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Sequence to sequence learning for performing number addition
Sequence to sequence learning for performing number addition
Author: Smerity and others
Date created: 2015/08/17
Last modified: 2024/02/13
Description: A model that learns to add strings of numbers, e.g. "535+61" -> "596".
Introduction
In this example, we train a model to learn to add two numbers, provided as strings.
Example:
- Input: "535+61"
- Output: "596"
Input may optionally be reversed, which was shown to increase performance in many tasks in: Learning to Execute and Sequence to Sequence Learning with Neural Networks.
Theoretically, sequence order inversion introduces shorter term dependencies between source and target for this problem.
Results:
For two digits (reversed):
- One layer LSTM (128 HN), 5k training examples = 99% train/test accuracy in 55 epochs
Three digits (reversed):
- One layer LSTM (128 HN), 50k training examples = 99% train/test accuracy in 100 epochs
Four digits (reversed):
- One layer LSTM (128 HN), 400k training examples = 99% train/test accuracy in 20 epochs
Five digits (reversed):
- One layer LSTM (128 HN), 550k training examples = 99% train/test accuracy in 30 epochs
Setup
import keras
from keras import layers
import numpy as np
# Parameters for the model and dataset.
TRAINING_SIZE = 50000
DIGITS = 3
REVERSE = True
# Maximum length of input is 'int + int' (e.g., '345+678'). Maximum length of
# int is DIGITS.
MAXLEN = DIGITS + 1 + DIGITS
Generate the data
class CharacterTable:
"""Given a set of characters:
+ Encode them to a one-hot integer representation
+ Decode the one-hot or integer representation to their character output
+ Decode a vector of probabilities to their character output
"""
def __init__(self, chars):
"""Initialize character table.
# Arguments
chars: Characters that can appear in the input.
"""
self.chars = sorted(set(chars))
self.char_indices = dict((c, i) for i, c in enumerate(self.chars))
self.indices_char = dict((i, c) for i, c in enumerate(self.chars))
def encode(self, C, num_rows):
"""One-hot encode given string C.
# Arguments
C: string, to be encoded.
num_rows: Number of rows in the returned one-hot encoding. This is
used to keep the # of rows for each data the same.
"""
x = np.zeros((num_rows, len(self.chars)))
for i, c in enumerate(C):
x[i, self.char_indices[c]] = 1
return x
def decode(self, x, calc_argmax=True):
"""Decode the given vector or 2D array to their character output.
# Arguments
x: A vector or a 2D array of probabilities or one-hot representations;
or a vector of character indices (used with `calc_argmax=False`).
calc_argmax: Whether to find the character index with maximum
probability, defaults to `True`.
"""
if calc_argmax:
x = x.argmax(axis=-1)
return "".join(self.indices_char[x] for x in x)
# All the numbers, plus sign and space for padding.
chars = "0123456789+ "
ctable = CharacterTable(chars)
questions = []
expected = []
seen = set()
print("Generating data...")
while len(questions) < TRAINING_SIZE:
f = lambda: int(
"".join(
np.random.choice(list("0123456789"))
for i in range(np.random.randint(1, DIGITS + 1))
)
)
a, b = f(), f()
# Skip any addition questions we've already seen
# Also skip any such that x+Y == Y+x (hence the sorting).
key = tuple(sorted((a, b)))
if key in seen:
continue
seen.add(key)
# Pad the data with spaces such that it is always MAXLEN.
q = "{}+{}".format(a, b)
query = q + " " * (MAXLEN - len(q))
ans = str(a + b)
# Answers can be of maximum size DIGITS + 1.
ans += " " * (DIGITS + 1 - len(ans))
if REVERSE:
# Reverse the query, e.g., '12+345 ' becomes ' 543+21'. (Note the
# space used for padding.)
query = query[::-1]
questions.append(query)
expected.append(ans)
print("Total questions:", len(questions))
Generating data...
