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output.py
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1403 lines (1281 loc) · 116 KB
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Python 3.7.5 (tags/v3.7.5:5c02a39a0b, Oct 15 2019, 00:11:34) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>>
= RESTART: C:\Users\Admin\Desktop\respiratory disease prediction\respiratory-disease.py
Finished feature extraction from 920 files
[['Bronchiectasis' 'Bronchiolitis' 'COPD' 'Healthy' 'Pneumonia' 'URTI']
['16' '13' '793' '35' '37' '23']]
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 39, 861, 16) 80
max_pooling2d (MaxPooling2D (None, 19, 430, 16) 0
)
dropout (Dropout) (None, 19, 430, 16) 0
conv2d_1 (Conv2D) (None, 18, 429, 32) 2080
max_pooling2d_1 (MaxPooling (None, 9, 214, 32) 0
2D)
dropout_1 (Dropout) (None, 9, 214, 32) 0
conv2d_2 (Conv2D) (None, 8, 213, 64) 8256
max_pooling2d_2 (MaxPooling (None, 4, 106, 64) 0
2D)
dropout_2 (Dropout) (None, 4, 106, 64) 0
conv2d_3 (Conv2D) (None, 3, 105, 128) 32896
max_pooling2d_3 (MaxPooling (None, 1, 52, 128) 0
2D)
dropout_3 (Dropout) (None, 1, 52, 128) 0
global_average_pooling2d (G (None, 128) 0
lobalAveragePooling2D)
dense (Dense) (None, 6) 774
=================================================================
Total params: 44,086
Trainable params: 44,086
Non-trainable params: 0
_________________________________________________________________
1/6 [====>.........................] - ETA: 1s - loss: 7.0706 - accuracy: 0.03122/6 [=========>....................] - ETA: 0s - loss: 6.6741 - accuracy: 0.01564/6 [===================>..........] - ETA: 0s - loss: 6.4873 - accuracy: 0.02345/6 [========================>.....] - ETA: 0s - loss: 6.2611 - accuracy: 0.03756/6 [==============================] - 1s 61ms/step - loss: 6.4874 - accuracy: 0.0380
Pre-training accuracy: 3.8043%
Epoch 1/10
1/6 [====>.........................] - ETA: 4s - loss: 9.5673 - accuracy: 0.07032/6 [=========>....................] - ETA: 1s - loss: 7.4910 - accuracy: 0.46093/6 [==============>...............] - ETA: 1s - loss: 6.6442 - accuracy: 0.60424/6 [===================>..........] - ETA: 0s - loss: 6.6031 - accuracy: 0.66995/6 [========================>.....] - ETA: 0s - loss: 6.1717 - accuracy: 0.71096/6 [==============================] - ETA: 0s - loss: 5.9844 - accuracy: 0.7271
Epoch 1: val_accuracy improved from -inf to 0.02174, saving model to mymodel2_01.h5
6/6 [==============================] - 3s 409ms/step - loss: 5.9844 - accuracy: 0.7271 - val_loss: 4.3830 - val_accuracy: 0.0217
Epoch 2/10
1/6 [====>.........................] - ETA: 1s - loss: 7.6516 - accuracy: 0.14842/6 [=========>....................] - ETA: 1s - loss: 5.9631 - accuracy: 0.32423/6 [==============>...............] - ETA: 1s - loss: 5.0247 - accuracy: 0.51564/6 [===================>..........] - ETA: 0s - loss: 4.8591 - accuracy: 0.60945/6 [========================>.....] - ETA: 0s - loss: 5.2926 - accuracy: 0.65626/6 [==============================] - ETA: 0s - loss: 5.2766 - accuracy: 0.6780
Epoch 2: val_accuracy improved from 0.02174 to 0.86413, saving model to mymodel2_02.h5
6/6 [==============================] - 2s 411ms/step - loss: 5.2766 - accuracy: 0.6780 - val_loss: 2.3422 - val_accuracy: 0.8641
Epoch 3/10
1/6 [====>.........................] - ETA: 1s - loss: 5.3186 - accuracy: 0.89062/6 [=========>....................] - ETA: 1s - loss: 4.8734 - accuracy: 0.86723/6 [==============>...............] - ETA: 1s - loss: 4.8628 - accuracy: 0.86984/6 [===================>..........] - ETA: 0s - loss: 4.8083 - accuracy: 0.86335/6 [========================>.....] - ETA: 0s - loss: 4.4658 - accuracy: 0.84066/6 [==============================] - ETA: 0s - loss: 4.2653 - accuracy: 0.8131
Epoch 3: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 405ms/step - loss: 4.2653 - accuracy: 0.8131 - val_loss: 1.7011 - val_accuracy: 0.7609
Epoch 4/10
1/6 [====>.........................] - ETA: 1s - loss: 3.0165 - accuracy: 0.77342/6 [=========>....................] - ETA: 1s - loss: 2.9507 - accuracy: 0.79303/6 [==============>...............] - ETA: 1s - loss: 2.7806 - accuracy: 0.81514/6 [===================>..........] - ETA: 0s - loss: 2.8686 - accuracy: 0.81645/6 [========================>.....] - ETA: 0s - loss: 2.7554 - accuracy: 0.82036/6 [==============================] - ETA: 0s - loss: 2.7148 - accuracy: 0.8226
Epoch 4: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 397ms/step - loss: 2.7148 - accuracy: 0.8226 - val_loss: 1.3075 - val_accuracy: 0.8641
Epoch 5/10
1/6 [====>.........................] - ETA: 1s - loss: 1.7113 - accuracy: 0.92192/6 [=========>....................] - ETA: 1s - loss: 2.2236 - accuracy: 0.89453/6 [==============>...............] - ETA: 1s - loss: 2.6060 - accuracy: 0.87504/6 [===================>..........] - ETA: 0s - loss: 2.1806 - accuracy: 0.88285/6 [========================>.....] - ETA: 0s - loss: 2.0264 - accuracy: 0.87976/6 [==============================] - ETA: 0s - loss: 2.0138 - accuracy: 0.8677
Epoch 5: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 392ms/step - loss: 2.0138 - accuracy: 0.8677 - val_loss: 1.1324 - val_accuracy: 0.7935
Epoch 6/10
1/6 [====>.........................] - ETA: 1s - loss: 0.9722 - accuracy: 0.85162/6 [=========>....................] - ETA: 1s - loss: 1.3049 - accuracy: 0.82033/6 [==============>...............] - ETA: 1s - loss: 1.1532 - accuracy: 0.82554/6 [===================>..........] - ETA: 0s - loss: 1.1154 - accuracy: 0.82235/6 [========================>.....] - ETA: 0s - loss: 1.1305 - accuracy: 0.82976/6 [==============================] - ETA: 0s - loss: 1.1926 - accuracy: 0.8213
Epoch 6: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 408ms/step - loss: 1.1926 - accuracy: 0.8213 - val_loss: 0.8228 - val_accuracy: 0.8478
Epoch 7/10
1/6 [====>.........................] - ETA: 1s - loss: 0.8392 - accuracy: 0.85942/6 [=========>....................] - ETA: 1s - loss: 1.0274 - accuracy: 0.83203/6 [==============>...............] - ETA: 1s - loss: 1.0153 - accuracy: 0.82814/6 [===================>..........] - ETA: 0s - loss: 0.9402 - accuracy: 0.83205/6 [========================>.....] - ETA: 0s - loss: 0.8560 - accuracy: 0.83446/6 [==============================] - ETA: 0s - loss: 0.8350 - accuracy: 0.8322
Epoch 7: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 395ms/step - loss: 0.8350 - accuracy: 0.8322 - val_loss: 0.9318 - val_accuracy: 0.8478
Epoch 8/10
1/6 [====>.........................] - ETA: 1s - loss: 0.6149 - accuracy: 0.86722/6 [=========>....................] - ETA: 1s - loss: 0.6614 - accuracy: 0.85553/6 [==============>...............] - ETA: 1s - loss: 0.6562 - accuracy: 0.85164/6 [===================>..........] - ETA: 0s - loss: 0.6710 - accuracy: 0.84385/6 [========================>.....] - ETA: 0s - loss: 0.6695 - accuracy: 0.84536/6 [==============================] - ETA: 0s - loss: 0.6421 - accuracy: 0.8554
Epoch 8: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 404ms/step - loss: 0.6421 - accuracy: 0.8554 - val_loss: 1.0121 - val_accuracy: 0.6957
Epoch 9/10
1/6 [====>.........................] - ETA: 1s - loss: 0.5826 - accuracy: 0.85942/6 [=========>....................] - ETA: 1s - loss: 0.5807 - accuracy: 0.85943/6 [==============>...............] - ETA: 1s - loss: 0.5966 - accuracy: 0.86204/6 [===================>..........] - ETA: 0s - loss: 0.6325 - accuracy: 0.85165/6 [========================>.....] - ETA: 0s - loss: 0.6722 - accuracy: 0.84696/6 [==============================] - ETA: 0s - loss: 0.6574 - accuracy: 0.8499
