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.

Please feel free to refer to basic concepts here:

• Udacity's course on Deep Learning https://www.udacity.com/course/deep-learning--ud730 by Google engineers: Arpan Chakraborty and Vincent Vanhoucke and their full video playlist:
  • https://www.youtube.com/watch?v=XB9NADf2wk&index=2&list=PLAwxTw4SYaPnOWPFT9ulXLuQrImzHfOV

Archived YouTube video of this live unedited lab-lecture:

Entering the 4th Dimension

Networks for Understanding Time-Oriented Patterns in Data

Common time-based problems include * Sequence modeling: "What comes next?" * Likely next letter, word, phrase, category, cound, action, value * Sequence-to-Sequence modeling: "What alternative sequence is a pattern match?" (i.e., similar probability distribution) * Machine translation, text-to-speech/speech-to-text, connected handwriting (specific scripts)

Simplified Approaches

• If we know all of the sequence states and the probabilities of state transition...
  • ... then we have a simple Markov Chain model.
• If we don't know all of the states or probabilities (yet) but can make constraining assumptions and acquire solid information from observing (sampling) them...
  • ... we can use a Hidden Markov Model approach.

These approached have only limited capacity because they are effectively stateless and so have some degree of "extreme retrograde amnesia."

Can we use a neural network to learn the "next" record in a sequence?

First approach, using what we already know, might look like * Clamp input sequence to a vector of neurons in a feed-forward network * Learn a model on the class of the next input record

Let's try it! This can work in some situations, although it's more of a setup and starting point for our next development.

We will make up a simple example of the English alphabet sequence wehere we try to predict the next alphabet from a sequence of length 3.

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)

('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')

# dataX is just a reindexing of the alphabets in consecutive triplets of numbers
dataX

Out[2]: 
[[0, 1, 2],
 [1, 2, 3],
 [2, 3, 4],
 [3, 4, 5],
 [4, 5, 6],
 [5, 6, 7],
 [6, 7, 8],
 [7, 8, 9],
 [8, 9, 10],
 [9, 10, 11],
 [10, 11, 12],
 [11, 12, 13],
 [12, 13, 14],
 [13, 14, 15],
 [14, 15, 16],
 [15, 16, 17],
 [16, 17, 18],
 [17, 18, 19],
 [18, 19, 20],
 [19, 20, 21],
 [20, 21, 22],
 [21, 22, 23],
 [22, 23, 24]]

dataY # just a reindexing of the following alphabet after each consecutive triplet of numbers

Out[3]: 
[3,
 4,
 5,
 6,
 7,
 8,
 9,
 10,
 11,
 12,
 13,
 14,
 15,
 16,
 17,
 18,
 19,
 20,
 21,
 22,
 23,
 24,
 25]

Train a network on that data:

import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM # <- this is the Long-Short-term memory layer
from keras.utils import np_utils

# begin data generation ------------------------------------------
# this is just a repeat of what we did above
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)
# end data generation ---------------------------------------------

X = numpy.reshape(dataX, (len(dataX), seq_length))
X = X / float(len(alphabet)) # normalize the mapping of alphabets from integers into [0, 1]
y = np_utils.to_categorical(dataY) # make the output we want to predict to be categorical

# keras architecturing of a feed forward dense or fully connected Neural Network
model = Sequential()
# draw the architecture of the network given by next two lines, hint: X.shape[1] = 3, y.shape[1] = 26
model.add(Dense(30, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(y.shape[1], activation='softmax'))

# keras compiling and fitting
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=1000, batch_size=5, verbose=2)

scores = model.evaluate(X, y)
print("Model Accuracy: %.2f " % scores[1])

for pattern in dataX:
    x = numpy.reshape(pattern, (1, len(pattern)))
    x = x / float(len(alphabet))
    prediction = model.predict(x, verbose=0) # get prediction from fitted model
    index = numpy.argmax(prediction)
    result = int_to_char[index]
    seq_in = [int_to_char[value] for value in pattern]
    print (seq_in, "->", result) # print the predicted outputs