Total questions: 50000
Vectorize the data
print("Vectorization...")
x = np.zeros((len(questions), MAXLEN, len(chars)), dtype=bool)
y = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype=bool)
for i, sentence in enumerate(questions):
x[i] = ctable.encode(sentence, MAXLEN)
for i, sentence in enumerate(expected):
y[i] = ctable.encode(sentence, DIGITS + 1)
# Shuffle (x, y) in unison as the later parts of x will almost all be larger
# digits.
indices = np.arange(len(y))
np.random.shuffle(indices)
x = x[indices]
y = y[indices]
# Explicitly set apart 10% for validation data that we never train over.
split_at = len(x) - len(x) // 10
(x_train, x_val) = x[:split_at], x[split_at:]
(y_train, y_val) = y[:split_at], y[split_at:]
print("Training Data:")
print(x_train.shape)
print(y_train.shape)
print("Validation Data:")
print(x_val.shape)
print(y_val.shape)
Vectorization...
Training Data:
(45000, 7, 12)
(45000, 4, 12)
Validation Data:
(5000, 7, 12)
(5000, 4, 12)
Build the model
print("Build model...")
num_layers = 1 # Try to add more LSTM layers!
model = keras.Sequential()
# "Encode" the input sequence using a LSTM, producing an output of size 128.
# Note: In a situation where your input sequences have a variable length,
# use input_shape=(None, num_feature).
model.add(layers.Input((MAXLEN, len(chars))))
model.add(layers.LSTM(128))
# As the decoder RNN's input, repeatedly provide with the last output of
# RNN for each time step. Repeat 'DIGITS + 1' times as that's the maximum
# length of output, e.g., when DIGITS=3, max output is 999+999=1998.
model.add(layers.RepeatVector(DIGITS + 1))
# The decoder RNN could be multiple layers stacked or a single layer.
for _ in range(num_layers):
# By setting return_sequences to True, return not only the last output but
# all the outputs so far in the form of (num_samples, timesteps,
# output_dim). This is necessary as TimeDistributed in the below expects
# the first dimension to be the timesteps.
model.add(layers.LSTM(128, return_sequences=True))
# Apply a dense layer to the every temporal slice of an input. For each of step
# of the output sequence, decide which character should be chosen.
model.add(layers.Dense(len(chars), activation="softmax"))
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.summary()
Build model...
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ lstm (LSTM) │ (None, 128) │ 72,192 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ repeat_vector (RepeatVector) │ (None, 4, 128) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ lstm_1 (LSTM) │ (None, 4, 128) │ 131,584 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense (Dense) │ (None, 4, 12) │ 1,548 │ └─────────────────────────────────┴───────────────────────────┴────────────┘
Total params: 205,324 (802.05 KB)
Trainable params: 205,324 (802.05 KB)
Non-trainable params: 0 (0.00 B)
Train the model
# Training parameters.
epochs = 30
batch_size = 32
# Formatting characters for results display.
green_color = "\033[92m"
red_color = "\033[91m"
end_char = "\033[0m"
# Train the model each generation and show predictions against the validation
# dataset.
for epoch in range(1, epochs):
print()
print("Iteration", epoch)
model.fit(
x_train,
y_train,
batch_size=batch_size,
epochs=1,
validation_data=(x_val, y_val),
)
