import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.utils import np_utils
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
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), seq_length, 1))
X = X / float(len(alphabet))
y = np_utils.to_categorical(dataY)
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=400, batch_size=1, verbose=2)
scores = model.evaluate(X, y)
print("Model Accuracy: %.2f%%" % (scores[1]*100))
for pattern in ['WBC', 'WKL', 'WTU', 'DWF', 'MWO', 'VWW', 'GHW', 'JKW', 'PQW']:
pattern = [char_to_int[c] for c in pattern]
x = numpy.reshape(pattern, (1, len(pattern), 1))
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)
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 /databricks/python/lib/python3.7/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 /databricks/python/lib/python3.7/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 1/400
- 1s - loss: 3.2607 - acc: 0.0435
Epoch 2/400
- 0s - loss: 3.2471 - acc: 0.0435
Epoch 3/400
- 0s - loss: 3.2406 - acc: 0.0435
Epoch 4/400
- 0s - loss: 3.2340 - acc: 0.0435
Epoch 5/400
- 0s - loss: 3.2268 - acc: 0.0435
Epoch 6/400
- 0s - loss: 3.2188 - acc: 0.0435
Epoch 7/400
- 0s - loss: 3.2119 - acc: 0.0000e+00
Epoch 8/400
- 0s - loss: 3.2032 - acc: 0.0000e+00
Epoch 9/400
- 0s - loss: 3.1923 - acc: 0.0000e+00
Epoch 10/400
- 0s - loss: 3.1813 - acc: 0.0000e+00
Epoch 11/400
- 0s - loss: 3.1704 - acc: 0.0000e+00
Epoch 12/400
- 0s - loss: 3.1563 - acc: 0.0000e+00
Epoch 13/400
- 0s - loss: 3.1418 - acc: 0.0435
Epoch 14/400
- 0s - loss: 3.1271 - acc: 0.0000e+00
Epoch 15/400
- 0s - loss: 3.1095 - acc: 0.0435
Epoch 16/400
- 0s - loss: 3.0919 - acc: 0.0435
Epoch 17/400
- 0s - loss: 3.0732 - acc: 0.0435
Epoch 18/400
- 0s - loss: 3.0534 - acc: 0.0435
Epoch 19/400
- 0s - loss: 3.0314 - acc: 0.0435
Epoch 20/400
- 0s - loss: 3.0114 - acc: 0.0000e+00
Epoch 21/400
- 0s - loss: 2.9832 - acc: 0.0000e+00
Epoch 22/400
- 0s - loss: 2.9516 - acc: 0.0435
Epoch 23/400
- 0s - loss: 2.9206 - acc: 0.0870
Epoch 24/400
- 0s - loss: 2.8855 - acc: 0.1304
Epoch 25/400
- 0s - loss: 2.8390 - acc: 0.0435
Epoch 26/400
- 0s - loss: 2.7914 - acc: 0.0435
Epoch 27/400
- 0s - loss: 2.7509 - acc: 0.0435
Epoch 28/400
- 0s - loss: 2.6969 - acc: 0.0870
Epoch 29/400
- 0s - loss: 2.6545 - acc: 0.0435
Epoch 30/400
- 0s - loss: 2.6108 - acc: 0.0870
Epoch 31/400
- 0s - loss: 2.5731 - acc: 0.0435
Epoch 32/400
- 0s - loss: 2.5345 - acc: 0.0870
Epoch 33/400
- 0s - loss: 2.5017 - acc: 0.0435
Epoch 34/400
- 0s - loss: 2.4686 - acc: 0.1304
Epoch 35/400
- 0s - loss: 2.4437 - acc: 0.0870
Epoch 36/400