('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 1/1000
0s - loss: 3.2624 - acc: 0.0000e+00
Epoch 2/1000
0s - loss: 3.2588 - acc: 0.0870
Epoch 3/1000
0s - loss: 3.2567 - acc: 0.0435
Epoch 4/1000
0s - loss: 3.2550 - acc: 0.0435
Epoch 5/1000
0s - loss: 3.2530 - acc: 0.0435
Epoch 6/1000
0s - loss: 3.2511 - acc: 0.0435
Epoch 7/1000
0s - loss: 3.2497 - acc: 0.0435
Epoch 8/1000
0s - loss: 3.2476 - acc: 0.0435
Epoch 9/1000
0s - loss: 3.2458 - acc: 0.0435
Epoch 10/1000
0s - loss: 3.2437 - acc: 0.0435
Epoch 11/1000
0s - loss: 3.2419 - acc: 0.0435
Epoch 12/1000
0s - loss: 3.2401 - acc: 0.0435
Epoch 13/1000
0s - loss: 3.2378 - acc: 0.0435
Epoch 14/1000
0s - loss: 3.2356 - acc: 0.0435
Epoch 15/1000
0s - loss: 3.2340 - acc: 0.0435
Epoch 16/1000
0s - loss: 3.2315 - acc: 0.0435
Epoch 17/1000
0s - loss: 3.2290 - acc: 0.0435
Epoch 18/1000
0s - loss: 3.2268 - acc: 0.0435
Epoch 19/1000
0s - loss: 3.2243 - acc: 0.0435
Epoch 20/1000
0s - loss: 3.2219 - acc: 0.0435
Epoch 21/1000
0s - loss: 3.2192 - acc: 0.0435
Epoch 22/1000
0s - loss: 3.2163 - acc: 0.0435
Epoch 23/1000
0s - loss: 3.2135 - acc: 0.0435
Epoch 24/1000
0s - loss: 3.2110 - acc: 0.0435
Epoch 25/1000
0s - loss: 3.2081 - acc: 0.0435
Epoch 26/1000
0s - loss: 3.2048 - acc: 0.0435
Epoch 27/1000
0s - loss: 3.2021 - acc: 0.0435
Epoch 28/1000
0s - loss: 3.1987 - acc: 0.0435
Epoch 29/1000
0s - loss: 3.1956 - acc: 0.0435
Epoch 30/1000
0s - loss: 3.1922 - acc: 0.0435
Epoch 31/1000
0s - loss: 3.1890 - acc: 0.0435
Epoch 32/1000
0s - loss: 3.1860 - acc: 0.0435
Epoch 33/1000
0s - loss: 3.1822 - acc: 0.0435
Epoch 34/1000
0s - loss: 3.1782 - acc: 0.0435
Epoch 35/1000
0s - loss: 3.1748 - acc: 0.0435
Epoch 36/1000
0s - loss: 3.1715 - acc: 0.0435
Epoch 37/1000
0s - loss: 3.1681 - acc: 0.0435
Epoch 38/1000
0s - loss: 3.1635 - acc: 0.0435
Epoch 39/1000
0s - loss: 3.1604 - acc: 0.0435
Epoch 40/1000
0s - loss: 3.1567 - acc: 0.0435
Epoch 41/1000
0s - loss: 3.1530 - acc: 0.0435
Epoch 42/1000
0s - loss: 3.1496 - acc: 0.0435
Epoch 43/1000
0s - loss: 3.1454 - acc: 0.0435
Epoch 44/1000
0s - loss: 3.1424 - acc: 0.0435
Epoch 45/1000
0s - loss: 3.1378 - acc: 0.0435
Epoch 46/1000
0s - loss: 3.1343 - acc: 0.0435
Epoch 47/1000
0s - loss: 3.1304 - acc: 0.0435
Epoch 48/1000
0s - loss: 3.1276 - acc: 0.0435
Epoch 49/1000
0s - loss: 3.1235 - acc: 0.0435
Epoch 50/1000
0s - loss: 3.1203 - acc: 0.0435
Epoch 51/1000
0s - loss: 3.1166 - acc: 0.0435
Epoch 52/1000
0s - loss: 3.1130 - acc: 0.0435
Epoch 53/1000
0s - loss: 3.1095 - acc: 0.0435
Epoch 54/1000
0s - loss: 3.1065 - acc: 0.0435
Epoch 55/1000
0s - loss: 3.1030 - acc: 0.0435
Epoch 56/1000
0s - loss: 3.0994 - acc: 0.0435
Epoch 57/1000
0s - loss: 3.0963 - acc: 0.0435
Epoch 58/1000
0s - loss: 3.0930 - acc: 0.0435
Epoch 59/1000
0s - loss: 3.0897 - acc: 0.0435
Epoch 60/1000
0s - loss: 3.0870 - acc: 0.0435
Epoch 61/1000
0s - loss: 3.0835 - acc: 0.0435
Epoch 62/1000
0s - loss: 3.0799 - acc: 0.0435
Epoch 63/1000
0s - loss: 3.0767 - acc: 0.0435
Epoch 64/1000
0s - loss: 3.0735 - acc: 0.0435
Epoch 65/1000
0s - loss: 3.0701 - acc: 0.0435
Epoch 66/1000
0s - loss: 3.0671 - acc: 0.0435
Epoch 67/1000
0s - loss: 3.0641 - acc: 0.0435
Epoch 68/1000
0s - loss: 3.0608 - acc: 0.0435
Epoch 69/1000
0s - loss: 3.0577 - acc: 0.0435
Epoch 70/1000
0s - loss: 3.0546 - acc: 0.0435
Epoch 71/1000
0s - loss: 3.0514 - acc: 0.0435
Epoch 72/1000
0s - loss: 3.0490 - acc: 0.0435
Epoch 73/1000
0s - loss: 3.0455 - acc: 0.0435
Epoch 74/1000
0s - loss: 3.0424 - acc: 0.0435
Epoch 75/1000
0s - loss: 3.0392 - acc: 0.0435
Epoch 76/1000
0s - loss: 3.0362 - acc: 0.0435
Epoch 77/1000
0s - loss: 3.0328 - acc: 0.0435
Epoch 78/1000
0s - loss: 3.0293 - acc: 0.0435
Epoch 79/1000
0s - loss: 3.0265 - acc: 0.0435
Epoch 80/1000
0s - loss: 3.0235 - acc: 0.0435
Epoch 81/1000
0s - loss: 3.0203 - acc: 0.0435
Epoch 82/1000
0s - loss: 3.0173 - acc: 0.0435
Epoch 83/1000
0s - loss: 3.0138 - acc: 0.0435
Epoch 84/1000
0s - loss: 3.0109 - acc: 0.0435
Epoch 85/1000
0s - loss: 3.0076 - acc: 0.0435
Epoch 86/1000
0s - loss: 3.0045 - acc: 0.0435
Epoch 87/1000
0s - loss: 3.0013 - acc: 0.0870
Epoch 88/1000
0s - loss: 2.9983 - acc: 0.0870
Epoch 89/1000
0s - loss: 2.9951 - acc: 0.0870
Epoch 90/1000
0s - loss: 2.9923 - acc: 0.0870
Epoch 91/1000
0s - loss: 2.9892 - acc: 0.0870
Epoch 92/1000
0s - loss: 2.9865 - acc: 0.0870
Epoch 93/1000
0s - loss: 2.9830 - acc: 0.0870
Epoch 94/1000
0s - loss: 2.9798 - acc: 0.0870
Epoch 95/1000
0s - loss: 2.9767 - acc: 0.0870
Epoch 96/1000
0s - loss: 2.9740 - acc: 0.0870
Epoch 97/1000
0s - loss: 2.9709 - acc: 0.0870
Epoch 98/1000
0s - loss: 2.9675 - acc: 0.0870
Epoch 99/1000
0s - loss: 2.9647 - acc: 0.0870
Epoch 100/1000
0s - loss: 2.9615 - acc: 0.0870
Epoch 101/1000
0s - loss: 2.9588 - acc: 0.0870
Epoch 102/1000
0s - loss: 2.9559 - acc: 0.0870
Epoch 103/1000
0s - loss: 2.9527 - acc: 0.0870
Epoch 104/1000
0s - loss: 2.9497 - acc: 0.0870
Epoch 105/1000
0s - loss: 2.9463 - acc: 0.0870
Epoch 106/1000
0s - loss: 2.9433 - acc: 0.0870
Epoch 107/1000
0s - loss: 2.9399 - acc: 0.0870
Epoch 108/1000
0s - loss: 2.9374 - acc: 0.0870
Epoch 109/1000
0s - loss: 2.9344 - acc: 0.0870
Epoch 110/1000
0s - loss: 2.9311 - acc: 0.0870
Epoch 111/1000
0s - loss: 2.9283 - acc: 0.0870
Epoch 112/1000
0s - loss: 2.9253 - acc: 0.0870
Epoch 113/1000
0s - loss: 2.9225 - acc: 0.0870
Epoch 114/1000
0s - loss: 2.9196 - acc: 0.0870
Epoch 115/1000
0s - loss: 2.9162 - acc: 0.1304
Epoch 116/1000
0s - loss: 2.9135 - acc: 0.1304
Epoch 117/1000
0s - loss: 2.9105 - acc: 0.1739
Epoch 118/1000
0s - loss: 2.9076 - acc: 0.1304
Epoch 119/1000
0s - loss: 2.9044 - acc: 0.1304
Epoch 120/1000
0s - loss: 2.9014 - acc: 0.1304
Epoch 121/1000
0s - loss: 2.8985 - acc: 0.1304
Epoch 122/1000
0s - loss: 2.8957 - acc: 0.1304
Epoch 123/1000
0s - loss: 2.8923 - acc: 0.1304
Epoch 124/1000
0s - loss: 2.8895 - acc: 0.0870
Epoch 125/1000
0s - loss: 2.8866 - acc: 0.0870
Epoch 126/1000
0s - loss: 2.8835 - acc: 0.0870
Epoch 127/1000
0s - loss: 2.8804 - acc: 0.0870
Epoch 128/1000
0s - loss: 2.8775 - acc: 0.0870
Epoch 129/1000
0s - loss: 2.8745 - acc: 0.0870
Epoch 130/1000
0s - loss: 2.8713 - acc: 0.0870
Epoch 131/1000
0s - loss: 2.8685 - acc: 0.0870
Epoch 132/1000
0s - loss: 2.8654 - acc: 0.0870
Epoch 133/1000
0s - loss: 2.8624 - acc: 0.0870
Epoch 134/1000
0s - loss: 2.8596 - acc: 0.0870
Epoch 135/1000
0s - loss: 2.8565 - acc: 0.0870
Epoch 136/1000
0s - loss: 2.8538 - acc: 0.0870
Epoch 137/1000
0s - loss: 2.8507 - acc: 0.0870
Epoch 138/1000
0s - loss: 2.8473 - acc: 0.0870
Epoch 139/1000
0s - loss: 2.8446 - acc: 0.0870
Epoch 140/1000
0s - loss: 2.8417 - acc: 0.0870
Epoch 141/1000
0s - loss: 2.8385 - acc: 0.0870
Epoch 142/1000
0s - loss: 2.8353 - acc: 0.0870
Epoch 143/1000
0s - loss: 2.8326 - acc: 0.0870
Epoch 144/1000
0s - loss: 2.8291 - acc: 0.0870
Epoch 145/1000
0s - loss: 2.8263 - acc: 0.0870
Epoch 146/1000
0s - loss: 2.8229 - acc: 0.0870
Epoch 147/1000
0s - loss: 2.8201 - acc: 0.0870
Epoch 148/1000
0s - loss: 2.8179 - acc: 0.0870
Epoch 149/1000
0s - loss: 2.8142 - acc: 0.0870
Epoch 150/1000
0s - loss: 2.8108 - acc: 0.0870
Epoch 151/1000
0s - loss: 2.8082 - acc: 0.0870
Epoch 152/1000
0s - loss: 2.8055 - acc: 0.0870
Epoch 153/1000
0s - loss: 2.8024 - acc: 0.0870
Epoch 154/1000
0s - loss: 2.7995 - acc: 0.0870
Epoch 155/1000
0s - loss: 2.7967 - acc: 0.0870
Epoch 156/1000
0s - loss: 2.7938 - acc: 0.0870
Epoch 157/1000
0s - loss: 2.7910 - acc: 0.0870
Epoch 158/1000
0s - loss: 2.7879 - acc: 0.0870
Epoch 159/1000
0s - loss: 2.7853 - acc: 0.0870
Epoch 160/1000
0s - loss: 2.7828 - acc: 0.0870
Epoch 161/1000
0s - loss: 2.7799 - acc: 0.0870
Epoch 162/1000
0s - loss: 2.7766 - acc: 0.0870
Epoch 163/1000
0s - loss: 2.7738 - acc: 0.0870
Epoch 164/1000
0s - loss: 2.7714 - acc: 0.1304
Epoch 165/1000
0s - loss: 2.7685 - acc: 0.1739
Epoch 166/1000
0s - loss: 2.7660 - acc: 0.1304
Epoch 167/1000
0s - loss: 2.7627 - acc: 0.1304
Epoch 168/1000
0s - loss: 2.7603 - acc: 0.1304
Epoch 169/1000
0s - loss: 2.7572 - acc: 0.1304
Epoch 170/1000
0s - loss: 2.7547 - acc: 0.1304
Epoch 171/1000
0s - loss: 2.7518 - acc: 0.1304
Epoch 172/1000
0s - loss: 2.7489 - acc: 0.1304
Epoch 173/1000
0s - loss: 2.7467 - acc: 0.1304
Epoch 174/1000
0s - loss: 2.7436 - acc: 0.1304
Epoch 175/1000
0s - loss: 2.7410 - acc: 0.1304
Epoch 176/1000
0s - loss: 2.7383 - acc: 0.0870
Epoch 177/1000
0s - loss: 2.7362 - acc: 0.0870
Epoch 178/1000
0s - loss: 2.7329 - acc: 0.0870
Epoch 179/1000
0s - loss: 2.7304 - acc: 0.0870
Epoch 180/1000
0s - loss: 2.7282 - acc: 0.0870
Epoch 181/1000
0s - loss: 2.7259 - acc: 0.1304
Epoch 182/1000
0s - loss: 2.7230 - acc: 0.1304
Epoch 183/1000
0s - loss: 2.7206 - acc: 0.0870
Epoch 184/1000
0s - loss: 2.7175 - acc: 0.0870
Epoch 185/1000
0s - loss: 2.7152 - acc: 0.0870
Epoch 186/1000
0s - loss: 2.7124 - acc: 0.1304
Epoch 187/1000
0s - loss: 2.7097 - acc: 0.1304
Epoch 188/1000
0s - loss: 2.7073 - acc: 0.1304
Epoch 189/1000
0s - loss: 2.7052 - acc: 0.1739
Epoch 190/1000
0s - loss: 2.7024 - acc: 0.1304
Epoch 191/1000
0s - loss: 2.6999 - acc: 0.1739
Epoch 192/1000
0s - loss: 2.6977 - acc: 0.1304
Epoch 193/1000
0s - loss: 2.6950 - acc: 0.1304
Epoch 194/1000
0s - loss: 2.6928 - acc: 0.1304
Epoch 195/1000
0s - loss: 2.6896 - acc: 0.1304
Epoch 196/1000
0s - loss: 2.6871 - acc: 0.1304
Epoch 197/1000
0s - loss: 2.6847 - acc: 0.1304
Epoch 198/1000
0s - loss: 2.6822 - acc: 0.1304
Epoch 199/1000
0s - loss: 2.6796 - acc: 0.1304
Epoch 200/1000
0s - loss: 2.6775 - acc: 0.1304
Epoch 201/1000
0s - loss: 2.6752 - acc: 0.1304
Epoch 202/1000
0s - loss: 2.6724 - acc: 0.1304
Epoch 203/1000
0s - loss: 2.6698 - acc: 0.1304
Epoch 204/1000
0s - loss: 2.6674 - acc: 0.1304
Epoch 205/1000
0s - loss: 2.6647 - acc: 0.1304
Epoch 206/1000
0s - loss: 2.6627 - acc: 0.1304
Epoch 207/1000
0s - loss: 2.6602 - acc: 0.1304
Epoch 208/1000
0s - loss: 2.6579 - acc: 0.1304
Epoch 209/1000
0s - loss: 2.6554 - acc: 0.1304
Epoch 210/1000
0s - loss: 2.6532 - acc: 0.1304
Epoch 211/1000
0s - loss: 2.6505 - acc: 0.1304
Epoch 212/1000
0s - loss: 2.6480 - acc: 0.1304
Epoch 213/1000
0s - loss: 2.6456 - acc: 0.1304
Epoch 214/1000
0s - loss: 2.6432 - acc: 0.1304
Epoch 215/1000
0s - loss: 2.6408 - acc: 0.1304
Epoch 216/1000
0s - loss: 2.6386 - acc: 0.1739
Epoch 217/1000
0s - loss: 2.6359 - acc: 0.1739
Epoch 218/1000
0s - loss: 2.6337 - acc: 0.1739
Epoch 219/1000
0s - loss: 2.6314 - acc: 0.1739
Epoch 220/1000
0s - loss: 2.6293 - acc: 0.1739
Epoch 221/1000
0s - loss: 2.6269 - acc: 0.1739
Epoch 222/1000
0s - loss: 2.6246 - acc: 0.1739
Epoch 223/1000
0s - loss: 2.6223 - acc: 0.1739
Epoch 224/1000
0s - loss: 2.6198 - acc: 0.1304
Epoch 225/1000
0s - loss: 2.6177 - acc: 0.1739
Epoch 226/1000
0s - loss: 2.6155 - acc: 0.1739
Epoch 227/1000
0s - loss: 2.6136 - acc: 0.1739
Epoch 228/1000
0s - loss: 2.6108 - acc: 0.1739
Epoch 229/1000
0s - loss: 2.6092 - acc: 0.1739
Epoch 230/1000
0s - loss: 2.6070 - acc: 0.1739
Epoch 231/1000
0s - loss: 2.6045 - acc: 0.1739
Epoch 232/1000
0s - loss: 2.6021 - acc: 0.1739
Epoch 233/1000
0s - loss: 2.6003 - acc: 0.1739
Epoch 234/1000
0s - loss: 2.5978 - acc: 0.1739
Epoch 235/1000
0s - loss: 2.5960 - acc: 0.1739
Epoch 236/1000
0s - loss: 2.5937 - acc: 0.1739
Epoch 237/1000
0s - loss: 2.5920 - acc: 0.1739
Epoch 238/1000
0s - loss: 2.5894 - acc: 0.1739
Epoch 239/1000
0s - loss: 2.5871 - acc: 0.1739
Epoch 240/1000
0s - loss: 2.5854 - acc: 0.1739
Epoch 241/1000
0s - loss: 2.5832 - acc: 0.1739
Epoch 242/1000
0s - loss: 2.5810 - acc: 0.1739
Epoch 243/1000
0s - loss: 2.5787 - acc: 0.1739
Epoch 244/1000
0s - loss: 2.5770 - acc: 0.1739
Epoch 245/1000
0s - loss: 2.5747 - acc: 0.1739
Epoch 246/1000
0s - loss: 2.5721 - acc: 0.1739
Epoch 247/1000
0s - loss: 2.5708 - acc: 0.1739
Epoch 248/1000
0s - loss: 2.5681 - acc: 0.1739
Epoch 249/1000
0s - loss: 2.5668 - acc: 0.1739
Epoch 250/1000
0s - loss: 2.5642 - acc: 0.1739
Epoch 251/1000
0s - loss: 2.5622 - acc: 0.1739
Epoch 252/1000
0s - loss: 2.5600 - acc: 0.1739
Epoch 253/1000
0s - loss: 2.5581 - acc: 0.1739
Epoch 254/1000
0s - loss: 2.5564 - acc: 0.2174