# Select 10 samples from the validation set at random so we can visualize
# errors.
for i in range(10):
ind = np.random.randint(0, len(x_val))
rowx, rowy = x_val[np.array([ind])], y_val[np.array([ind])]
preds = np.argmax(model.predict(rowx, verbose=0), axis=-1)
q = ctable.decode(rowx[0])
correct = ctable.decode(rowy[0])
guess = ctable.decode(preds[0], calc_argmax=False)
print("Q", q[::-1] if REVERSE else q, end=" ")
print("T", correct, end=" ")
if correct == guess:
print(f"{green_color}☑ {guess}{end_char}")
else:
print(f"{red_color}☒ {guess}{end_char}")
Iteration 1
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 10s 6ms/step - accuracy: 0.3258 - loss: 1.8801 - val_accuracy: 0.4268 - val_loss: 1.5506
Q 499+58 T 557 ☒ 511
Q 51+638 T 689 ☒ 662
Q 87+12 T 99 ☒ 11
Q 259+55 T 314 ☒ 561
Q 704+87 T 791 ☒ 811
Q 988+67 T 1055 ☒ 101
Q 94+116 T 210 ☒ 111
Q 724+4 T 728 ☒ 777
Q 8+673 T 681 ☒ 772
Q 8+991 T 999 ☒ 900
Iteration 2
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.4688 - loss: 1.4235 - val_accuracy: 0.5846 - val_loss: 1.1293
Q 379+6 T 385 ☒ 387
Q 15+504 T 519 ☒ 525
Q 552+299 T 851 ☒ 727
Q 664+0 T 664 ☒ 667
Q 500+257 T 757 ☒ 797
Q 50+818 T 868 ☒ 861
Q 310+691 T 1001 ☒ 900
Q 378+548 T 926 ☒ 827
Q 46+59 T 105 ☒ 122
Q 49+817 T 866 ☒ 871
Iteration 3
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.6053 - loss: 1.0648 - val_accuracy: 0.6665 - val_loss: 0.9070
Q 1+266 T 267 ☒ 260
Q 73+257 T 330 ☒ 324
Q 421+628 T 1049 ☒ 1022
Q 85+590 T 675 ☒ 660
Q 66+34 T 100 ☒ 90
Q 256+639 T 895 ☒ 890
Q 6+677 T 683 ☑ 683
Q 162+637 T 799 ☒ 792
Q 5+324 T 329 ☒ 337
Q 848+34 T 882 ☒ 889
Iteration 4
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.6781 - loss: 0.8751 - val_accuracy: 0.7037 - val_loss: 0.8092
Q 677+1 T 678 ☒ 676
Q 1+531 T 532 ☒ 535
Q 699+60 T 759 ☒ 756
Q 475+139 T 614 ☒ 616
Q 327+592 T 919 ☒ 915
Q 48+912 T 960 ☒ 956
Q 520+78 T 598 ☒ 505
Q 318+8 T 326 ☒ 327
Q 914+53 T 967 ☒ 966
Q 734+0 T 734 ☒ 733
Iteration 5
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.7142 - loss: 0.7807 - val_accuracy: 0.7164 - val_loss: 0.7622
Q 150+337 T 487 ☒ 489
Q 72+934 T 1006 ☒ 1005
Q 171+62 T 233 ☒ 231
Q 108+21 T 129 ☒ 135
Q 755+896 T 1651 ☒ 1754
Q 117+1 T 118 ☒ 119
Q 148+95 T 243 ☒ 241
Q 719+956 T 1675 ☒ 1684
Q 656+43 T 699 ☒ 695
Q 368+8 T 376 ☒ 372
Iteration 6
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.7377 - loss: 0.7157 - val_accuracy: 0.7541 - val_loss: 0.6684
Q 945+364 T 1309 ☒ 1305
Q 762+96 T 858 ☒ 855
Q 5+650 T 655 ☑ 655
Q 52+680 T 732 ☒ 735
Q 77+724 T 801 ☒ 800
Q 46+739 T 785 ☑ 785
Q 843+43 T 886 ☒ 885
Q 158+3 T 161 ☒ 160
Q 426+711 T 1137 ☒ 1138
Q 157+41 T 198 ☒ 190
Iteration 7
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.7642 - loss: 0.6462 - val_accuracy: 0.7955 - val_loss: 0.5433
