Lower Back Pain Symptoms Detection using NN

Photo by Colleen Tracey on Dribbble

310 Observations, 13 Attributes (12 Numeric Predictors, 1 Binary Class Attribute — No Demographics)

Lower back pain can be caused by a variety of problems with any part of the complex, interconnected network of spinal muscles, nerves, bones, discs, or tendons in the lumbar spine. Typical sources of low back pain include:

  • The large nerve roots in the low back that go to the legs may be irritated
  • The smaller nerves that supply the low back may be irritated
  • The large paired lower back muscles may be strained
  • The bones, ligaments, or joints may be damaged
  • An intervertebral disc may be degenerating

An irritation or problem with any of these structures can cause lower back pain and/or pain that radiates or is referred to other parts of the body. Many lower back problems also cause back muscle spasms, which don’t sound like much but can cause severe pain and disability.

While lower back pain is extremely common, the symptoms and severity of lower back pain vary greatly. A simple lower back muscle strain might be excruciating enough to necessitate an emergency room visit, while a degenerating disc might cause only mild, intermittent discomfort.

This data set is about identifying a person is abnormal or normal using collected physical spine details/data.

Data Visualisation

RangeIndex: 310 entries, 0 to 309
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 pelvic_incidence 310 non-null float64
1 pelvic_tilt 310 non-null float64
2 lumbar_lordosis_angle 310 non-null float64
3 sacral_slope 310 non-null float64
4 pelvic_radius 310 non-null float64
5 degree_spondylolisthesis 310 non-null float64
6 pelvic_slope 310 non-null float64
7 direct_tilt 310 non-null float64
8 thoracic_slope 310 non-null float64
9 cervical_tilt 310 non-null float64
10 sacrum_angle 310 non-null float64
11 scoliosis_slope 310 non-null float64
12 class 310 non-null int64
dtypes: float64(12), int64(1)
memory usage: 31.6 KB

Model Architecture

Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_2 (Dense) (None, 64) 832
_________________________________________________________________
dropout_1 (Dropout) (None, 64) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 65
=================================================================
Total params: 897
Trainable params: 897
Non-trainable params: 0

Training the model

Epoch 1/300
6/6 - 0s - loss: 0.7166 - accuracy: 0.5459 - val_loss: 0.6021 - val_accuracy: 0.6596
Epoch 2/300
6/6 - 0s - loss: 0.6368 - accuracy: 0.6486 - val_loss: 0.5782 - val_accuracy: 0.7021
Epoch 3/300
6/6 - 0s - loss: 0.6219 - accuracy: 0.6054 - val_loss: 0.5605 - val_accuracy: 0.7234
Epoch 4/300
6/6 - 0s - loss: 0.5943 - accuracy: 0.6649 - val_loss: 0.5454 - val_accuracy: 0.7447
Epoch 5/300
6/6 - 0s - loss: 0.5552 - accuracy: 0.7027 - val_loss: 0.5329 - val_accuracy: 0.7660
Epoch 6/300
6/6 - 0s - loss: 0.5593 - accuracy: 0.6919 - val_loss: 0.5221 - val_accuracy: 0.7447
Epoch 7/300
