0.6522
- Epoch 363/500
- - 0s - loss: 1.8096 - acc: 0.5652
- Epoch 364/500
- - 0s - loss: 1.8078 - acc: 0.6087
- Epoch 365/500
- - 0s - loss: 1.8069 - acc: 0.5652
- Epoch 366/500
- - 0s - loss: 1.8060 - acc: 0.6522
- Epoch 367/500
- - 0s - loss: 1.8041 - acc: 0.6087
- Epoch 368/500
- - 0s - loss: 1.8021 - acc: 0.6957
- Epoch 369/500
- - 0s - loss: 1.8003 - acc: 0.6957
- Epoch 370/500
- - 0s - loss: 1.8004 - acc: 0.6957
- Epoch 371/500
- - 0s - loss: 1.7980 - acc: 0.5652
- Epoch 372/500
- - 0s - loss: 1.7977 - acc: 0.6522
- Epoch 373/500
- - 0s - loss: 1.7946 - acc: 0.6957
- Epoch 374/500
- - 0s - loss: 1.7930 - acc: 0.6957
- Epoch 375/500
- - 0s - loss: 1.7939 - acc: 0.6957
- Epoch 376/500
- - 0s - loss: 1.7907 - acc: 0.6087
- Epoch 377/500
- - 0s - loss: 1.7892 - acc: 0.6522
- Epoch 378/500
- - 0s - loss: 1.7899 - acc: 0.6087
- Epoch 379/500
- - 0s - loss: 1.7861 - acc: 0.6522
- Epoch 380/500
- - 0s - loss: 1.7871 - acc: 0.6522
- Epoch 381/500
- - 0s - loss: 1.7870 - acc: 0.6087
- Epoch 382/500
- - 0s - loss: 1.7850 - acc: 0.7391
- Epoch 383/500
- - 0s - loss: 1.7811 - acc: 0.6957
- Epoch 384/500
- - 0s - loss: 1.7812 - acc: 0.6522
- Epoch 385/500
- - 0s - loss: 1.7824 - acc: 0.7391
- Epoch 386/500
- - 0s - loss: 1.7790 - acc: 0.6522
- Epoch 387/500
- - 0s - loss: 1.7762 - acc: 0.6957
- Epoch 388/500
- - 0s - loss: 1.7761 - acc: 0.7826
- Epoch 389/500
- - 0s - loss: 1.7763 - acc: 0.6957
- Epoch 390/500
- - 0s - loss: 1.7740 - acc: 0.6957
- Epoch 391/500
- - 0s - loss: 1.7719 - acc: 0.6957
- Epoch 392/500
- - 0s - loss: 1.7698 - acc: 0.6957
- Epoch 393/500
- - 0s - loss: 1.7712 - acc: 0.6522
- Epoch 394/500
- - 0s - loss: 1.7673 - acc: 0.6522
- Epoch 395/500
- - 0s - loss: 1.7690 - acc: 0.6957
- Epoch 396/500
- - 0s - loss: 1.7659 - acc: 0.6522
- Epoch 397/500
- - 0s - loss: 1.7666 - acc: 0.6087
- Epoch 398/500
- - 0s - loss: 1.7657 - acc: 0.6087
- Epoch 399/500
- - 0s - loss: 1.7630 - acc: 0.6957
- Epoch 400/500
- - 0s - loss: 1.7623 - acc: 0.6522
- Epoch 401/500
- - 0s - loss: 1.7604 - acc: 0.6957
- Epoch 402/500
- - 0s - loss: 1.7576 - acc: 0.7391
- Epoch 403/500
- - 0s - loss: 1.7580 - acc: 0.6522
- Epoch 404/500
- - 0s - loss: 1.7584 - acc: 0.6957
- Epoch 405/500
- - 0s - loss: 1.7561 - acc: 0.6522
- Epoch 406/500
- - 0s - loss: 1.7555 - acc: 0.6522
- Epoch 407/500
- - 0s - loss: 1.7526 - acc: 0.8261
- Epoch 408/500
- - 0s - loss: 1.7531 - acc: 0.6957
- Epoch 409/500
- - 0s - loss: 1.7507 - acc: 0.6957
- Epoch 410/500
- - 0s - loss: 1.7508 - acc: 0.7391
- Epoch 411/500
- - 0s - loss: 1.7495 - acc: 0.6957
- Epoch 412/500
- - 0s - loss: 1.7495 - acc: 0.7391