Q 822+27 T 849 ☑ 849
Q 82+495 T 577 ☒ 563
Q 9+366 T 375 ☒ 373
Q 9+598 T 607 ☒ 696
Q 186+41 T 227 ☒ 226
Q 920+920 T 1840 ☒ 1846
Q 445+345 T 790 ☒ 797
Q 783+588 T 1371 ☒ 1360
Q 36+473 T 509 ☒ 502
Q 354+61 T 415 ☒ 416
Iteration 8
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.8326 - loss: 0.4626 - val_accuracy: 0.9069 - val_loss: 0.2744
Q 458+154 T 612 ☑ 612
Q 309+19 T 328 ☑ 328
Q 808+97 T 905 ☑ 905
Q 28+736 T 764 ☑ 764
Q 28+79 T 107 ☑ 107
Q 44+84 T 128 ☒ 129
Q 744+13 T 757 ☑ 757
Q 24+996 T 1020 ☒ 1011
Q 8+193 T 201 ☒ 101
Q 483+9 T 492 ☒ 491
Iteration 9
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9365 - loss: 0.2275 - val_accuracy: 0.9657 - val_loss: 0.1393
Q 330+61 T 391 ☑ 391
Q 207+82 T 289 ☒ 299
Q 23+234 T 257 ☑ 257
Q 690+567 T 1257 ☑ 1257
Q 293+97 T 390 ☒ 380
Q 312+868 T 1180 ☑ 1180
Q 956+40 T 996 ☑ 996
Q 97+105 T 202 ☒ 203
Q 365+44 T 409 ☑ 409
Q 76+639 T 715 ☑ 715
Iteration 10
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 7s 5ms/step - accuracy: 0.9717 - loss: 0.1223 - val_accuracy: 0.9744 - val_loss: 0.0965
Q 123+143 T 266 ☑ 266
Q 599+1 T 600 ☑ 600
Q 729+237 T 966 ☑ 966
Q 51+120 T 171 ☑ 171
Q 97+672 T 769 ☑ 769
Q 840+5 T 845 ☑ 845
Q 86+494 T 580 ☒ 570
Q 278+51 T 329 ☑ 329
Q 8+832 T 840 ☑ 840
Q 383+9 T 392 ☑ 392
Iteration 11
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 7s 5ms/step - accuracy: 0.9842 - loss: 0.0729 - val_accuracy: 0.9808 - val_loss: 0.0690
Q 181+923 T 1104 ☑ 1104
Q 747+24 T 771 ☑ 771
Q 6+65 T 71 ☑ 71
Q 75+994 T 1069 ☑ 1069
Q 712+587 T 1299 ☑ 1299
Q 977+10 T 987 ☑ 987
Q 742+24 T 766 ☑ 766
Q 215+44 T 259 ☑ 259
Q 817+683 T 1500 ☑ 1500
Q 102+48 T 150 ☒ 140
Iteration 12
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9820 - loss: 0.0695 - val_accuracy: 0.9823 - val_loss: 0.0596
Q 819+885 T 1704 ☒ 1604
Q 34+20 T 54 ☑ 54
Q 9+996 T 1005 ☑ 1005
Q 915+811 T 1726 ☑ 1726
Q 166+640 T 806 ☑ 806
Q 229+82 T 311 ☑ 311
Q 1+418 T 419 ☑ 419
Q 552+28 T 580 ☑ 580
Q 279+733 T 1012 ☑ 1012
Q 756+734 T 1490 ☑ 1490
Iteration 13
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9836 - loss: 0.0587 - val_accuracy: 0.9941 - val_loss: 0.0296
Q 793+0 T 793 ☑ 793
Q 79+48 T 127 ☑ 127
Q 484+92 T 576 ☑ 576
Q 39+655 T 694 ☑ 694
Q 64+708 T 772 ☑ 772
Q 568+341 T 909 ☑ 909
Q 9+918 T 927 ☑ 927
Q 48+912 T 960 ☑ 960
Q 31+289 T 320 ☑ 320
Q 378+548 T 926 ☑ 926
Iteration 14
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9915 - loss: 0.0353 - val_accuracy: 0.9901 - val_loss: 0.0358
Q 318+8 T 326 ☒ 325
Q 886+63 T 949 ☒ 959
Q 77+8 T 85 ☑ 85
Q 418+40 T 458 ☑ 458
Q 30+32 T 62 ☑ 62
Q 541+93 T 634 ☑ 634
Q 6+7 T 13 ☒ 14
Q 670+74 T 744 ☑ 744
Q 97+57 T 154 ☑ 154
Q 60+13 T 73 ☑ 73