Epoch 255/1000
0s - loss: 2.5544 - acc: 0.2174
Epoch 256/1000
0s - loss: 2.5523 - acc: 0.2174
Epoch 257/1000
0s - loss: 2.5508 - acc: 0.2174
Epoch 258/1000
0s - loss: 2.5485 - acc: 0.2174
Epoch 259/1000
0s - loss: 2.5466 - acc: 0.2174
Epoch 260/1000
0s - loss: 2.5444 - acc: 0.2174
Epoch 261/1000
0s - loss: 2.5428 - acc: 0.2174
Epoch 262/1000
0s - loss: 2.5406 - acc: 0.2174
Epoch 263/1000
0s - loss: 2.5387 - acc: 0.2174
Epoch 264/1000
0s - loss: 2.5365 - acc: 0.2174
Epoch 265/1000
0s - loss: 2.5347 - acc: 0.2174
Epoch 266/1000
0s - loss: 2.5330 - acc: 0.2174
Epoch 267/1000
0s - loss: 2.5310 - acc: 0.2174
Epoch 268/1000
0s - loss: 2.5297 - acc: 0.2174
Epoch 269/1000
0s - loss: 2.5271 - acc: 0.2174
Epoch 270/1000
0s - loss: 2.5254 - acc: 0.2174
Epoch 271/1000
0s - loss: 2.5237 - acc: 0.2174
Epoch 272/1000
0s - loss: 2.5223 - acc: 0.2174
Epoch 273/1000
0s - loss: 2.5204 - acc: 0.2174
Epoch 274/1000
0s - loss: 2.5179 - acc: 0.2174
Epoch 275/1000
0s - loss: 2.5167 - acc: 0.2174
Epoch 276/1000
0s - loss: 2.5141 - acc: 0.2174
Epoch 277/1000
0s - loss: 2.5126 - acc: 0.2174
Epoch 278/1000
0s - loss: 2.5111 - acc: 0.2174
Epoch 279/1000
0s - loss: 2.5091 - acc: 0.2174
Epoch 280/1000
0s - loss: 2.5070 - acc: 0.2174
Epoch 281/1000
0s - loss: 2.5056 - acc: 0.2174
Epoch 282/1000
0s - loss: 2.5036 - acc: 0.2174
Epoch 283/1000
0s - loss: 2.5014 - acc: 0.2174
Epoch 284/1000
0s - loss: 2.4996 - acc: 0.2174
Epoch 285/1000
0s - loss: 2.4982 - acc: 0.2174
Epoch 286/1000
0s - loss: 2.4966 - acc: 0.2174
Epoch 287/1000
0s - loss: 2.4946 - acc: 0.2174
Epoch 288/1000
0s - loss: 2.4923 - acc: 0.1739
Epoch 289/1000
0s - loss: 2.4907 - acc: 0.1739
Epoch 290/1000
0s - loss: 2.4894 - acc: 0.1739
Epoch 291/1000
0s - loss: 2.4879 - acc: 0.1739
Epoch 292/1000
0s - loss: 2.4856 - acc: 0.1739
Epoch 293/1000
0s - loss: 2.4839 - acc: 0.1739
Epoch 294/1000
0s - loss: 2.4820 - acc: 0.1739
Epoch 295/1000
0s - loss: 2.4807 - acc: 0.1739
Epoch 296/1000
0s - loss: 2.4788 - acc: 0.1739
Epoch 297/1000
0s - loss: 2.4772 - acc: 0.1739
Epoch 298/1000
0s - loss: 2.4754 - acc: 0.1739
Epoch 299/1000
0s - loss: 2.4737 - acc: 0.1739
Epoch 300/1000
0s - loss: 2.4719 - acc: 0.1739
Epoch 301/1000
0s - loss: 2.4706 - acc: 0.1739
Epoch 302/1000
0s - loss: 2.4682 - acc: 0.1739
Epoch 303/1000
0s - loss: 2.4666 - acc: 0.1739
Epoch 304/1000
0s - loss: 2.4652 - acc: 0.1739
Epoch 305/1000
0s - loss: 2.4635 - acc: 0.1739
Epoch 306/1000
0s - loss: 2.4616 - acc: 0.2174
Epoch 307/1000
0s - loss: 2.4603 - acc: 0.1739
Epoch 308/1000
0s - loss: 2.4585 - acc: 0.2174
Epoch 309/1000
0s - loss: 2.4568 - acc: 0.1739
Epoch 310/1000
0s - loss: 2.4551 - acc: 0.1739
Epoch 311/1000
0s - loss: 2.4528 - acc: 0.1739
Epoch 312/1000
0s - loss: 2.4524 - acc: 0.2174
Epoch 313/1000
0s - loss: 2.4498 - acc: 0.1739
Epoch 314/1000
0s - loss: 2.4485 - acc: 0.1739
Epoch 315/1000
0s - loss: 2.4466 - acc: 0.2174
Epoch 316/1000
0s - loss: 2.4454 - acc: 0.1739
Epoch 317/1000
0s - loss: 2.4444 - acc: 0.1739
Epoch 318/1000
0s - loss: 2.4424 - acc: 0.1739
Epoch 319/1000
0s - loss: 2.4408 - acc: 0.1739
Epoch 320/1000
0s - loss: 2.4390 - acc: 0.1739
Epoch 321/1000
0s - loss: 2.4376 - acc: 0.1739
Epoch 322/1000
0s - loss: 2.4364 - acc: 0.1739
Epoch 323/1000
0s - loss: 2.4343 - acc: 0.1739
Epoch 324/1000
0s - loss: 2.4327 - acc: 0.1739
Epoch 325/1000
0s - loss: 2.4312 - acc: 0.1739
Epoch 326/1000
0s - loss: 2.4298 - acc: 0.1739
Epoch 327/1000
0s - loss: 2.4283 - acc: 0.1739
Epoch 328/1000
0s - loss: 2.4266 - acc: 0.1739
Epoch 329/1000
0s - loss: 2.4253 - acc: 0.1739
Epoch 330/1000
0s - loss: 2.4232 - acc: 0.1739
Epoch 331/1000
0s - loss: 2.4220 - acc: 0.1739
Epoch 332/1000
0s - loss: 2.4200 - acc: 0.2174
Epoch 333/1000
0s - loss: 2.4190 - acc: 0.2174
Epoch 334/1000
0s - loss: 2.4172 - acc: 0.2174
Epoch 335/1000
0s - loss: 2.4154 - acc: 0.1739
Epoch 336/1000
0s - loss: 2.4139 - acc: 0.1739
Epoch 337/1000
0s - loss: 2.4118 - acc: 0.1739
Epoch 338/1000
0s - loss: 2.4110 - acc: 0.1739
Epoch 339/1000
0s - loss: 2.4099 - acc: 0.2174
Epoch 340/1000
0s - loss: 2.4083 - acc: 0.2174
Epoch 341/1000
0s - loss: 2.4074 - acc: 0.2174
Epoch 342/1000
0s - loss: 2.4052 - acc: 0.2174
Epoch 343/1000
0s - loss: 2.4037 - acc: 0.1739
Epoch 344/1000
0s - loss: 2.4022 - acc: 0.1739
Epoch 345/1000
0s - loss: 2.4004 - acc: 0.1739
Epoch 346/1000
0s - loss: 2.3993 - acc: 0.1739
Epoch 347/1000
0s - loss: 2.3976 - acc: 0.1739
Epoch 348/1000
0s - loss: 2.3963 - acc: 0.1739
Epoch 349/1000
0s - loss: 2.3946 - acc: 0.2174
Epoch 350/1000
0s - loss: 2.3935 - acc: 0.2174
Epoch 351/1000
0s - loss: 2.3914 - acc: 0.2174
Epoch 352/1000
0s - loss: 2.3906 - acc: 0.2174
Epoch 353/1000
0s - loss: 2.3887 - acc: 0.2174
Epoch 354/1000
0s - loss: 2.3873 - acc: 0.2174
Epoch 355/1000
0s - loss: 2.3860 - acc: 0.2174
Epoch 356/1000
0s - loss: 2.3842 - acc: 0.2174
Epoch 357/1000
0s - loss: 2.3826 - acc: 0.2609
Epoch 358/1000
0s - loss: 2.3818 - acc: 0.2609
Epoch 359/1000
0s - loss: 2.3798 - acc: 0.2609
Epoch 360/1000
0s - loss: 2.3784 - acc: 0.2609
Epoch 361/1000
0s - loss: 2.3770 - acc: 0.2609
Epoch 362/1000
0s - loss: 2.3759 - acc: 0.2609
Epoch 363/1000
0s - loss: 2.3744 - acc: 0.2174
Epoch 364/1000
0s - loss: 2.3731 - acc: 0.2174
Epoch 365/1000
0s - loss: 2.3716 - acc: 0.2609
Epoch 366/1000
0s - loss: 2.3702 - acc: 0.2609
Epoch 367/1000
0s - loss: 2.3686 - acc: 0.2609
Epoch 368/1000
0s - loss: 2.3670 - acc: 0.2609
Epoch 369/1000
0s - loss: 2.3654 - acc: 0.2174
Epoch 370/1000
0s - loss: 2.3644 - acc: 0.2174
Epoch 371/1000
0s - loss: 2.3634 - acc: 0.2174
Epoch 372/1000
0s - loss: 2.3614 - acc: 0.2174
Epoch 373/1000
0s - loss: 2.3602 - acc: 0.2174
Epoch 374/1000
0s - loss: 2.3582 - acc: 0.2174
Epoch 375/1000
0s - loss: 2.3569 - acc: 0.2174
Epoch 376/1000
0s - loss: 2.3562 - acc: 0.2174
Epoch 377/1000
0s - loss: 2.3547 - acc: 0.2609
Epoch 378/1000
0s - loss: 2.3533 - acc: 0.2609
Epoch 379/1000
0s - loss: 2.3515 - acc: 0.2609
Epoch 380/1000
0s - loss: 2.3502 - acc: 0.2609
Epoch 381/1000
0s - loss: 2.3493 - acc: 0.2609
Epoch 382/1000
0s - loss: 2.3479 - acc: 0.3043
Epoch 383/1000
0s - loss: 2.3465 - acc: 0.3043
Epoch 384/1000
0s - loss: 2.3452 - acc: 0.3043
Epoch 385/1000
0s - loss: 2.3437 - acc: 0.3043
Epoch 386/1000
0s - loss: 2.3424 - acc: 0.3043
Epoch 387/1000
0s - loss: 2.3405 - acc: 0.3043
Epoch 388/1000
0s - loss: 2.3390 - acc: 0.3043
Epoch 389/1000
0s - loss: 2.3383 - acc: 0.3043
Epoch 390/1000
0s - loss: 2.3369 - acc: 0.3043
Epoch 391/1000
0s - loss: 2.3357 - acc: 0.3043
Epoch 392/1000
0s - loss: 2.3344 - acc: 0.2609
Epoch 393/1000
0s - loss: 2.3326 - acc: 0.2609
Epoch 394/1000
0s - loss: 2.3316 - acc: 0.2609
Epoch 395/1000
0s - loss: 2.3301 - acc: 0.2609
Epoch 396/1000
0s - loss: 2.3288 - acc: 0.2609
Epoch 397/1000
0s - loss: 2.3276 - acc: 0.3043
Epoch 398/1000
0s - loss: 2.3263 - acc: 0.3043
Epoch 399/1000
0s - loss: 2.3249 - acc: 0.3043
Epoch 400/1000
0s - loss: 2.3234 - acc: 0.3043
Epoch 401/1000
0s - loss: 2.3221 - acc: 0.3043
Epoch 402/1000
0s - loss: 2.3213 - acc: 0.3043
Epoch 403/1000
0s - loss: 2.3196 - acc: 0.3043
Epoch 404/1000
0s - loss: 2.3182 - acc: 0.3043
Epoch 405/1000
0s - loss: 2.3174 - acc: 0.3043
Epoch 406/1000
0s - loss: 2.3158 - acc: 0.2609
Epoch 407/1000
0s - loss: 2.3151 - acc: 0.2609
Epoch 408/1000
0s - loss: 2.3132 - acc: 0.2609
Epoch 409/1000
0s - loss: 2.3123 - acc: 0.2609
Epoch 410/1000
0s - loss: 2.3108 - acc: 0.3043
Epoch 411/1000
0s - loss: 2.3092 - acc: 0.3043
Epoch 412/1000
0s - loss: 2.3085 - acc: 0.3043
Epoch 413/1000
0s - loss: 2.3072 - acc: 0.3043
Epoch 414/1000
0s - loss: 2.3061 - acc: 0.3043
Epoch 415/1000
0s - loss: 2.3045 - acc: 0.2609
Epoch 416/1000
0s - loss: 2.3028 - acc: 0.2609
Epoch 417/1000
0s - loss: 2.3017 - acc: 0.2609
Epoch 418/1000
0s - loss: 2.3003 - acc: 0.2609
Epoch 419/1000
0s - loss: 2.2998 - acc: 0.2174
Epoch 420/1000
0s - loss: 2.2984 - acc: 0.2174
Epoch 421/1000
0s - loss: 2.2968 - acc: 0.2609
Epoch 422/1000
0s - loss: 2.2960 - acc: 0.2609
Epoch 423/1000
0s - loss: 2.2946 - acc: 0.2174
Epoch 424/1000
0s - loss: 2.2931 - acc: 0.2174
Epoch 425/1000
0s - loss: 2.2921 - acc: 0.2174
Epoch 426/1000
0s - loss: 2.2905 - acc: 0.2609
Epoch 427/1000
0s - loss: 2.2896 - acc: 0.3043
Epoch 428/1000
0s - loss: 2.2880 - acc: 0.3043
Epoch 429/1000
0s - loss: 2.2873 - acc: 0.3043
Epoch 430/1000
0s - loss: 2.2859 - acc: 0.3043
Epoch 431/1000
0s - loss: 2.2847 - acc: 0.3043
Epoch 432/1000
0s - loss: 2.2835 - acc: 0.3043
Epoch 433/1000
0s - loss: 2.2822 - acc: 0.3043
Epoch 434/1000
0s - loss: 2.2810 - acc: 0.3043
Epoch 435/1000
0s - loss: 2.2802 - acc: 0.3043
Epoch 436/1000
0s - loss: 2.2786 - acc: 0.3043
Epoch 437/1000
0s - loss: 2.2775 - acc: 0.3043
Epoch 438/1000
0s - loss: 2.2765 - acc: 0.3043
Epoch 439/1000
0s - loss: 2.2753 - acc: 0.3043
Epoch 440/1000
0s - loss: 2.2744 - acc: 0.3043
Epoch 441/1000
0s - loss: 2.2725 - acc: 0.3043
Epoch 442/1000
0s - loss: 2.2711 - acc: 0.3043
Epoch 443/1000
0s - loss: 2.2703 - acc: 0.3043
Epoch 444/1000
0s - loss: 2.2684 - acc: 0.3043
Epoch 445/1000
0s - loss: 2.2682 - acc: 0.3043
Epoch 446/1000
0s - loss: 2.2669 - acc: 0.3043
Epoch 447/1000
0s - loss: 2.2654 - acc: 0.3043
Epoch 448/1000
0s - loss: 2.2640 - acc: 0.3043
Epoch 449/1000
0s - loss: 2.2633 - acc: 0.3043
Epoch 450/1000
0s - loss: 2.2618 - acc: 0.3043
Epoch 451/1000
0s - loss: 2.2609 - acc: 0.3043
Epoch 452/1000
0s - loss: 2.2600 - acc: 0.3043
Epoch 453/1000
0s - loss: 2.2585 - acc: 0.3043
Epoch 454/1000
0s - loss: 2.2567 - acc: 0.3043
Epoch 455/1000
0s - loss: 2.2559 - acc: 0.3043
Epoch 456/1000
0s - loss: 2.2548 - acc: 0.3043
Epoch 457/1000
0s - loss: 2.2535 - acc: 0.3043
Epoch 458/1000
0s - loss: 2.2531 - acc: 0.3478
Epoch 459/1000
0s - loss: 2.2513 - acc: 0.3043
Epoch 460/1000
0s - loss: 2.2503 - acc: 0.3043
Epoch 461/1000
0s - loss: 2.2488 - acc: 0.3043
Epoch 462/1000
0s - loss: 2.2475 - acc: 0.3478
Epoch 463/1000
0s - loss: 2.2470 - acc: 0.3478
Epoch 464/1000
0s - loss: 2.2461 - acc: 0.3478
Epoch 465/1000
0s - loss: 2.2447 - acc: 0.3478
Epoch 466/1000
0s - loss: 2.2439 - acc: 0.3478
Epoch 467/1000
0s - loss: 2.2427 - acc: 0.3478
Epoch 468/1000
0s - loss: 2.2410 - acc: 0.3478
Epoch 469/1000
0s - loss: 2.2402 - acc: 0.3478
Epoch 470/1000
0s - loss: 2.2393 - acc: 0.3478
Epoch 471/1000
0s - loss: 2.2380 - acc: 0.3478
Epoch 472/1000
0s - loss: 2.2371 - acc: 0.3478
Epoch 473/1000
0s - loss: 2.2356 - acc: 0.3478
Epoch 474/1000
0s - loss: 2.2343 - acc: 0.3478
Epoch 475/1000
0s - loss: 2.2337 - acc: 0.3478
Epoch 476/1000
0s - loss: 2.2322 - acc: 0.3913
Epoch 477/1000
0s - loss: 2.2308 - acc: 0.3913
Epoch 478/1000
0s - loss: 2.2293 - acc: 0.3913
Epoch 479/1000
0s - loss: 2.2289 - acc: 0.3913
Epoch 480/1000
0s - loss: 2.2280 - acc: 0.3913
Epoch 481/1000
0s - loss: 2.2265 - acc: 0.3913
Epoch 482/1000
0s - loss: 2.2259 - acc: 0.3913
Epoch 483/1000
0s - loss: 2.2240 - acc: 0.3913
Epoch 484/1000
0s - loss: 2.2233 - acc: 0.3913
Epoch 485/1000
0s - loss: 2.2217 - acc: 0.3913
Epoch 486/1000
0s - loss: 2.2209 - acc: 0.3913
Epoch 487/1000
0s - loss: 2.2196 - acc: 0.3913
Epoch 488/1000
0s - loss: 2.2183 - acc: 0.3913
Epoch 489/1000
0s - loss: 2.2173 - acc: 0.3913
Epoch 490/1000
0s - loss: 2.2157 - acc: 0.3913
Epoch 491/1000
0s - loss: 2.2158 - acc: 0.3913
Epoch 492/1000
0s - loss: 2.2143 - acc: 0.3478
Epoch 493/1000
0s - loss: 2.2131 - acc: 0.3478
Epoch 494/1000
0s - loss: 2.2120 - acc: 0.3478
Epoch 495/1000
0s - loss: 2.2116 - acc: 0.3478
Epoch 496/1000
0s - loss: 2.2096 - acc: 0.3478
Epoch 497/1000
0s - loss: 2.2084 - acc: 0.3478
Epoch 498/1000
0s - loss: 2.2078 - acc: 0.3478
Epoch 499/1000
0s - loss: 2.2063 - acc: 0.3478
Epoch 500/1000
0s - loss: 2.2053 - acc: 0.3478
Epoch 501/1000
0s - loss: 2.2040 - acc: 0.3478
Epoch 502/1000
0s - loss: 2.2032 - acc: 0.3478
Epoch 503/1000
0s - loss: 2.2020 - acc: 0.3478
Epoch 504/1000
0s - loss: 2.2013 - acc: 0.3478
Epoch 505/1000
0s - loss: 2.2002 - acc: 0.3478
Epoch 506/1000
0s - loss: 2.1993 - acc: 0.3478
Epoch 507/1000