Epoch 9: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 411ms/step - loss: 0.6574 - accuracy: 0.8499 - val_loss: 0.9091 - val_accuracy: 0.8370
Epoch 10/10
1/6 [====>.........................] - ETA: 1s - loss: 0.6537 - accuracy: 0.83592/6 [=========>....................] - ETA: 1s - loss: 0.6464 - accuracy: 0.84383/6 [==============>...............] - ETA: 1s - loss: 0.6058 - accuracy: 0.85164/6 [===================>..........] - ETA: 0s - loss: 0.5737 - accuracy: 0.85745/6 [========================>.....] - ETA: 0s - loss: 0.6007 - accuracy: 0.85006/6 [==============================] - ETA: 0s - loss: 0.5961 - accuracy: 0.8472
Epoch 10: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 406ms/step - loss: 0.5961 - accuracy: 0.8472 - val_loss: 0.9360 - val_accuracy: 0.8261
Training completed in time: 0:00:24.549375
Training Accuracy: 79.39972877502441
Testing Accuracy: 82.6086938381195
1/6 [====>.........................] - ETA: 0s - loss: 0.8524 - accuracy: 0.81252/6 [=========>....................] - ETA: 0s - loss: 0.8734 - accuracy: 0.82814/6 [===================>..........] - ETA: 0s - loss: 0.9024 - accuracy: 0.84386/6 [==============================] - ETA: 0s - loss: 0.9360 - accuracy: 0.82616/6 [==============================] - 0s 41ms/step - loss: 0.9360 - accuracy: 0.8261
Epoch 1/200
1/6 [====>.........................] - ETA: 3s - loss: 0.6729 - accuracy: 0.81252/6 [=========>....................] - ETA: 1s - loss: 0.5350 - accuracy: 0.85943/6 [==============>...............] - ETA: 1s - loss: 0.5585 - accuracy: 0.85164/6 [===================>..........] - ETA: 0s - loss: 0.5401 - accuracy: 0.85555/6 [========================>.....] - ETA: 0s - loss: 0.5322 - accuracy: 0.85786/6 [==============================] - ETA: 0s - loss: 0.5355 - accuracy: 0.8595
Epoch 1: val_accuracy did not improve from 0.86413
6/6 [==============================] - 3s 404ms/step - loss: 0.5355 - accuracy: 0.8595 - val_loss: 0.8314 - val_accuracy: 0.8370
Epoch 2/200
1/6 [====>.........................] - ETA: 1s - loss: 0.5410 - accuracy: 0.87502/6 [=========>....................] - ETA: 1s - loss: 0.5864 - accuracy: 0.84773/6 [==============>...............] - ETA: 1s - loss: 0.5724 - accuracy: 0.84904/6 [===================>..........] - ETA: 0s - loss: 0.5460 - accuracy: 0.85745/6 [========================>.....] - ETA: 0s - loss: 0.5396 - accuracy: 0.85946/6 [==============================] - ETA: 0s - loss: 0.5350 - accuracy: 0.8595
Epoch 2: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 412ms/step - loss: 0.5350 - accuracy: 0.8595 - val_loss: 0.8097 - val_accuracy: 0.8424
Epoch 3/200
1/6 [====>.........................] - ETA: 2s - loss: 0.5544 - accuracy: 0.83592/6 [=========>....................] - ETA: 1s - loss: 0.5308 - accuracy: 0.83983/6 [==============>...............] - ETA: 1s - loss: 0.5199 - accuracy: 0.85424/6 [===================>..........] - ETA: 0s - loss: 0.5118 - accuracy: 0.85745/6 [========================>.....] - ETA: 0s - loss: 0.4938 - accuracy: 0.86256/6 [==============================] - ETA: 0s - loss: 0.5094 - accuracy: 0.8568
Epoch 3: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 406ms/step - loss: 0.5094 - accuracy: 0.8568 - val_loss: 0.7795 - val_accuracy: 0.8315
Epoch 4/200
1/6 [====>.........................] - ETA: 1s - loss: 0.4972 - accuracy: 0.84382/6 [=========>....................] - ETA: 1s - loss: 0.5317 - accuracy: 0.83593/6 [==============>...............] - ETA: 1s - loss: 0.4886 - accuracy: 0.85424/6 [===================>..........] - ETA: 0s - loss: 0.5025 - accuracy: 0.85355/6 [========================>.....] - ETA: 0s - loss: 0.5107 - accuracy: 0.84536/6 [==============================] - ETA: 0s - loss: 0.4963 - accuracy: 0.8486
Epoch 4: val_accuracy did not improve from 0.86413
6/6 [==============================] - 3s 494ms/step - loss: 0.4963 - accuracy: 0.8486 - val_loss: 0.7240 - val_accuracy: 0.8424
Epoch 5/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4771 - accuracy: 0.85162/6 [=========>....................] - ETA: 4s - loss: 0.3904 - accuracy: 0.88283/6 [==============>...............] - ETA: 3s - loss: 0.4254 - accuracy: 0.87504/6 [===================>..........] - ETA: 2s - loss: 0.4822 - accuracy: 0.85945/6 [========================>.....] - ETA: 1s - loss: 0.5070 - accuracy: 0.85006/6 [==============================] - ETA: 0s - loss: 0.4980 - accuracy: 0.8513
Epoch 5: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4980 - accuracy: 0.8513 - val_loss: 0.7233 - val_accuracy: 0.8424
Epoch 6/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4374 - accuracy: 0.84382/6 [=========>....................] - ETA: 4s - loss: 0.4307 - accuracy: 0.85943/6 [==============>...............] - ETA: 3s - loss: 0.4681 - accuracy: 0.84904/6 [===================>..........] - ETA: 2s - loss: 0.4664 - accuracy: 0.85355/6 [========================>.....] - ETA: 1s - loss: 0.4748 - accuracy: 0.84536/6 [==============================] - ETA: 0s - loss: 0.4777 - accuracy: 0.8417
Epoch 6: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4777 - accuracy: 0.8417 - val_loss: 0.6515 - val_accuracy: 0.8478
Epoch 7/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4312 - accuracy: 0.86722/6 [=========>....................] - ETA: 4s - loss: 0.4239 - accuracy: 0.87113/6 [==============>...............] - ETA: 3s - loss: 0.4482 - accuracy: 0.86724/6 [===================>..........] - ETA: 2s - loss: 0.4592 - accuracy: 0.85945/6 [========================>.....] - ETA: 1s - loss: 0.4598 - accuracy: 0.85946/6 [==============================] - ETA: 0s - loss: 0.4583 - accuracy: 0.8622
Epoch 7: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4583 - accuracy: 0.8622 - val_loss: 0.6410 - val_accuracy: 0.8478
Epoch 8/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4714 - accuracy: 0.82812/6 [=========>....................] - ETA: 4s - loss: 0.4182 - accuracy: 0.85943/6 [==============>...............] - ETA: 3s - loss: 0.4371 - accuracy: 0.85684/6 [===================>..........] - ETA: 2s - loss: 0.4687 - accuracy: 0.85165/6 [========================>.....] - ETA: 1s - loss: 0.4590 - accuracy: 0.85476/6 [==============================] - ETA: 0s - loss: 0.4464 - accuracy: 0.8568
Epoch 8: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4464 - accuracy: 0.8568 - val_loss: 0.6028 - val_accuracy: 0.8478
Epoch 9/200
1/6 [====>.........................] - ETA: 5s - loss: 0.3929 - accuracy: 0.87502/6 [=========>....................] - ETA: 4s - loss: 0.3554 - accuracy: 0.89063/6 [==============>...............] - ETA: 3s - loss: 0.3371 - accuracy: 0.89324/6 [===================>..........] - ETA: 2s - loss: 0.4023 - accuracy: 0.87505/6 [========================>.....] - ETA: 1s - loss: 0.3959 - accuracy: 0.87976/6 [==============================] - ETA: 0s - loss: 0.4427 - accuracy: 0.8677
Epoch 9: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4427 - accuracy: 0.8677 - val_loss: 0.6011 - val_accuracy: 0.8533
Epoch 10/200
1/6 [====>.........................] - ETA: 5s - loss: 0.3306 - accuracy: 0.88282/6 [=========>....................] - ETA: 4s - loss: 0.4787 - accuracy: 0.82813/6 [==============>...............] - ETA: 3s - loss: 0.4547 - accuracy: 0.84114/6 [===================>..........] - ETA: 2s - loss: 0.4406 - accuracy: 0.85555/6 [========================>.....] - ETA: 1s - loss: 0.4558 - accuracy: 0.85626/6 [==============================] - ETA: 0s - loss: 0.4472 - accuracy: 0.8581
Epoch 10: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4472 - accuracy: 0.8581 - val_loss: 0.5535 - val_accuracy: 0.8533
Epoch 11/200