Iteration 15
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9911 - loss: 0.0335 - val_accuracy: 0.9934 - val_loss: 0.0262
Q 24+533 T 557 ☑ 557
Q 324+44 T 368 ☑ 368
Q 63+505 T 568 ☑ 568
Q 670+74 T 744 ☑ 744
Q 58+359 T 417 ☑ 417
Q 16+428 T 444 ☑ 444
Q 17+99 T 116 ☑ 116
Q 779+903 T 1682 ☑ 1682
Q 40+576 T 616 ☑ 616
Q 947+773 T 1720 ☑ 1720
Iteration 16
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9968 - loss: 0.0175 - val_accuracy: 0.9901 - val_loss: 0.0360
Q 315+155 T 470 ☑ 470
Q 594+950 T 1544 ☑ 1544
Q 372+37 T 409 ☑ 409
Q 537+47 T 584 ☑ 584
Q 8+263 T 271 ☑ 271
Q 81+500 T 581 ☑ 581
Q 75+270 T 345 ☑ 345
Q 0+796 T 796 ☑ 796
Q 655+965 T 1620 ☑ 1620
Q 384+1 T 385 ☑ 385
Iteration 17
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9972 - loss: 0.0148 - val_accuracy: 0.9924 - val_loss: 0.0278
Q 168+83 T 251 ☑ 251
Q 951+53 T 1004 ☑ 1004
Q 400+37 T 437 ☑ 437
Q 996+473 T 1469 ☒ 1569
Q 996+847 T 1843 ☑ 1843
Q 842+550 T 1392 ☑ 1392
Q 479+72 T 551 ☑ 551
Q 753+782 T 1535 ☑ 1535
Q 99+188 T 287 ☑ 287
Q 2+974 T 976 ☑ 976
Iteration 18
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 7s 5ms/step - accuracy: 0.9929 - loss: 0.0258 - val_accuracy: 0.9973 - val_loss: 0.0135
Q 380+62 T 442 ☑ 442
Q 774+305 T 1079 ☑ 1079
Q 248+272 T 520 ☑ 520
Q 479+736 T 1215 ☑ 1215
Q 859+743 T 1602 ☑ 1602
Q 667+20 T 687 ☑ 687
Q 932+56 T 988 ☑ 988
Q 740+31 T 771 ☑ 771
Q 588+88 T 676 ☑ 676
Q 109+57 T 166 ☑ 166
Iteration 19
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9977 - loss: 0.0116 - val_accuracy: 0.9571 - val_loss: 0.1416
Q 635+89 T 724 ☑ 724
Q 50+818 T 868 ☑ 868
Q 37+622 T 659 ☑ 659
Q 913+49 T 962 ☑ 962
Q 641+962 T 1603 ☒ 1503
Q 11+626 T 637 ☑ 637
Q 20+405 T 425 ☑ 425
Q 667+208 T 875 ☑ 875
Q 89+794 T 883 ☑ 883
Q 234+55 T 289 ☑ 289
Iteration 20
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9947 - loss: 0.0194 - val_accuracy: 0.9967 - val_loss: 0.0136
Q 5+777 T 782 ☑ 782
Q 1+266 T 267 ☑ 267
Q 579+1 T 580 ☑ 580
Q 665+6 T 671 ☑ 671
Q 210+546 T 756 ☑ 756
Q 660+86 T 746 ☑ 746
Q 75+349 T 424 ☑ 424
Q 984+36 T 1020 ☑ 1020
Q 4+367 T 371 ☑ 371
Q 249+213 T 462 ☑ 462
Iteration 21
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 7s 5ms/step - accuracy: 0.9987 - loss: 0.0081 - val_accuracy: 0.9840 - val_loss: 0.0481
Q 228+95 T 323 ☑ 323
Q 72+18 T 90 ☑ 90
Q 34+687 T 721 ☑ 721
Q 932+0 T 932 ☑ 932
Q 933+54 T 987 ☑ 987
Q 735+455 T 1190 ☑ 1190
Q 790+70 T 860 ☑ 860
Q 416+36 T 452 ☒ 462
Q 194+110 T 304 ☑ 304
Q 349+70 T 419 ☑ 419
Iteration 22
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 40s 28ms/step - accuracy: 0.9902 - loss: 0.0326 - val_accuracy: 0.9947 - val_loss: 0.0190
Q 95+237 T 332 ☑ 332
Q 5+188 T 193 ☑ 193
Q 19+931 T 950 ☑ 950
Q 38+499 T 537 ☑ 537