6/6 - 0s - loss: 0.5467 - accuracy: 0.7405 - val_loss: 0.5118 - val_accuracy: 0.7872
Epoch 8/300
6/6 - 0s - loss: 0.5265 - accuracy: 0.7135 - val_loss: 0.5025 - val_accuracy: 0.7872
Epoch 9/300
6/6 - 0s - loss: 0.5398 - accuracy: 0.6919 - val_loss: 0.4960 - val_accuracy: 0.7660
Epoch 10/300
6/6 - 0s - loss: 0.5498 - accuracy: 0.6973 - val_loss: 0.4897 - val_accuracy: 0.8085
Epoch 11/300
6/6 - 0s - loss: 0.4946 - accuracy: 0.7622 - val_loss: 0.4851 - val_accuracy: 0.8298
Epoch 12/300
6/6 - 0s - loss: 0.5060 - accuracy: 0.7568 - val_loss: 0.4797 - val_accuracy: 0.8298
Epoch 13/300
6/6 - 0s - loss: 0.4608 - accuracy: 0.7838 - val_loss: 0.4737 - val_accuracy: 0.8298
Epoch 14/300
6/6 - 0s - loss: 0.4819 - accuracy: 0.7135 - val_loss: 0.4693 - val_accuracy: 0.8298
Epoch 15/300
6/6 - 0s - loss: 0.4976 - accuracy: 0.7514 - val_loss: 0.4655 - val_accuracy: 0.8298
Epoch 16/300
6/6 - 0s - loss: 0.4558 - accuracy: 0.7892 - val_loss: 0.4606 - val_accuracy: 0.8298
Epoch 17/300
6/6 - 0s - loss: 0.4805 - accuracy: 0.7514 - val_loss: 0.4570 - val_accuracy: 0.8298
Epoch 18/300
6/6 - 0s - loss: 0.4572 - accuracy: 0.7838 - val_loss: 0.4540 - val_accuracy: 0.8511
Epoch 19/300
6/6 - 0s - loss: 0.4444 - accuracy: 0.7946 - val_loss: 0.4505 - val_accuracy: 0.8511
Epoch 20/300
6/6 - 0s - loss: 0.4352 - accuracy: 0.8108 - val_loss: 0.4463 - val_accuracy: 0.8511
Epoch 21/300
6/6 - 0s - loss: 0.4483 - accuracy: 0.7946 - val_loss: 0.4420 - val_accuracy: 0.8511
Epoch 22/300
6/6 - 0s - loss: 0.4647 - accuracy: 0.7676 - val_loss: 0.4389 - val_accuracy: 0.8511
Epoch 23/300
6/6 - 0s - loss: 0.4542 - accuracy: 0.7514 - val_loss: 0.4358 - val_accuracy: 0.8511
Epoch 24/300
6/6 - 0s - loss: 0.4375 - accuracy: 0.7838 - val_loss: 0.4323 - val_accuracy: 0.8511
Epoch 25/300
6/6 - 0s - loss: 0.4156 - accuracy: 0.8216 - val_loss: 0.4297 - val_accuracy: 0.8511
Epoch 26/300
6/6 - 0s - loss: 0.3976 - accuracy: 0.8595 - val_loss: 0.4262 - val_accuracy: 0.8511
Epoch 27/300
6/6 - 0s - loss: 0.4309 - accuracy: 0.8000 - val_loss: 0.4230 - val_accuracy: 0.8511
Epoch 28/300
6/6 - 0s - loss: 0.4066 - accuracy: 0.8108 - val_loss: 0.4198 - val_accuracy: 0.8511
Epoch 29/300
6/6 - 0s - loss: 0.4341 - accuracy: 0.7622 - val_loss: 0.4186 - val_accuracy: 0.8511
Epoch 30/300
6/6 - 0s - loss: 0.4214 - accuracy: 0.8270 - val_loss: 0.4170 - val_accuracy: 0.8511
Epoch 31/300
6/6 - 0s - loss: 0.4117 - accuracy: 0.8000 - val_loss: 0.4153 - val_accuracy: 0.8511
Epoch 32/300
6/6 - 0s - loss: 0.3978 - accuracy: 0.8486 - val_loss: 0.4131 - val_accuracy: 0.8511
Epoch 33/300
6/6 - 0s - loss: 0.4011 - accuracy: 0.8216 - val_loss: 0.4115 - val_accuracy: 0.8511
Epoch 34/300
6/6 - 0s - loss: 0.3865 - accuracy: 0.8378 - val_loss: 0.4081 - val_accuracy: 0.8511
Epoch 35/300
6/6 - 0s - loss: 0.3925 - accuracy: 0.8486 - val_loss: 0.4057 - val_accuracy: 0.8511
Epoch 36/300
6/6 - 0s - loss: 0.4040 - accuracy: 0.8108 - val_loss: 0.4035 - val_accuracy: 0.8511
Epoch 37/300
6/6 - 0s - loss: 0.3939 - accuracy: 0.8000 - val_loss: 0.4009 - val_accuracy: 0.8511
Epoch 38/300