- 0s - loss: 2.4134 - acc: 0.1304
Epoch 37/400
- 0s - loss: 2.3951 - acc: 0.1304
Epoch 38/400
- 0s - loss: 2.3646 - acc: 0.1304
Epoch 39/400
- 0s - loss: 2.3386 - acc: 0.1304
Epoch 40/400
- 0s - loss: 2.3184 - acc: 0.1739
Epoch 41/400
- 0s - loss: 2.2917 - acc: 0.1304
Epoch 42/400
- 0s - loss: 2.2671 - acc: 0.1304
Epoch 43/400
- 0s - loss: 2.2335 - acc: 0.2174
Epoch 44/400
- 0s - loss: 2.2171 - acc: 0.1739
Epoch 45/400
- 0s - loss: 2.1917 - acc: 0.1739
Epoch 46/400
- 0s - loss: 2.1575 - acc: 0.1739
Epoch 47/400
- 0s - loss: 2.1401 - acc: 0.1739
Epoch 48/400
- 0s - loss: 2.1176 - acc: 0.0870
Epoch 49/400
- 0s - loss: 2.0948 - acc: 0.2174
Epoch 50/400
- 0s - loss: 2.0782 - acc: 0.2174
Epoch 51/400
- 0s - loss: 2.0612 - acc: 0.2174
Epoch 52/400
- 0s - loss: 2.0398 - acc: 0.3043
Epoch 53/400
- 0s - loss: 2.0150 - acc: 0.2174
Epoch 54/400
- 0s - loss: 1.9948 - acc: 0.2609
Epoch 55/400
- 0s - loss: 1.9834 - acc: 0.2609
Epoch 56/400
- 0s - loss: 1.9608 - acc: 0.3478
Epoch 57/400
- 0s - loss: 1.9438 - acc: 0.2609
Epoch 58/400
- 0s - loss: 1.9316 - acc: 0.3913
Epoch 59/400
- 0s - loss: 1.9126 - acc: 0.2609
Epoch 60/400
- 0s - loss: 1.9025 - acc: 0.3043
Epoch 61/400
- 0s - loss: 1.8868 - acc: 0.2609
Epoch 62/400
- 0s - loss: 1.8789 - acc: 0.2174
Epoch 63/400
- 0s - loss: 1.8545 - acc: 0.2174
Epoch 64/400
- 0s - loss: 1.8475 - acc: 0.3043
Epoch 65/400
- 0s - loss: 1.8338 - acc: 0.2609
Epoch 66/400
- 0s - loss: 1.8265 - acc: 0.3478
Epoch 67/400
- 0s - loss: 1.8118 - acc: 0.3043
Epoch 68/400
- 0s - loss: 1.7896 - acc: 0.3478
Epoch 69/400
- 0s - loss: 1.7823 - acc: 0.3043
Epoch 70/400
- 0s - loss: 1.7655 - acc: 0.4783
Epoch 71/400
- 0s - loss: 1.7583 - acc: 0.4348
Epoch 72/400
- 0s - loss: 1.7516 - acc: 0.3478
Epoch 73/400
- 0s - loss: 1.7415 - acc: 0.3043
Epoch 74/400
- 0s - loss: 1.7327 - acc: 0.4348
Epoch 75/400
- 0s - loss: 1.7153 - acc: 0.3913
Epoch 76/400
- 0s - loss: 1.7080 - acc: 0.3478
Epoch 77/400
- 0s - loss: 1.6930 - acc: 0.3478
Epoch 78/400
- 0s - loss: 1.6934 - acc: 0.4348
Epoch 79/400
- 0s - loss: 1.6783 - acc: 0.4348
Epoch 80/400
- 0s - loss: 1.6727 - acc: 0.4783
Epoch 81/400
- 0s - loss: 1.6616 - acc: 0.4348
Epoch 82/400
- 0s - loss: 1.6524 - acc: 0.4348
Epoch 83/400
- 0s - loss: 1.6392 - acc: 0.5652
Epoch 84/400
- 0s - loss: 1.6321 - acc: 0.6522
Epoch 85/400
- 0s - loss: 1.6241 - acc: 0.5217
Epoch 86/400
- 0s - loss: 1.6140 - acc: 0.6522
Epoch 87/400
- 0s - loss: 1.6054 - acc: 0.5652
Epoch 88/400
- 0s - loss: 1.5969 - acc: 0.6087
Epoch 89/400
- 0s - loss: 1.5884 - acc: 0.5652
Epoch 90/400
- 0s - loss: 1.5831 - acc: 0.5217
