SDS-2.2, Scalable Data Science
This is used in a non-profit educational setting with kind permission of Adam Breindel.
This is not licensed by Adam for use in a for-profit setting. Please contact Adam directly at [email protected] to request or report such use cases or abuses.
A few minor modifications and additional mathematical statistical pointers have been added by Raazesh Sainudiin when teaching PhD students in Uppsala University.
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') Epoch 1/400 0s - loss: 3.2754 - acc: 0.0000e+00 Epoch 2/400 0s - loss: 3.2605 - acc: 0.0435 Epoch 3/400 0s - loss: 3.2533 - acc: 0.0435 Epoch 4/400 0s - loss: 3.2470 - acc: 0.0435 Epoch 5/400 0s - loss: 3.2404 - acc: 0.0435 Epoch 6/400 0s - loss: 3.2345 - acc: 0.0435 Epoch 7/400 0s - loss: 3.2273 - acc: 0.0435 Epoch 8/400 0s - loss: 3.2202 - acc: 0.0435 Epoch 9/400 0s - loss: 3.2125 - acc: 0.0435 Epoch 10/400 0s - loss: 3.2043 - acc: 0.0435 Epoch 11/400 0s - loss: 3.1951 - acc: 0.0435 Epoch 12/400 0s - loss: 3.1857 - acc: 0.0435 Epoch 13/400 0s - loss: 3.1731 - acc: 0.0435 Epoch 14/400 0s - loss: 3.1614 - acc: 0.0435 Epoch 15/400 0s - loss: 3.1469 - acc: 0.0435 Epoch 16/400 0s - loss: 3.1320 - acc: 0.0435 Epoch 17/400 0s - loss: 3.1151 - acc: 0.0435 Epoch 18/400 0s - loss: 3.0969 - acc: 0.0435 Epoch 19/400 0s - loss: 3.0793 - acc: 0.0435 Epoch 20/400 0s - loss: 3.0609 - acc: 0.0435 Epoch 21/400 0s - loss: 3.0459 - acc: 0.0435 Epoch 22/400 0s - loss: 3.0171 - acc: 0.0435 Epoch 23/400 0s - loss: 2.9935 - acc: 0.0435 Epoch 24/400 0s - loss: 2.9669 - acc: 0.0870 Epoch 25/400 0s - loss: 2.9331 - acc: 0.0870 Epoch 26/400 0s - loss: 2.8985 - acc: 0.1304 Epoch 27/400 0s - loss: 2.8610 - acc: 0.0870 Epoch 28/400 0s - loss: 2.8236 - acc: 0.1304 Epoch 29/400 0s - loss: 2.7759 - acc: 0.1304 Epoch 30/400 0s - loss: 2.7317 - acc: 0.1304 Epoch 31/400 0s - loss: 2.6916 - acc: 0.1304 Epoch 32/400 0s - loss: 2.6509 - acc: 0.1304 Epoch 33/400 0s - loss: 2.6134 - acc: 0.0870 Epoch 34/400 0s - loss: 2.5798 - acc: 0.1304 Epoch 35/400 0s - loss: 2.5445 - acc: 0.0870 Epoch 36/400 0s - loss: 2.5142 - acc: 0.0870 Epoch 37/400 0s - loss: 2.4821 - acc: 0.1304 Epoch 38/400 0s - loss: 2.4543 - acc: 0.1304 Epoch 39/400 0s - loss: 2.4259 - acc: 0.1304 Epoch 40/400 0s - loss: 2.4005 - acc: 0.0870 Epoch 41/400 0s - loss: 2.3685 - acc: 0.1304 Epoch 42/400 0s - loss: 2.3379 - acc: 