6/6 - 0s - loss: 0.3957 - accuracy: 0.8108 - val_loss: 0.3989 - val_accuracy: 0.8511
Epoch 39/300
6/6 - 0s - loss: 0.3757 - accuracy: 0.8162 - val_loss: 0.3969 - val_accuracy: 0.8723
Epoch 40/300
6/6 - 0s - loss: 0.3871 - accuracy: 0.8108 - val_loss: 0.3955 - val_accuracy: 0.8723
Epoch 41/300
6/6 - 0s - loss: 0.3691 - accuracy: 0.8270 - val_loss: 0.3940 - val_accuracy: 0.8723
Epoch 42/300
6/6 - 0s - loss: 0.3626 - accuracy: 0.8270 - val_loss: 0.3919 - val_accuracy: 0.8723
Epoch 43/300
6/6 - 0s - loss: 0.3743 - accuracy: 0.8216 - val_loss: 0.3913 - val_accuracy: 0.8723
Epoch 44/300
6/6 - 0s - loss: 0.3628 - accuracy: 0.8757 - val_loss: 0.3891 - val_accuracy: 0.8723
Epoch 45/300
6/6 - 0s - loss: 0.3656 - accuracy: 0.8162 - val_loss: 0.3881 - val_accuracy: 0.8723
Epoch 46/300
6/6 - 0s - loss: 0.3919 - accuracy: 0.8162 - val_loss: 0.3856 - val_accuracy: 0.8723
Epoch 47/300
6/6 - 0s - loss: 0.3532 - accuracy: 0.8216 - val_loss: 0.3839 - val_accuracy: 0.8511
Epoch 48/300
6/6 - 0s - loss: 0.3530 - accuracy: 0.8324 - val_loss: 0.3821 - val_accuracy: 0.8723
Epoch 49/300
6/6 - 0s - loss: 0.3422 - accuracy: 0.8432 - val_loss: 0.3798 - val_accuracy: 0.8511
Epoch 50/300
6/6 - 0s - loss: 0.3561 - accuracy: 0.8108 - val_loss: 0.3785 - val_accuracy: 0.8511
Epoch 51/300
6/6 - 0s - loss: 0.3392 - accuracy: 0.8108 - val_loss: 0.3779 - val_accuracy: 0.8511
Epoch 52/300
6/6 - 0s - loss: 0.3407 - accuracy: 0.8216 - val_loss: 0.3769 - val_accuracy: 0.8511
Epoch 53/300
6/6 - 0s - loss: 0.3677 - accuracy: 0.8324 - val_loss: 0.3757 - val_accuracy: 0.8511
Epoch 54/300
6/6 - 0s - loss: 0.3256 - accuracy: 0.8649 - val_loss: 0.3750 - val_accuracy: 0.8511
Epoch 55/300
6/6 - 0s - loss: 0.3272 - accuracy: 0.8432 - val_loss: 0.3732 - val_accuracy: 0.8511
Epoch 56/300
6/6 - 0s - loss: 0.3432 - accuracy: 0.8432 - val_loss: 0.3726 - val_accuracy: 0.8511
Epoch 57/300
6/6 - 0s - loss: 0.3403 - accuracy: 0.8324 - val_loss: 0.3714 - val_accuracy: 0.8511
Epoch 58/300
6/6 - 0s - loss: 0.3551 - accuracy: 0.8649 - val_loss: 0.3708 - val_accuracy: 0.8511
Epoch 59/300
6/6 - 0s - loss: 0.3418 - accuracy: 0.8486 - val_loss: 0.3690 - val_accuracy: 0.8511
Epoch 60/300
6/6 - 0s - loss: 0.3349 - accuracy: 0.8595 - val_loss: 0.3673 - val_accuracy: 0.8511
Epoch 61/300
6/6 - 0s - loss: 0.3002 - accuracy: 0.8595 - val_loss: 0.3665 - val_accuracy: 0.8511
Epoch 62/300
6/6 - 0s - loss: 0.2995 - accuracy: 0.8595 - val_loss: 0.3647 - val_accuracy: 0.8511
Epoch 63/300
6/6 - 0s - loss: 0.3044 - accuracy: 0.8865 - val_loss: 0.3648 - val_accuracy: 0.8511
Epoch 64/300
6/6 - 0s - loss: 0.3257 - accuracy: 0.8486 - val_loss: 0.3642 - val_accuracy: 0.8511
Epoch 65/300
6/6 - 0s - loss: 0.2982 - accuracy: 0.8811 - val_loss: 0.3629 - val_accuracy: 0.8511
Epoch 66/300
6/6 - 0s - loss: 0.3240 - accuracy: 0.8595 - val_loss: 0.3614 - val_accuracy: 0.8511
Epoch 67/300
6/6 - 0s - loss: 0.3357 - accuracy: 0.8486 - val_loss: 0.3595 - val_accuracy: 0.8511
Epoch 68/300
6/6 - 0s - loss: 0.3034 - accuracy: 0.8649 - val_loss: 0.3580 - val_accuracy: 0.8511
Epoch 69/300
