implement an 3-digit subtractor by Neural Network
ref:https://github.com/IKMLab/Adder-practice
pip install -r requirements.txt
python main.py
Generating data...
Total addition questions: 200000
['3-93 ', '6-5 ', '3-71 ', '5-2 ', '187-5 '] ['-90 ', '1 ', '-68 ', '3 ', '182 ']
Vectorization...
Training Data:
(45000, 7, 12)
(45000, 4, 12)
Validation Data:
(5000, 7, 12)
(5000, 4, 12)
Testing Data:
(150000, 7, 12)
(150000, 4, 12)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_1 (LSTM) (None, 128) 72192
_________________________________________________________________
repeat_vector_1 (RepeatVecto (None, 4, 128) 0
_________________________________________________________________
lstm_2 (LSTM) (None, 4, 128) 131584
_________________________________________________________________
lstm_3 (LSTM) (None, 4, 128) 131584
_________________________________________________________________
time_distributed_1 (TimeDist (None, 4, 12) 1548
_________________________________________________________________
activation_1 (Activation) (None, 4, 12) 0
=================================================================
Total params: 336,908
Trainable params: 336,908
Non-trainable params: 0
_________________________________________________________________
--------------------------------------------------
Iteration 0
Train on 45000 samples, validate on 5000 samples
Epoch 1/1
128/45000 [..............................] - ETA: 8:30 - loss: 2.4848 - acc: 0 384/45000 [..............................] - ETA: 2:56 - loss: 2.4824 - acc: 0 640/45000 [..............................] - ETA: 1:50 - loss: 2.4782 - acc: 0 896/45000 [..............................] - ETA: 1:21 - loss: 2.4737 - acc: 0 1152/45000 [..............................] - ETA: 1:05 - loss: 2.4664 - acc: 0 1408/45000 [..............................] - ETA: 55s - loss: 2.4590 - acc: 0. 1664/45000 [>.............................] - ETA: 48s - loss: 2.4444 - acc: 0. 1920/45000 [>.............................] - ETA: 43s - loss: 2.4332 - acc: 0. 2176/45000 [>.............................] - ETA: 39s - loss: 2.4205 - acc: 0. 2432/45000 [>.............................] - ETA: 35s - loss: 2.4130 - acc: 0. 2688/45000 [>.............................] - ETA: 33s - loss: 2.4039 - acc: 0. 2944/45000 [>.............................] - ETA: 31s - loss: 2.3991 - acc: 0. 3200/45000 [=>............................] - 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ETA: 0s - loss: 1.9554 - acc: 0.343392/45000 [===========================>..] - ETA: 0s - loss: 1.9537 - acc: 0.343648/45000 [============================>.] - ETA: 0s - loss: 1.9522 - acc: 0.343904/45000 [============================>.] - ETA: 0s - loss: 1.9507 - acc: 0.344160/45000 [============================>.] - ETA: 0s - loss: 1.9490 - acc: 0.344416/45000 [============================>.] - ETA: 0s - loss: 1.9474 - acc: 0.344672/45000 [============================>.] - ETA: 0s - loss: 1.9460 - acc: 0.344928/45000 [============================>.] - ETA: 0s - loss: 1.9446 - acc: 0.345000/45000 [==============================] - 12s 275us/step - loss: 1.9442 - acc: 0.3103 - val_loss: 1.6933 - val_acc: 0.3744
Q 89-8 T 81 ☒ 366
Q 0-956 T -956 ☒ -886
Q 531-920 T -389 ☒ -266
Q 324-878 T -554 ☒ -466
Q 797-194 T 603 ☒ 366
Q 538-309 T 229 ☒ -1
Q 21-863 T -842 ☒ -866
Q 15-71 T -56 ☒ -1
Q 1-732 T -731 ☒ -666
Q 66-409 T -343 ☒ -466
--------------------------------------------------
Q 713-263 T 450 ☑ 450
Q 534-886 T -352 ☑ -352
Q 557-274 T 283 ☑ 283
Q 606-649 T -43 ☑ -43
Q 797-663 T 134 ☑ 134
Q 759-379 T 380 ☑ 380
Q 952-277 T 675 ☑ 675
Q 639-565 T 74 ☑ 74
Q 972-450 T 522 ☑ 522
Q 49-464 T -415 ☒ -523
Q 202-717 T -515 ☑ -515
Q 720-939 T -219 ☑ -219
Q 430-66 T 364 ☒ -236
Q 558-410 T 148 ☑ 148
Q 766-216 T 550 ☑ 550
Q 386-303 T 83 ☑ 83
Q 813-432 T 381 ☑ 381
Q 848-642 T 206 ☑ 206
Q 921-942 T -21 ☑ -21
Q 822-820 T 2 ☒ 1
...
MSG : Accuracy is 0.8609