0.1304 Epoch 43/400 0s - loss: 2.3107 - acc: 0.1304 Epoch 44/400 0s - loss: 2.2847 - acc: 0.1304 Epoch 45/400 0s - loss: 2.2603 - acc: 0.1304 Epoch 46/400 0s - loss: 2.2349 - acc: 0.1304 Epoch 47/400 0s - loss: 2.2082 - acc: 0.1304 Epoch 48/400 0s - loss: 2.1838 - acc: 0.1739 Epoch 49/400 0s - loss: 2.1651 - acc: 0.1304 Epoch 50/400 0s - loss: 2.1458 - acc: 0.1304 Epoch 51/400 0s - loss: 2.1273 - acc: 0.1304 Epoch 52/400 0s - loss: 2.1024 - acc: 0.1739 Epoch 53/400 0s - loss: 2.0876 - acc: 0.1739 Epoch 54/400 0s - loss: 2.0608 - acc: 0.1304 Epoch 55/400 0s - loss: 2.0524 - acc: 0.2174 Epoch 56/400 0s - loss: 2.0220 - acc: 0.2174 Epoch 57/400 0s - loss: 2.0093 - acc: 0.3043 Epoch 58/400 0s - loss: 1.9878 - acc: 0.2609 Epoch 59/400 0s - loss: 1.9765 - acc: 0.2174 Epoch 60/400 0s - loss: 1.9638 - acc: 0.2609 Epoch 61/400 0s - loss: 1.9433 - acc: 0.2174 Epoch 62/400 0s - loss: 1.9250 - acc: 0.3913 Epoch 63/400 0s - loss: 1.9182 - acc: 0.2174 Epoch 64/400 0s - loss: 1.8968 - acc: 0.4348 Epoch 65/400 0s - loss: 1.8848 - acc: 0.2609 Epoch 66/400 0s - loss: 1.8733 - acc: 0.3043 Epoch 67/400 0s - loss: 1.8573 - acc: 0.3043 Epoch 68/400 0s - loss: 1.8468 - acc: 0.3913 Epoch 69/400 0s - loss: 1.8292 - acc: 0.3478 Epoch 70/400 0s - loss: 1.8210 - acc: 0.3913 Epoch 71/400 0s - loss: 1.8048 - acc: 0.3913 Epoch 72/400 0s - loss: 1.8039 - acc: 0.3043 Epoch 73/400 0s - loss: 1.7860 - acc: 0.3478 Epoch 74/400 0s - loss: 1.7774 - acc: 0.3478 Epoch 75/400 0s - loss: 1.7643 - acc: 0.3913 Epoch 76/400 0s - loss: 1.7528 - acc: 0.5217 Epoch 77/400 0s - loss: 1.7423 - acc: 0.4783 Epoch 78/400 0s - loss: 1.7387 - acc: 0.3913 Epoch 79/400 0s - loss: 1.7281 - acc: 0.3913 Epoch 80/400 0s - loss: 1.7052 - acc: 0.4783 Epoch 81/400 0s - loss: 1.7095 - acc: 0.3478 Epoch 82/400 0s - loss: 1.6930 - acc: 0.4783 Epoch 83/400 0s - loss: 1.6776 - acc: 0.5217 Epoch 84/400 0s - loss: 1.6728 - acc: 0.4783 Epoch 85/400 0s - loss: 1.6603 - acc: 0.5217 Epoch 86/400 0s - loss: 1.6446 - acc: 0.5217 Epoch 87/400 0s - loss: 1.6423 - acc: 0.5217 Epoch 88/400 0s - loss: 1.6368 - acc: 0.5217 Epoch 89/400 0s - loss: 1.6279 - acc: 0.5217 Epoch 90/400 0s - loss: 1.6140 - acc: 0.5217 Epoch 91/400 0s - loss: 1.6029 - acc: 0.4348 Epoch 92/400 0s - loss: 1.6050 - acc: 0.5652 Epoch 93/400 0s - loss: 1.5897 - acc: 0.6087 Epoch 94/400 0s - loss: 1.5799 - acc: 0.6087 Epoch 95/400 0s - loss: 