Epoch 91/400
- 0s - loss: 1.5750 - acc: 0.5652
Epoch 92/400
- 0s - loss: 1.5625 - acc: 0.6522
Epoch 93/400
- 0s - loss: 1.5559 - acc: 0.6957
Epoch 94/400
- 0s - loss: 1.5512 - acc: 0.6957
Epoch 95/400
- 0s - loss: 1.5378 - acc: 0.6957
Epoch 96/400
- 0s - loss: 1.5266 - acc: 0.7391
Epoch 97/400
- 0s - loss: 1.5172 - acc: 0.6522
Epoch 98/400
- 0s - loss: 1.5146 - acc: 0.6522
Epoch 99/400
- 0s - loss: 1.5055 - acc: 0.6522
Epoch 100/400
- 0s - loss: 1.4920 - acc: 0.6957
Epoch 101/400
- 0s - loss: 1.4909 - acc: 0.7826
Epoch 102/400
- 0s - loss: 1.4820 - acc: 0.6957
Epoch 103/400
- 0s - loss: 1.4706 - acc: 0.6957
Epoch 104/400
- 0s - loss: 1.4739 - acc: 0.7391
Epoch 105/400
- 0s - loss: 1.4650 - acc: 0.6957
Epoch 106/400
- 0s - loss: 1.4545 - acc: 0.7391
Epoch 107/400
- 0s - loss: 1.4526 - acc: 0.7391
Epoch 108/400
- 0s - loss: 1.4383 - acc: 0.7391
Epoch 109/400
- 0s - loss: 1.4341 - acc: 0.6957
Epoch 110/400
- 0s - loss: 1.4214 - acc: 0.6957
Epoch 111/400
- 0s - loss: 1.4173 - acc: 0.7826
Epoch 112/400
- 0s - loss: 1.4146 - acc: 0.7391
Epoch 113/400
- 0s - loss: 1.4028 - acc: 0.6957
Epoch 114/400
- 0s - loss: 1.3965 - acc: 0.7391
Epoch 115/400
- 0s - loss: 1.3840 - acc: 0.6957
Epoch 116/400
- 0s - loss: 1.3815 - acc: 0.6957
Epoch 117/400
- 0s - loss: 1.3780 - acc: 0.6957
Epoch 118/400
- 0s - loss: 1.3642 - acc: 0.7826
Epoch 119/400
- 0s - loss: 1.3611 - acc: 0.6957
Epoch 120/400
- 0s - loss: 1.3554 - acc: 0.8261
Epoch 121/400
- 0s - loss: 1.3459 - acc: 0.7826
Epoch 122/400
- 0s - loss: 1.3397 - acc: 0.6957
Epoch 123/400
- 0s - loss: 1.3315 - acc: 0.7391
Epoch 124/400
- 0s - loss: 1.3299 - acc: 0.7391
Epoch 125/400
- 0s - loss: 1.3273 - acc: 0.6957
Epoch 126/400
- 0s - loss: 1.3126 - acc: 0.7391
Epoch 127/400
- 0s - loss: 1.3127 - acc: 0.8261
Epoch 128/400
- 0s - loss: 1.3052 - acc: 0.7826
Epoch 129/400
- 0s - loss: 1.3013 - acc: 0.7826
Epoch 130/400
- 0s - loss: 1.2956 - acc: 0.8261
Epoch 131/400
- 0s - loss: 1.2823 - acc: 0.8261
Epoch 132/400
- 0s - loss: 1.2840 - acc: 0.7826
Epoch 133/400
- 0s - loss: 1.2661 - acc: 0.7826
Epoch 134/400
- 0s - loss: 1.2652 - acc: 0.7826
Epoch 135/400
- 0s - loss: 1.2592 - acc: 0.8696
Epoch 136/400
- 0s - loss: 1.2529 - acc: 0.8261
Epoch 137/400
- 0s - loss: 1.2499 - acc: 0.8261
Epoch 138/400
- 0s - loss: 1.2379 - acc: 0.8261
Epoch 139/400
- 0s - loss: 1.2483 - acc: 0.8261
Epoch 140/400
- 0s - loss: 1.2352 - acc: 0.8261
Epoch 141/400
- 0s - loss: 1.2215 - acc: 0.8261
Epoch 142/400
- 0s - loss: 1.2234 - acc: 0.7826
Epoch 143/400
- 0s - loss: 1.2134 - acc: 0.8261
Epoch 144/400