6/6 - 0s - loss: 0.2895 - accuracy: 0.8757 - val_loss: 0.3577 - val_accuracy: 0.8511
Epoch 70/300
6/6 - 0s - loss: 0.3174 - accuracy: 0.8541 - val_loss: 0.3570 - val_accuracy: 0.8511
Epoch 71/300
6/6 - 0s - loss: 0.3005 - accuracy: 0.8378 - val_loss: 0.3554 - val_accuracy: 0.8511
Epoch 72/300
6/6 - 0s - loss: 0.2806 - accuracy: 0.8919 - val_loss: 0.3545 - val_accuracy: 0.8511
Epoch 73/300
6/6 - 0s - loss: 0.3287 - accuracy: 0.8378 - val_loss: 0.3540 - val_accuracy: 0.8511
Epoch 74/300
6/6 - 0s - loss: 0.3122 - accuracy: 0.8865 - val_loss: 0.3523 - val_accuracy: 0.8511
Epoch 75/300
6/6 - 0s - loss: 0.3267 - accuracy: 0.8541 - val_loss: 0.3520 - val_accuracy: 0.8723
Epoch 76/300
6/6 - 0s - loss: 0.2921 - accuracy: 0.8703 - val_loss: 0.3509 - val_accuracy: 0.8723
Epoch 77/300
6/6 - 0s - loss: 0.2946 - accuracy: 0.8486 - val_loss: 0.3501 - val_accuracy: 0.8723
Epoch 78/300
6/6 - 0s - loss: 0.2761 - accuracy: 0.8919 - val_loss: 0.3495 - val_accuracy: 0.8723
Epoch 79/300
6/6 - 0s - loss: 0.2965 - accuracy: 0.8757 - val_loss: 0.3496 - val_accuracy: 0.8723
Epoch 80/300
6/6 - 0s - loss: 0.2925 - accuracy: 0.8757 - val_loss: 0.3496 - val_accuracy: 0.8723
Epoch 81/300
6/6 - 0s - loss: 0.2692 - accuracy: 0.8865 - val_loss: 0.3484 - val_accuracy: 0.8723
Epoch 82/300
6/6 - 0s - loss: 0.2746 - accuracy: 0.8973 - val_loss: 0.3469 - val_accuracy: 0.8723
Epoch 83/300
6/6 - 0s - loss: 0.2742 - accuracy: 0.8865 - val_loss: 0.3464 - val_accuracy: 0.8723
Epoch 84/300
6/6 - 0s - loss: 0.2943 - accuracy: 0.8811 - val_loss: 0.3462 - val_accuracy: 0.8723
Epoch 85/300
6/6 - 0s - loss: 0.2927 - accuracy: 0.8703 - val_loss: 0.3457 - val_accuracy: 0.8723
Epoch 86/300
6/6 - 0s - loss: 0.2872 - accuracy: 0.8541 - val_loss: 0.3438 - val_accuracy: 0.8723
Epoch 87/300
6/6 - 0s - loss: 0.2777 - accuracy: 0.8865 - val_loss: 0.3437 - val_accuracy: 0.8723
Epoch 88/300
6/6 - 0s - loss: 0.3031 - accuracy: 0.8541 - val_loss: 0.3442 - val_accuracy: 0.8723
Epoch 89/300
6/6 - 0s - loss: 0.2455 - accuracy: 0.8973 - val_loss: 0.3439 - val_accuracy: 0.8723
Epoch 90/300
6/6 - 0s - loss: 0.2816 - accuracy: 0.8865 - val_loss: 0.3442 - val_accuracy: 0.8723
Epoch 91/300
6/6 - 0s - loss: 0.2755 - accuracy: 0.8811 - val_loss: 0.3445 - val_accuracy: 0.8723
Epoch 92/300
6/6 - 0s - loss: 0.2569 - accuracy: 0.9135 - val_loss: 0.3435 - val_accuracy: 0.8723
Epoch 93/300
6/6 - 0s - loss: 0.2719 - accuracy: 0.8865 - val_loss: 0.3428 - val_accuracy: 0.8723
Epoch 94/300
6/6 - 0s - loss: 0.2738 - accuracy: 0.8811 - val_loss: 0.3419 - val_accuracy: 0.8723
Epoch 95/300
6/6 - 0s - loss: 0.2590 - accuracy: 0.8973 - val_loss: 0.3414 - val_accuracy: 0.8511
Epoch 96/300
6/6 - 0s - loss: 0.2954 - accuracy: 0.8486 - val_loss: 0.3422 - val_accuracy: 0.8511
Epoch 97/300
6/6 - 0s - loss: 0.2550 - accuracy: 0.8757 - val_loss: 0.3418 - val_accuracy: 0.8511
Epoch 98/300
6/6 - 0s - loss: 0.2690 - accuracy: 0.8649 - val_loss: 0.3411 - val_accuracy: 0.8511
Epoch 99/300
6/6 - 0s - loss: 0.2666 - accuracy: 0.9081 - val_loss: 0.3402 - val_accuracy: 0.8511
Epoch 100/300