1/6 [====>.........................] - ETA: 5s - loss: 0.3499 - accuracy: 0.89062/6 [=========>....................] - ETA: 4s - loss: 0.3951 - accuracy: 0.88283/6 [==============>...............] - ETA: 3s - loss: 0.4376 - accuracy: 0.86464/6 [===================>..........] - ETA: 2s - loss: 0.4797 - accuracy: 0.85945/6 [========================>.....] - ETA: 1s - loss: 0.4416 - accuracy: 0.86726/6 [==============================] - ETA: 0s - loss: 0.4560 - accuracy: 0.8649
Epoch 11: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4560 - accuracy: 0.8649 - val_loss: 0.5788 - val_accuracy: 0.8587
Epoch 12/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4573 - accuracy: 0.85162/6 [=========>....................] - ETA: 4s - loss: 0.4945 - accuracy: 0.84383/6 [==============>...............] - ETA: 3s - loss: 0.5069 - accuracy: 0.82814/6 [===================>..........] - ETA: 2s - loss: 0.4604 - accuracy: 0.84965/6 [========================>.....] - ETA: 1s - loss: 0.4408 - accuracy: 0.85626/6 [==============================] - ETA: 0s - loss: 0.4375 - accuracy: 0.8568
Epoch 12: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4375 - accuracy: 0.8568 - val_loss: 0.5763 - val_accuracy: 0.8478
Epoch 13/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4670 - accuracy: 0.83592/6 [=========>....................] - ETA: 4s - loss: 0.3950 - accuracy: 0.85553/6 [==============>...............] - ETA: 3s - loss: 0.4106 - accuracy: 0.85684/6 [===================>..........] - ETA: 2s - loss: 0.4110 - accuracy: 0.85945/6 [========================>.....] - ETA: 1s - loss: 0.4248 - accuracy: 0.85946/6 [==============================] - ETA: 0s - loss: 0.4158 - accuracy: 0.8622
Epoch 13: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4158 - accuracy: 0.8622 - val_loss: 0.5609 - val_accuracy: 0.8424
Epoch 14/200
1/6 [====>.........................] - ETA: 5s - loss: 0.5133 - accuracy: 0.82812/6 [=========>....................] - ETA: 4s - loss: 0.4634 - accuracy: 0.84773/6 [==============>...............] - ETA: 3s - loss: 0.3900 - accuracy: 0.87504/6 [===================>..........] - ETA: 2s - loss: 0.4070 - accuracy: 0.86915/6 [========================>.....] - ETA: 1s - loss: 0.4249 - accuracy: 0.86256/6 [==============================] - ETA: 0s - loss: 0.4195 - accuracy: 0.8622
Epoch 14: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4195 - accuracy: 0.8622 - val_loss: 0.5618 - val_accuracy: 0.8478
Epoch 15/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4727 - accuracy: 0.83592/6 [=========>....................] - ETA: 4s - loss: 0.4821 - accuracy: 0.83983/6 [==============>...............] - ETA: 3s - loss: 0.4221 - accuracy: 0.86464/6 [===================>..........] - ETA: 2s - loss: 0.4189 - accuracy: 0.86335/6 [========================>.....] - ETA: 1s - loss: 0.4064 - accuracy: 0.86876/6 [==============================] - ETA: 0s - loss: 0.4062 - accuracy: 0.8636
Epoch 15: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4062 - accuracy: 0.8636 - val_loss: 0.5471 - val_accuracy: 0.8478
Epoch 16/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4157 - accuracy: 0.83592/6 [=========>....................] - ETA: 4s - loss: 0.3940 - accuracy: 0.86723/6 [==============>...............] - ETA: 3s - loss: 0.3908 - accuracy: 0.87244/6 [===================>..........] - ETA: 2s - loss: 0.4091 - accuracy: 0.87115/6 [========================>.....] - ETA: 1s - loss: 0.4310 - accuracy: 0.86876/6 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8718
Epoch 16: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4132 - accuracy: 0.8718 - val_loss: 0.5538 - val_accuracy: 0.8424
Epoch 17/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4900 - accuracy: 0.82812/6 [=========>....................] - ETA: 4s - loss: 0.4232 - accuracy: 0.85553/6 [==============>...............] - ETA: 3s - loss: 0.4178 - accuracy: 0.85684/6 [===================>..........] - ETA: 2s - loss: 0.4124 - accuracy: 0.86525/6 [========================>.....] - ETA: 1s - loss: 0.3935 - accuracy: 0.87036/6 [==============================] - ETA: 0s - loss: 0.4111 - accuracy: 0.8649
Epoch 17: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4111 - accuracy: 0.8649 - val_loss: 0.5278 - val_accuracy: 0.8587
Epoch 18/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4443 - accuracy: 0.85942/6 [=========>....................] - ETA: 4s - loss: 0.4378 - accuracy: 0.87113/6 [==============>...............] - ETA: 3s - loss: 0.4314 - accuracy: 0.86464/6 [===================>..........] - ETA: 2s - loss: 0.4499 - accuracy: 0.85745/6 [========================>.....] - ETA: 1s - loss: 0.4291 - accuracy: 0.86256/6 [==============================] - ETA: 0s - loss: 0.4083 - accuracy: 0.8718
Epoch 18: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.4083 - accuracy: 0.8718 - val_loss: 0.5480 - val_accuracy: 0.8370
Epoch 19/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4759 - accuracy: 0.85942/6 [=========>....................] - ETA: 4s - loss: 0.4271 - accuracy: 0.85553/6 [==============>...............] - ETA: 3s - loss: 0.4267 - accuracy: 0.85424/6 [===================>..........] - ETA: 2s - loss: 0.3809 - accuracy: 0.87305/6 [========================>.....] - ETA: 1s - loss: 0.3905 - accuracy: 0.86566/6 [==============================] - ETA: 0s - loss: 0.3933 - accuracy: 0.8636
Epoch 19: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.3933 - accuracy: 0.8636 - val_loss: 0.5361 - val_accuracy: 0.8424
Epoch 20/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4140 - accuracy: 0.87502/6 [=========>....................] - ETA: 4s - loss: 0.4226 - accuracy: 0.87113/6 [==============>...............] - ETA: 3s - loss: 0.3830 - accuracy: 0.88024/6 [===================>..........] - ETA: 2s - loss: 0.3825 - accuracy: 0.87895/6 [========================>.....] - ETA: 1s - loss: 0.3826 - accuracy: 0.87666/6 [==============================] - ETA: 0s - loss: 0.3867 - accuracy: 0.8718
Epoch 20: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.3867 - accuracy: 0.8718 - val_loss: 0.5461 - val_accuracy: 0.8370
Epoch 21/200
1/6 [====>.........................] - ETA: 5s - loss: 0.4187 - accuracy: 0.88282/6 [=========>....................] - ETA: 4s - loss: 0.3628 - accuracy: 0.89453/6 [==============>...............] - ETA: 3s - loss: 0.3678 - accuracy: 0.89324/6 [===================>..........] - ETA: 2s - loss: 0.3779 - accuracy: 0.88485/6 [========================>.....] - ETA: 1s - loss: 0.3991 - accuracy: 0.87196/6 [==============================] - ETA: 0s - loss: 0.3929 - accuracy: 0.8745
Epoch 21: val_accuracy did not improve from 0.86413
6/6 [==============================] - 7s 1s/step - loss: 0.3929 - accuracy: 0.8745 - val_loss: 0.5224 - val_accuracy: 0.8587
Epoch 22/200
1/6 [====>.........................] - ETA: 5s - loss: 0.3153 - accuracy: 0.89062/6 [=========>....................] - ETA: 4s - loss: 0.3635 - accuracy: 0.87893/6 [==============>...............] - ETA: 3s - loss: 0.3521 - accuracy: 0.87504/6 [===================>..........] - ETA: 2s - loss: 0.3919 - accuracy: 0.85745/6 [========================>.....] - ETA: 1s - loss: 0.3760 - accuracy: 0.86876/6 [==============================] - ETA: 0s - loss: 0.3793 - accuracy: 0.8663
Epoch 22: val_accuracy did not improve from 0.86413
6/6 [==============================] - 6s 931ms/step - loss: 0.3793 - accuracy: 0.8663 - val_loss: 0.5490 - val_accuracy: 0.8424
Epoch 23/200