- 0s - loss: 1.2076 - acc: 0.8261
Epoch 145/400
- 0s - loss: 1.2023 - acc: 0.8261
Epoch 146/400
- 0s - loss: 1.1932 - acc: 0.8261
Epoch 147/400
- 0s - loss: 1.1943 - acc: 0.8696
Epoch 148/400
- 0s - loss: 1.1852 - acc: 0.8696
Epoch 149/400
- 0s - loss: 1.1806 - acc: 0.7826
Epoch 150/400
- 0s - loss: 1.1755 - acc: 0.8261
Epoch 151/400
- 0s - loss: 1.1730 - acc: 0.8696
Epoch 152/400
- 0s - loss: 1.1625 - acc: 0.8261
Epoch 153/400
- 0s - loss: 1.1569 - acc: 0.9130
Epoch 154/400
- 0s - loss: 1.1530 - acc: 0.8261
Epoch 155/400
- 0s - loss: 1.1432 - acc: 0.8261
Epoch 156/400
- 0s - loss: 1.1481 - acc: 0.8261
Epoch 157/400
- 0s - loss: 1.1401 - acc: 0.8696
Epoch 158/400
- 0s - loss: 1.1241 - acc: 0.8696
Epoch 159/400
- 0s - loss: 1.1240 - acc: 0.9130
Epoch 160/400
- 0s - loss: 1.1125 - acc: 0.9130
Epoch 161/400
- 0s - loss: 1.1103 - acc: 0.8696
Epoch 162/400
- 0s - loss: 1.1038 - acc: 0.8696
Epoch 163/400
- 0s - loss: 1.0996 - acc: 0.8696
Epoch 164/400
- 0s - loss: 1.0889 - acc: 0.8696
Epoch 165/400
- 0s - loss: 1.0917 - acc: 0.8261
Epoch 166/400
- 0s - loss: 1.0825 - acc: 0.8261
Epoch 167/400
- 0s - loss: 1.0885 - acc: 0.8261
Epoch 168/400
- 0s - loss: 1.0763 - acc: 0.8696
Epoch 169/400
- 0s - loss: 1.0647 - acc: 0.8696
Epoch 170/400
- 0s - loss: 1.0602 - acc: 0.8696
Epoch 171/400
- 0s - loss: 1.0542 - acc: 0.8696
Epoch 172/400
- 0s - loss: 1.0479 - acc: 0.8261
Epoch 173/400
- 0s - loss: 1.0519 - acc: 0.8696
Epoch 174/400
- 0s - loss: 1.0456 - acc: 0.9130
Epoch 175/400
- 0s - loss: 1.0316 - acc: 0.9130
Epoch 176/400
- 0s - loss: 1.0308 - acc: 0.9130
Epoch 177/400
- 0s - loss: 1.0253 - acc: 0.9130
Epoch 178/400
- 0s - loss: 1.0219 - acc: 0.9130
Epoch 179/400
- 0s - loss: 1.0136 - acc: 0.9130
Epoch 180/400
- 0s - loss: 1.0060 - acc: 0.9130
Epoch 181/400
- 0s - loss: 1.0015 - acc: 0.9130
Epoch 182/400
- 0s - loss: 1.0028 - acc: 0.8696
Epoch 183/400
- 0s - loss: 0.9979 - acc: 0.8696
Epoch 184/400
- 0s - loss: 0.9935 - acc: 0.9130
Epoch 185/400
- 0s - loss: 0.9851 - acc: 0.9130
Epoch 186/400
- 0s - loss: 0.9750 - acc: 0.8696
Epoch 187/400
- 0s - loss: 0.9704 - acc: 0.8696
Epoch 188/400
- 0s - loss: 0.9661 - acc: 0.9130
Epoch 189/400
- 0s - loss: 0.9695 - acc: 0.8696
Epoch 190/400
- 0s - loss: 0.9577 - acc: 0.9130
Epoch 191/400
- 0s - loss: 0.9603 - acc: 0.9130
Epoch 192/400
- 0s - loss: 0.9503 - acc: 0.9130
Epoch 193/400
- 0s - loss: 0.9416 - acc: 0.8696
Epoch 194/400
- 0s - loss: 0.9378 - acc: 0.9130
Epoch 195/400
- 0s - loss: 0.9346 - acc: 0.8696
Epoch 196/400
- 0s - loss: 0.9361 - acc: 0.9130
Epoch 197/400
- 0s - loss: 0.9275 - acc: 0.8261