Q 25+21 T 46 ☑ 46
Q 55+85 T 140 ☑ 140
Q 555+7 T 562 ☑ 562
Q 83+873 T 956 ☑ 956
Q 95+527 T 622 ☑ 622
Q 556+558 T 1114 ☑ 1114
Iteration 23
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9835 - loss: 0.0572 - val_accuracy: 0.9962 - val_loss: 0.0141
Q 48+413 T 461 ☑ 461
Q 71+431 T 502 ☑ 502
Q 892+534 T 1426 ☑ 1426
Q 934+201 T 1135 ☑ 1135
Q 898+967 T 1865 ☒ 1855
Q 958+0 T 958 ☑ 958
Q 23+179 T 202 ☑ 202
Q 138+60 T 198 ☑ 198
Q 718+5 T 723 ☑ 723
Q 816+514 T 1330 ☑ 1330
Iteration 24
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 20s 14ms/step - accuracy: 0.9932 - loss: 0.0255 - val_accuracy: 0.9932 - val_loss: 0.0243
Q 4+583 T 587 ☑ 587
Q 49+466 T 515 ☑ 515
Q 920+26 T 946 ☑ 946
Q 624+813 T 1437 ☑ 1437
Q 87+315 T 402 ☑ 402
Q 368+73 T 441 ☑ 441
Q 86+833 T 919 ☑ 919
Q 528+423 T 951 ☑ 951
Q 0+705 T 705 ☑ 705
Q 581+928 T 1509 ☑ 1509
Iteration 25
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9908 - loss: 0.0303 - val_accuracy: 0.9944 - val_loss: 0.0169
Q 107+34 T 141 ☑ 141
Q 998+90 T 1088 ☑ 1088
Q 71+520 T 591 ☑ 591
Q 91+996 T 1087 ☑ 1087
Q 94+69 T 163 ☑ 163
Q 108+21 T 129 ☑ 129
Q 785+60 T 845 ☑ 845
Q 71+628 T 699 ☑ 699
Q 294+9 T 303 ☑ 303
Q 399+34 T 433 ☑ 433
Iteration 26
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 5ms/step - accuracy: 0.9965 - loss: 0.0139 - val_accuracy: 0.9979 - val_loss: 0.0094
Q 19+133 T 152 ☑ 152
Q 841+3 T 844 ☑ 844
Q 698+6 T 704 ☑ 704
Q 942+28 T 970 ☑ 970
Q 81+735 T 816 ☑ 816
Q 325+14 T 339 ☑ 339
Q 790+64 T 854 ☑ 854
Q 4+839 T 843 ☑ 843
Q 505+96 T 601 ☑ 601
Q 917+42 T 959 ☑ 959
Iteration 27
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 72s 51ms/step - accuracy: 0.9952 - loss: 0.0173 - val_accuracy: 0.9992 - val_loss: 0.0036
Q 71+628 T 699 ☑ 699
Q 791+9 T 800 ☑ 800
Q 19+148 T 167 ☑ 167
Q 7+602 T 609 ☑ 609
Q 6+566 T 572 ☑ 572
Q 437+340 T 777 ☑ 777
Q 614+533 T 1147 ☑ 1147
Q 948+332 T 1280 ☑ 1280
Q 56+619 T 675 ☑ 675
Q 86+251 T 337 ☑ 337
Iteration 28
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 8s 6ms/step - accuracy: 0.9964 - loss: 0.0124 - val_accuracy: 0.9990 - val_loss: 0.0047
Q 2+572 T 574 ☑ 574
Q 437+96 T 533 ☑ 533
Q 15+224 T 239 ☑ 239
Q 16+655 T 671 ☑ 671
Q 714+5 T 719 ☑ 719
Q 645+417 T 1062 ☑ 1062
Q 25+919 T 944 ☑ 944
Q 89+329 T 418 ☑ 418
Q 22+513 T 535 ☑ 535
Q 497+983 T 1480 ☑ 1480
Iteration 29
1407/1407 ━━━━━━━━━━━━━━━━━━━━ 7s 5ms/step - accuracy: 0.9970 - loss: 0.0106 - val_accuracy: 0.9990 - val_loss: 0.0048
Q 2+962 T 964 ☑ 964
Q 6+76 T 82 ☑ 82
Q 986+20 T 1006 ☑ 1006
Q 727+49 T 776 ☑ 776
Q 948+332 T 1280 ☑ 1280
Q 921+463 T 1384 ☑ 1384
Q 77+556 T 633 ☑ 633
Q 133+849 T 982 ☑ 982
Q 301+478 T 779 ☑ 779
Q 3+243 T 246 ☑ 246
You'll get to 99+% validation accuracy after ~30 epochs.