1/6 [====>.........................] - ETA: 1s - loss: 0.4598 - accuracy: 0.85942/6 [=========>....................] - ETA: 1s - loss: 0.4141 - accuracy: 0.86723/6 [==============>...............] - ETA: 1s - loss: 0.3802 - accuracy: 0.86724/6 [===================>..........] - ETA: 0s - loss: 0.4073 - accuracy: 0.85945/6 [========================>.....] - ETA: 0s - loss: 0.4008 - accuracy: 0.85946/6 [==============================] - ETA: 0s - loss: 0.3843 - accuracy: 0.8663
Epoch 23: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 385ms/step - loss: 0.3843 - accuracy: 0.8663 - val_loss: 0.4938 - val_accuracy: 0.8533
Epoch 24/200
1/6 [====>.........................] - ETA: 1s - loss: 0.4954 - accuracy: 0.84382/6 [=========>....................] - ETA: 1s - loss: 0.4216 - accuracy: 0.86333/6 [==============>...............] - ETA: 1s - loss: 0.4096 - accuracy: 0.86724/6 [===================>..........] - ETA: 0s - loss: 0.4223 - accuracy: 0.85555/6 [========================>.....] - ETA: 0s - loss: 0.3907 - accuracy: 0.86726/6 [==============================] - ETA: 0s - loss: 0.3935 - accuracy: 0.8636
Epoch 24: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 387ms/step - loss: 0.3935 - accuracy: 0.8636 - val_loss: 0.5419 - val_accuracy: 0.8478
Epoch 25/200
1/6 [====>.........................] - ETA: 1s - loss: 0.2982 - accuracy: 0.91412/6 [=========>....................] - ETA: 1s - loss: 0.3984 - accuracy: 0.87503/6 [==============>...............] - ETA: 1s - loss: 0.3828 - accuracy: 0.88024/6 [===================>..........] - ETA: 0s - loss: 0.3667 - accuracy: 0.88095/6 [========================>.....] - ETA: 0s - loss: 0.3873 - accuracy: 0.87036/6 [==============================] - ETA: 0s - loss: 0.3805 - accuracy: 0.8731
Epoch 25: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 393ms/step - loss: 0.3805 - accuracy: 0.8731 - val_loss: 0.5051 - val_accuracy: 0.8587
Epoch 26/200
1/6 [====>.........................] - ETA: 1s - loss: 0.3476 - accuracy: 0.86722/6 [=========>....................] - ETA: 1s - loss: 0.3601 - accuracy: 0.87113/6 [==============>...............] - ETA: 1s - loss: 0.3944 - accuracy: 0.86724/6 [===================>..........] - ETA: 0s - loss: 0.3888 - accuracy: 0.86915/6 [========================>.....] - ETA: 0s - loss: 0.3834 - accuracy: 0.87346/6 [==============================] - ETA: 0s - loss: 0.3854 - accuracy: 0.8690
Epoch 26: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 395ms/step - loss: 0.3854 - accuracy: 0.8690 - val_loss: 0.5111 - val_accuracy: 0.8587
Epoch 27/200
1/6 [====>.........................] - ETA: 1s - loss: 0.3430 - accuracy: 0.87502/6 [=========>....................] - ETA: 1s - loss: 0.4043 - accuracy: 0.8672Traceback (most recent call last):
File "C:\Users\Admin\Desktop\respiratory disease prediction\respiratory-disease.py", line 296, in <module>
validation_data=(x_test, y_test), callbacks=callbacks, verbose=1)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 150, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 880, in __call__
result = self._call(*args, **kwds)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 919, in _call
results = self._variable_creation_fn(*args, **kwds)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compiler.py", line 135, in __call__
filtered_flat_args, captured_inputs=concrete_function.captured_inputs) # pylint: disable=protected-access
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\polymorphic_function\monomorphic_function.py", line 1746, in _call_flat
ctx, args, cancellation_manager=cancellation_manager))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\polymorphic_function\monomorphic_function.py", line 383, in call
ctx=ctx)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 53, in quick_execute
inputs, attrs, num_outputs)
KeyboardInterrupt
>>>
======================== RESTART: C:\Users\Admin\Desktop\respiratory disease prediction\respiratory-disease.py ========================
Finished feature extraction from 920 files
[['Bronchiectasis' 'Bronchiolitis' 'COPD' 'Healthy' 'Pneumonia' 'URTI']
['16' '13' '793' '35' '37' '23']]
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 39, 861, 16) 80
max_pooling2d (MaxPooling2D (None, 19, 430, 16) 0
)
dropout (Dropout) (None, 19, 430, 16) 0
conv2d_1 (Conv2D) (None, 18, 429, 32) 2080
max_pooling2d_1 (MaxPooling (None, 9, 214, 32) 0
2D)
dropout_1 (Dropout) (None, 9, 214, 32) 0
conv2d_2 (Conv2D) (None, 8, 213, 64) 8256
max_pooling2d_2 (MaxPooling (None, 4, 106, 64) 0
2D)
dropout_2 (Dropout) (None, 4, 106, 64) 0
conv2d_3 (Conv2D) (None, 3, 105, 128) 32896
max_pooling2d_3 (MaxPooling (None, 1, 52, 128) 0
2D)
dropout_3 (Dropout) (None, 1, 52, 128) 0
global_average_pooling2d (G (None, 128) 0
lobalAveragePooling2D)
dense (Dense) (None, 6) 774
=================================================================
Total params: 44,086
Trainable params: 44,086
Non-trainable params: 0
_________________________________________________________________
1/6 [====>.........................] - ETA: 1s - loss: 6.7907 - accuracy: 0.03123/6 [==============>...............] - ETA: 0s - loss: 6.0311 - accuracy: 0.01044/6 [===================>..........] - ETA: 0s - loss: 5.9136 - accuracy: 0.00786/6 [==============================] - ETA: 0s - loss: 6.1382 - accuracy: 0.01636/6 [==============================] - 0s 43ms/step - loss: 6.1382 - accuracy: 0.0163
Pre-training accuracy: 1.6304%
Epoch 1/10
1/6 [====>.........................] - ETA: 4s - loss: 8.8742 - accuracy: 0.03122/6 [=========>....................] - ETA: 1s - loss: 6.7263 - accuracy: 0.44923/6 [==============>...............] - ETA: 1s - loss: 6.5141 - accuracy: 0.57554/6 [===================>..........] - ETA: 0s - loss: 6.2876 - accuracy: 0.64265/6 [========================>.....] - ETA: 0s - loss: 5.5282 - accuracy: 0.68126/6 [==============================] - ETA: 0s - loss: 5.1452 - accuracy: 0.6917
Epoch 1: val_accuracy improved from -inf to 0.72283, saving model to mymodel2_01.h5
6/6 [==============================] - 3s 417ms/step - loss: 5.1452 - accuracy: 0.6917 - val_loss: 2.4809 - val_accuracy: 0.7228
Epoch 2/10
1/6 [====>.........................] - ETA: 1s - loss: 5.9216 - accuracy: 0.52342/6 [=========>....................] - ETA: 1s - loss: 5.1863 - accuracy: 0.65233/6 [==============>...............] - ETA: 1s - loss: 4.3226 - accuracy: 0.73964/6 [===================>..........] - ETA: 0s - loss: 4.2035 - accuracy: 0.77935/6 [========================>.....] - ETA: 0s - loss: 4.0278 - accuracy: 0.79376/6 [==============================] - ETA: 0s - loss: 4.1741 - accuracy: 0.7981
Epoch 2: val_accuracy improved from 0.72283 to 0.86413, saving model to mymodel2_02.h5
6/6 [==============================] - 2s 395ms/step - loss: 4.1741 - accuracy: 0.7981 - val_loss: 1.9853 - val_accuracy: 0.8641
Epoch 3/10
1/6 [====>.........................] - ETA: 1s - loss: 4.2994 - accuracy: 0.81252/6 [=========>....................] - ETA: 1s - loss: 3.1663 - accuracy: 0.82423/6 [==============>...............] - ETA: 1s - loss: 3.4710 - accuracy: 0.81774/6 [===================>..........] - ETA: 0s - loss: 3.5397 - accuracy: 0.81455/6 [========================>.....] - ETA: 0s - loss: 3.4304 - accuracy: 0.82036/6 [==============================] - ETA: 0s - loss: 3.2460 - accuracy: 0.8104
Epoch 3: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 392ms/step - loss: 3.2460 - accuracy: 0.8104 - val_loss: 1.2897 - val_accuracy: 0.8152
Epoch 4/10
1/6 [====>.........................] - ETA: 1s - loss: 3.1985 - accuracy: 0.77342/6 [=========>....................] - ETA: 1s - loss: 3.0076 - accuracy: 0.74613/6 [==============>...............] - ETA: 1s - loss: 2.5264 - accuracy: 0.79174/6 [===================>..........] - ETA: 0s - loss: 2.1622 - accuracy: 0.81055/6 [========================>.....] - ETA: 0s - loss: 2.2004 - accuracy: 0.80626/6 [==============================] - ETA: 0s - loss: 2.1038 - accuracy: 0.8035