0s - loss: 2.1981 - acc: 0.3913
Epoch 508/1000
0s - loss: 2.1968 - acc: 0.3478
Epoch 509/1000
0s - loss: 2.1957 - acc: 0.3913
Epoch 510/1000
0s - loss: 2.1943 - acc: 0.4348
Epoch 511/1000
0s - loss: 2.1935 - acc: 0.4348
Epoch 512/1000
0s - loss: 2.1920 - acc: 0.4348
Epoch 513/1000
0s - loss: 2.1916 - acc: 0.4348
Epoch 514/1000
0s - loss: 2.1898 - acc: 0.4348
Epoch 515/1000
0s - loss: 2.1891 - acc: 0.4348
Epoch 516/1000
0s - loss: 2.1882 - acc: 0.3913
Epoch 517/1000
0s - loss: 2.1875 - acc: 0.3913
Epoch 518/1000
0s - loss: 2.1863 - acc: 0.4348
Epoch 519/1000
0s - loss: 2.1849 - acc: 0.4348
Epoch 520/1000
0s - loss: 2.1846 - acc: 0.4348
Epoch 521/1000
0s - loss: 2.1831 - acc: 0.4348
Epoch 522/1000
0s - loss: 2.1823 - acc: 0.4348
Epoch 523/1000
0s - loss: 2.1811 - acc: 0.4348
Epoch 524/1000
0s - loss: 2.1795 - acc: 0.4783
Epoch 525/1000
0s - loss: 2.1798 - acc: 0.4348
Epoch 526/1000
0s - loss: 2.1776 - acc: 0.3913
Epoch 527/1000
0s - loss: 2.1773 - acc: 0.3913
Epoch 528/1000
0s - loss: 2.1752 - acc: 0.3913
Epoch 529/1000
0s - loss: 2.1744 - acc: 0.3913
Epoch 530/1000
0s - loss: 2.1736 - acc: 0.3913
Epoch 531/1000
0s - loss: 2.1726 - acc: 0.4348
Epoch 532/1000
0s - loss: 2.1721 - acc: 0.4348
Epoch 533/1000
0s - loss: 2.1710 - acc: 0.3913
Epoch 534/1000
0s - loss: 2.1694 - acc: 0.3913
Epoch 535/1000
0s - loss: 2.1684 - acc: 0.3913
Epoch 536/1000
0s - loss: 2.1668 - acc: 0.3913
Epoch 537/1000
0s - loss: 2.1667 - acc: 0.3913
Epoch 538/1000
0s - loss: 2.1658 - acc: 0.3913
Epoch 539/1000
0s - loss: 2.1647 - acc: 0.3913
Epoch 540/1000
0s - loss: 2.1634 - acc: 0.3913
Epoch 541/1000
0s - loss: 2.1622 - acc: 0.3913
Epoch 542/1000
0s - loss: 2.1617 - acc: 0.3913
Epoch 543/1000
0s - loss: 2.1598 - acc: 0.3478
Epoch 544/1000
0s - loss: 2.1594 - acc: 0.4348
Epoch 545/1000
0s - loss: 2.1588 - acc: 0.3913
Epoch 546/1000
0s - loss: 2.1567 - acc: 0.3913
Epoch 547/1000
0s - loss: 2.1563 - acc: 0.3913
Epoch 548/1000
0s - loss: 2.1553 - acc: 0.3913
Epoch 549/1000
0s - loss: 2.1549 - acc: 0.4348
Epoch 550/1000
0s - loss: 2.1531 - acc: 0.4348
Epoch 551/1000
0s - loss: 2.1529 - acc: 0.4348
Epoch 552/1000
0s - loss: 2.1518 - acc: 0.3913
Epoch 553/1000
0s - loss: 2.1507 - acc: 0.3913
Epoch 554/1000
0s - loss: 2.1498 - acc: 0.3913
Epoch 555/1000
0s - loss: 2.1485 - acc: 0.4783
Epoch 556/1000
0s - loss: 2.1474 - acc: 0.4783
Epoch 557/1000
0s - loss: 2.1464 - acc: 0.4348
Epoch 558/1000
0s - loss: 2.1453 - acc: 0.4348
Epoch 559/1000
0s - loss: 2.1446 - acc: 0.4348
Epoch 560/1000
0s - loss: 2.1431 - acc: 0.4348
Epoch 561/1000
0s - loss: 2.1427 - acc: 0.4348
Epoch 562/1000
0s - loss: 2.1415 - acc: 0.4348
Epoch 563/1000
0s - loss: 2.1407 - acc: 0.4348
Epoch 564/1000
0s - loss: 2.1396 - acc: 0.4783
Epoch 565/1000
0s - loss: 2.1387 - acc: 0.4783
Epoch 566/1000
0s - loss: 2.1374 - acc: 0.4783
Epoch 567/1000
0s - loss: 2.1365 - acc: 0.4783
Epoch 568/1000
0s - loss: 2.1359 - acc: 0.4783
Epoch 569/1000
0s - loss: 2.1349 - acc: 0.4783
Epoch 570/1000
0s - loss: 2.1335 - acc: 0.4783
Epoch 571/1000
0s - loss: 2.1324 - acc: 0.4783
Epoch 572/1000
0s - loss: 2.1314 - acc: 0.4348
Epoch 573/1000
0s - loss: 2.1308 - acc: 0.4348
Epoch 574/1000
0s - loss: 2.1302 - acc: 0.4348
Epoch 575/1000
0s - loss: 2.1291 - acc: 0.4348
Epoch 576/1000
0s - loss: 2.1280 - acc: 0.4348
Epoch 577/1000
0s - loss: 2.1267 - acc: 0.4348
Epoch 578/1000
0s - loss: 2.1256 - acc: 0.4348
Epoch 579/1000
0s - loss: 2.1252 - acc: 0.4348
Epoch 580/1000
0s - loss: 2.1246 - acc: 0.4348
Epoch 581/1000
0s - loss: 2.1234 - acc: 0.4348
Epoch 582/1000
0s - loss: 2.1219 - acc: 0.4348
Epoch 583/1000
0s - loss: 2.1214 - acc: 0.4348
Epoch 584/1000
0s - loss: 2.1206 - acc: 0.4348
Epoch 585/1000
0s - loss: 2.1192 - acc: 0.4348
Epoch 586/1000
0s - loss: 2.1180 - acc: 0.4348
Epoch 587/1000
0s - loss: 2.1178 - acc: 0.4348
Epoch 588/1000
0s - loss: 2.1163 - acc: 0.4348
Epoch 589/1000
0s - loss: 2.1160 - acc: 0.4348
Epoch 590/1000
0s - loss: 2.1152 - acc: 0.4348
Epoch 591/1000
0s - loss: 2.1139 - acc: 0.4348
Epoch 592/1000
0s - loss: 2.1132 - acc: 0.3913
Epoch 593/1000
0s - loss: 2.1123 - acc: 0.4348
Epoch 594/1000
0s - loss: 2.1109 - acc: 0.4348
Epoch 595/1000
0s - loss: 2.1105 - acc: 0.4348
Epoch 596/1000
0s - loss: 2.1091 - acc: 0.4348
Epoch 597/1000
0s - loss: 2.1089 - acc: 0.4348
Epoch 598/1000
0s - loss: 2.1081 - acc: 0.4783
Epoch 599/1000
0s - loss: 2.1068 - acc: 0.5217
Epoch 600/1000
0s - loss: 2.1060 - acc: 0.4783
Epoch 601/1000
0s - loss: 2.1048 - acc: 0.4348
Epoch 602/1000
0s - loss: 2.1040 - acc: 0.4348
Epoch 603/1000
0s - loss: 2.1033 - acc: 0.4783
Epoch 604/1000
0s - loss: 2.1025 - acc: 0.5217
Epoch 605/1000
0s - loss: 2.1014 - acc: 0.4783
Epoch 606/1000
0s - loss: 2.1002 - acc: 0.4783
Epoch 607/1000
0s - loss: 2.0992 - acc: 0.4783
Epoch 608/1000
0s - loss: 2.0991 - acc: 0.4783
Epoch 609/1000
0s - loss: 2.0977 - acc: 0.4348
Epoch 610/1000
0s - loss: 2.0966 - acc: 0.4783
Epoch 611/1000
0s - loss: 2.0965 - acc: 0.4783
Epoch 612/1000
0s - loss: 2.0952 - acc: 0.4783
Epoch 613/1000
0s - loss: 2.0937 - acc: 0.4783
Epoch 614/1000
0s - loss: 2.0930 - acc: 0.4783
Epoch 615/1000
0s - loss: 2.0929 - acc: 0.4783
Epoch 616/1000
0s - loss: 2.0913 - acc: 0.4348
Epoch 617/1000
0s - loss: 2.0897 - acc: 0.4783
Epoch 618/1000
0s - loss: 2.0890 - acc: 0.4783
Epoch 619/1000
0s - loss: 2.0885 - acc: 0.5217
Epoch 620/1000
0s - loss: 2.0874 - acc: 0.4783
Epoch 621/1000
0s - loss: 2.0862 - acc: 0.4783
Epoch 622/1000
0s - loss: 2.0862 - acc: 0.4783
Epoch 623/1000
0s - loss: 2.0854 - acc: 0.4783
Epoch 624/1000
0s - loss: 2.0840 - acc: 0.4783
Epoch 625/1000
0s - loss: 2.0832 - acc: 0.5217
Epoch 626/1000
0s - loss: 2.0823 - acc: 0.5217
Epoch 627/1000
0s - loss: 2.0810 - acc: 0.5652
Epoch 628/1000
0s - loss: 2.0804 - acc: 0.5652
Epoch 629/1000
0s - loss: 2.0794 - acc: 0.5652
Epoch 630/1000
0s - loss: 2.0790 - acc: 0.5217
Epoch 631/1000
0s - loss: 2.0775 - acc: 0.5217
Epoch 632/1000
0s - loss: 2.0765 - acc: 0.5217
Epoch 633/1000
0s - loss: 2.0758 - acc: 0.5652
Epoch 634/1000
0s - loss: 2.0748 - acc: 0.5652
Epoch 635/1000
0s - loss: 2.0747 - acc: 0.4783
Epoch 636/1000
0s - loss: 2.0735 - acc: 0.4348
Epoch 637/1000
0s - loss: 2.0728 - acc: 0.4348
Epoch 638/1000
0s - loss: 2.0722 - acc: 0.4348
Epoch 639/1000
0s - loss: 2.0708 - acc: 0.4348
Epoch 640/1000
0s - loss: 2.0703 - acc: 0.4783
Epoch 641/1000
0s - loss: 2.0684 - acc: 0.5652
Epoch 642/1000
0s - loss: 2.0680 - acc: 0.5652
Epoch 643/1000
0s - loss: 2.0671 - acc: 0.5217
Epoch 644/1000
0s - loss: 2.0665 - acc: 0.5217
Epoch 645/1000
0s - loss: 2.0655 - acc: 0.5217
Epoch 646/1000
0s - loss: 2.0654 - acc: 0.4783
Epoch 647/1000
0s - loss: 2.0645 - acc: 0.4348
Epoch 648/1000
0s - loss: 2.0635 - acc: 0.4348
Epoch 649/1000
0s - loss: 2.0619 - acc: 0.3913
Epoch 650/1000
0s - loss: 2.0611 - acc: 0.4348
Epoch 651/1000
0s - loss: 2.0603 - acc: 0.4783
Epoch 652/1000
0s - loss: 2.0591 - acc: 0.4783
Epoch 653/1000
0s - loss: 2.0601 - acc: 0.4348
Epoch 654/1000
0s - loss: 2.0581 - acc: 0.4783
Epoch 655/1000
0s - loss: 2.0576 - acc: 0.5217
Epoch 656/1000
0s - loss: 2.0568 - acc: 0.4783
Epoch 657/1000
0s - loss: 2.0560 - acc: 0.4783
Epoch 658/1000
0s - loss: 2.0550 - acc: 0.3913
Epoch 659/1000
0s - loss: 2.0543 - acc: 0.4348
Epoch 660/1000
0s - loss: 2.0530 - acc: 0.4348
Epoch 661/1000
0s - loss: 2.0529 - acc: 0.4783
Epoch 662/1000
0s - loss: 2.0517 - acc: 0.4348
Epoch 663/1000
0s - loss: 2.0502 - acc: 0.4348
Epoch 664/1000
0s - loss: 2.0495 - acc: 0.4348
Epoch 665/1000
0s - loss: 2.0489 - acc: 0.4348
Epoch 666/1000
0s - loss: 2.0481 - acc: 0.4348
Epoch 667/1000
0s - loss: 2.0472 - acc: 0.3913
Epoch 668/1000
0s - loss: 2.0464 - acc: 0.3913
Epoch 669/1000
0s - loss: 2.0455 - acc: 0.3913
Epoch 670/1000
0s - loss: 2.0450 - acc: 0.3913
Epoch 671/1000
0s - loss: 2.0433 - acc: 0.3913
Epoch 672/1000
0s - loss: 2.0429 - acc: 0.4348
Epoch 673/1000
0s - loss: 2.0417 - acc: 0.4348
Epoch 674/1000
0s - loss: 2.0412 - acc: 0.4348
Epoch 675/1000
0s - loss: 2.0404 - acc: 0.4783
Epoch 676/1000
0s - loss: 2.0398 - acc: 0.4783
Epoch 677/1000
0s - loss: 2.0386 - acc: 0.5652
Epoch 678/1000
0s - loss: 2.0383 - acc: 0.5652
Epoch 679/1000
0s - loss: 2.0377 - acc: 0.5652
Epoch 680/1000
0s - loss: 2.0364 - acc: 0.5652
Epoch 681/1000
0s - loss: 2.0352 - acc: 0.4348
Epoch 682/1000
0s - loss: 2.0350 - acc: 0.5217
Epoch 683/1000
0s - loss: 2.0342 - acc: 0.5652
Epoch 684/1000
0s - loss: 2.0336 - acc: 0.5217
Epoch 685/1000
0s - loss: 2.0334 - acc: 0.4783
Epoch 686/1000
0s - loss: 2.0315 - acc: 0.5217
Epoch 687/1000
0s - loss: 2.0312 - acc: 0.4783
Epoch 688/1000
0s - loss: 2.0305 - acc: 0.5217
Epoch 689/1000
0s - loss: 2.0293 - acc: 0.4783
Epoch 690/1000
0s - loss: 2.0296 - acc: 0.5652
Epoch 691/1000
0s - loss: 2.0274 - acc: 0.5652
Epoch 692/1000
0s - loss: 2.0268 - acc: 0.5652
Epoch 693/1000
0s - loss: 2.0262 - acc: 0.4783
Epoch 694/1000
0s - loss: 2.0250 - acc: 0.4348
Epoch 695/1000
0s - loss: 2.0245 - acc: 0.4348
Epoch 696/1000
0s - loss: 2.0234 - acc: 0.4783
Epoch 697/1000
0s - loss: 2.0224 - acc: 0.5217
Epoch 698/1000
0s - loss: 2.0219 - acc: 0.5217
Epoch 699/1000
0s - loss: 2.0214 - acc: 0.5217
Epoch 700/1000
0s - loss: 2.0198 - acc: 0.5217
Epoch 701/1000
0s - loss: 2.0194 - acc: 0.5217
Epoch 702/1000
0s - loss: 2.0185 - acc: 0.5217
Epoch 703/1000
0s - loss: 2.0185 - acc: 0.4783
Epoch 704/1000
0s - loss: 2.0178 - acc: 0.4783
Epoch 705/1000
0s - loss: 2.0164 - acc: 0.5652
Epoch 706/1000
0s - loss: 2.0151 - acc: 0.5652
Epoch 707/1000
0s - loss: 2.0148 - acc: 0.5652
Epoch 708/1000
0s - loss: 2.0140 - acc: 0.5217
Epoch 709/1000
0s - loss: 2.0124 - acc: 0.4783
Epoch 710/1000
0s - loss: 2.0126 - acc: 0.4783
Epoch 711/1000
0s - loss: 2.0115 - acc: 0.4348
Epoch 712/1000
0s - loss: 2.0117 - acc: 0.4348
Epoch 713/1000
0s - loss: 2.0101 - acc: 0.4783
Epoch 714/1000
0s - loss: 2.0096 - acc: 0.4783
Epoch 715/1000
0s - loss: 2.0093 - acc: 0.4783
Epoch 716/1000
0s - loss: 2.0078 - acc: 0.4783
Epoch 717/1000
0s - loss: 2.0066 - acc: 0.5652
Epoch 718/1000
0s - loss: 2.0059 - acc: 0.5652
Epoch 719/1000
0s - loss: 2.0058 - acc: 0.5217
Epoch 720/1000
0s - loss: 2.0050 - acc: 0.5217
Epoch 721/1000
0s - loss: 2.0046 - acc: 0.5217
Epoch 722/1000
0s - loss: 2.0036 - acc: 0.5217
Epoch 723/1000
0s - loss: 2.0024 - acc: 0.5217
Epoch 724/1000
0s - loss: 2.0016 - acc: 0.5217
Epoch 725/1000
0s - loss: 2.0008 - acc: 0.5652
Epoch 726/1000
0s - loss: 2.0006 - acc: 0.5217
Epoch 727/1000
0s - loss: 1.9986 - acc: 0.5652
Epoch 728/1000
0s - loss: 1.9988 - acc: 0.5652
Epoch 729/1000
0s - loss: 1.9981 - acc: 0.6087
Epoch 730/1000
0s - loss: 1.9969 - acc: 0.5652
Epoch 731/1000
0s - loss: 1.9962 - acc: 0.5652
Epoch 732/1000
0s - loss: 1.9954 - acc: 0.5652
Epoch 733/1000
0s - loss: 1.9946 - acc: 0.5217
Epoch 734/1000
0s - loss: 1.9936 - acc: 0.4783
Epoch 735/1000
0s - loss: 1.9916 - acc: 0.4783
Epoch 736/1000
0s - loss: 1.9926 - acc: 0.5217
Epoch 737/1000
0s - loss: 1.9922 - acc: 0.4783
Epoch 738/1000
0s - loss: 1.9910 - acc: 0.4783
Epoch 739/1000
0s - loss: 1.9900 - acc: 0.4783
Epoch 740/1000
0s - loss: 1.9897 - acc: 0.5217
Epoch 741/1000
0s - loss: 1.9889 - acc: 0.5652
Epoch 742/1000
0s - loss: 1.9883 - acc: 0.6087
Epoch 743/1000
0s - loss: 1.9876 - acc: 0.5652
Epoch 744/1000
0s - loss: 1.9870 - acc: 0.5652
Epoch 745/1000
0s - loss: 1.9856 - acc: 0.4783
Epoch 746/1000
0s - loss: 1.9852 - acc: 0.5217
Epoch 747/1000
0s - loss: 1.9841 - acc: 0.5217
Epoch 748/1000
0s - loss: 1.9834 - acc: 0.5652
Epoch 749/1000
0s - loss: 1.9829 - acc: 0.5217
Epoch 750/1000
0s - loss: 1.9827 - acc: 0.5217
Epoch 751/1000
0s - loss: 1.9807 - acc: 0.5217
Epoch 752/1000
0s - loss: 1.9805 - acc: 0.5217
Epoch 753/1000
0s - loss: 1.9795 - acc: 0.5217
Epoch 754/1000
0s - loss: 1.9793 - acc: 0.4783
Epoch 755/1000
0s - loss: 1.9781 - acc: 0.5652
Epoch 756/1000
0s - loss: 1.9770 - acc: 0.5652
Epoch 757/1000
0s - loss: 1.9768 - acc: 0.5652
Epoch 758/1000
0s - loss: 1.9750 - acc: 0.5217
Epoch 759/1000
0s - loss: 1.9751 - acc: 0.4783