- Epoch 413/500
- - 0s - loss: 1.7469 - acc: 0.6957
- Epoch 414/500
- - 0s - loss: 1.7459 - acc: 0.6522
- Epoch 415/500
- - 0s - loss: 1.7434 - acc: 0.6957
- Epoch 416/500
- - 0s - loss: 1.7414 - acc: 0.6522
- Epoch 417/500
- - 0s - loss: 1.7393 - acc: 0.6957
- Epoch 418/500
- - 0s - loss: 1.7383 - acc: 0.6522
- Epoch 419/500
- - 0s - loss: 1.7388 - acc: 0.6957
- Epoch 420/500
- - 0s - loss: 1.7389 - acc: 0.6087
- Epoch 421/500
- - 0s - loss: 1.7379 - acc: 0.6957
- Epoch 422/500
- - 0s - loss: 1.7335 - acc: 0.6957
- Epoch 423/500
- - 0s - loss: 1.7331 - acc: 0.7391
- Epoch 424/500
- - 0s - loss: 1.7339 - acc: 0.6957
- Epoch 425/500
- - 0s - loss: 1.7338 - acc: 0.7391
- Epoch 426/500
- - 0s - loss: 1.7303 - acc: 0.6957
- Epoch 427/500
- - 0s - loss: 1.7278 - acc: 0.7826
- Epoch 428/500
- - 0s - loss: 1.7274 - acc: 0.6522
- Epoch 429/500
- - 0s - loss: 1.7277 - acc: 0.7391
- Epoch 430/500
- - 0s - loss: 1.7264 - acc: 0.6957
- Epoch 431/500
- - 0s - loss: 1.7249 - acc: 0.6522
- Epoch 432/500
- - 0s - loss: 1.7245 - acc: 0.6522
- Epoch 433/500
- - 0s - loss: 1.7202 - acc: 0.7391
- Epoch 434/500
- - 0s - loss: 1.7201 - acc: 0.6522
- Epoch 435/500
- - 0s - loss: 1.7186 - acc: 0.7391
- Epoch 436/500
- - 0s - loss: 1.7177 - acc: 0.8261
- Epoch 437/500
- - 0s - loss: 1.7187 - acc: 0.7391
- Epoch 438/500
- - 0s - loss: 1.7170 - acc: 0.7391
- Epoch 439/500
- - 0s - loss: 1.7148 - acc: 0.7391
- Epoch 440/500
- - 0s - loss: 1.7130 - acc: 0.6957
- Epoch 441/500
- - 0s - loss: 1.7140 - acc: 0.8261
- Epoch 442/500
- - 0s - loss: 1.7124 - acc: 0.7826
- Epoch 443/500
- - 0s - loss: 1.7077 - acc: 0.7826
- Epoch 444/500
- - 0s - loss: 1.7108 - acc: 0.6957
- Epoch 445/500
- - 0s - loss: 1.7080 - acc: 0.7391
- Epoch 446/500
- - 0s - loss: 1.7068 - acc: 0.7391
- Epoch 447/500
- - 0s - loss: 1.7061 - acc: 0.6522
- Epoch 448/500
- - 0s - loss: 1.7056 - acc: 0.6957
- Epoch 449/500
- - 0s - loss: 1.7052 - acc: 0.6957
- Epoch 450/500
- - 0s - loss: 1.7015 - acc: 0.7391
- Epoch 451/500
- - 0s - loss: 1.7008 - acc: 0.7391
- Epoch 452/500
- - 0s - loss: 1.6998 - acc: 0.6957
- Epoch 453/500
- - 0s - loss: 1.7005 - acc: 0.7391
- Epoch 454/500
- - 0s - loss: 1.6990 - acc: 0.7826
- Epoch 455/500
- - 0s - loss: 1.6948 - acc: 0.6957
- Epoch 456/500
- - 0s - loss: 1.6984 - acc: 0.8261
- Epoch 457/500
- - 0s - loss: 1.6917 - acc: 0.7826
- Epoch 458/500
- - 0s - loss: 1.6947 - acc: 0.6087
- Epoch 459/500
- - 0s - loss: 1.6923 - acc: 0.7826
- Epoch 460/500
- - 0s - loss: 1.6934 - acc: 0.7391
- Epoch 461/500
- - 0s - loss: 1.6918 - acc: 0.7391
- Epoch 462/500
- - 0s - loss: 1.6893 - acc: 0.7391