6/6 - 0s - loss: 0.2444 - accuracy: 0.9189 - val_loss: 0.3400 - val_accuracy: 0.8511
Epoch 101/300
6/6 - 0s - loss: 0.2583 - accuracy: 0.8919 - val_loss: 0.3397 - val_accuracy: 0.8511
Epoch 102/300
6/6 - 0s - loss: 0.2664 - accuracy: 0.8919 - val_loss: 0.3392 - val_accuracy: 0.8723
Epoch 103/300
6/6 - 0s - loss: 0.2740 - accuracy: 0.8757 - val_loss: 0.3390 - val_accuracy: 0.8511
Epoch 104/300
6/6 - 0s - loss: 0.2466 - accuracy: 0.9189 - val_loss: 0.3404 - val_accuracy: 0.8511
Epoch 105/300
6/6 - 0s - loss: 0.2290 - accuracy: 0.8919 - val_loss: 0.3405 - val_accuracy: 0.8511
Epoch 106/300
6/6 - 0s - loss: 0.2584 - accuracy: 0.8919 - val_loss: 0.3407 - val_accuracy: 0.8511
Epoch 107/300
6/6 - 0s - loss: 0.2584 - accuracy: 0.8757 - val_loss: 0.3407 - val_accuracy: 0.8298
Epoch 108/300
6/6 - 0s - loss: 0.2571 - accuracy: 0.8919 - val_loss: 0.3401 - val_accuracy: 0.8511
Epoch 109/300
6/6 - 0s - loss: 0.2656 - accuracy: 0.8757 - val_loss: 0.3386 - val_accuracy: 0.8511
Epoch 110/300
6/6 - 0s - loss: 0.2426 - accuracy: 0.9081 - val_loss: 0.3386 - val_accuracy: 0.8723
Epoch 111/300
6/6 - 0s - loss: 0.2528 - accuracy: 0.8811 - val_loss: 0.3387 - val_accuracy: 0.8511
Epoch 112/300
6/6 - 0s - loss: 0.2512 - accuracy: 0.8811 - val_loss: 0.3385 - val_accuracy: 0.8511
Epoch 113/300
6/6 - 0s - loss: 0.2446 - accuracy: 0.9027 - val_loss: 0.3392 - val_accuracy: 0.8511
Epoch 114/300
6/6 - 0s - loss: 0.2299 - accuracy: 0.9081 - val_loss: 0.3380 - val_accuracy: 0.8511
Epoch 115/300
6/6 - 0s - loss: 0.2534 - accuracy: 0.8703 - val_loss: 0.3383 - val_accuracy: 0.8511
Epoch 116/300
6/6 - 0s - loss: 0.2283 - accuracy: 0.9027 - val_loss: 0.3382 - val_accuracy: 0.8511
Epoch 117/300
6/6 - 0s - loss: 0.2402 - accuracy: 0.9081 - val_loss: 0.3373 - val_accuracy: 0.8511
Epoch 118/300
6/6 - 0s - loss: 0.2223 - accuracy: 0.9081 - val_loss: 0.3355 - val_accuracy: 0.8511
Epoch 119/300
6/6 - 0s - loss: 0.2255 - accuracy: 0.9081 - val_loss: 0.3352 - val_accuracy: 0.8511
Epoch 120/300
6/6 - 0s - loss: 0.2284 - accuracy: 0.9135 - val_loss: 0.3362 - val_accuracy: 0.8511
Epoch 121/300
6/6 - 0s - loss: 0.2429 - accuracy: 0.8757 - val_loss: 0.3367 - val_accuracy: 0.8298
Epoch 122/300
6/6 - 0s - loss: 0.2275 - accuracy: 0.9081 - val_loss: 0.3359 - val_accuracy: 0.8511
Epoch 123/300
6/6 - 0s - loss: 0.2213 - accuracy: 0.9405 - val_loss: 0.3367 - val_accuracy: 0.8298
Epoch 124/300
6/6 - 0s - loss: 0.2169 - accuracy: 0.9243 - val_loss: 0.3364 - val_accuracy: 0.8298
Epoch 125/300
6/6 - 0s - loss: 0.2517 - accuracy: 0.9027 - val_loss: 0.3365 - val_accuracy: 0.8511
Epoch 126/300
6/6 - 0s - loss: 0.2190 - accuracy: 0.9243 - val_loss: 0.3370 - val_accuracy: 0.8511
Epoch 127/300
6/6 - 0s - loss: 0.2200 - accuracy: 0.9027 - val_loss: 0.3360 - val_accuracy: 0.8511
Epoch 128/300
6/6 - 0s - loss: 0.2271 - accuracy: 0.8973 - val_loss: 0.3360 - val_accuracy: 0.8511
Epoch 129/300
6/6 - 0s - loss: 0.2454 - accuracy: 0.8703 - val_loss: 0.3369 - val_accuracy: 0.8298
Epoch 130/300
6/6 - 0s - loss: 0.2287 - accuracy: 0.9081 - val_loss: 0.3374 - val_accuracy: 0.8298
Epoch 131/300