Epoch 760/1000
0s - loss: 1.9741 - acc: 0.4348
Epoch 761/1000
0s - loss: 1.9732 - acc: 0.4348
Epoch 762/1000
0s - loss: 1.9728 - acc: 0.4348
Epoch 763/1000
0s - loss: 1.9720 - acc: 0.4783
Epoch 764/1000
0s - loss: 1.9716 - acc: 0.4783
Epoch 765/1000
0s - loss: 1.9704 - acc: 0.4783
Epoch 766/1000
0s - loss: 1.9701 - acc: 0.4783
Epoch 767/1000
0s - loss: 1.9699 - acc: 0.5217
Epoch 768/1000
0s - loss: 1.9683 - acc: 0.4783
Epoch 769/1000
0s - loss: 1.9676 - acc: 0.4783
Epoch 770/1000
0s - loss: 1.9668 - acc: 0.4783
Epoch 771/1000
0s - loss: 1.9664 - acc: 0.4348
Epoch 772/1000
0s - loss: 1.9664 - acc: 0.4348
Epoch 773/1000
0s - loss: 1.9653 - acc: 0.4348
Epoch 774/1000
0s - loss: 1.9646 - acc: 0.4348
Epoch 775/1000
0s - loss: 1.9647 - acc: 0.4783
Epoch 776/1000
0s - loss: 1.9623 - acc: 0.5217
Epoch 777/1000
0s - loss: 1.9619 - acc: 0.5217
Epoch 778/1000
0s - loss: 1.9613 - acc: 0.4783
Epoch 779/1000
0s - loss: 1.9604 - acc: 0.4783
Epoch 780/1000
0s - loss: 1.9598 - acc: 0.4348
Epoch 781/1000
0s - loss: 1.9595 - acc: 0.4348
Epoch 782/1000
0s - loss: 1.9592 - acc: 0.4348
Epoch 783/1000
0s - loss: 1.9578 - acc: 0.4348
Epoch 784/1000
0s - loss: 1.9570 - acc: 0.4348
Epoch 785/1000
0s - loss: 1.9558 - acc: 0.4348
Epoch 786/1000
0s - loss: 1.9558 - acc: 0.4348
Epoch 787/1000
0s - loss: 1.9555 - acc: 0.4348
Epoch 788/1000
0s - loss: 1.9548 - acc: 0.3913
Epoch 789/1000
0s - loss: 1.9536 - acc: 0.4348
Epoch 790/1000
0s - loss: 1.9534 - acc: 0.4783
Epoch 791/1000
0s - loss: 1.9529 - acc: 0.4783
Epoch 792/1000
0s - loss: 1.9519 - acc: 0.4783
Epoch 793/1000
0s - loss: 1.9518 - acc: 0.4348
Epoch 794/1000
0s - loss: 1.9507 - acc: 0.4348
Epoch 795/1000
0s - loss: 1.9494 - acc: 0.4348
Epoch 796/1000
0s - loss: 1.9492 - acc: 0.4348
Epoch 797/1000
0s - loss: 1.9486 - acc: 0.4348
Epoch 798/1000
0s - loss: 1.9472 - acc: 0.4348
Epoch 799/1000
0s - loss: 1.9464 - acc: 0.4348
Epoch 800/1000
0s - loss: 1.9454 - acc: 0.3913
Epoch 801/1000
0s - loss: 1.9450 - acc: 0.4348
Epoch 802/1000
0s - loss: 1.9449 - acc: 0.4348
Epoch 803/1000
0s - loss: 1.9444 - acc: 0.4348
Epoch 804/1000
0s - loss: 1.9428 - acc: 0.4348
Epoch 805/1000
0s - loss: 1.9427 - acc: 0.4348
Epoch 806/1000
0s - loss: 1.9421 - acc: 0.3913
Epoch 807/1000
0s - loss: 1.9406 - acc: 0.4348
Epoch 808/1000
0s - loss: 1.9401 - acc: 0.4348
Epoch 809/1000
0s - loss: 1.9398 - acc: 0.4783
Epoch 810/1000
0s - loss: 1.9382 - acc: 0.4783
Epoch 811/1000
0s - loss: 1.9384 - acc: 0.4348
Epoch 812/1000
0s - loss: 1.9370 - acc: 0.4783
Epoch 813/1000
0s - loss: 1.9364 - acc: 0.5217
Epoch 814/1000
0s - loss: 1.9361 - acc: 0.5217
Epoch 815/1000
0s - loss: 1.9364 - acc: 0.5652
Epoch 816/1000
0s - loss: 1.9348 - acc: 0.5652
Epoch 817/1000
0s - loss: 1.9343 - acc: 0.5217
Epoch 818/1000
0s - loss: 1.9327 - acc: 0.5652
Epoch 819/1000
0s - loss: 1.9328 - acc: 0.5217
Epoch 820/1000
0s - loss: 1.9328 - acc: 0.4783
Epoch 821/1000
0s - loss: 1.9313 - acc: 0.4783
Epoch 822/1000
0s - loss: 1.9309 - acc: 0.4783
Epoch 823/1000
0s - loss: 1.9298 - acc: 0.4783
Epoch 824/1000
0s - loss: 1.9282 - acc: 0.5217
Epoch 825/1000
0s - loss: 1.9286 - acc: 0.5217
Epoch 826/1000
0s - loss: 1.9269 - acc: 0.4783
Epoch 827/1000
0s - loss: 1.9274 - acc: 0.5217
Epoch 828/1000
0s - loss: 1.9270 - acc: 0.4783
Epoch 829/1000
0s - loss: 1.9262 - acc: 0.5217
Epoch 830/1000
0s - loss: 1.9252 - acc: 0.5217
Epoch 831/1000
0s - loss: 1.9251 - acc: 0.5652
Epoch 832/1000
0s - loss: 1.9239 - acc: 0.5217
Epoch 833/1000
0s - loss: 1.9237 - acc: 0.5652
Epoch 834/1000
0s - loss: 1.9224 - acc: 0.5652
Epoch 835/1000
0s - loss: 1.9213 - acc: 0.5217
Epoch 836/1000
0s - loss: 1.9203 - acc: 0.5217
Epoch 837/1000
0s - loss: 1.9210 - acc: 0.5217
Epoch 838/1000
0s - loss: 1.9193 - acc: 0.4783
Epoch 839/1000
0s - loss: 1.9194 - acc: 0.5217
Epoch 840/1000
0s - loss: 1.9185 - acc: 0.5217
Epoch 841/1000
0s - loss: 1.9170 - acc: 0.5217
Epoch 842/1000
0s - loss: 1.9170 - acc: 0.4783
Epoch 843/1000
0s - loss: 1.9160 - acc: 0.4783
Epoch 844/1000
0s - loss: 1.9149 - acc: 0.5652
Epoch 845/1000
0s - loss: 1.9151 - acc: 0.5217
Epoch 846/1000
0s - loss: 1.9147 - acc: 0.4783
Epoch 847/1000
0s - loss: 1.9143 - acc: 0.5652
Epoch 848/1000
0s - loss: 1.9129 - acc: 0.5652
Epoch 849/1000
0s - loss: 1.9134 - acc: 0.5652
Epoch 850/1000
0s - loss: 1.9115 - acc: 0.5652
Epoch 851/1000
0s - loss: 1.9111 - acc: 0.4783
Epoch 852/1000
0s - loss: 1.9109 - acc: 0.5217
Epoch 853/1000
0s - loss: 1.9103 - acc: 0.5652
Epoch 854/1000
0s - loss: 1.9091 - acc: 0.5652
Epoch 855/1000
0s - loss: 1.9085 - acc: 0.4783
Epoch 856/1000
0s - loss: 1.9076 - acc: 0.4783
Epoch 857/1000
0s - loss: 1.9074 - acc: 0.4783
Epoch 858/1000
0s - loss: 1.9072 - acc: 0.4783
Epoch 859/1000
0s - loss: 1.9061 - acc: 0.4783
Epoch 860/1000
0s - loss: 1.9057 - acc: 0.5217
Epoch 861/1000
0s - loss: 1.9047 - acc: 0.5217
Epoch 862/1000
0s - loss: 1.9046 - acc: 0.5217
Epoch 863/1000
0s - loss: 1.9034 - acc: 0.5217
Epoch 864/1000
0s - loss: 1.9028 - acc: 0.5652
Epoch 865/1000
0s - loss: 1.9017 - acc: 0.5652
Epoch 866/1000
0s - loss: 1.9012 - acc: 0.6087
Epoch 867/1000
0s - loss: 1.9005 - acc: 0.5652
Epoch 868/1000
0s - loss: 1.8999 - acc: 0.5652
Epoch 869/1000
0s - loss: 1.8991 - acc: 0.4783
Epoch 870/1000
0s - loss: 1.8983 - acc: 0.5217
Epoch 871/1000
0s - loss: 1.8980 - acc: 0.5217
Epoch 872/1000
0s - loss: 1.8978 - acc: 0.5217
Epoch 873/1000
0s - loss: 1.8968 - acc: 0.5217
Epoch 874/1000
0s - loss: 1.8961 - acc: 0.5217
Epoch 875/1000
0s - loss: 1.8958 - acc: 0.5217
Epoch 876/1000
0s - loss: 1.8949 - acc: 0.5217
Epoch 877/1000
0s - loss: 1.8942 - acc: 0.5217
Epoch 878/1000
0s - loss: 1.8930 - acc: 0.4783
Epoch 879/1000
0s - loss: 1.8919 - acc: 0.4783
Epoch 880/1000
0s - loss: 1.8919 - acc: 0.4783
Epoch 881/1000
0s - loss: 1.8909 - acc: 0.4783
Epoch 882/1000
0s - loss: 1.8906 - acc: 0.4783
Epoch 883/1000
0s - loss: 1.8910 - acc: 0.4783
Epoch 884/1000
0s - loss: 1.8900 - acc: 0.5217
Epoch 885/1000
0s - loss: 1.8890 - acc: 0.5217
Epoch 886/1000
0s - loss: 1.8880 - acc: 0.4783
Epoch 887/1000
0s - loss: 1.8880 - acc: 0.4783
Epoch 888/1000
0s - loss: 1.8875 - acc: 0.4783
Epoch 889/1000
0s - loss: 1.8864 - acc: 0.6522
Epoch 890/1000
0s - loss: 1.8855 - acc: 0.5652
Epoch 891/1000
0s - loss: 1.8848 - acc: 0.5652
Epoch 892/1000
0s - loss: 1.8835 - acc: 0.5652
Epoch 893/1000
0s - loss: 1.8841 - acc: 0.5217
Epoch 894/1000
0s - loss: 1.8841 - acc: 0.4783
Epoch 895/1000
0s - loss: 1.8834 - acc: 0.5217
Epoch 896/1000
0s - loss: 1.8821 - acc: 0.4783
Epoch 897/1000
0s - loss: 1.8825 - acc: 0.4783
Epoch 898/1000
0s - loss: 1.8807 - acc: 0.6087
Epoch 899/1000
0s - loss: 1.8809 - acc: 0.6087
Epoch 900/1000
0s - loss: 1.8799 - acc: 0.5217
Epoch 901/1000
0s - loss: 1.8788 - acc: 0.5217
Epoch 902/1000
0s - loss: 1.8780 - acc: 0.5217
Epoch 903/1000
0s - loss: 1.8774 - acc: 0.5652
Epoch 904/1000
0s - loss: 1.8761 - acc: 0.5652
Epoch 905/1000
0s - loss: 1.8756 - acc: 0.6087
Epoch 906/1000
0s - loss: 1.8759 - acc: 0.5652
Epoch 907/1000
0s - loss: 1.8751 - acc: 0.5217
Epoch 908/1000
0s - loss: 1.8746 - acc: 0.4783
Epoch 909/1000
0s - loss: 1.8730 - acc: 0.4783
Epoch 910/1000
0s - loss: 1.8723 - acc: 0.4783
Epoch 911/1000
0s - loss: 1.8730 - acc: 0.4783
Epoch 912/1000
0s - loss: 1.8721 - acc: 0.4783
Epoch 913/1000
0s - loss: 1.8715 - acc: 0.4783
Epoch 914/1000
0s - loss: 1.8713 - acc: 0.4783
Epoch 915/1000
0s - loss: 1.8704 - acc: 0.4783
Epoch 916/1000
0s - loss: 1.8695 - acc: 0.4783
Epoch 917/1000
0s - loss: 1.8686 - acc: 0.4783
Epoch 918/1000
0s - loss: 1.8684 - acc: 0.5217
Epoch 919/1000
0s - loss: 1.8676 - acc: 0.5217
Epoch 920/1000
0s - loss: 1.8666 - acc: 0.5217
Epoch 921/1000
0s - loss: 1.8660 - acc: 0.5217
Epoch 922/1000
0s - loss: 1.8658 - acc: 0.5217
Epoch 923/1000
0s - loss: 1.8659 - acc: 0.4783
Epoch 924/1000
0s - loss: 1.8643 - acc: 0.4783
Epoch 925/1000
0s - loss: 1.8637 - acc: 0.4783
Epoch 926/1000
0s - loss: 1.8633 - acc: 0.5217
Epoch 927/1000
0s - loss: 1.8625 - acc: 0.5217
Epoch 928/1000
0s - loss: 1.8614 - acc: 0.5217
Epoch 929/1000
0s - loss: 1.8616 - acc: 0.4783
Epoch 930/1000
0s - loss: 1.8609 - acc: 0.5217
Epoch 931/1000
0s - loss: 1.8599 - acc: 0.5217
Epoch 932/1000
0s - loss: 1.8588 - acc: 0.5217
Epoch 933/1000
0s - loss: 1.8591 - acc: 0.5217
Epoch 934/1000
0s - loss: 1.8589 - acc: 0.5217
Epoch 935/1000
0s - loss: 1.8578 - acc: 0.5217
Epoch 936/1000
0s - loss: 1.8573 - acc: 0.5217
Epoch 937/1000
0s - loss: 1.8565 - acc: 0.5217
Epoch 938/1000
0s - loss: 1.8567 - acc: 0.5217
Epoch 939/1000
0s - loss: 1.8554 - acc: 0.5652
Epoch 940/1000
0s - loss: 1.8552 - acc: 0.5217
Epoch 941/1000
0s - loss: 1.8546 - acc: 0.5217
Epoch 942/1000
0s - loss: 1.8536 - acc: 0.5652
Epoch 943/1000
0s - loss: 1.8533 - acc: 0.5652
Epoch 944/1000
0s - loss: 1.8535 - acc: 0.5217
Epoch 945/1000
0s - loss: 1.8517 - acc: 0.5217
Epoch 946/1000
0s - loss: 1.8512 - acc: 0.5217
Epoch 947/1000
0s - loss: 1.8505 - acc: 0.5217
Epoch 948/1000
0s - loss: 1.8501 - acc: 0.5217
Epoch 949/1000
0s - loss: 1.8492 - acc: 0.5217
Epoch 950/1000
0s - loss: 1.8482 - acc: 0.5217
Epoch 951/1000
0s - loss: 1.8484 - acc: 0.6087
Epoch 952/1000
0s - loss: 1.8476 - acc: 0.5652
Epoch 953/1000
0s - loss: 1.8462 - acc: 0.5652
Epoch 954/1000
0s - loss: 1.8466 - acc: 0.5652
Epoch 955/1000
0s - loss: 1.8460 - acc: 0.5217
Epoch 956/1000
0s - loss: 1.8453 - acc: 0.5217
Epoch 957/1000
0s - loss: 1.8447 - acc: 0.5217
Epoch 958/1000
0s - loss: 1.8437 - acc: 0.5217
Epoch 959/1000
0s - loss: 1.8432 - acc: 0.5217
Epoch 960/1000
0s - loss: 1.8424 - acc: 0.5652
Epoch 961/1000
0s - loss: 1.8412 - acc: 0.5652
Epoch 962/1000
0s - loss: 1.8424 - acc: 0.5652
Epoch 963/1000
0s - loss: 1.8401 - acc: 0.6087
Epoch 964/1000
0s - loss: 1.8412 - acc: 0.6522
Epoch 965/1000
0s - loss: 1.8402 - acc: 0.6522
Epoch 966/1000
0s - loss: 1.8395 - acc: 0.5652
Epoch 967/1000
0s - loss: 1.8385 - acc: 0.5652
Epoch 968/1000
0s - loss: 1.8383 - acc: 0.5652
Epoch 969/1000
0s - loss: 1.8374 - acc: 0.5652
Epoch 970/1000
0s - loss: 1.8361 - acc: 0.5652
Epoch 971/1000
0s - loss: 1.8361 - acc: 0.5217
Epoch 972/1000
0s - loss: 1.8366 - acc: 0.5217
Epoch 973/1000
0s - loss: 1.8361 - acc: 0.6087
Epoch 974/1000
0s - loss: 1.8341 - acc: 0.5652
Epoch 975/1000
0s - loss: 1.8339 - acc: 0.5217
Epoch 976/1000
0s - loss: 1.8333 - acc: 0.5217
Epoch 977/1000
0s - loss: 1.8334 - acc: 0.4783
Epoch 978/1000
0s - loss: 1.8322 - acc: 0.4783
Epoch 979/1000
0s - loss: 1.8327 - acc: 0.5217
Epoch 980/1000
0s - loss: 1.8312 - acc: 0.5652
Epoch 981/1000
0s - loss: 1.8315 - acc: 0.5217
Epoch 982/1000
0s - loss: 1.8304 - acc: 0.4783
Epoch 983/1000
0s - loss: 1.8300 - acc: 0.4783
Epoch 984/1000
0s - loss: 1.8291 - acc: 0.5217
Epoch 985/1000
0s - loss: 1.8284 - acc: 0.5217
Epoch 986/1000
0s - loss: 1.8267 - acc: 0.5217
Epoch 987/1000
0s - loss: 1.8273 - acc: 0.5217
Epoch 988/1000
0s - loss: 1.8266 - acc: 0.5217
Epoch 989/1000
0s - loss: 1.8263 - acc: 0.4783
Epoch 990/1000
0s - loss: 1.8254 - acc: 0.5217
Epoch 991/1000
0s - loss: 1.8250 - acc: 0.4783
Epoch 992/1000
0s - loss: 1.8241 - acc: 0.4783
Epoch 993/1000
0s - loss: 1.8235 - acc: 0.4783
Epoch 994/1000
0s - loss: 1.8223 - acc: 0.4783
Epoch 995/1000
0s - loss: 1.8224 - acc: 0.5217
Epoch 996/1000
0s - loss: 1.8223 - acc: 0.5217
Epoch 997/1000
0s - loss: 1.8211 - acc: 0.5652
Epoch 998/1000
0s - loss: 1.8199 - acc: 0.6087
Epoch 999/1000
0s - loss: 1.8204 - acc: 0.5652
Epoch 1000/1000
0s - loss: 1.8194 - acc: 0.5652
23/23 [==============================] - 0s
Model Accuracy: 0.61 
(['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'], '->', 'K')
(['J', 'K', 'L'], '->', 'M')
(['K', 'L', 'M'], '->', 'O')
(['L', 'M', 'N'], '->', 'O')
(['M', 'N', 'O'], '->', 'O')
(['N', 'O', 'P'], '->', 'Q')
(['O', 'P', 'Q'], '->', 'S')
(['P', 'Q', 'R'], '->', 'S')
(['Q', 'R', 'S'], '->', 'T')
(['R', 'S', 'T'], '->', 'V')
(['S', 'T', 'U'], '->', 'Y')
(['T', 'U', 'V'], '->', 'Y')
(['U', 'V', 'W'], '->', 'Y')
(['V', 'W', 'X'], '->', 'Z')
(['W', 'X', 'Y'], '->', 'Z')