- Epoch 463/500
- - 0s - loss: 1.6865 - acc: 0.6957
- Epoch 464/500
- - 0s - loss: 1.6843 - acc: 0.6957
- Epoch 465/500
- - 0s - loss: 1.6856 - acc: 0.7391
- Epoch 466/500
- - 0s - loss: 1.6861 - acc: 0.7391
- Epoch 467/500
- - 0s - loss: 1.6828 - acc: 0.7826
- Epoch 468/500
- - 0s - loss: 1.6819 - acc: 0.7826
- Epoch 469/500
- - 0s - loss: 1.6800 - acc: 0.8261
- Epoch 470/500
- - 0s - loss: 1.6785 - acc: 0.7826
- Epoch 471/500
- - 0s - loss: 1.6795 - acc: 0.8261
- Epoch 472/500
- - 0s - loss: 1.6761 - acc: 0.7391
- Epoch 473/500
- - 0s - loss: 1.6770 - acc: 0.8261
- Epoch 474/500
- - 0s - loss: 1.6755 - acc: 0.8261
- Epoch 475/500
- - 0s - loss: 1.6722 - acc: 0.7826
- Epoch 476/500
- - 0s - loss: 1.6703 - acc: 0.7826
- Epoch 477/500
- - 0s - loss: 1.6705 - acc: 0.7391
- Epoch 478/500
- - 0s - loss: 1.6700 - acc: 0.7826
- Epoch 479/500
- - 0s - loss: 1.6676 - acc: 0.8696
- Epoch 480/500
- - 0s - loss: 1.6700 - acc: 0.7826
- Epoch 481/500
- - 0s - loss: 1.6695 - acc: 0.7826
- Epoch 482/500
- - 0s - loss: 1.6668 - acc: 0.6957
- Epoch 483/500
- - 0s - loss: 1.6669 - acc: 0.7391
- Epoch 484/500
- - 0s - loss: 1.6657 - acc: 0.6957
- Epoch 485/500
- - 0s - loss: 1.6640 - acc: 0.7391
- Epoch 486/500
- - 0s - loss: 1.6613 - acc: 0.7391
- Epoch 487/500
- - 0s - loss: 1.6623 - acc: 0.7826
- Epoch 488/500
- - 0s - loss: 1.6612 - acc: 0.6957
- Epoch 489/500
- - 0s - loss: 1.6574 - acc: 0.7391
- Epoch 490/500
- - 0s - loss: 1.6580 - acc: 0.7826
- Epoch 491/500
- - 0s - loss: 1.6575 - acc: 0.7826
- Epoch 492/500
- - 0s - loss: 1.6556 - acc: 0.8261
- Epoch 493/500
- - 0s - loss: 1.6568 - acc: 0.7391
- Epoch 494/500
- - 0s - loss: 1.6551 - acc: 0.7391
- Epoch 495/500
- - 0s - loss: 1.6500 - acc: 0.8261
- Epoch 496/500
- - 0s - loss: 1.6521 - acc: 0.7391
- Epoch 497/500
- - 0s - loss: 1.6502 - acc: 0.7391
- Epoch 498/500
- - 0s - loss: 1.6516 - acc: 0.8261
- Epoch 499/500
- - 0s - loss: 1.6491 - acc: 0.7826
- Epoch 500/500
- - 0s - loss: 1.6453 - acc: 0.7826
- Model Accuracy: 86.96%
- ['A', 'B', 'C'] -> D
- ['B', 'C', 'D'] -> E
- ['C', 'D', 'E'] -> F
- ['D', 'E', 'F'] -> G
- ['E', 'F', 'G'] -> H
- ['F', 'G', 'H'] -> I
- ['G', 'H', 'I'] -> J
- ['H', 'I', 'J'] -> K
- ['I', 'J', 'K'] -> L
- ['J', 'K', 'L'] -> M
- ['K', 'L', 'M'] -> N
- ['L', 'M', 'N'] -> O
- ['M', 'N', 'O'] -> P
- ['N', 'O', 'P'] -> Q
- ['O', 'P', 'Q'] -> R
- ['P', 'Q', 'R'] -> S
- ['Q', 'R', 'S'] -> T
- ['R', 'S', 'T'] -> U
- ['S', 'T', 'U'] -> V
- ['T', 'U', 'V'] -> X
- ['U', 'V', 'W'] -> Z
- ['V', 'W', 'X'] -> Z
- ['W', 'X', 'Y'] -> Z
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