Epoch 198/400
- 0s - loss: 0.9279 - acc: 0.8696
Epoch 199/400
- 0s - loss: 0.9258 - acc: 0.9130
Epoch 200/400
- 0s - loss: 0.9116 - acc: 0.9130
Epoch 201/400
- 0s - loss: 0.9087 - acc: 0.9130
Epoch 202/400
- 0s - loss: 0.9065 - acc: 0.8696
Epoch 203/400
- 0s - loss: 0.8957 - acc: 0.9130
Epoch 204/400
- 0s - loss: 0.8991 - acc: 0.9130
Epoch 205/400
- 0s - loss: 0.8937 - acc: 0.9130
Epoch 206/400
- 0s - loss: 0.8840 - acc: 0.9130
Epoch 207/400
- 0s - loss: 0.8844 - acc: 0.9130
Epoch 208/400
- 0s - loss: 0.8731 - acc: 0.9130
Epoch 209/400
- 0s - loss: 0.8804 - acc: 0.9130
Epoch 210/400
- 0s - loss: 0.8659 - acc: 0.9565
Epoch 211/400
- 0s - loss: 0.8685 - acc: 0.9565
Epoch 212/400
- 0s - loss: 0.8635 - acc: 0.9130
Epoch 213/400
- 0s - loss: 0.8611 - acc: 0.9130
Epoch 214/400
- 0s - loss: 0.8532 - acc: 0.9130
Epoch 215/400
- 0s - loss: 0.8483 - acc: 0.8696
Epoch 216/400
- 0s - loss: 0.8428 - acc: 0.8696
Epoch 217/400
- 0s - loss: 0.8376 - acc: 0.9130
Epoch 218/400
- 0s - loss: 0.8372 - acc: 0.9130
Epoch 219/400
- 0s - loss: 0.8347 - acc: 0.9130
Epoch 220/400
- 0s - loss: 0.8289 - acc: 0.8696
Epoch 221/400
- 0s - loss: 0.8210 - acc: 0.9565
Epoch 222/400
- 0s - loss: 0.8175 - acc: 0.9565
Epoch 223/400
- 0s - loss: 0.8194 - acc: 0.9130
Epoch 224/400
- 0s - loss: 0.8044 - acc: 0.8696
Epoch 225/400
- 0s - loss: 0.8063 - acc: 0.8696
Epoch 226/400
- 0s - loss: 0.8011 - acc: 0.9130
Epoch 227/400
- 0s - loss: 0.7963 - acc: 0.9130
Epoch 228/400
- 0s - loss: 0.7921 - acc: 0.9130
Epoch 229/400
- 0s - loss: 0.7878 - acc: 0.9130
Epoch 230/400
- 0s - loss: 0.7911 - acc: 0.8696
Epoch 231/400
- 0s - loss: 0.7852 - acc: 0.9130
Epoch 232/400
- 0s - loss: 0.7812 - acc: 0.9130
Epoch 233/400
- 0s - loss: 0.7741 - acc: 0.9130
Epoch 234/400
- 0s - loss: 0.7719 - acc: 0.8696
Epoch 235/400
- 0s - loss: 0.7711 - acc: 0.9130
Epoch 236/400
- 0s - loss: 0.7593 - acc: 0.9565
Epoch 237/400
- 0s - loss: 0.7581 - acc: 0.9130
Epoch 238/400
- 0s - loss: 0.7562 - acc: 0.9130
Epoch 239/400
- 0s - loss: 0.7577 - acc: 0.9130
Epoch 240/400
- 0s - loss: 0.7453 - acc: 0.8696
Epoch 241/400
- 0s - loss: 0.7404 - acc: 0.9130
Epoch 242/400
- 0s - loss: 0.7340 - acc: 0.9130
Epoch 243/400
- 0s - loss: 0.7358 - acc: 0.9565
Epoch 244/400
- 0s - loss: 0.7353 - acc: 0.9130
Epoch 245/400
- 0s - loss: 0.7353 - acc: 0.9565
Epoch 246/400
- 0s - loss: 0.7292 - acc: 0.9130
Epoch 247/400
- 0s - loss: 0.7270 - acc: 0.9565
Epoch 248/400
- 0s - loss: 0.7298 - acc: 0.9130
Epoch 249/400
- 0s - loss: 0.7172 - acc: 0.9130
Epoch 250/400
- 0s - loss: 0.7166 - acc: 0.9130
Epoch 251/400