X.shape[1], y.shape[1] # get a sense of the shapes to understand the network architecture

Out[3]: (3, 26)

The network does learn, and could be trained to get a good accuracy. But what's really going on here?

Let's leave aside for a moment the simplistic training data (one fun experiment would be to create corrupted sequences and augment the data with those, forcing the network to pay attention to the whole sequence).

Because the model is fundamentally symmetric and stateless (in terms of the sequence; naturally it has weights), this model would need to learn every sequential feature relative to every single sequence position. That seems difficult, inflexible, and inefficient.

Maybe we could add layers, neurons, and extra connections to mitigate parts of the problem. We could also do things like a 1D convolution to pick up frequencies and some patterns.

But instead, it might make more sense to explicitly model the sequential nature of the data (a bit like how we explictly modeled the 2D nature of image data with CNNs).

Recurrent Neural Network Concept

Let's take the neuron's output from one time (t) and feed it into that same neuron at a later time (t+1), in combination with other relevant inputs. Then we would have a neuron with memory.

We can weight the "return" of that value and train the weight -- so the neuron learns how important the previous value is relative to the current one.

Different neurons might learn to "remember" different amounts of prior history.

This concept is called a Recurrent Neural Network, originally developed around the 1980s.

Let's recall some pointers from the crash intro to Deep learning.

Watch following videos now for 12 minutes for the fastest introduction to RNNs and LSTMs

Udacity: Deep Learning by Vincent Vanhoucke - Recurrent Neural network

Recurrent neural network

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

http://karpathy.github.io/2015/05/21/rnn-effectiveness/ ***

LSTM - Long short term memory

GRU - Gated recurrent unit

http://arxiv.org/pdf/1406.1078v3.pdf

Training a Recurrent Neural Network

We can train an RNN using backpropagation with a minor twist: since RNN neurons with different states over time can be "unrolled" (i.e., are analogous) to a sequence of neurons with the "remember" weight linking directly forward from (t) to (t+1), we can backpropagate through time as well as the physical layers of the network.

This is, in fact, called Backpropagation Through Time (BPTT)

The idea is sound but -- since it creates patterns similar to very deep networks -- it suffers from the same challenges: * Vanishing gradient * Exploding gradient * Saturation * etc.

i.e., many of the same problems with early deep feed-forward networks having lots of weights.

10 steps back in time for a single layer is a not as bad as 10 layers (since there are fewer connections and, hence, weights) but it does get expensive.

ASIDE: Hierarchical and Recursive Networks, Bidirectional RNN

Network topologies can be built to reflect the relative structure of the data we are modeling. E.g., for natural language, grammar constraints mean that both hierarchy and (limited) recursion may allow a physically smaller model to achieve more effective capacity.

A bi-directional RNN includes values from previous and subsequent time steps. This is less strange than it sounds at first: after all, in many problems, such as sentence translation (where BiRNNs are very popular) we usually have the entire source sequence at one time. In that case, a BiDiRNN is really just saying that both prior and subsequent words can influence the interpretation of each word, something we humans take for granted.

Recent versions of neural net libraries have support for bidirectional networks, although you may need to write (or locate) a little code yourself if you want to experiment with hierarchical networks.

Long Short-Term Memory (LSTM)

"Pure" RNNs were never very successful. Sepp Hochreiter and Jürgen Schmidhuber (1997) made a game-changing contribution with the publication of the Long Short-Term Memory unit. How game changing? It's effectively state of the art today.

^{(Credit and much thanks to Chris Olah, http://colah.github.io/about.html, Research Scientist at Google Brain, for publishing the following excellent diagrams!)}

In the following diagrams, pay close attention that the output value is "split" for graphical purposes -- so the two h* arrows/signals coming out are the same signal.*

RNN Cell:

LSTM Cell:

An LSTM unit is a neuron with some bonus features: * Cell state propagated across time * Input, Output, Forget gates * Learns retention/discard of cell state * Admixture of new data * Output partly distinct from state * Use of addition (not multiplication) to combine input and cell state allows state to propagate unimpeded across time (addition of gradient)

ASIDE: Variations on LSTM

... include "peephole" where gate functions have direct access to cell state; convolutional; and bidirectional, where we can "cheat" by letting neurons learn from future time steps and not just previous time steps.

Slow down ... exactly what's getting added to where? For a step-by-step walk through, read Chris Olah's full post http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Do LSTMs Work Reasonably Well?

Yes! These architectures are in production (2017) for deep-learning-enabled products at Baidu, Google, Microsoft, Apple, and elsewhere. They are used to solve problems in time series analysis, speech recognition and generation, connected handwriting, grammar, music, and robot control systems.

Let's Code an LSTM Variant of our Sequence Lab

(this great demo example courtesy of Jason Brownlee: http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/)

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)

# Let’s define an LSTM network with 32 units and an output layer with a softmax activation function for making predictions. 
# a naive implementation of LSTM
model = Sequential()
model.add(LSTM(32, input_shape=(X.shape[1], X.shape[2]))) # <- LSTM layer...
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 dataX:
    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)