6/6 - 0s - loss: 0.2650 - accuracy: 0.8757 - val_loss: 0.3374 - val_accuracy: 0.8511
Epoch 132/300
6/6 - 0s - loss: 0.2261 - accuracy: 0.8973 - val_loss: 0.3377 - val_accuracy: 0.8298
Epoch 133/300
6/6 - 0s - loss: 0.2360 - accuracy: 0.8919 - val_loss: 0.3377 - val_accuracy: 0.8298
Epoch 134/300
6/6 - 0s - loss: 0.2148 - accuracy: 0.9243 - val_loss: 0.3370 - val_accuracy: 0.8298
Epoch 135/300
6/6 - 0s - loss: 0.2683 - accuracy: 0.8649 - val_loss: 0.3368 - val_accuracy: 0.8723
Epoch 136/300
6/6 - 0s - loss: 0.2332 - accuracy: 0.8973 - val_loss: 0.3367 - val_accuracy: 0.8511
Epoch 137/300
6/6 - 0s - loss: 0.2279 - accuracy: 0.8973 - val_loss: 0.3369 - val_accuracy: 0.8511
Epoch 138/300
6/6 - 0s - loss: 0.2347 - accuracy: 0.9189 - val_loss: 0.3364 - val_accuracy: 0.8511
Epoch 139/300
6/6 - 0s - loss: 0.2156 - accuracy: 0.9135 - val_loss: 0.3366 - val_accuracy: 0.8511
Epoch 140/300
6/6 - 0s - loss: 0.2239 - accuracy: 0.9027 - val_loss: 0.3367 - val_accuracy: 0.8723
Epoch 141/300
6/6 - 0s - loss: 0.2209 - accuracy: 0.9135 - val_loss: 0.3362 - val_accuracy: 0.8511
Epoch 142/300
6/6 - 0s - loss: 0.2121 - accuracy: 0.9027 - val_loss: 0.3363 - val_accuracy: 0.8723
Epoch 143/300
6/6 - 0s - loss: 0.2233 - accuracy: 0.9135 - val_loss: 0.3366 - val_accuracy: 0.8723
Epoch 144/300
6/6 - 0s - loss: 0.2164 - accuracy: 0.9081 - val_loss: 0.3379 - val_accuracy: 0.8511
Epoch 145/300
6/6 - 0s - loss: 0.2154 - accuracy: 0.9027 - val_loss: 0.3384 - val_accuracy: 0.8298
Epoch 146/300
6/6 - 0s - loss: 0.2107 - accuracy: 0.9297 - val_loss: 0.3372 - val_accuracy: 0.8511
Epoch 147/300
6/6 - 0s - loss: 0.2303 - accuracy: 0.8973 - val_loss: 0.3365 - val_accuracy: 0.8511
Epoch 148/300
6/6 - 0s - loss: 0.1974 - accuracy: 0.9243 - val_loss: 0.3366 - val_accuracy: 0.8511
Epoch 149/300
6/6 - 0s - loss: 0.2202 - accuracy: 0.9027 - val_loss: 0.3367 - val_accuracy: 0.8511
Epoch 150/300
6/6 - 0s - loss: 0.1924 - accuracy: 0.9243 - val_loss: 0.3380 - val_accuracy: 0.8511
Epoch 151/300
6/6 - 0s - loss: 0.2088 - accuracy: 0.9135 - val_loss: 0.3383 - val_accuracy: 0.8511
Epoch 152/300
6/6 - 0s - loss: 0.2012 - accuracy: 0.9189 - val_loss: 0.3385 - val_accuracy: 0.8511
Epoch 153/300
6/6 - 0s - loss: 0.2023 - accuracy: 0.9351 - val_loss: 0.3390 - val_accuracy: 0.8511
Epoch 154/300
6/6 - 0s - loss: 0.2095 - accuracy: 0.9027 - val_loss: 0.3390 - val_accuracy: 0.8511
Epoch 155/300
6/6 - 0s - loss: 0.2077 - accuracy: 0.8973 - val_loss: 0.3395 - val_accuracy: 0.8511
Epoch 156/300
6/6 - 0s - loss: 0.1907 - accuracy: 0.9243 - val_loss: 0.3382 - val_accuracy: 0.8511
Epoch 157/300
6/6 - 0s - loss: 0.1980 - accuracy: 0.9243 - val_loss: 0.3390 - val_accuracy: 0.8511
Epoch 158/300
6/6 - 0s - loss: 0.1965 - accuracy: 0.8919 - val_loss: 0.3389 - val_accuracy: 0.8511
Epoch 159/300
6/6 - 0s - loss: 0.2122 - accuracy: 0.9027 - val_loss: 0.3381 - val_accuracy: 0.8511
Epoch 160/300
6/6 - 0s - loss: 0.2072 - accuracy: 0.9189 - val_loss: 0.3382 - val_accuracy: 0.8511
Epoch 161/300
6/6 - 0s - loss: 0.2023 - accuracy: 0.9297 - val_loss: 0.3386 - val_accuracy: 0.8511