1.5733 - acc: 0.6522 Epoch 96/400 0s - loss: 1.5675 - acc: 0.5652 Epoch 97/400 0s - loss: 1.5568 - acc: 0.6522 Epoch 98/400 0s - loss: 1.5497 - acc: 0.6522 Epoch 99/400 0s - loss: 1.5377 - acc: 0.6957 Epoch 100/400 0s - loss: 1.5336 - acc: 0.7391 Epoch 101/400 0s - loss: 1.5230 - acc: 0.6087 Epoch 102/400 0s - loss: 1.5188 - acc: 0.6957 Epoch 103/400 0s - loss: 1.5106 - acc: 0.6087 Epoch 104/400 0s - loss: 1.4985 - acc: 0.6087 Epoch 105/400 0s - loss: 1.4934 - acc: 0.6087 Epoch 106/400 0s - loss: 1.4816 - acc: 0.6957 Epoch 107/400 0s - loss: 1.4782 - acc: 0.6522 Epoch 108/400 0s - loss: 1.4646 - acc: 0.7826 Epoch 109/400 0s - loss: 1.4680 - acc: 0.6087 Epoch 110/400 0s - loss: 1.4589 - acc: 0.6957 Epoch 111/400 0s - loss: 1.4480 - acc: 0.7391 Epoch 112/400 0s - loss: 1.4424 - acc: 0.8261 Epoch 113/400 0s - loss: 1.4343 - acc: 0.7826 Epoch 114/400 0s - loss: 1.4287 - acc: 0.7391 Epoch 115/400 0s - loss: 1.4189 - acc: 0.6957 Epoch 116/400 0s - loss: 1.4169 - acc: 0.6957 Epoch 117/400 0s - loss: 1.4082 - acc: 0.6522 Epoch 118/400 0s - loss: 1.3956 - acc: 0.7826 Epoch 119/400 0s - loss: 1.3907 - acc: 0.6957 Epoch 120/400 0s - loss: 1.3888 - acc: 0.7391 Epoch 121/400 0s - loss: 1.3803 - acc: 0.7391 Epoch 122/400 0s - loss: 1.3739 - acc: 0.6957 Epoch 123/400 0s - loss: 1.3734 - acc: 0.7391 Epoch 124/400 0s - loss: 1.3684 - acc: 0.7391 Epoch 125/400 0s - loss: 1.3591 - acc: 0.7391 Epoch 126/400 0s - loss: 1.3483 - acc: 0.7826 Epoch 127/400 0s - loss: 1.3419 - acc: 0.6522 Epoch 128/400 0s - loss: 1.3414 - acc: 0.6957 Epoch 129/400 0s - loss: 1.3355 - acc: 0.7826 Epoch 130/400 0s - loss: 1.3250 - acc: 0.8261 Epoch 131/400 0s - loss: 1.3229 - acc: 0.7826 Epoch 132/400 0s - loss: 1.3222 - acc: 0.7391 Epoch 133/400 0s - loss: 1.3140 - acc: 0.7391 Epoch 134/400 0s - loss: 1.3002 - acc: 0.8696 Epoch 135/400 0s - loss: 1.2944 - acc: 0.7826 Epoch 136/400 0s - loss: 1.2899 - acc: 0.7391 Epoch 137/400 0s - loss: 1.2843 - acc: 0.8261 Epoch 138/400 0s - loss: 1.2723 - acc: 0.7826 Epoch 139/400 0s - loss: 1.2669 - acc: 0.7826 Epoch 140/400 0s - loss: 1.2664 - acc: 0.8261 Epoch 141/400 0s - loss: 1.2633 - acc: 0.7826 Epoch 142/400 0s - loss: 1.2487 - acc: 0.7826 Epoch 143/400 0s - loss: 1.2486 - acc: 0.7826 Epoch 144/400 0s - loss: 1.2456 - acc: 0.7826 Epoch 145/400 0s - loss: 1.2351 - acc: 0.8261 Epoch 146/400 0s - loss: 1.2233 - acc: 0.7826 Epoch 