('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: 3.2743 - acc: 0.0435
Epoch 2/400
0s - loss: 3.2586 - acc: 0.0000e+00
Epoch 3/400
0s - loss: 3.2516 - acc: 0.0435
Epoch 4/400
0s - loss: 3.2441 - acc: 0.0435
Epoch 5/400
0s - loss: 3.2361 - acc: 0.0435
Epoch 6/400
0s - loss: 3.2288 - acc: 0.0435
Epoch 7/400
0s - loss: 3.2203 - acc: 0.0435
Epoch 8/400
0s - loss: 3.2117 - acc: 0.0435
Epoch 9/400
0s - loss: 3.2021 - acc: 0.0435
Epoch 10/400
0s - loss: 3.1926 - acc: 0.0435
Epoch 11/400
0s - loss: 3.1793 - acc: 0.0000e+00
Epoch 12/400
0s - loss: 3.1653 - acc: 0.0000e+00
Epoch 13/400
0s - loss: 3.1483 - acc: 0.0435
Epoch 14/400
0s - loss: 3.1319 - acc: 0.0435
Epoch 15/400
0s - loss: 3.1135 - acc: 0.0435
Epoch 16/400
0s - loss: 3.0969 - acc: 0.0435
Epoch 17/400
0s - loss: 3.0802 - acc: 0.0435
Epoch 18/400
0s - loss: 3.0600 - acc: 0.0435
Epoch 19/400
0s - loss: 3.0419 - acc: 0.0435
Epoch 20/400
0s - loss: 3.0283 - acc: 0.0435
Epoch 21/400
0s - loss: 3.0073 - acc: 0.0435
Epoch 22/400
0s - loss: 2.9810 - acc: 0.0435
Epoch 23/400
0s - loss: 2.9582 - acc: 0.0870
Epoch 24/400
0s - loss: 2.9321 - acc: 0.0870
Epoch 25/400
0s - loss: 2.9076 - acc: 0.1304
Epoch 26/400
0s - loss: 2.8777 - acc: 0.1304
Epoch 27/400
0s - loss: 2.8500 - acc: 0.1304
Epoch 28/400
0s - loss: 2.8169 - acc: 0.0870
Epoch 29/400
0s - loss: 2.7848 - acc: 0.0870
Epoch 30/400
0s - loss: 2.7524 - acc: 0.0870
Epoch 31/400
0s - loss: 2.7157 - acc: 0.1304
Epoch 32/400
0s - loss: 2.6844 - acc: 0.0870
Epoch 33/400
0s - loss: 2.6552 - acc: 0.0435
Epoch 34/400
0s - loss: 2.6242 - acc: 0.0870
Epoch 35/400
0s - loss: 2.5939 - acc: 0.0870
Epoch 36/400
0s - loss: 2.5631 - acc: 0.1739
Epoch 37/400
0s - loss: 2.5370 - acc: 0.1304
Epoch 38/400
0s - loss: 2.5116 - acc: 0.0435
Epoch 39/400
0s - loss: 2.4850 - acc: 0.0870
Epoch 40/400
0s - loss: 2.4573 - acc: 0.0870
Epoch 41/400
0s - loss: 2.4295 - acc: 0.1304
Epoch 42/400
0s - loss: 2.4097 - acc: 0.0870
Epoch 43/400
0s - loss: 2.3830 - acc: 0.0870
Epoch 44/400
0s - loss: 2.3630 - acc: 0.1739
Epoch 45/400
0s - loss: 2.3409 - acc: 0.1739
Epoch 46/400
0s - loss: 2.3123 - acc: 0.1739
Epoch 47/400
0s - loss: 2.2926 - acc: 0.2174
Epoch 48/400
0s - loss: 2.2692 - acc: 0.2174
Epoch 49/400
0s - loss: 2.2514 - acc: 0.1739
Epoch 50/400
0s - loss: 2.2240 - acc: 0.2174
Epoch 51/400
0s - loss: 2.2059 - acc: 0.3043
Epoch 52/400
0s - loss: 2.1808 - acc: 0.1739
Epoch 53/400
0s - loss: 2.1677 - acc: 0.2609
Epoch 54/400
0s - loss: 2.1425 - acc: 0.2174
Epoch 55/400
0s - loss: 2.1271 - acc: 0.2609
Epoch 56/400
0s - loss: 2.1025 - acc: 0.2174
Epoch 57/400
0s - loss: 2.0917 - acc: 0.1739
Epoch 58/400
0s - loss: 2.0679 - acc: 0.3043
Epoch 59/400
0s - loss: 2.0529 - acc: 0.2609
Epoch 60/400
0s - loss: 2.0383 - acc: 0.3043
Epoch 61/400
0s - loss: 2.0245 - acc: 0.2609
Epoch 62/400
0s - loss: 1.9908 - acc: 0.2609
Epoch 63/400
0s - loss: 1.9841 - acc: 0.2174
Epoch 64/400
0s - loss: 1.9660 - acc: 0.2609
Epoch 65/400
0s - loss: 1.9557 - acc: 0.3043
Epoch 66/400
0s - loss: 1.9353 - acc: 0.3478
Epoch 67/400
0s - loss: 1.9223 - acc: 0.3043
Epoch 68/400
0s - loss: 1.9027 - acc: 0.3913
Epoch 69/400
0s - loss: 1.8819 - acc: 0.4783
Epoch 70/400
0s - loss: 1.8755 - acc: 0.4348
Epoch 71/400
0s - loss: 1.8581 - acc: 0.3913
Epoch 72/400
0s - loss: 1.8466 - acc: 0.3478
Epoch 73/400
0s - loss: 1.8299 - acc: 0.3478
Epoch 74/400
0s - loss: 1.8144 - acc: 0.4348
Epoch 75/400
0s - loss: 1.7991 - acc: 0.4783
Epoch 76/400
0s - loss: 1.7894 - acc: 0.4348
Epoch 77/400
0s - loss: 1.7762 - acc: 0.3913
Epoch 78/400
0s - loss: 1.7623 - acc: 0.3913
Epoch 79/400
0s - loss: 1.7544 - acc: 0.5217
Epoch 80/400
0s - loss: 1.7341 - acc: 0.6522
Epoch 81/400
0s - loss: 1.7215 - acc: 0.4783
Epoch 82/400
0s - loss: 1.7217 - acc: 0.4348
Epoch 83/400
0s - loss: 1.7050 - acc: 0.4348
Epoch 84/400
0s - loss: 1.6934 - acc: 0.3913
Epoch 85/400
0s - loss: 1.6764 - acc: 0.5217
Epoch 86/400
0s - loss: 1.6713 - acc: 0.4783
Epoch 87/400
0s - loss: 1.6584 - acc: 0.4348
Epoch 88/400
0s - loss: 1.6584 - acc: 0.4783
Epoch 89/400
0s - loss: 1.6426 - acc: 0.6522
Epoch 90/400
0s - loss: 1.6340 - acc: 0.5652
Epoch 91/400
0s - loss: 1.6225 - acc: 0.5652
Epoch 92/400
0s - loss: 1.6102 - acc: 0.6087
Epoch 93/400
0s - loss: 1.6009 - acc: 0.6522
Epoch 94/400
0s - loss: 1.6024 - acc: 0.4783
Epoch 95/400
0s - loss: 1.5845 - acc: 0.5652
Epoch 96/400
0s - loss: 1.5774 - acc: 0.6087
Epoch 97/400
0s - loss: 1.5728 - acc: 0.5217
Epoch 98/400
0s - loss: 1.5606 - acc: 0.6522
Epoch 99/400
0s - loss: 1.5581 - acc: 0.6087
Epoch 100/400
0s - loss: 1.5504 - acc: 0.5217
Epoch 101/400
0s - loss: 1.5345 - acc: 0.6522
Epoch 102/400
0s - loss: 1.5317 - acc: 0.6087
Epoch 103/400
0s - loss: 1.5255 - acc: 0.6522
Epoch 104/400
0s - loss: 1.5155 - acc: 0.6087
Epoch 105/400
0s - loss: 1.5071 - acc: 0.6087
Epoch 106/400
0s - loss: 1.4973 - acc: 0.6522
Epoch 107/400
0s - loss: 1.4914 - acc: 0.6087
Epoch 108/400
0s - loss: 1.4756 - acc: 0.6957
Epoch 109/400
0s - loss: 1.4717 - acc: 0.6522
Epoch 110/400
0s - loss: 1.4654 - acc: 0.6522
Epoch 111/400
0s - loss: 1.4631 - acc: 0.6522
Epoch 112/400
0s - loss: 1.4436 - acc: 0.6087
Epoch 113/400
0s - loss: 1.4416 - acc: 0.6087
Epoch 114/400
0s - loss: 1.4367 - acc: 0.6522
Epoch 115/400
0s - loss: 1.4240 - acc: 0.6522
Epoch 116/400
0s - loss: 1.4160 - acc: 0.6522
Epoch 117/400
0s - loss: 1.4207 - acc: 0.7391
Epoch 118/400
0s - loss: 1.4087 - acc: 0.6957
Epoch 119/400
0s - loss: 1.3908 - acc: 0.8261
Epoch 120/400
0s - loss: 1.4012 - acc: 0.6957
Epoch 121/400
0s - loss: 1.3864 - acc: 0.7391
Epoch 122/400
0s - loss: 1.3738 - acc: 0.7391
Epoch 123/400
0s - loss: 1.3751 - acc: 0.7391
Epoch 124/400
0s - loss: 1.3631 - acc: 0.7391
Epoch 125/400
0s - loss: 1.3601 - acc: 0.6957
Epoch 126/400
0s - loss: 1.3559 - acc: 0.7391
Epoch 127/400
0s - loss: 1.3523 - acc: 0.6522
Epoch 128/400
0s - loss: 1.3407 - acc: 0.8261
Epoch 129/400
0s - loss: 1.3344 - acc: 0.7826
Epoch 130/400
0s - loss: 1.3207 - acc: 0.7391
Epoch 131/400
0s - loss: 1.3185 - acc: 0.6522
Epoch 132/400
0s - loss: 1.3149 - acc: 0.7391
Epoch 133/400
0s - loss: 1.3060 - acc: 0.7826
Epoch 134/400
0s - loss: 1.2958 - acc: 0.8261
Epoch 135/400
0s - loss: 1.2924 - acc: 0.7826
Epoch 136/400
0s - loss: 1.2810 - acc: 0.7826
Epoch 137/400
0s - loss: 1.2812 - acc: 0.7826
Epoch 138/400
0s - loss: 1.2766 - acc: 0.7826
Epoch 139/400
0s - loss: 1.2774 - acc: 0.7391
Epoch 140/400
0s - loss: 1.2650 - acc: 0.7826
Epoch 141/400
0s - loss: 1.2537 - acc: 0.6957
Epoch 142/400
0s - loss: 1.2554 - acc: 0.7391
Epoch 143/400
0s - loss: 1.2400 - acc: 0.7826
Epoch 144/400
0s - loss: 1.2441 - acc: 0.7826
Epoch 145/400
0s - loss: 1.2429 - acc: 0.7391
Epoch 146/400
0s - loss: 1.2346 - acc: 0.7826
Epoch 147/400
0s - loss: 1.2283 - acc: 0.7391
Epoch 148/400
0s - loss: 1.2205 - acc: 0.7826
Epoch 149/400
0s - loss: 1.2170 - acc: 0.8261
Epoch 150/400
0s - loss: 1.2034 - acc: 0.8261
Epoch 151/400
0s - loss: 1.1980 - acc: 0.8696
Epoch 152/400
0s - loss: 1.1997 - acc: 0.7826
Epoch 153/400
0s - loss: 1.1896 - acc: 0.8261
Epoch 154/400
0s - loss: 1.1875 - acc: 0.7826
Epoch 155/400
0s - loss: 1.1804 - acc: 0.7826
Epoch 156/400
0s - loss: 1.1722 - acc: 0.7391
Epoch 157/400
0s - loss: 1.1683 - acc: 0.7826
Epoch 158/400
0s - loss: 1.1695 - acc: 0.7826
Epoch 159/400
0s - loss: 1.1658 - acc: 0.8696
Epoch 160/400
0s - loss: 1.1545 - acc: 0.8696
Epoch 161/400
0s - loss: 1.1484 - acc: 0.7826
Epoch 162/400
0s - loss: 1.1481 - acc: 0.8261
Epoch 163/400
0s - loss: 1.1380 - acc: 0.9130
Epoch 164/400
0s - loss: 1.1335 - acc: 0.9130
Epoch 165/400
0s - loss: 1.1334 - acc: 0.8696
Epoch 166/400
0s - loss: 1.1307 - acc: 0.8261
Epoch 167/400
0s - loss: 1.1232 - acc: 0.8261
Epoch 168/400
0s - loss: 1.1192 - acc: 0.8261
Epoch 169/400
0s - loss: 1.1100 - acc: 0.8696
Epoch 170/400
0s - loss: 1.1034 - acc: 0.9130
Epoch 171/400
0s - loss: 1.1050 - acc: 0.8261
Epoch 172/400
0s - loss: 1.0945 - acc: 0.9130
Epoch 173/400
0s - loss: 1.0874 - acc: 0.8696
Epoch 174/400
0s - loss: 1.0870 - acc: 0.9130
Epoch 175/400
0s - loss: 1.0844 - acc: 0.8696
Epoch 176/400
0s - loss: 1.0787 - acc: 0.7826
Epoch 177/400
0s - loss: 1.0700 - acc: 0.9130
Epoch 178/400
0s - loss: 1.0641 - acc: 0.7826
Epoch 179/400
0s - loss: 1.0618 - acc: 0.8696
Epoch 180/400
0s - loss: 1.0549 - acc: 0.9130
Epoch 181/400
0s - loss: 1.0520 - acc: 0.8696
Epoch 182/400
0s - loss: 1.0470 - acc: 0.9130
Epoch 183/400
0s - loss: 1.0408 - acc: 0.8261
Epoch 184/400
0s - loss: 1.0369 - acc: 0.8261
Epoch 185/400
0s - loss: 1.0347 - acc: 0.8261
Epoch 186/400
0s - loss: 1.0267 - acc: 0.9130
Epoch 187/400
0s - loss: 1.0250 - acc: 0.8696
Epoch 188/400
0s - loss: 1.0177 - acc: 0.9130
Epoch 189/400
0s - loss: 1.0121 - acc: 0.8696
Epoch 190/400
0s - loss: 1.0109 - acc: 0.9130
Epoch 191/400
0s - loss: 1.0108 - acc: 0.9130
Epoch 192/400
0s - loss: 0.9983 - acc: 0.8696
Epoch 193/400
0s - loss: 1.0024 - acc: 0.9130
Epoch 194/400
0s - loss: 0.9909 - acc: 0.9565
Epoch 195/400
0s - loss: 0.9919 - acc: 0.9130
Epoch 196/400
0s - loss: 0.9900 - acc: 0.8696
Epoch 197/400
0s - loss: 0.9831 - acc: 0.9130
Epoch 198/400
0s - loss: 0.9760 - acc: 0.9130
Epoch 199/400
0s - loss: 0.9719 - acc: 0.8261
Epoch 200/400
0s - loss: 0.9693 - acc: 0.8696
Epoch 201/400
0s - loss: 0.9626 - acc: 0.8696
Epoch 202/400
0s - loss: 0.9559 - acc: 0.9130
Epoch 203/400
0s - loss: 0.9547 - acc: 0.9130
Epoch 204/400
0s - loss: 0.9546 - acc: 0.8261
Epoch 205/400
0s - loss: 0.9417 - acc: 0.8696
Epoch 206/400
0s - loss: 0.9437 - acc: 0.8696
Epoch 207/400
0s - loss: 0.9479 - acc: 0.9130
Epoch 208/400
0s - loss: 0.9315 - acc: 0.9130
Epoch 209/400
0s - loss: 0.9294 - acc: 0.8696
Epoch 210/400
0s - loss: 0.9275 - acc: 0.9130
Epoch 211/400
0s - loss: 0.9209 - acc: 0.9130
Epoch 212/400
0s - loss: 0.9140 - acc: 0.8261
Epoch 213/400
0s - loss: 0.9152 - acc: 0.8261
Epoch 214/400
0s - loss: 0.9085 - acc: 0.9565
Epoch 215/400
0s - loss: 0.9109 - acc: 0.9130
Epoch 216/400
0s - loss: 0.8986 - acc: 0.9565
Epoch 217/400
0s - loss: 0.9070 - acc: 0.8696
Epoch 218/400
0s - loss: 0.8911 - acc: 0.8696
Epoch 219/400
0s - loss: 0.8951 - acc: 0.8261
Epoch 220/400
0s - loss: 0.8834 - acc: 0.9130
Epoch 221/400
0s - loss: 0.8808 - acc: 0.9130
Epoch 222/400
0s - loss: 0.8745 - acc: 0.8261
Epoch 223/400
0s - loss: 0.8695 - acc: 0.8696
Epoch 224/400
0s - loss: 0.8679 - acc: 0.8696
Epoch 225/400
0s - loss: 0.8731 - acc: 0.8696
Epoch 226/400
0s - loss: 0.8598 - acc: 0.8696
Epoch 227/400
0s - loss: 0.8542 - acc: 0.9565
Epoch 228/400
0s - loss: 0.8545 - acc: 0.9565
Epoch 229/400
0s - loss: 0.8501 - acc: 0.9130
Epoch 230/400
0s - loss: 0.8572 - acc: 0.9565
Epoch 231/400
0s - loss: 0.8541 - acc: 0.8696
Epoch 232/400
0s - loss: 0.8435 - acc: 0.9130
Epoch 233/400
0s - loss: 0.8386 - acc: 0.9130
Epoch 234/400
0s - loss: 0.8345 - acc: 0.8261
Epoch 235/400
0s - loss: 0.8275 - acc: 0.9130
Epoch 236/400
0s - loss: 0.8183 - acc: 0.9565
Epoch 237/400
0s - loss: 0.8159 - acc: 0.9130
Epoch 238/400
0s - loss: 0.8180 - acc: 0.9130
Epoch 239/400
0s - loss: 0.8133 - acc: 0.9565
Epoch 240/400
0s - loss: 0.8061 - acc: 0.9565
Epoch 241/400
0s - loss: 0.8066 - acc: 0.8696
Epoch 242/400
0s - loss: 0.8010 - acc: 0.9565
Epoch 243/400
0s - loss: 0.7944 - acc: 0.8696
Epoch 244/400
0s - loss: 0.7976 - acc: 0.8696
Epoch 245/400
0s - loss: 0.7874 - acc: 0.8696
Epoch 246/400
0s - loss: 0.7844 - acc: 0.9130
Epoch 247/400
0s - loss: 0.7784 - acc: 0.9130
Epoch 248/400
0s - loss: 0.7813 - acc: 0.8696
Epoch 249/400
0s - loss: 0.7746 - acc: 0.8696
Epoch 250/400
0s - loss: 0.7759 - acc: 0.8696
Epoch 251/400
0s - loss: 0.7639 - acc: 0.9130
Epoch 252/400
0s - loss: 0.7679 - acc: 0.9130
Epoch 253/400
0s - loss: 0.7583 - acc: 0.9565
Epoch 254/400
0s - loss: 0.7524 - acc: 0.9565
Epoch 255/400
0s - loss: 0.7491 - acc: 0.9130
Epoch 256/400
0s - loss: 0.7545 - acc: 0.9565
Epoch 257/400
0s - loss: 0.7560 - acc: 0.9565
Epoch 258/400
0s - loss: 0.7480 - acc: 0.8696
Epoch 259/400
0s - loss: 0.7346 - acc: 0.9130
Epoch 260/400
0s - loss: 0.7394 - acc: 0.9565
Epoch 261/400
0s - loss: 0.7319 - acc: 0.9565
Epoch 262/400
0s - loss: 0.7261 - acc: 0.9130
Epoch 263/400
0s - loss: 0.7262 - acc: 0.9565
Epoch 264/400
0s - loss: 0.7237 - acc: 0.9565
Epoch 265/400
0s - loss: 0.7194 - acc: 0.9565
Epoch 266/400
0s - loss: 0.7169 - acc: 0.9565
Epoch 267/400
0s - loss: 0.7194 - acc: 0.9130
Epoch 268/400
0s - loss: 0.7137 - acc: 0.9130
Epoch 269/400
0s - loss: 0.7024 - acc: 0.9130
Epoch 270/400
0s - loss: 0.6985 - acc: 1.0000
Epoch 271/400
0s - loss: 0.6999 - acc: 0.9130
Epoch 272/400
0s - loss: 0.6928 - acc: 0.9130
Epoch 273/400
0s - loss: 0.6908 - acc: 0.9130
Epoch 274/400
0s - loss: 0.6869 - acc: 0.9565
Epoch 275/400
0s - loss: 0.6850 - acc: 0.9565
Epoch 276/400
0s - loss: 0.6853 - acc: 0.9130
Epoch 277/400
0s - loss: 0.6771 - acc: 0.9565
Epoch 278/400
0s - loss: 0.6819 - acc: 0.9565
Epoch 279/400
0s - loss: 0.6730 - acc: 0.9130
Epoch 280/400
0s - loss: 0.6750 - acc: 0.9565
Epoch 281/400
0s - loss: 0.6668 - acc: 0.9565
Epoch 282/400
0s - loss: 0.6681 - acc: 0.9565
Epoch 283/400
0s - loss: 0.6619 - acc: 0.9130
Epoch 284/400
0s - loss: 0.6566 - acc: 0.9130
Epoch 285/400
0s - loss: 0.6577 - acc: 0.9565
Epoch 286/400
0s - loss: 0.6541 - acc: 0.9130
Epoch 287/400
0s - loss: 0.6557 - acc: 0.9130
Epoch 288/400
0s - loss: 0.6504 - acc: 0.9565
Epoch 289/400
0s - loss: 0.6478 - acc: 0.9565
Epoch 290/400
0s - loss: 0.6401 - acc: 0.9130
Epoch 291/400
0s - loss: 0.6375 - acc: 0.9130
Epoch 292/400
0s - loss: 0.6381 - acc: 0.9130
Epoch 293/400
0s - loss: 0.6302 - acc: 0.9130
Epoch 294/400
0s - loss: 0.6268 - acc: 0.9565
Epoch 295/400
0s - loss: 0.6243 - acc: 0.9565
Epoch 296/400
0s - loss: 0.6259 - acc: 1.0000
Epoch 297/400
0s - loss: 0.6258 - acc: 0.9130
Epoch 298/400
0s - loss: 0.6176 - acc: 0.9565
Epoch 299/400
0s - loss: 0.6166 - acc: 0.9565
Epoch 300/400
0s - loss: 0.6122 - acc: 0.9565
Epoch 301/400
0s - loss: 0.6079 - acc: 1.0000
Epoch 302/400
0s - loss: 0.6090 - acc: 0.9130
Epoch 303/400
0s - loss: 0.6068 - acc: 1.0000
Epoch 304/400
0s - loss: 0.6046 - acc: 0.9130
Epoch 305/400
0s - loss: 0.5953 - acc: 0.9130
Epoch 306/400
0s - loss: 0.5975 - acc: 0.9565
Epoch 307/400
0s - loss: 0.5980 - acc: 0.9130
Epoch 308/400
0s - loss: 0.5904 - acc: 0.9130
Epoch 309/400
0s - loss: 0.5873 - acc: 0.9565
Epoch 310/400
0s - loss: 0.5882 - acc: 0.9130
Epoch 311/400
0s - loss: 0.5809 - acc: 0.9565
Epoch 312/400
0s - loss: 0.5785 - acc: 1.0000
Epoch 313/400
0s - loss: 0.5739 - acc: 1.0000
Epoch 314/400
0s - loss: 0.5763 - acc: 0.9130
Epoch 315/400
0s - loss: 0.5720 - acc: 0.9130
Epoch 316/400
0s - loss: 0.5694 - acc: 1.0000
Epoch 317/400
0s - loss: 0.5692 - acc: 0.9565
Epoch 318/400
0s - loss: 0.5673 - acc: 0.9565
Epoch 319/400
0s - loss: 0.5622 - acc: 0.9565
Epoch 320/400
0s - loss: 0.5620 - acc: 0.9565
Epoch 321/400
0s - loss: 0.5639 - acc: 0.9565
Epoch 322/400
0s - loss: 0.5637 - acc: 1.0000
Epoch 323/400
0s - loss: 0.5539 - acc: 0.9130
Epoch 324/400
0s - loss: 0.5500 - acc: 0.9130
Epoch 325/400
0s - loss: 0.5503 - acc: 0.9565
Epoch 326/400
0s - loss: 0.5500 - acc: 1.0000
Epoch 327/400
0s - loss: 0.5457 - acc: 0.9130
Epoch 328/400
0s - loss: 0.5495 - acc: 0.9130
Epoch 329/400
0s - loss: 0.5386 - acc: 0.9565
Epoch 330/400
0s - loss: 0.5372 - acc: 0.9565
Epoch 331/400
0s - loss: 0.5326 - acc: 1.0000
Epoch 332/400
0s - loss: 0.5325 - acc: 0.9565
Epoch 333/400
0s - loss: 0.5314 - acc: 1.0000
Epoch 334/400
0s - loss: 0.5308 - acc: 1.0000
Epoch 335/400
0s - loss: 0.5222 - acc: 1.0000
Epoch 336/400
0s - loss: 0.5188 - acc: 1.0000
Epoch 337/400
0s - loss: 0.5223 - acc: 0.9565
Epoch 338/400
0s - loss: 0.5160 - acc: 0.9130
Epoch 339/400
0s - loss: 0.5225 - acc: 0.9130
Epoch 340/400
0s - loss: 0.5185 - acc: 0.9565
Epoch 341/400
0s - loss: 0.5132 - acc: 0.9565
Epoch 342/400
0s - loss: 0.5172 - acc: 1.0000
Epoch 343/400
0s - loss: 0.5129 - acc: 1.0000
Epoch 344/400
0s - loss: 0.5087 - acc: 0.9565
Epoch 345/400
0s - loss: 0.5109 - acc: 0.9130
Epoch 346/400
0s - loss: 0.5060 - acc: 0.9565
Epoch 347/400
0s - loss: 0.5037 - acc: 0.9565
Epoch 348/400
0s - loss: 0.5007 - acc: 0.9565
Epoch 349/400
0s - loss: 0.4991 - acc: 0.9565
Epoch 350/400
0s - loss: 0.4937 - acc: 0.9565
Epoch 351/400
0s - loss: 0.4909 - acc: 0.9565
Epoch 352/400
0s - loss: 0.4932 - acc: 1.0000
Epoch 353/400
0s - loss: 0.4860 - acc: 0.9565
Epoch 354/400
0s - loss: 0.4910 - acc: 0.9565
Epoch 355/400
0s - loss: 0.4851 - acc: 1.0000
Epoch 356/400
0s - loss: 0.4872 - acc: 0.9130
Epoch 357/400
0s - loss: 0.4806 - acc: 1.0000
Epoch 358/400
0s - loss: 0.4778 - acc: 0.9565
Epoch 359/400
0s - loss: 0.4817 - acc: 1.0000
Epoch 360/400
0s - loss: 0.4736 - acc: 0.9565
Epoch 361/400
0s - loss: 0.4748 - acc: 1.0000
Epoch 362/400
0s - loss: 0.4734 - acc: 1.0000
Epoch 363/400
0s - loss: 0.4729 - acc: 1.0000
Epoch 364/400
0s - loss: 0.4739 - acc: 0.9565
Epoch 365/400
0s - loss: 0.4714 - acc: 0.9565
Epoch 366/400
0s - loss: 0.4665 - acc: 0.9130
Epoch 367/400
0s - loss: 0.4648 - acc: 1.0000
Epoch 368/400
0s - loss: 0.4696 - acc: 0.9565
Epoch 369/400
0s - loss: 0.4650 - acc: 0.9130
Epoch 370/400
0s - loss: 0.4579 - acc: 0.9565
Epoch 371/400
0s - loss: 0.4562 - acc: 1.0000
Epoch 372/400
0s - loss: 0.4562 - acc: 1.0000
Epoch 373/400
0s - loss: 0.4575 - acc: 0.9565
Epoch 374/400
0s - loss: 0.4541 - acc: 0.9130
Epoch 375/400
0s - loss: 0.4528 - acc: 1.0000
Epoch 376/400
0s - loss: 0.4483 - acc: 1.0000
Epoch 377/400
0s - loss: 0.4477 - acc: 1.0000
Epoch 378/400
0s - loss: 0.4482 - acc: 0.9565
Epoch 379/400
0s - loss: 0.4422 - acc: 0.9565
Epoch 380/400
0s - loss: 0.4400 - acc: 0.9565
Epoch 381/400
0s - loss: 0.4416 - acc: 1.0000
Epoch 382/400
0s - loss: 0.4365 - acc: 1.0000
Epoch 383/400
0s - loss: 0.4410 - acc: 1.0000
Epoch 384/400
0s - loss: 0.4383 - acc: 1.0000
Epoch 385/400
0s - loss: 0.4348 - acc: 0.9565
Epoch 386/400
0s - loss: 0.4353 - acc: 1.0000
Epoch 387/400
0s - loss: 0.4314 - acc: 0.9130
Epoch 388/400
0s - loss: 0.4381 - acc: 0.9565
Epoch 389/400
0s - loss: 0.4314 - acc: 1.0000
Epoch 390/400
0s - loss: 0.4319 - acc: 0.9130
Epoch 391/400
0s - loss: 0.4292 - acc: 0.9565
Epoch 392/400
0s - loss: 0.4490 - acc: 0.9565
Epoch 393/400
0s - loss: 0.4347 - acc: 0.9565
Epoch 394/400
0s - loss: 0.4517 - acc: 0.9565
Epoch 395/400
0s - loss: 0.4294 - acc: 0.9130
Epoch 396/400
0s - loss: 0.4178 - acc: 0.9565
Epoch 397/400
0s - loss: 0.4142 - acc: 0.9565
Epoch 398/400
0s - loss: 0.4175 - acc: 1.0000
Epoch 399/400
0s - loss: 0.4123 - acc: 1.0000
Epoch 400/400
0s - loss: 0.4123 - acc: 0.9565
23/23 [==============================] - 0s
Model Accuracy: 100.00%
(['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'], '->', 'W')
(['U', 'V', 'W'], '->', 'X')
(['V', 'W', 'X'], '->', 'Y')
(['W', 'X', 'Y'], '->', 'Z')