Epoch 4: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 394ms/step - loss: 2.1038 - accuracy: 0.8035 - val_loss: 1.1862 - val_accuracy: 0.8641
Epoch 5/10
1/6 [====>.........................] - ETA: 1s - loss: 3.1467 - accuracy: 0.78912/6 [=========>....................] - ETA: 1s - loss: 2.3154 - accuracy: 0.82423/6 [==============>...............] - ETA: 1s - loss: 2.1596 - accuracy: 0.82034/6 [===================>..........] - ETA: 0s - loss: 2.1212 - accuracy: 0.81645/6 [========================>.....] - ETA: 0s - loss: 1.8788 - accuracy: 0.81256/6 [==============================] - ETA: 0s - loss: 1.7073 - accuracy: 0.8240
Epoch 5: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 396ms/step - loss: 1.7073 - accuracy: 0.8240 - val_loss: 0.9651 - val_accuracy: 0.8587
Epoch 6/10
1/6 [====>.........................] - ETA: 1s - loss: 1.3807 - accuracy: 0.82032/6 [=========>....................] - ETA: 1s - loss: 1.3484 - accuracy: 0.83203/6 [==============>...............] - ETA: 1s - loss: 1.5948 - accuracy: 0.82554/6 [===================>..........] - ETA: 0s - loss: 1.4143 - accuracy: 0.83795/6 [========================>.....] - ETA: 0s - loss: 1.3974 - accuracy: 0.84226/6 [==============================] - ETA: 0s - loss: 1.3228 - accuracy: 0.8472
Epoch 6: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 393ms/step - loss: 1.3228 - accuracy: 0.8472 - val_loss: 1.0354 - val_accuracy: 0.8370
Epoch 7/10
1/6 [====>.........................] - ETA: 1s - loss: 1.0184 - accuracy: 0.84382/6 [=========>....................] - ETA: 1s - loss: 1.0622 - accuracy: 0.83593/6 [==============>...............] - ETA: 1s - loss: 1.1391 - accuracy: 0.82814/6 [===================>..........] - ETA: 0s - loss: 1.0382 - accuracy: 0.82815/6 [========================>.....] - ETA: 0s - loss: 1.0649 - accuracy: 0.81566/6 [==============================] - ETA: 0s - loss: 1.0263 - accuracy: 0.8226
Epoch 7: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 396ms/step - loss: 1.0263 - accuracy: 0.8226 - val_loss: 0.9931 - val_accuracy: 0.8533
Epoch 8/10
1/6 [====>.........................] - ETA: 1s - loss: 0.6058 - accuracy: 0.90622/6 [=========>....................] - ETA: 1s - loss: 0.6153 - accuracy: 0.90233/6 [==============>...............] - ETA: 1s - loss: 0.7479 - accuracy: 0.87244/6 [===================>..........] - ETA: 0s - loss: 0.7761 - accuracy: 0.86915/6 [========================>.....] - ETA: 0s - loss: 0.8225 - accuracy: 0.86096/6 [==============================] - ETA: 0s - loss: 0.8572 - accuracy: 0.8499
Epoch 8: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 403ms/step - loss: 0.8572 - accuracy: 0.8499 - val_loss: 1.0040 - val_accuracy: 0.8478
Epoch 9/10
1/6 [====>.........................] - ETA: 1s - loss: 0.6495 - accuracy: 0.82032/6 [=========>....................] - ETA: 1s - loss: 0.6598 - accuracy: 0.82033/6 [==============>...............] - ETA: 1s - loss: 0.6987 - accuracy: 0.83074/6 [===================>..........] - ETA: 0s - loss: 0.6920 - accuracy: 0.83985/6 [========================>.....] - ETA: 0s - loss: 0.6756 - accuracy: 0.84066/6 [==============================] - ETA: 0s - loss: 0.6861 - accuracy: 0.8390
Epoch 9: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 402ms/step - loss: 0.6861 - accuracy: 0.8390 - val_loss: 1.1101 - val_accuracy: 0.8424
Epoch 10/10
1/6 [====>.........................] - ETA: 2s - loss: 0.5363 - accuracy: 0.88282/6 [=========>....................] - ETA: 1s - loss: 0.6090 - accuracy: 0.86723/6 [==============>...............] - ETA: 1s - loss: 0.6241 - accuracy: 0.85944/6 [===================>..........] - ETA: 0s - loss: 0.6274 - accuracy: 0.86135/6 [========================>.....] - ETA: 0s - loss: 0.6407 - accuracy: 0.85786/6 [==============================] - ETA: 0s - loss: 0.6478 - accuracy: 0.8527
Epoch 10: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 410ms/step - loss: 0.6478 - accuracy: 0.8527 - val_loss: 1.3441 - val_accuracy: 0.6902
Training completed in time: 0:00:24.482991
Training Accuracy: 66.8485701084137
Testing Accuracy: 69.021737575531
1/6 [====>.........................] - ETA: 0s - loss: 1.3776 - accuracy: 0.65622/6 [=========>....................] - ETA: 0s - loss: 1.3395 - accuracy: 0.68754/6 [===================>..........] - ETA: 0s - loss: 1.3050 - accuracy: 0.71096/6 [==============================] - ETA: 0s - loss: 1.3441 - accuracy: 0.69026/6 [==============================] - 0s 40ms/step - loss: 1.3441 - accuracy: 0.6902
Epoch 1/10
1/6 [====>.........................] - ETA: 3s - loss: 0.5889 - accuracy: 0.85162/6 [=========>....................] - ETA: 1s - loss: 0.6321 - accuracy: 0.82813/6 [==============>...............] - ETA: 1s - loss: 0.6131 - accuracy: 0.82814/6 [===================>..........] - ETA: 0s - loss: 0.6216 - accuracy: 0.83015/6 [========================>.....] - ETA: 0s - loss: 0.6070 - accuracy: 0.82976/6 [==============================] - ETA: 0s - loss: 0.6085 - accuracy: 0.8322
Epoch 1: val_accuracy did not improve from 0.86413
6/6 [==============================] - 3s 411ms/step - loss: 0.6085 - accuracy: 0.8322 - val_loss: 1.2291 - val_accuracy: 0.7065
Epoch 2/10
1/6 [====>.........................] - ETA: 1s - loss: 0.6494 - accuracy: 0.82812/6 [=========>....................] - ETA: 1s - loss: 0.5202 - accuracy: 0.86333/6 [==============>...............] - ETA: 1s - loss: 0.5466 - accuracy: 0.85164/6 [===================>..........] - ETA: 0s - loss: 0.6091 - accuracy: 0.83405/6 [========================>.....] - ETA: 0s - loss: 0.5767 - accuracy: 0.84066/6 [==============================] - ETA: 0s - loss: 0.5686 - accuracy: 0.8431
Epoch 2: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 406ms/step - loss: 0.5686 - accuracy: 0.8431 - val_loss: 1.1621 - val_accuracy: 0.7554
Epoch 3/10
1/6 [====>.........................] - ETA: 1s - loss: 0.5258 - accuracy: 0.88282/6 [=========>....................] - ETA: 1s - loss: 0.5655 - accuracy: 0.85943/6 [==============>...............] - ETA: 1s - loss: 0.5406 - accuracy: 0.86204/6 [===================>..........] - ETA: 0s - loss: 0.5593 - accuracy: 0.84775/6 [========================>.....] - ETA: 0s - loss: 0.5539 - accuracy: 0.84696/6 [==============================] - ETA: 0s - loss: 0.5442 - accuracy: 0.8486
Epoch 3: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 407ms/step - loss: 0.5442 - accuracy: 0.8486 - val_loss: 1.0674 - val_accuracy: 0.7935
Epoch 4/10
1/6 [====>.........................] - ETA: 1s - loss: 0.5672 - accuracy: 0.82812/6 [=========>....................] - ETA: 1s - loss: 0.5392 - accuracy: 0.83983/6 [==============>...............] - ETA: 1s - loss: 0.5366 - accuracy: 0.83854/6 [===================>..........] - ETA: 0s - loss: 0.5311 - accuracy: 0.84185/6 [========================>.....] - ETA: 0s - loss: 0.5166 - accuracy: 0.84536/6 [==============================] - ETA: 0s - loss: 0.5069 - accuracy: 0.8486
Epoch 4: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 404ms/step - loss: 0.5069 - accuracy: 0.8486 - val_loss: 1.0396 - val_accuracy: 0.7826
Epoch 5/10
1/6 [====>.........................] - ETA: 1s - loss: 0.4416 - accuracy: 0.85162/6 [=========>....................] - ETA: 1s - loss: 0.4779 - accuracy: 0.85163/6 [==============>...............] - ETA: 1s - loss: 0.4742 - accuracy: 0.85424/6 [===================>..........] - ETA: 0s - loss: 0.4665 - accuracy: 0.86135/6 [========================>.....] - ETA: 0s - loss: 0.4953 - accuracy: 0.85626/6 [==============================] - ETA: 0s - loss: 0.4972 - accuracy: 0.8554