147/400 0s - loss: 1.2244 - acc: 0.8261 Epoch 148/400 0s - loss: 1.2115 - acc: 0.8261 Epoch 149/400 0s - loss: 1.2059 - acc: 0.8261 Epoch 150/400 0s - loss: 1.1988 - acc: 0.7826 Epoch 151/400 0s - loss: 1.1978 - acc: 0.8261 Epoch 152/400 0s - loss: 1.1928 - acc: 0.7826 Epoch 153/400 0s - loss: 1.1813 - acc: 0.8261 Epoch 154/400 0s - loss: 1.1858 - acc: 0.8261 Epoch 155/400 0s - loss: 1.1776 - acc: 0.7826 Epoch 156/400 0s - loss: 1.1724 - acc: 0.8696 Epoch 157/400 0s - loss: 1.1686 - acc: 0.8696 Epoch 158/400 0s - loss: 1.1629 - acc: 0.8696 Epoch 159/400 0s - loss: 1.1553 - acc: 0.8696 Epoch 160/400 0s - loss: 1.1483 - acc: 0.8261 Epoch 161/400 0s - loss: 1.1470 - acc: 0.7826 Epoch 162/400 0s - loss: 1.1314 - acc: 0.8261 Epoch 163/400 0s - loss: 1.1339 - acc: 0.7826 Epoch 164/400 0s - loss: 1.1337 - acc: 0.8261 Epoch 165/400 0s - loss: 1.1224 - acc: 0.8696 Epoch 166/400 0s - loss: 1.1214 - acc: 0.8261 Epoch 167/400 0s - loss: 1.1189 - acc: 0.8261 Epoch 168/400 0s - loss: 1.1118 - acc: 0.8696 Epoch 169/400 0s - loss: 1.1065 - acc: 0.9130 Epoch 170/400 0s - loss: 1.0941 - acc: 0.9130 Epoch 171/400 0s - loss: 1.0917 - acc: 0.8696 Epoch 172/400 0s - loss: 1.0808 - acc: 0.8696 Epoch 173/400 0s - loss: 1.0836 - acc: 0.8696 Epoch 174/400 0s - loss: 1.0705 - acc: 0.8261 Epoch 175/400 0s - loss: 1.0609 - acc: 0.8696 Epoch 176/400 0s - loss: 1.0694 - acc: 0.7826 Epoch 177/400 0s - loss: 1.0591 - acc: 0.8696 Epoch 178/400 0s - loss: 1.0523 - acc: 0.9130 Epoch 179/400 0s - loss: 1.0498 - acc: 0.8261 Epoch 180/400 0s - loss: 1.0455 - acc: 0.8261 Epoch 181/400 0s - loss: 1.0473 - acc: 0.8261 Epoch 182/400 0s - loss: 1.0413 - acc: 0.8696 Epoch 183/400 0s - loss: 1.0246 - acc: 0.8261 Epoch 184/400 0s - loss: 1.0243 - acc: 0.8696 Epoch 185/400 0s - loss: 1.0261 - acc: 0.8696 Epoch 186/400 0s - loss: 1.0187 - acc: 0.8261 Epoch 187/400 0s - loss: 1.0087 - acc: 0.8696 Epoch 188/400 0s - loss: 1.0028 - acc: 0.9130 Epoch 189/400 0s - loss: 0.9928 - acc: 0.9130 Epoch 190/400 0s - loss: 0.9925 - acc: 0.9130 Epoch 191/400 0s - loss: 0.9868 - acc: 0.9130 Epoch 192/400 0s - loss: 0.9807 - acc: 0.8696 Epoch 193/400 0s - loss: 0.9789 - acc: 0.8696 Epoch 194/400 0s - loss: 0.9742 - acc: 0.9130 Epoch 195/400 0s - loss: 0.9653 - acc: 0.8696 Epoch 196/400 0s - loss: 0.9668 - acc: 0.8696 Epoch 197/400 0s - loss: 0.9640 - acc: 0.9130 Epoch 198/400 0s - loss: 0.9573 - acc: 0.7826 Epoch 199/400 0s - loss: 0.9461 - acc: 0.9130 Epoch 200/400 0s - loss: 0.9386 - acc: 0.8696 Epoch 201/400 0s - loss: 0.9415 - acc: 0.9130 Epoch 202/400 0s - loss: 0.9299 - acc: 0.9130 Epoch 203/400 0s - loss: 0.9226 - acc: 0.9565 Epoch 204/400 0s - loss: 0.9304 - acc: 0.9130 Epoch 205/400 0s - loss: 0.9176 - acc: 0.9130 Epoch 206/400 0s - loss: 0.9255 - acc: 0.9565 Epoch 207/400 0s - loss: 0.9133 - acc: 0.9130 Epoch 208/400 0s - loss: 0.9048 - acc: 0.9130 Epoch 209/400 0s - loss: 0.9015 - acc: 0.9130 Epoch 210/400 0s - loss: 0.8925 - acc: 0.9130 Epoch 211/400 0s - loss: 0.8931 - acc: 0.9130 Epoch 212/400 0s - loss: 0.8890 - acc: 0.9130 Epoch 213/400 0s - loss: 0.8807 - acc: 0.9565 Epoch 214/400 0s - loss: 0.8740 - acc: 0.8696 Epoch 215/400 0s - loss: 0.8757 - acc: 0.9130 Epoch 216/400 0s - loss: 0.8719 - acc: 0.8696 Epoch 217/400 0s - loss: 0.8650 - acc: 0.9565 Epoch 218/400 0s - loss: 0.8576 - acc: 0.9565 Epoch 219/400 0s - loss: 0.8509 - acc: 0.9565 Epoch 220/400 0s - loss: 0.8436 - acc: 0.9130 Epoch 221/400 0s - loss: 0.8426 - acc: 0.9130 Epoch 222/400 0s - loss: 0.8421 - acc: 0.9565 Epoch 223/400 0s - loss: 0.8311 - acc: 0.9130 Epoch 224/400 0s - loss: 0.8289 - acc: 0.9130 Epoch 225/400 0s - loss: 0.8293 - acc: 0.9130 Epoch 226/400 0s - loss: 0.8243 - acc: 0.9130 Epoch 227/400 0s - loss: 0.8245 - acc: 0.9130 Epoch 228/400 0s - loss: 0.8143 - acc: 0.9130 Epoch 229/400 0s - loss: 0.8130 - acc: 0.9565 Epoch 230/400 0s - loss: 0.8077 - acc: 0.9565 Epoch 231/400 0s - loss: 0.7966 - acc: 0.9130 Epoch 232/400 0s - loss: 0.7960 - acc: 0.9130 Epoch 233/400 0s - loss: 0.7924 - acc: 0.9130 Epoch 234/400 0s - loss: 0.7862 - acc: 0.9130 Epoch 235/400 0s - loss: 0.7874 - acc: 0.9130 Epoch 236/400 0s - loss: 0.7842 - acc: 0.9565 Epoch 237/400 0s - loss: 0.7763 - acc: 0.9565 Epoch 238/400 0s - loss: 0.7729 - acc: 0.9565 Epoch 239/400 0s - loss: 0.7658 - acc: 0.9130 Epoch 240/400 0s - loss: 0.7597 - acc: 0.9565 Epoch 241/400 0s - loss: 0.7560 - acc: 0.9130 Epoch 242/400 0s - loss: 0.7562 - acc: 0.9565 Epoch 243/400 0s - loss: 0.7511 - acc: 0.9565 Epoch 244/400 0s - loss: 0.7458 - acc: 0.9565 Epoch 245/400 0s - loss: 0.7476 - acc: 0.9565 Epoch 246/400 0s - loss: 0.7385 - acc: 0.9565 Epoch 247/400 0s - loss: 0.7382 - acc: 0.9130 Epoch 248/400 0s - loss: 0.7268 - acc: 0.9565 Epoch 249/400 0s - loss: 0.7262 - acc: 0.9565 Epoch 250/400 