- 0s - loss: 0.7117 - acc: 0.9565
Epoch 252/400
- 0s - loss: 0.7037 - acc: 0.9130
Epoch 253/400
- 0s - loss: 0.7029 - acc: 0.9565
Epoch 254/400
- 0s - loss: 0.6932 - acc: 0.9565
Epoch 255/400
- 0s - loss: 0.6989 - acc: 0.9130
Epoch 256/400
- 0s - loss: 0.6965 - acc: 0.9130
Epoch 257/400
- 0s - loss: 0.6896 - acc: 0.9130
Epoch 258/400
- 0s - loss: 0.6913 - acc: 0.9565
Epoch 259/400
- 0s - loss: 0.6849 - acc: 0.9130
Epoch 260/400
- 0s - loss: 0.6786 - acc: 0.9565
Epoch 261/400
- 0s - loss: 0.6836 - acc: 0.8696
Epoch 262/400
- 0s - loss: 0.6725 - acc: 0.8696
Epoch 263/400
- 0s - loss: 0.6712 - acc: 0.9130
Epoch 264/400
- 0s - loss: 0.6651 - acc: 0.9130
Epoch 265/400
- 0s - loss: 0.6574 - acc: 0.9565
Epoch 266/400
- 0s - loss: 0.6620 - acc: 0.9130
Epoch 267/400
- 0s - loss: 0.6564 - acc: 0.9565
Epoch 268/400
- 0s - loss: 0.6523 - acc: 0.9565
Epoch 269/400
- 0s - loss: 0.6537 - acc: 0.9130
Epoch 270/400
- 0s - loss: 0.6547 - acc: 0.9565
Epoch 271/400
- 0s - loss: 0.6499 - acc: 0.9130
Epoch 272/400
- 0s - loss: 0.6469 - acc: 0.8696
Epoch 273/400
- 0s - loss: 0.6391 - acc: 0.9565
Epoch 274/400
- 0s - loss: 0.6390 - acc: 0.9565
Epoch 275/400
- 0s - loss: 0.6343 - acc: 0.9130
Epoch 276/400
- 0s - loss: 0.6300 - acc: 0.9130
Epoch 277/400
- 0s - loss: 0.6300 - acc: 0.9565
Epoch 278/400
- 0s - loss: 0.6331 - acc: 0.9130
Epoch 279/400
- 0s - loss: 0.6311 - acc: 0.9130
Epoch 280/400
- 0s - loss: 0.6272 - acc: 0.9130
Epoch 281/400
- 0s - loss: 0.6205 - acc: 0.9130
Epoch 282/400
- 0s - loss: 0.6135 - acc: 0.9130
Epoch 283/400
- 0s - loss: 0.6132 - acc: 0.9130
Epoch 284/400
- 0s - loss: 0.6079 - acc: 0.9565
Epoch 285/400
- 0s - loss: 0.6115 - acc: 0.9130
Epoch 286/400
- 0s - loss: 0.6090 - acc: 0.8696
Epoch 287/400
- 0s - loss: 0.6026 - acc: 0.9130
Epoch 288/400
- 0s - loss: 0.5981 - acc: 0.9130
Epoch 289/400
- 0s - loss: 0.5947 - acc: 0.9565
Epoch 290/400
- 0s - loss: 0.5904 - acc: 0.9130
Epoch 291/400
- 0s - loss: 0.5904 - acc: 0.9130
Epoch 292/400
- 0s - loss: 0.5871 - acc: 0.9130
Epoch 293/400
- 0s - loss: 0.5827 - acc: 0.9130
Epoch 294/400
- 0s - loss: 0.5773 - acc: 0.9130
Epoch 295/400
- 0s - loss: 0.5772 - acc: 0.9130
Epoch 296/400
- 0s - loss: 0.5729 - acc: 0.9565
Epoch 297/400
- 0s - loss: 0.5747 - acc: 0.9130
Epoch 298/400
- 0s - loss: 0.5716 - acc: 0.8696
Epoch 299/400
- 0s - loss: 0.5679 - acc: 0.9130
Epoch 300/400
- 0s - loss: 0.5679 - acc: 0.9565
Epoch 301/400
- 0s - loss: 0.5658 - acc: 0.9565
Epoch 302/400
- 0s - loss: 0.5644 - acc: 0.9565
Epoch 303/400
- 0s - loss: 0.5600 - acc: 0.9565
Epoch 304/400
- 0s - loss: 0.5549 - acc: 0.9565