Epoch 162/300
6/6 - 0s - loss: 0.2032 - accuracy: 0.9135 - val_loss: 0.3410 - val_accuracy: 0.8511
Epoch 163/300
6/6 - 0s - loss: 0.2131 - accuracy: 0.9135 - val_loss: 0.3415 - val_accuracy: 0.8511
Epoch 164/300
6/6 - 0s - loss: 0.1970 - accuracy: 0.9135 - val_loss: 0.3415 - val_accuracy: 0.8511
Epoch 165/300
6/6 - 0s - loss: 0.2181 - accuracy: 0.9135 - val_loss: 0.3399 - val_accuracy: 0.8511
Epoch 166/300
6/6 - 0s - loss: 0.1877 - accuracy: 0.9243 - val_loss: 0.3395 - val_accuracy: 0.8511
Epoch 167/300
6/6 - 0s - loss: 0.1807 - accuracy: 0.9135 - val_loss: 0.3415 - val_accuracy: 0.8511
Epoch 168/300
6/6 - 0s - loss: 0.2099 - accuracy: 0.8757 - val_loss: 0.3412 - val_accuracy: 0.8511
Epoch 169/300
6/6 - 0s - loss: 0.2395 - accuracy: 0.8865 - val_loss: 0.3406 - val_accuracy: 0.8511
Epoch 170/300
6/6 - 0s - loss: 0.1857 - accuracy: 0.9135 - val_loss: 0.3413 - val_accuracy: 0.8511
Epoch 171/300
6/6 - 0s - loss: 0.2014 - accuracy: 0.9135 - val_loss: 0.3422 - val_accuracy: 0.8511
Epoch 172/300
6/6 - 0s - loss: 0.1805 - accuracy: 0.9297 - val_loss: 0.3421 - val_accuracy: 0.8511
Epoch 173/300
6/6 - 0s - loss: 0.2118 - accuracy: 0.9027 - val_loss: 0.3430 - val_accuracy: 0.8511
Epoch 174/300
6/6 - 0s - loss: 0.1988 - accuracy: 0.9027 - val_loss: 0.3411 - val_accuracy: 0.8511
Epoch 175/300
6/6 - 0s - loss: 0.2002 - accuracy: 0.9297 - val_loss: 0.3430 - val_accuracy: 0.8511
Epoch 176/300
6/6 - 0s - loss: 0.2119 - accuracy: 0.9027 - val_loss: 0.3431 - val_accuracy: 0.8511
Epoch 177/300
6/6 - 0s - loss: 0.1944 - accuracy: 0.9135 - val_loss: 0.3431 - val_accuracy: 0.8511
Epoch 178/300
6/6 - 0s - loss: 0.1769 - accuracy: 0.9135 - val_loss: 0.3445 - val_accuracy: 0.8511
Epoch 179/300
6/6 - 0s - loss: 0.1768 - accuracy: 0.9297 - val_loss: 0.3439 - val_accuracy: 0.8511
Epoch 180/300
6/6 - 0s - loss: 0.1973 - accuracy: 0.9135 - val_loss: 0.3448 - val_accuracy: 0.8511
Epoch 181/300
6/6 - 0s - loss: 0.2035 - accuracy: 0.8865 - val_loss: 0.3443 - val_accuracy: 0.8511
Epoch 182/300
6/6 - 0s - loss: 0.1858 - accuracy: 0.9297 - val_loss: 0.3450 - val_accuracy: 0.8511
Epoch 183/300
6/6 - 0s - loss: 0.1796 - accuracy: 0.9297 - val_loss: 0.3453 - val_accuracy: 0.8511
Epoch 184/300
6/6 - 0s - loss: 0.1903 - accuracy: 0.9351 - val_loss: 0.3466 - val_accuracy: 0.8511
Epoch 185/300
6/6 - 0s - loss: 0.2121 - accuracy: 0.8973 - val_loss: 0.3477 - val_accuracy: 0.8511
Epoch 186/300
6/6 - 0s - loss: 0.2039 - accuracy: 0.8973 - val_loss: 0.3469 - val_accuracy: 0.8511
Epoch 187/300
6/6 - 0s - loss: 0.1832 - accuracy: 0.9459 - val_loss: 0.3474 - val_accuracy: 0.8511
Epoch 188/300
6/6 - 0s - loss: 0.2091 - accuracy: 0.9135 - val_loss: 0.3493 - val_accuracy: 0.8511
Epoch 189/300
6/6 - 0s - loss: 0.1891 - accuracy: 0.9189 - val_loss: 0.3489 - val_accuracy: 0.8511
Epoch 190/300
6/6 - 0s - loss: 0.1675 - accuracy: 0.9514 - val_loss: 0.3503 - val_accuracy: 0.8511
Epoch 191/300
6/6 - 0s - loss: 0.1683 - accuracy: 0.9243 - val_loss: 0.3526 - val_accuracy: 0.8511
Epoch 192/300
6/6 - 0s - loss: 0.1939 - accuracy: 0.9243 - val_loss: 0.3522 - val_accuracy: 0.8511