0s - loss: 0.7177 - acc: 0.9565 Epoch 251/400 0s - loss: 0.7214 - acc: 0.9130 Epoch 252/400 0s - loss: 0.7255 - acc: 0.9565 Epoch 253/400 0s - loss: 0.7105 - acc: 0.9130 Epoch 254/400 0s - loss: 0.7026 - acc: 0.9565 Epoch 255/400 0s - loss: 0.7076 - acc: 0.9565 Epoch 256/400 0s - loss: 0.6981 - acc: 0.9565 Epoch 257/400 0s - loss: 0.6958 - acc: 0.9565 Epoch 258/400 0s - loss: 0.6942 - acc: 0.9565 Epoch 259/400 0s - loss: 0.6828 - acc: 0.9565 Epoch 260/400 0s - loss: 0.6807 - acc: 0.9565 Epoch 261/400 0s - loss: 0.6821 - acc: 0.9565 Epoch 262/400 0s - loss: 0.6742 - acc: 0.9565 Epoch 263/400 0s - loss: 0.6726 - acc: 0.9565 Epoch 264/400 0s - loss: 0.6665 - acc: 0.9565 Epoch 265/400 0s - loss: 0.6648 - acc: 0.9565 Epoch 266/400 0s - loss: 0.6609 - acc: 0.9565 Epoch 267/400 0s - loss: 0.6596 - acc: 0.9565 Epoch 268/400 0s - loss: 0.6481 - acc: 0.9565 Epoch 269/400 0s - loss: 0.6480 - acc: 0.9565 Epoch 270/400 0s - loss: 0.6562 - acc: 0.9565 Epoch 271/400 0s - loss: 0.6531 - acc: 0.9565 Epoch 272/400 0s - loss: 0.6440 - acc: 0.9565 Epoch 273/400 0s - loss: 0.6373 - acc: 0.9565 Epoch 274/400 0s - loss: 0.6335 - acc: 0.9565 Epoch 275/400 0s - loss: 0.6306 - acc: 0.9565 Epoch 276/400 0s - loss: 0.6285 - acc: 0.9565 Epoch 277/400 0s - loss: 0.6269 - acc: 0.9565 Epoch 278/400 0s - loss: 0.6244 - acc: 1.0000 Epoch 279/400 0s - loss: 0.6191 - acc: 0.9565 Epoch 280/400 0s - loss: 0.6137 - acc: 0.9565 Epoch 281/400 0s - loss: 0.6104 - acc: 0.9565 Epoch 282/400 0s - loss: 0.6024 - acc: 0.9565 Epoch 283/400 0s - loss: 0.6011 - acc: 0.9565 Epoch 284/400 0s - loss: 0.5986 - acc: 0.9565 Epoch 285/400 0s - loss: 0.5967 - acc: 0.9565 Epoch 286/400 0s - loss: 0.5897 - acc: 0.9565 Epoch 287/400 0s - loss: 0.5892 - acc: 0.9565 Epoch 288/400 0s - loss: 0.5838 - acc: 0.9565 Epoch 289/400 0s - loss: 0.5785 - acc: 0.9565 Epoch 290/400 0s - loss: 0.5789 - acc: 0.9565 Epoch 291/400 0s - loss: 0.5783 - acc: 0.9565 Epoch 292/400 0s - loss: 0.5755 - acc: 0.9565 Epoch 293/400 0s - loss: 0.5686 - acc: 0.9565 Epoch 294/400 0s - loss: 0.5693 - acc: 0.9565 Epoch 295/400 0s - loss: 0.5630 - acc: 0.9565 Epoch 296/400 0s - loss: 0.5601 - acc: 0.9565 Epoch 297/400 0s - loss: 0.5581 - acc: 0.9565 Epoch 298/400 0s - loss: 0.5536 - acc: 0.9565 Epoch 299/400 0s - loss: 0.5537 - acc: 1.0000 Epoch 300/400 0s - loss: 0.5537 - acc: 0.9565 Epoch 301/400 0s - loss: 0.5415 - acc: 