Epoch 305/400
- 0s - loss: 0.5510 - acc: 0.9565
Epoch 306/400
- 0s - loss: 0.5513 - acc: 0.9565
Epoch 307/400
- 0s - loss: 0.5472 - acc: 0.9565
Epoch 308/400
- 0s - loss: 0.5464 - acc: 0.9130
Epoch 309/400
- 0s - loss: 0.5446 - acc: 0.8696
Epoch 310/400
- 0s - loss: 0.5411 - acc: 0.9565
Epoch 311/400
- 0s - loss: 0.5372 - acc: 0.9565
Epoch 312/400
- 0s - loss: 0.5379 - acc: 0.9130
Epoch 313/400
- 0s - loss: 0.5337 - acc: 0.9130
Epoch 314/400
- 0s - loss: 0.5371 - acc: 0.9130
Epoch 315/400
- 0s - loss: 0.5290 - acc: 0.9130
Epoch 316/400
- 0s - loss: 0.5274 - acc: 0.9130
Epoch 317/400
- 0s - loss: 0.5197 - acc: 0.8696
Epoch 318/400
- 0s - loss: 0.5299 - acc: 0.9130
Epoch 319/400
- 0s - loss: 0.5251 - acc: 0.9565
Epoch 320/400
- 0s - loss: 0.5215 - acc: 0.9130
Epoch 321/400
- 0s - loss: 0.5203 - acc: 0.9565
Epoch 322/400
- 0s - loss: 0.5182 - acc: 0.9130
Epoch 323/400
- 0s - loss: 0.5135 - acc: 0.9565
Epoch 324/400
- 0s - loss: 0.5142 - acc: 0.8696
Epoch 325/400
- 0s - loss: 0.5101 - acc: 0.9565
Epoch 326/400
- 0s - loss: 0.5012 - acc: 0.9565
Epoch 327/400
- 0s - loss: 0.5000 - acc: 0.9565
Epoch 328/400
- 0s - loss: 0.4999 - acc: 0.9565
Epoch 329/400
- 0s - loss: 0.4978 - acc: 0.9565
Epoch 330/400
- 0s - loss: 0.4955 - acc: 0.9130
Epoch 331/400
- 0s - loss: 0.4916 - acc: 0.9130
Epoch 332/400
- 0s - loss: 0.4904 - acc: 0.9565
Epoch 333/400
- 0s - loss: 0.4870 - acc: 0.9130
Epoch 334/400
- 0s - loss: 0.4878 - acc: 0.9130
Epoch 335/400
- 0s - loss: 0.4846 - acc: 0.9130
Epoch 336/400
- 0s - loss: 0.4838 - acc: 0.8696
Epoch 337/400
- 0s - loss: 0.4833 - acc: 0.9130
Epoch 338/400
- 0s - loss: 0.4807 - acc: 0.8696
Epoch 339/400
- 0s - loss: 0.4764 - acc: 0.9130
Epoch 340/400
- 0s - loss: 0.4760 - acc: 0.9565
Epoch 341/400
- 0s - loss: 0.4800 - acc: 0.9130
Epoch 342/400
- 0s - loss: 0.4741 - acc: 0.9565
Epoch 343/400
- 0s - loss: 0.4706 - acc: 1.0000
Epoch 344/400
- 0s - loss: 0.4670 - acc: 1.0000
Epoch 345/400
- 0s - loss: 0.4660 - acc: 0.9130
Epoch 346/400
- 0s - loss: 0.4626 - acc: 0.9130
Epoch 347/400
- 0s - loss: 0.4616 - acc: 0.9130
Epoch 348/400
- 0s - loss: 0.4610 - acc: 0.9565
Epoch 349/400
- 0s - loss: 0.4540 - acc: 0.9130
Epoch 350/400
- 0s - loss: 0.4575 - acc: 0.9565
Epoch 351/400
- 0s - loss: 0.4511 - acc: 0.9565
Epoch 352/400
- 0s - loss: 0.4551 - acc: 0.9130
Epoch 353/400
- 0s - loss: 0.4520 - acc: 0.9565
Epoch 354/400
- 0s - loss: 0.4468 - acc: 0.9565
Epoch 355/400
- 0s - loss: 0.4560 - acc: 0.9565
Epoch 356/400
- 0s - loss: 0.4442 - acc: 0.9565
Epoch 357/400
- 0s - loss: 0.4432 - acc: 0.9130
Epoch 358/400