Epoch 5: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 398ms/step - loss: 0.4972 - accuracy: 0.8554 - val_loss: 0.9663 - val_accuracy: 0.7880
Epoch 6/10
1/6 [====>.........................] - ETA: 2s - loss: 0.4177 - accuracy: 0.86722/6 [=========>....................] - ETA: 1s - loss: 0.4898 - accuracy: 0.84383/6 [==============>...............] - ETA: 1s - loss: 0.4844 - accuracy: 0.84114/6 [===================>..........] - ETA: 0s - loss: 0.4994 - accuracy: 0.83205/6 [========================>.....] - ETA: 0s - loss: 0.4978 - accuracy: 0.83446/6 [==============================] - ETA: 0s - loss: 0.4859 - accuracy: 0.8390
Epoch 6: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 404ms/step - loss: 0.4859 - accuracy: 0.8390 - val_loss: 0.9344 - val_accuracy: 0.7935
Epoch 7/10
1/6 [====>.........................] - ETA: 1s - loss: 0.4241 - accuracy: 0.87502/6 [=========>....................] - ETA: 1s - loss: 0.3924 - accuracy: 0.87503/6 [==============>...............] - ETA: 1s - loss: 0.4018 - accuracy: 0.87244/6 [===================>..........] - ETA: 0s - loss: 0.4351 - accuracy: 0.86525/6 [========================>.....] - ETA: 0s - loss: 0.4574 - accuracy: 0.85786/6 [==============================] - ETA: 0s - loss: 0.4989 - accuracy: 0.8486
Epoch 7: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 415ms/step - loss: 0.4989 - accuracy: 0.8486 - val_loss: 0.8674 - val_accuracy: 0.7880
Epoch 8/10
1/6 [====>.........................] - ETA: 1s - loss: 0.3254 - accuracy: 0.87502/6 [=========>....................] - ETA: 1s - loss: 0.4994 - accuracy: 0.83203/6 [==============>...............] - ETA: 1s - loss: 0.4585 - accuracy: 0.84904/6 [===================>..........] - ETA: 0s - loss: 0.4533 - accuracy: 0.85165/6 [========================>.....] - ETA: 0s - loss: 0.4524 - accuracy: 0.85476/6 [==============================] - ETA: 0s - loss: 0.4683 - accuracy: 0.8486
Epoch 8: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 415ms/step - loss: 0.4683 - accuracy: 0.8486 - val_loss: 0.8014 - val_accuracy: 0.8261
Epoch 9/10
1/6 [====>.........................] - ETA: 2s - loss: 0.5526 - accuracy: 0.83592/6 [=========>....................] - ETA: 1s - loss: 0.5032 - accuracy: 0.83983/6 [==============>...............] - ETA: 1s - loss: 0.4717 - accuracy: 0.85684/6 [===================>..........] - ETA: 0s - loss: 0.4930 - accuracy: 0.84775/6 [========================>.....] - ETA: 0s - loss: 0.4828 - accuracy: 0.85316/6 [==============================] - ETA: 0s - loss: 0.4625 - accuracy: 0.8595
Epoch 9: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 414ms/step - loss: 0.4625 - accuracy: 0.8595 - val_loss: 0.8461 - val_accuracy: 0.8043
Epoch 10/10
1/6 [====>.........................] - ETA: 1s - loss: 0.4344 - accuracy: 0.84382/6 [=========>....................] - ETA: 1s - loss: 0.3995 - accuracy: 0.86333/6 [==============>...............] - ETA: 1s - loss: 0.4699 - accuracy: 0.85424/6 [===================>..........] - ETA: 0s - loss: 0.4744 - accuracy: 0.84965/6 [========================>.....] - ETA: 0s - loss: 0.4537 - accuracy: 0.85626/6 [==============================] - ETA: 0s - loss: 0.4606 - accuracy: 0.8540
Epoch 10: val_accuracy did not improve from 0.86413
6/6 [==============================] - 2s 411ms/step - loss: 0.4606 - accuracy: 0.8540 - val_loss: 0.6885 - val_accuracy: 0.8315
1/6 [====>.........................] - ETA: 0s3/6 [==============>...............] - ETA: 0s5/6 [========================>.....] - ETA: 0s6/6 [==============================] - 0s 27ms/step
Warning (from warnings module):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\metrics\_classification.py", line 1318
_warn_prf(average, modifier, msg_start, len(result))
UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
Warning (from warnings module):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\metrics\_classification.py", line 1318
_warn_prf(average, modifier, msg_start, len(result))
UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
Warning (from warnings module):
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\metrics\_classification.py", line 1318
_warn_prf(average, modifier, msg_start, len(result))
UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
precision recall f1-score support
Bronchiectasis 1.00 0.33 0.50 3
Bronchiolitis 0.00 0.00 0.00 3
COPD 0.95 0.94 0.94 159
Healthy 0.00 0.00 0.00 7
Pneumonia 0.12 0.43 0.19 7
URTI 0.00 0.00 0.00 5
accuracy 0.83 184
macro avg 0.35 0.28 0.27 184
weighted avg 0.84 0.83 0.83 184
[[ 1 0 2 0 0 0]
[ 0 0 1 0 2 0]
[ 0 0 149 0 8 2]
[ 0 0 0 0 7 0]
[ 0 0 4 0 3 0]
[ 0 0 1 0 4 0]]
>>> model
<keras.engine.sequential.Sequential object at 0x00000150B2B3C3C8>
>>> features
array([[[-4.25929626e+02, -4.74543427e+02, -5.26268555e+02, ...,
-5.21922974e+02, -5.18371826e+02, -5.24104553e+02],
[ 7.59593506e+01, 9.57673111e+01, 1.07862137e+02, ...,
1.13700607e+02, 1.18766586e+02, 1.06290695e+02],
[ 7.67765198e+01, 8.34067230e+01, 7.01855469e+01, ...,
7.70387115e+01, 8.18043213e+01, 7.64635773e+01],
...,
[ 3.02516103e-01, 7.77786791e-01, 3.45391810e-01, ...,
-6.93254948e+00, -3.68614340e+00, -1.21863639e+00],
[ 3.72309828e+00, 4.03583717e+00, 2.32282257e+00, ...,
-2.24807978e+00, -2.22762990e+00, -9.33637738e-01],
[ 2.72636032e+00, 3.96600580e+00, 4.09331608e+00, ...,
3.13393116e+00, -9.88742232e-01, -4.00402451e+00]],
[[-5.23843079e+02, -5.57566833e+02, -5.70132507e+02, ...,
-4.97284393e+02, -4.91398682e+02, -5.07024902e+02],
[ 6.80857086e+01, 9.42855377e+01, 1.08900223e+02, ...,
1.78229462e+02, 1.89745941e+02, 1.70870193e+02],
[ 6.97112885e+01, 6.79071198e+01, 6.13862648e+01, ...,
6.72552185e+01, 8.14682465e+01, 8.44948502e+01],
...,
[ 7.93595791e-01, 1.69063663e+00, 1.93637061e+00, ...,
2.97555184e+00, 2.80524445e+00, 1.38320661e+00],
[ 5.70877075e+00, 3.02921534e+00, 2.32096910e+00, ...,
-2.30543375e-01, 1.13518804e-01, -1.66258466e+00],
[ 5.59621382e+00, 1.56999946e+00, 8.22276652e-01, ...,
-4.43165779e+00, -1.62131524e+00, -4.19453573e+00]],
[[-4.67062286e+02, -4.74796997e+02, -5.33539612e+02, ...,
-5.59948914e+02, -5.58220581e+02, -5.42373962e+02],
[ 2.19346832e+02, 2.14409271e+02, 1.60924133e+02, ...,
1.65051483e+02, 1.66153748e+02, 1.73107452e+02],
[ 5.15500717e+01, 5.26684875e+01, 4.51723480e+01, ...,
9.69564590e+01, 9.60886993e+01, 8.55847931e+01],
...,
[ 5.43332291e+00, 5.14263201e+00, 4.27001905e+00, ...,
2.08396769e+00, 1.47119272e+00, -5.84330082e-01],
[ 3.02171707e+00, 3.54979134e+00, 4.02570486e+00, ...,
2.18397045e+00, 2.40084362e+00, 3.69970703e+00],
[ 3.54913306e+00, 3.35440493e+00, 2.66989541e+00, ...,
9.57578659e-01, 3.04545522e+00, 4.69646072e+00]],
...,
[[-5.41270874e+02, -5.44161621e+02, -5.44428772e+02, ...,
-5.38873901e+02, -5.43086060e+02, -5.48335388e+02],
[ 7.41401062e+01, 7.74765167e+01, 7.75629578e+01, ...,
8.38977814e+01, 7.93003464e+01, 7.16372528e+01],
[ 5.34874191e+01, 5.32623672e+01, 5.39372253e+01, ...,
5.53442230e+01, 5.38246803e+01, 4.91301422e+01],
...,
[-3.42850268e-01, 1.77218056e+00, 6.22304201e+00, ...,
5.32452106e+00, 5.33961391e+00, 4.00897408e+00],
[-1.89268148e+00, 1.51159382e+00, 6.30345201e+00, ...,
2.60764480e+00, 2.15536618e+00, 2.54276085e+00],
[-4.30831432e-01, 1.54389453e+00, 4.32400894e+00, ...,
1.13124728e+00, -9.20630038e-01, -2.54349142e-01]],
[[-4.58573120e+02, -4.96709534e+02, -5.07816956e+02, ...,
-5.19135437e+02, -5.10667419e+02, -4.92354004e+02],
[ 4.34720230e+01, 5.52355957e+01, 5.81193695e+01, ...,
4.49364586e+01, 5.60589981e+01, 7.08909760e+01],
[ 5.81432152e+01, 5.37707558e+01, 4.81697350e+01, ...,
4.14378586e+01, 5.04584122e+01, 6.44260025e+01],
...,