0.9565 Epoch 302/400 0s - loss: 0.5410 - acc: 1.0000 Epoch 303/400 0s - loss: 0.5474 - acc: 0.9565 Epoch 304/400 0s - loss: 0.5402 - acc: 0.9565 Epoch 305/400 0s - loss: 0.5329 - acc: 0.9565 Epoch 306/400 0s - loss: 0.5242 - acc: 1.0000 Epoch 307/400 0s - loss: 0.5274 - acc: 0.9565 Epoch 308/400 0s - loss: 0.5310 - acc: 0.9565 Epoch 309/400 0s - loss: 0.5223 - acc: 0.9565 Epoch 310/400 0s - loss: 0.5141 - acc: 0.9565 Epoch 311/400 0s - loss: 0.5150 - acc: 0.9565 Epoch 312/400 0s - loss: 0.5119 - acc: 0.9565 Epoch 313/400 0s - loss: 0.5121 - acc: 0.9565 Epoch 314/400 0s - loss: 0.5088 - acc: 0.9565 Epoch 315/400 0s - loss: 0.5025 - acc: 0.9565 Epoch 316/400 0s - loss: 0.5018 - acc: 0.9565 Epoch 317/400 0s - loss: 0.5065 - acc: 0.9565 Epoch 318/400 0s - loss: 0.4982 - acc: 1.0000 Epoch 319/400 0s - loss: 0.5061 - acc: 0.9565 Epoch 320/400 0s - loss: 0.4979 - acc: 0.9565 Epoch 321/400 0s - loss: 0.4934 - acc: 1.0000 Epoch 322/400 0s - loss: 0.4836 - acc: 0.9565 Epoch 323/400 0s - loss: 0.4811 - acc: 1.0000 Epoch 324/400 0s - loss: 0.4752 - acc: 1.0000 Epoch 325/400 0s - loss: 0.4775 - acc: 1.0000 Epoch 326/400 0s - loss: 0.4736 - acc: 1.0000 Epoch 327/400 0s - loss: 0.4713 - acc: 0.9565 Epoch 328/400 0s - loss: 0.4682 - acc: 0.9565 Epoch 329/400 0s - loss: 0.4686 - acc: 0.9565 Epoch 330/400 0s - loss: 0.4685 - acc: 1.0000 Epoch 331/400 0s - loss: 0.4586 - acc: 1.0000 Epoch 332/400 0s - loss: 0.4625 - acc: 1.0000 Epoch 333/400 0s - loss: 0.4610 - acc: 0.9565 Epoch 334/400 0s - loss: 0.4550 - acc: 1.0000 Epoch 335/400 0s - loss: 0.4511 - acc: 0.9565 Epoch 336/400 0s - loss: 0.4502 - acc: 0.9565 Epoch 337/400 0s - loss: 0.4476 - acc: 0.9565 Epoch 338/400 0s - loss: 0.4409 - acc: 1.0000 Epoch 339/400 0s - loss: 0.4459 - acc: 1.0000 Epoch 340/400 0s - loss: 0.4438 - acc: 1.0000 Epoch 341/400 0s - loss: 0.4373 - acc: 1.0000 Epoch 342/400 0s - loss: 0.4333 - acc: 1.0000 Epoch 343/400 0s - loss: 0.4358 - acc: 1.0000 Epoch 344/400 0s - loss: 0.4305 - acc: 1.0000 Epoch 345/400 0s - loss: 0.4283 - acc: 1.0000 Epoch 346/400 0s - loss: 0.4219 - acc: 1.0000 Epoch 347/400 0s - loss: 0.4292 - acc: 0.9565 Epoch 348/400 0s - loss: 0.4220 - acc: 0.9565 Epoch 349/400 0s - loss: 0.4224 - acc: 1.0000 Epoch 350/400 0s - loss: 0.4216 - acc: 0.9565 Epoch 351/400 0s - loss: 0.4148 - acc: 1.0000 Epoch 352/400 0s - loss: 0.4076 - acc: 0.9565 Epoch 353/400 0s - loss: 0.4106 - acc: 1.0000 Epoch 354/400 0s - loss: 0.4091 - acc: 1.0000 Epoch 355/400 0s - loss: 0.4096 - acc: 1.0000 Epoch 356/400 0s - loss: 0.4078 - acc: 0.9565 Epoch 357/400 0s - loss: 0.4021 - acc: 1.0000 Epoch 358/400 0s - loss: 0.3979 - acc: 0.9565 Epoch 359/400 0s - loss: 0.3957 - acc: 0.9565 Epoch 360/400 0s - loss: 0.3937 - acc: 1.0000 Epoch 361/400 0s - loss: 0.3927 - acc: 1.0000 Epoch 362/400 0s - loss: 0.3878 - acc: 0.9565 Epoch 363/400 0s - loss: 0.3879 - acc: 1.0000 Epoch 364/400 0s - loss: 0.3882 - acc: 1.0000 Epoch 365/400 0s - loss: 0.3817 - acc: 1.0000 Epoch 366/400 0s - loss: 0.3822 - acc: 1.0000 Epoch 367/400 0s - loss: 0.3773 - acc: 1.0000 Epoch 368/400 0s - loss: 0.3770 - acc: 1.0000 Epoch 369/400 0s - loss: 0.3787 - acc: 1.0000 Epoch 370/400 0s - loss: 0.3731 - acc: 1.0000 Epoch 371/400 0s - loss: 0.3704 - acc: 1.0000 Epoch 372/400 0s - loss: 0.3694 - acc: 0.9565 Epoch 373/400 0s - loss: 0.3666 - acc: 1.0000 Epoch 374/400 0s - loss: 0.3621 - acc: 1.0000 Epoch 375/400 0s - loss: 0.3631 - acc: 1.0000 Epoch 376/400 0s - loss: 0.3634 - acc: 1.0000 Epoch 377/400 0s - loss: 0.3634 - acc: 1.0000 Epoch 378/400 0s - loss: 0.3577 - acc: 1.0000 Epoch 379/400 0s - loss: 0.3568 - acc: 0.9565 Epoch 380/400 0s - loss: 0.3537 - acc: 1.0000 Epoch 381/400 0s - loss: 0.3521 - acc: 1.0000 Epoch 382/400 0s - loss: 0.3603 - acc: 1.0000 Epoch 383/400 0s - loss: 0.3721 - acc: 1.0000 Epoch 384/400 0s - loss: 0.3637 - acc: 1.0000 Epoch 385/400 0s - loss: 0.3499 - acc: 1.0000 Epoch 386/400 0s - loss: 0.3426 - acc: 1.0000 Epoch 387/400 0s - loss: 0.3396 - acc: 1.0000 Epoch 388/400 0s - loss: 0.3380 - acc: 1.0000 Epoch 389/400 0s - loss: 0.3431 - acc: 1.0000 Epoch 390/400 0s - loss: 0.3352 - acc: 1.0000 Epoch 391/400 0s - loss: 0.3366 - acc: 1.0000 Epoch 392/400 0s - loss: 0.3360 - acc: 1.0000 Epoch 393/400 0s - loss: 0.3333 - acc: 1.0000 Epoch 394/400 0s - loss: 0.3276 - acc: 1.0000 Epoch 395/400 0s - loss: 0.3262 - acc: 1.0000 Epoch 396/400 0s - loss: 0.3222 - acc: 1.0000 Epoch 397/400 0s - loss: 0.3202 - acc: 1.0000 Epoch 398/400 0s - loss: 0.3222 - acc: 1.0000 Epoch 399/400 0s - loss: 0.3219 - acc: 1.0000 Epoch 400/400 0s - loss: 0.3246 - acc: 1.0000 23/23 [==============================] - 0s Model Accuracy: 100.00% (['W', 'B', 'C'], '->', 'Y') (['W', 'K', 'L'], '->', 'Y') (['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')