- 0s - loss: 0.4408 - acc: 0.9130
Epoch 359/400
- 0s - loss: 0.4396 - acc: 0.9565
Epoch 360/400
- 0s - loss: 0.4364 - acc: 0.9565
Epoch 361/400
- 0s - loss: 0.4306 - acc: 0.9565
Epoch 362/400
- 0s - loss: 0.4337 - acc: 0.9565
Epoch 363/400
- 0s - loss: 0.4315 - acc: 0.9565
Epoch 364/400
- 0s - loss: 0.4252 - acc: 0.9565
Epoch 365/400
- 0s - loss: 0.4291 - acc: 0.9565
Epoch 366/400
- 0s - loss: 0.4274 - acc: 0.9130
Epoch 367/400
- 0s - loss: 0.4264 - acc: 0.9130
Epoch 368/400
- 0s - loss: 0.4245 - acc: 0.9130
Epoch 369/400
- 0s - loss: 0.4270 - acc: 0.9565
Epoch 370/400
- 0s - loss: 0.4252 - acc: 0.9130
Epoch 371/400
- 0s - loss: 0.4296 - acc: 1.0000
Epoch 372/400
- 0s - loss: 0.4262 - acc: 0.9565
Epoch 373/400
- 0s - loss: 0.4189 - acc: 0.9565
Epoch 374/400
- 0s - loss: 0.4171 - acc: 0.9130
Epoch 375/400
- 0s - loss: 0.4085 - acc: 0.9130
Epoch 376/400
- 0s - loss: 0.4077 - acc: 0.9565
Epoch 377/400
- 0s - loss: 0.4039 - acc: 0.9565
Epoch 378/400
- 0s - loss: 0.4024 - acc: 0.9565
Epoch 379/400
- 0s - loss: 0.4016 - acc: 0.9565
Epoch 380/400
- 0s - loss: 0.4024 - acc: 0.9130
Epoch 381/400
- 0s - loss: 0.3991 - acc: 1.0000
Epoch 382/400
- 0s - loss: 0.3974 - acc: 0.9565
Epoch 383/400
- 0s - loss: 0.3954 - acc: 0.9130
Epoch 384/400
- 0s - loss: 0.3988 - acc: 0.9565
Epoch 385/400
- 0s - loss: 0.3927 - acc: 0.9565
Epoch 386/400
- 0s - loss: 0.3928 - acc: 1.0000
Epoch 387/400
- 0s - loss: 0.3945 - acc: 1.0000
Epoch 388/400
- 0s - loss: 0.3926 - acc: 0.9565
Epoch 389/400
- 0s - loss: 0.3907 - acc: 0.9130
Epoch 390/400
- 0s - loss: 0.3883 - acc: 0.9565
Epoch 391/400
- 0s - loss: 0.3824 - acc: 0.9130
Epoch 392/400
- 0s - loss: 0.3811 - acc: 0.9565
Epoch 393/400
- 0s - loss: 0.3794 - acc: 0.9565
Epoch 394/400
- 0s - loss: 0.3830 - acc: 0.9565
Epoch 395/400
- 0s - loss: 0.3786 - acc: 1.0000
Epoch 396/400
- 0s - loss: 0.3767 - acc: 1.0000
Epoch 397/400
- 0s - loss: 0.3764 - acc: 0.9565
Epoch 398/400
- 0s - loss: 0.3751 - acc: 0.9565
Epoch 399/400
- 0s - loss: 0.3719 - acc: 1.0000
Epoch 400/400
- 0s - loss: 0.3684 - acc: 1.0000
23/23 [==============================] - 0s 4ms/step
Model Accuracy: 100.00%
['W', 'B', 'C'] -> Z
['W', 'K', 'L'] -> Z
['W', 'T', 'U'] -> Z
['D', 'W', 'F'] -> I
['M', 'W', 'O'] -> Q
['V', 'W', 'W'] -> Y
['G', 'H', 'W'] -> J
['J', 'K', 'W'] -> M
['P', 'Q', 'W'] -> S
ScaDaMaLe Course site and book
This is a 2019-2021 augmentation and update of Adam Breindel's initial notebooks.
Thanks to Christian von Koch and William Anzén for their contributions towards making these materials Spark 3.0.1 and Python 3+ compliant.