[-2.88646054e+00, -1.09168315e+00, 1.45189559e+00, ...,
5.07071543e+00, 2.71075153e+00, 5.29224873e-01],
[-1.16138351e+00, -1.37643099e+00, -2.22292125e-01, ...,
5.57315683e+00, 3.66302824e+00, 3.11602068e+00],
[ 4.13072109e+00, 1.73481643e+00, -8.28740478e-01, ...,
5.38905430e+00, 5.32864189e+00, 4.76255846e+00]],
[[-4.63947205e+02, -5.06420807e+02, -5.30272217e+02, ...,
-4.87844055e+02, -4.94908234e+02, -5.02326721e+02],
[ 5.94968414e+01, 7.20262756e+01, 7.27158966e+01, ...,
1.27960289e+02, 1.21901215e+02, 1.13207626e+02],
[ 9.67137985e+01, 7.95487366e+01, 4.92426414e+01, ...,
9.11189346e+01, 9.17825623e+01, 8.76653366e+01],
...,
[ 5.43445587e+00, 5.16458416e+00, 2.66037107e+00, ...,
1.54472208e+00, 3.80879593e+00, 5.92417908e+00],
[ 4.44979382e+00, 6.18138695e+00, 6.13529301e+00, ...,
1.75754809e+00, 2.25188851e+00, 2.87845802e+00],
[ 2.81087875e+00, 5.56319666e+00, 5.75641632e+00, ...,
3.64545369e+00, -3.16145539e-01, -2.14443612e+00]]],
dtype=float32)
>>>
>>>
>>>
>>>
>>>
>>>
>>> features[0]
array([[-4.25929626e+02, -4.74543427e+02, -5.26268555e+02, ...,
-5.21922974e+02, -5.18371826e+02, -5.24104553e+02],
[ 7.59593506e+01, 9.57673111e+01, 1.07862137e+02, ...,
1.13700607e+02, 1.18766586e+02, 1.06290695e+02],
[ 7.67765198e+01, 8.34067230e+01, 7.01855469e+01, ...,
7.70387115e+01, 8.18043213e+01, 7.64635773e+01],
...,
[ 3.02516103e-01, 7.77786791e-01, 3.45391810e-01, ...,
-6.93254948e+00, -3.68614340e+00, -1.21863639e+00],
[ 3.72309828e+00, 4.03583717e+00, 2.32282257e+00, ...,
-2.24807978e+00, -2.22762990e+00, -9.33637738e-01],
[ 2.72636032e+00, 3.96600580e+00, 4.09331608e+00, ...,
3.13393116e+00, -9.88742232e-01, -4.00402451e+00]], dtype=float32)
>>> model
<keras.engine.sequential.Sequential object at 0x00000150B2B3C3C8>
>>> model.predict(features[0])
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
model.predict(features[0])
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Temp\__autograph_generated_fileaf8plzgv.py", line 15, in tf__predict_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
ValueError: in user code:
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2137, in predict_function *
return step_function(self, iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2123, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2111, in run_step **
outputs = model.predict_step(data)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2079, in predict_step
return self(x, training=False)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\input_spec.py", line 296, in assert_input_compatibility
f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 40, 862, 1), found shape=(None, 862)
>>> features[0].shape
(40, 862)
>>> features[0][0].shape
(862,)
>>> features.shape
(920, 40, 862)
>>> model.predict(features)
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array([[0.4078127 , 0.00282191, 0.13897394, 0.0089603 , 0.28684512,
0.15458606],
[0.40426606, 0.00647673, 0.11705599, 0.016038 , 0.37072113,
0.08544216],
[0.42276993, 0.00464415, 0.14508007, 0.009912 , 0.33064893,
0.08694487],
...,
[0.39288533, 0.00896468, 0.13463864, 0.02007602, 0.34003273,
0.1034026 ],
[0.3623898 , 0.00830106, 0.129323 , 0.0246962 , 0.37518653,
0.10010346],
[0.3638835 , 0.00732298, 0.11790882, 0.02370702, 0.38074946,
0.10642824]], dtype=float32)
>>> model.predict(features[0])
Traceback (most recent call last):
File "<pyshell#15>", line 1, in <module>
model.predict(features[0])
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 53, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'sequential/conv2d/Relu' defined at (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 155, in main
ret = method(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 550, in runcode
exec(code, self.locals)
File "<pyshell#15>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2350, in predict
tmp_batch_outputs = self.predict_function(iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2137, in predict_function
return step_function(self, iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2123, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2111, in run_step
outputs = model.predict_step(data)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2079, in predict_step
return self(x, training=False)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 561, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\sequential.py", line 413, in call
return super().call(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 511, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 668, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\layers\convolutional\base_conv.py", line 314, in call
return self.activation(outputs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\activations.py", line 318, in relu
x, alpha=alpha, max_value=max_value, threshold=threshold
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend.py", line 5369, in relu
x = tf.nn.relu(x)
Node: 'sequential/conv2d/Relu'
convolution input must be 4-dimensional: [32,862]
[[{{node sequential/conv2d/Relu}}]] [Op:__inference_predict_function_5992]
>>> model.predict([features[0]])
Traceback (most recent call last):
File "<pyshell#16>", line 1, in <module>
model.predict([features[0]])
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Temp\__autograph_generated_fileaf8plzgv.py", line 15, in tf__predict_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
ValueError: in user code:
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2137, in predict_function *
return step_function(self, iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2123, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2111, in run_step **
outputs = model.predict_step(data)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2079, in predict_step
return self(x, training=False)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\input_spec.py", line 296, in assert_input_compatibility
f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 40, 862, 1), found shape=(None, 862)
>>> model.predict(np.array([features[0]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 33ms/step
array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
>>> model.predict(np.array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
)
Traceback (most recent call last):
File "<pyshell#18>", line 2, in <module>
0.15458609]], dtype=float32)
NameError: name 'float32' is not defined
>>> model.predict(np.array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]]))
Traceback (most recent call last):
File "<pyshell#24>", line 2, in <module>
0.15458609]]))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 53, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'sequential/conv2d/Relu' defined at (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 155, in main
ret = method(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 550, in runcode
exec(code, self.locals)
File "<pyshell#15>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2350, in predict
tmp_batch_outputs = self.predict_function(iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2137, in predict_function
return step_function(self, iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2123, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2111, in run_step
outputs = model.predict_step(data)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2079, in predict_step
return self(x, training=False)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 561, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\sequential.py", line 413, in call
return super().call(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 511, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 668, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\layers\convolutional\base_conv.py", line 314, in call