(X.shape[1], X.shape[2]) # the input shape to LSTM layer with 32 neurons is given by dimensions of time-steps and features

Out[5]: (3, 1)

X.shape[0], y.shape[1] # number of examples and number of categorical outputs

Out[7]: (23, 26)

Memory and context

If this network is learning the way we would like, it should be robust to noise and also understand the relative context (in this case, where a prior letter occurs in the sequence).

I.e., we should be able to give it corrupted sequences, and it should produce reasonably correct predictions.

Make the following change to the code to test this out:

You Try!

• We'll use "W" for our erroneous/corrupted data element
• Add code at the end to predict on the following sequences:
  • 'WBC', 'WKL', 'WTU', 'DWF', 'MWO', 'VWW', 'GHW', 'JKW', 'PQW'
• Notice any pattern? Hard to tell from a small sample, but if you play with it (trying sequences from different places in the alphabet, or different "corruption" letters, you'll notice patterns that give a hint at what the network is learning

The solution is in 060_DLByABr_05a-LSTM-Solution if you are lazy right now or get stuck.

Pretty cool... BUT

This alphabet example does seem a bit like "tennis without the net" since the original goal was to develop networks that could extract patterns from complex, ambiguous content like natural language or music, and we've been playing with a sequence (Roman alphabet) that is 100% deterministic and tiny in size.

First, go ahead and start 061_DLByABr_05b-LSTM-Language since it will take several minutes to produce its first output.

This latter script is taken 100% exactly as-is from the Keras library examples folder (https://github.com/fchollet/keras/blob/master/examples/lstm*text*generation.py) and uses precisely the logic we just learned, in order to learn and synthesize English language text from a single-author corpuse. The amazing thing is that the text is learned and generated one letter at a time, just like we did with the alphabet.

Compared to our earlier examples... * there is a minor difference in the way the inputs are encoded, using 1-hot vectors * and there is a significant difference in the way the outputs (predictions) are generated: instead of taking just the most likely output class (character) via argmax as we did before, this time we are treating the output as a distribution and sampling from the distribution.

Let's take a look at the code ... but even so, this will probably be something to come back to after fika or a long break, as the training takes about 5 minutes per epoch (late 2013 MBP CPU) and we need around 20 epochs (80 minutes!) to get good output.

import sys
sys.exit(0) #just to keep from accidentally running this code (that is already in 061_DLByABr_05b-LSTM-Language) HERE

'''Example script to generate text from Nietzsche's writings.

At least 20 epochs are required before the generated text
starts sounding coherent.

It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.

If you try this script on new data, make sure your corpus
has at least ~100k characters. ~1M is better.
'''

from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys

path = "../data/nietzsche.txt"
text = open(path).read().lower()
print('corpus length:', len(text))

chars = sorted(list(set(text)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

# cut the text in semi-redundant sequences of maxlen characters
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i: i + maxlen])
    next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))

print('Vectorization...')
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1


# build the model: a single LSTM
print('Build model...')
model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars))))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))

optimizer = RMSprop(lr=0.01)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)


def sample(preds, temperature=1.0):
    # helper function to sample an index from a probability array
    preds = np.asarray(preds).astype('float64')
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1, preds, 1)
    return np.argmax(probas)

# train the model, output generated text after each iteration
for iteration in range(1, 60):
    print()
    print('-' * 50)
    print('Iteration', iteration)
    model.fit(X, y, batch_size=128, epochs=1)

    start_index = random.randint(0, len(text) - maxlen - 1)

    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print()
        print('----- diversity:', diversity)

        generated = ''
        sentence = text[start_index: start_index + maxlen]
        generated += sentence
        print('----- Generating with seed: "' + sentence + '"')
        sys.stdout.write(generated)

        for i in range(400):
            x = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x[0, t, char_indices[char]] = 1.

            preds = model.predict(x, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]

            generated += next_char
            sentence = sentence[1:] + next_char

            sys.stdout.write(next_char)
            sys.stdout.flush()
        print()

Gated Recurrent Unit (GRU)

In 2014, a new, promising design for RNN units called Gated Recurrent Unit was published (https://arxiv.org/abs/1412.3555)

GRUs have performed similarly to LSTMs, but are slightly simpler in design:

• GRU has just two gates: "update" and "reset" (instead of the input, output, and forget in LSTM)
• update controls how to modify (weight and keep) cell state
• reset controls how new input is mixed (weighted) with/against memorized state
• there is no output gate, so the cell state is propagated out -- i.e., there is no "hidden" state that is separate from the generated output state

Which one should you use for which applications? The jury is still out -- this is an area for experimentation!

Using GRUs in Keras

... is as simple as using the built-in GRU class (https://keras.io/layers/recurrent/)

If you are working with RNNs, spend some time with docs to go deeper, as we have just barely scratched the surface here, and there are many "knobs" to turn that will help things go right (or wrong).

'''Example script to generate text from Nietzsche's writings.

At least 20 epochs are required before the generated text
starts sounding coherent.

It is recommended to run this script on GPU, as recurrent
networks are quite computationally intensive.

If you try this script on new data, make sure your corpus
has at least ~100k characters. ~1M is better.
'''

from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import LSTM
from keras.optimizers import RMSprop
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys

path = get_file('nietzsche.txt', origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
text = open(path).read().lower()
print('corpus length:', len(text))

chars = sorted(list(set(text)))
print('total chars:', len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))

# cut the text in semi-redundant sequences of maxlen characters
maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i: i + maxlen])
    next_chars.append(text[i + maxlen])
print('nb sequences:', len(sentences))

print('Vectorization...')
X = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        X[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1

corpus length: 600901
total chars: 59
nb sequences: 200287
Vectorization...

len(sentences), maxlen, len(chars)

Out[11]: (200287, 40, 59)

X

Out[10]: 
array([[[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ..., 
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ..., 
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [ True, False, False, ..., False, False, False],
        [ True, False, False, ..., False, False, False],
        ..., 
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       ..., 
       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ..., 
        [False,  True, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ..., 
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]],

       [[False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        ..., 
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False],
        [False, False, False, ..., False, False, False]]], dtype=bool)

What Does Our Nietzsche Generator Produce?

Here are snapshots from middle and late in a training run.

Iteration 19

Iteration 19
Epoch 1/1
200287/200287 [==============================] - 262s - loss: 1.3908     

----- diversity: 0.2
----- Generating with seed: " apart from the value of such assertions"
 apart from the value of such assertions of the present of the supersially and the soul. the spirituality of the same of the soul. the protect and in the states to the supersially and the soul, in the supersially the supersially and the concerning and in the most conscience of the soul. the soul. the concerning and the substances, and the philosophers in the sing"--that is the most supersiall and the philosophers of the supersially of t

----- diversity: 0.5
----- Generating with seed: " apart from the value of such assertions"
 apart from the value of such assertions are more there is the scientific modern to the head in the concerning in the same old will of the excited of science. many all the possible concerning such laugher according to when the philosophers sense of men of univerself, the most lacked same depresse in the point, which is desires of a "good (who has senses on that one experiencess which use the concerning and in the respect of the same ori

----- diversity: 1.0
----- Generating with seed: " apart from the value of such assertions"
 apart from the value of such assertions expressions--are interest person from indeed to ordinapoon as or one of
the uphamy, state is rivel stimromannes are lot man of soul"--modile what he woulds hope in a riligiation, is conscience, and you amy, surposit to advanced torturily
and whorlon and perressing for accurcted with a lot us in view, of its own vanity of their natest"--learns, and dis predeceared from and leade, for oted those wi

----- diversity: 1.2
----- Generating with seed: " apart from the value of such assertions"
 apart from the value of such assertions of
rutould chinates
rested exceteds to more saarkgs testure carevan, accordy owing before fatherly rifiny,
thrurgins of novelts "frous inventive earth as dire!ition he
shate out of itst sacrifice, in this
mectalical
inworle, you
adome enqueres to its ighter. he often. once even with ded threaten"! an eebirelesifist.

lran innoting
with we canone acquire at them crarulents who had prote will out t

Iteration 32

Iteration 32
Epoch 1/1
200287/200287 [==============================] - 255s - loss: 1.3830     

----- diversity: 0.2
----- Generating with seed: " body, as a part of this external
world,"
 body, as a part of this external
world, and in the great present of the sort of the strangern that is and in the sologies and the experiences and the present of the present and science of the probably a subject of the subject of the morality and morality of the soul the experiences the morality of the experiences of the conscience in the soul and more the experiences the strangere and present the rest the strangere and individual of th

----- diversity: 0.5
----- Generating with seed: " body, as a part of this external
world,"
 body, as a part of this external
world, and in the morality of which we knows upon the english and insigning things be exception of
consequences of the man and explained its more in the senses for the same ordinary and the sortarians and subjects and simily in a some longing the destiny ordinary. man easily that has been the some subject and say, and and and and does not to power as all the reasonable and distinction of this one betray

----- diversity: 1.0
----- Generating with seed: " body, as a part of this external
world,"
 body, as a part of this external
world, surrespossifilice view and life fundamental worthing more sirer. holestly
and whan to be
dream. in whom hand that one downgk edplenius will almost eyes brocky that we wills stupid dor
oborbbill to be dimorable
great excet of ifysabless. the good take the historical yet right by guntend, and which fuens the irrelias in literals in finally to the same flild, conditioned when where prom. it has behi

----- diversity: 1.2
----- Generating with seed: " body, as a part of this external
world,"
 body, as a part of this external
world, easily achosed time mantur makeches on this
vanity, obcame-scompleises. but inquire-calr ever powerfully smorais: too-wantse; when thoue
conducting
unconstularly without least gainstyfyerfulled to wo
has upos
among uaxqunct what is mell "loves and
lamacity what mattery of upon the a. and which oasis seour schol
to power: the passion sparabrated will. in his europers raris! what seems to these her

Take alook at the anomalous behavior that started late in the training on one run ... What might have happened?

Iteration 38

Iteration 38
Epoch 1/1
200287/200287 [==============================] - 256s - loss: 7.6662     

----- diversity: 0.2
----- Generating with seed: "erable? for there is no
longer any ought"
erable? for there is no
longer any oughteesen a a  a= at ae i is es4 iei aatee he a a ac  oyte  in ioie  aan a atoe aie ion a atias a ooe o e tin exanat moe ao is aon e a ntiere t i in ate an on a  e as the a ion aisn ost  aed i  i ioiesn les?ane i ee to i o ate   o igice thi io an a xen an ae an teane one ee e alouieis asno oie on i a a ae s as n io a an e a ofe e  oe ehe it aiol  s a aeio st ior ooe an io e  ot io  o i  aa9em aan ev a

----- diversity: 0.5
----- Generating with seed: "erable? for there is no
longer any ought"
erable? for there is no
longer any oughteese a on eionea] aooooi ate uo e9l hoe atae s in eaae an  on io]e nd ast aais  ta e  od iia ng ac ee er ber  in ==st a se is ao  o e as aeian iesee tee otiane o oeean a ieatqe o  asnone anc 
 oo a t
tee sefiois to an at in ol asnse an o e e oo  ie oae asne at a ait iati oese se a e p ie peen iei ien   o oot inees engied evone t oen oou atipeem a sthen ion assise ti a a s itos io ae an  eees as oi

----- diversity: 1.0
----- Generating with seed: "erable? for there is no
longer any ought"
erable? for there is no
longer any oughteena te e ore te beosespeehsha ieno atit e ewge ou ino oo oee coatian aon ie ac aalle e a o  die eionae oa att uec a acae ao a  an eess as
 o  i a io  a   oe a  e is as oo in ene xof o  oooreeg ta m eon al iii n p daesaoe n ite o ane tio oe anoo t ane
s i e tioo ise s a asi e ana ooe ote soueeon io on atieaneyc ei it he se it is ao e an ime  ane on eronaa ee itouman io e ato an ale  a mae taoa ien

----- diversity: 1.2
----- Generating with seed: "erable? for there is no
longer any ought"
erable? for there is no
longer any oughti o aa e2senoees yi i e datssateal toeieie e a o zanato aal arn aseatli oeene aoni le eoeod t aes a isoee tap  e o . is  oi astee an ea titoe e a exeeee thui itoan ain eas a e bu inen ao ofa ie e e7n anae ait ie a ve  er inen  ite
as oe of  heangi eestioe orasb e fie o o o  a  eean o ot odeerean io io oae ooe ne " e  istee esoonae e terasfioees asa ehainoet at e ea ai esoon   ano a p eesas e aitie

(raaz)

'Mind the Hype' around AI

Pay attention to biases in various media.

• Guardian: 'He began to eat Hermione's family': bot tries to write Harry Potter book – and fails in magic ways After being fed all seven Potter tales, a predictive keyboard has produced a tale that veers from almost genuine to gloriously bonkers
  • https://www.theguardian.com/books/booksblog/2017/dec/13/harry-potter-botnik-jk-rowling
• Business Insider: There is a new chapter in Harry Potter's story — and it was written by artificial intelligence ... The writing is mostly weird and borderline comical, but the machine managed to partly reproduce original writer J.K. Rowling's writing style.
  • http://nordic.businessinsider.com/there-is-a-new-chapter-in-harry-potters-story-and-it-was-written-by-artificial-intelligence-2017-12

When your managers get "psyched" about how AI will solve all the problems and your sales teams are dreaming hard - keep it cool and manage their expectations as a practical data scientist who is humbled by the hard reality of additions, multiplications and conditionals under the hood.

'Mind the Ethics'

Don't forget to ask how your data science pipelines could adversely affect peoples: * A great X-mas gift to yourself: https://weaponsofmathdestructionbook.com/ * Another one to make you braver and calmer: https://www.schneier.com/books/dataandgoliath/

Don't forget that Data Scientists can be put behind bars for "following orders" from your boss to "make magic happen". * https://www.datasciencecentral.com/profiles/blogs/doing-illegal-data-science-without-knowing-it * https://timesofindia.indiatimes.com/india/forecast-of-poll-results-illegal-election-commission/articleshow/57927839.cms * https://spectrum.ieee.org/cars-that-think/at-work/education/vw-scandal-shocking-but-not-surprising-ethicists-say * ...