return self.activation(outputs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\activations.py", line 318, in relu
x, alpha=alpha, max_value=max_value, threshold=threshold
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend.py", line 5369, in relu
x = tf.nn.relu(x)
Node: 'sequential/conv2d/Relu'
convolution input must be 4-dimensional: [1,6]
[[{{node sequential/conv2d/Relu}}]] [Op:__inference_predict_function_5992]
>>> model.predict(np.array([features[0]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 21ms/step
array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
>>> model.predict(np.array([features[0][0]]))
Traceback (most recent call last):
File "<pyshell#26>", line 1, in <module>
model.predict(np.array([features[0][0]]))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\python\eager\execute.py", line 53, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'sequential/conv2d/Relu' defined at (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 155, in main
ret = method(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\idlelib\run.py", line 550, in runcode
exec(code, self.locals)
File "<pyshell#15>", line 1, in <module>
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2350, in predict
tmp_batch_outputs = self.predict_function(iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2137, in predict_function
return step_function(self, iterator)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2123, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2111, in run_step
outputs = model.predict_step(data)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 2079, in predict_step
return self(x, training=False)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\training.py", line 561, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\sequential.py", line 413, in call
return super().call(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 511, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\functional.py", line 668, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\engine\base_layer.py", line 1132, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\layers\convolutional\base_conv.py", line 314, in call
return self.activation(outputs)
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\activations.py", line 318, in relu
x, alpha=alpha, max_value=max_value, threshold=threshold
File "C:\Users\Admin\AppData\Local\Programs\Python\Python37\lib\site-packages\keras\backend.py", line 5369, in relu
x = tf.nn.relu(x)
Node: 'sequential/conv2d/Relu'
convolution input must be 4-dimensional: [1,862]
[[{{node sequential/conv2d/Relu}}]] [Op:__inference_predict_function_5992]
>>> model.predict(np.array([features[0]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 17ms/step
array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
>>> model.predict(np.array([features[1]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 37ms/step
array([[0.40426624, 0.00647673, 0.11705601, 0.01603799, 0.37072092,
0.0854421 ]], dtype=float32)
>>> lables
Traceback (most recent call last):
File "<pyshell#29>", line 1, in <module>
lables
NameError: name 'lables' is not defined
>>> labels
array(['URTI', 'URTI', 'Healthy', 'Asthma', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'URTI', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'LRTI', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Bronchiectasis',
'Bronchiectasis', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'LRTI', 'Bronchiectasis', 'Bronchiectasis', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'URTI',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy',
'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia', 'Healthy',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy',
'Healthy', 'Healthy', 'COPD', 'URTI', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'URTI',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'Pneumonia', 'Pneumonia', 'Pneumonia', 'Pneumonia', 'Healthy',
'URTI', 'URTI', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'Pneumonia', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy', 'Healthy', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'URTI', 'Bronchiolitis', 'Bronchiolitis', 'Bronchiolitis',
'URTI', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy',
'Healthy', 'Healthy', 'Healthy', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'Bronchiolitis', 'Bronchiolitis', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'URTI', 'URTI', 'URTI', 'URTI',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Bronchiolitis',
'Bronchiolitis', 'Bronchiectasis', 'Bronchiectasis',
'Bronchiectasis', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'Healthy', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'Bronchiolitis', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'Healthy', 'Healthy', 'Healthy', 'Healthy', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'URTI', 'URTI', 'URTI',
'URTI', 'COPD', 'URTI', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Bronchiectasis', 'URTI',
'URTI', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'Bronchiectasis', 'Bronchiectasis', 'Bronchiectasis',
'Bronchiectasis', 'Bronchiectasis', 'Bronchiectasis', 'Healthy',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'Bronchiolitis', 'Bronchiolitis', 'Bronchiolitis',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy', 'Healthy',
'URTI', 'URTI', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'Healthy',
'Bronchiectasis', 'Bronchiectasis', 'Bronchiolitis',
'Bronchiolitis', 'Healthy', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'Pneumonia', 'Pneumonia', 'Pneumonia',
'Pneumonia', 'Pneumonia', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD', 'COPD',
'COPD', 'COPD', 'Healthy', 'Healthy', 'Healthy', 'Pneumonia',
'Pneumonia', 'Pneumonia'], dtype='<U14')
>>> set(labels)
{'Bronchiectasis', 'Healthy', 'Asthma', 'LRTI', 'URTI', 'COPD', 'Pneumonia', 'Bronchiolitis'}
>>> model.predict(np.array([features[0]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 17ms/step
array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
>>> len(model.predict(np.array([features[0]])))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 42ms/step
1
>>> model.predict(np.array([features[0]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 17ms/step
array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)
>>> print(len(array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)))
Traceback (most recent call last):
File "<pyshell#37>", line 1, in <module>
print(len(array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
NameError: name 'array' is not defined
>>> print(len(np.array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]], dtype=float32)))
Traceback (most recent call last):
File "<pyshell#38>", line 2, in <module>
0.15458609]], dtype=float32)))
NameError: name 'float32' is not defined
>>> print(len(np.array([[0.40781286, 0.00282191, 0.13897392, 0.0089603 , 0.28684494,
0.15458609]])))
1
>>> max_pooling2d_2 (MaxPooling (None, 4, 106, 64) 0
SyntaxError: invalid syntax
>>>
>>>
>>> model.predict(np.array([features[11]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 14ms/step
array([[0.34432802, 0.00814287, 0.38072217, 0.00841277, 0.18814225,
0.07025192]], dtype=float32)
>>> model.predict(np.array([features[11]]))
1/1 [==============================] - ETA: 0s1/1 [==============================] - 0s 18ms/step
array([[0.34432802, 0.00814287, 0.38072217, 0.00841277, 0.18814225,
0.07025192]], dtype=float32)
>>> 0.34432802<0.38072217
True
>>>