Epoch 193/300
6/6 - 0s - loss: 0.1709 - accuracy: 0.9568 - val_loss: 0.3520 - val_accuracy: 0.8511
Epoch 194/300
6/6 - 0s - loss: 0.2060 - accuracy: 0.9081 - val_loss: 0.3533 - val_accuracy: 0.8511
Epoch 195/300
6/6 - 0s - loss: 0.1773 - accuracy: 0.9189 - val_loss: 0.3537 - val_accuracy: 0.8511
Epoch 196/300
6/6 - 0s - loss: 0.1970 - accuracy: 0.9081 - val_loss: 0.3532 - val_accuracy: 0.8511
Epoch 197/300
6/6 - 0s - loss: 0.1882 - accuracy: 0.9297 - val_loss: 0.3526 - val_accuracy: 0.8511
Epoch 198/300
6/6 - 0s - loss: 0.1971 - accuracy: 0.9027 - val_loss: 0.3520 - val_accuracy: 0.8511
Epoch 199/300
6/6 - 0s - loss: 0.1787 - accuracy: 0.9027 - val_loss: 0.3522 - val_accuracy: 0.8298
Epoch 200/300
6/6 - 0s - loss: 0.2034 - accuracy: 0.9027 - val_loss: 0.3514 - val_accuracy: 0.8298
Epoch 201/300
6/6 - 0s - loss: 0.2088 - accuracy: 0.8973 - val_loss: 0.3502 - val_accuracy: 0.8511
Epoch 202/300
6/6 - 0s - loss: 0.1552 - accuracy: 0.9351 - val_loss: 0.3527 - val_accuracy: 0.8511
Epoch 203/300
6/6 - 0s - loss: 0.1557 - accuracy: 0.9622 - val_loss: 0.3536 - val_accuracy: 0.8511
Epoch 204/300
6/6 - 0s - loss: 0.1663 - accuracy: 0.9189 - val_loss: 0.3544 - val_accuracy: 0.8511
Epoch 205/300
6/6 - 0s - loss: 0.2064 - accuracy: 0.8919 - val_loss: 0.3536 - val_accuracy: 0.8511
Epoch 206/300
6/6 - 0s - loss: 0.1771 - accuracy: 0.9243 - val_loss: 0.3538 - val_accuracy: 0.8511
Epoch 207/300
6/6 - 0s - loss: 0.1993 - accuracy: 0.9135 - val_loss: 0.3543 - val_accuracy: 0.8511
Epoch 208/300
6/6 - 0s - loss: 0.1927 - accuracy: 0.9189 - val_loss: 0.3541 - val_accuracy: 0.8511
Epoch 209/300
6/6 - 0s - loss: 0.1821 - accuracy: 0.9189 - val_loss: 0.3547 - val_accuracy: 0.8298
Epoch 210/300
6/6 - 0s - loss: 0.1756 - accuracy: 0.9351 - val_loss: 0.3556 - val_accuracy: 0.8298
Epoch 211/300
6/6 - 0s - loss: 0.1944 - accuracy: 0.9027 - val_loss: 0.3556 - val_accuracy: 0.8298
Epoch 212/300
6/6 - 0s - loss: 0.1835 - accuracy: 0.9081 - val_loss: 0.3566 - val_accuracy: 0.8511
Epoch 213/300
6/6 - 0s - loss: 0.1777 - accuracy: 0.9243 - val_loss: 0.3571 - val_accuracy: 0.8511
Epoch 214/300
6/6 - 0s - loss: 0.1423 - accuracy: 0.9568 - val_loss: 0.3588 - val_accuracy: 0.8511
Epoch 215/300
6/6 - 0s - loss: 0.1694 - accuracy: 0.9405 - val_loss: 0.3585 - val_accuracy: 0.8511
Epoch 216/300
6/6 - 0s - loss: 0.1823 - accuracy: 0.9189 - val_loss: 0.3587 - val_accuracy: 0.8511
Epoch 217/300
6/6 - 0s - loss: 0.1892 - accuracy: 0.9189 - val_loss: 0.3587 - val_accuracy: 0.8511
Epoch 218/300
6/6 - 0s - loss: 0.1895 - accuracy: 0.9405 - val_loss: 0.3593 - val_accuracy: 0.8511
Epoch 219/300
6/6 - 0s - loss: 0.1786 - accuracy: 0.9243 - val_loss: 0.3597 - val_accuracy: 0.8511
Epoch 220/300
6/6 - 0s - loss: 0.1756 - accuracy: 0.9297 - val_loss: 0.3602 - val_accuracy: 0.8298
Epoch 221/300
6/6 - 0s - loss: 0.1772 - accuracy: 0.9189 - val_loss: 0.3604 - val_accuracy: 0.8298
Epoch 222/300
6/6 - 0s - loss: 0.1753 - accuracy: 0.9135 - val_loss: 0.3607 - val_accuracy: 0.8298
Epoch 223/300
6/6 - 0s - loss: 0.1562 - accuracy: 0.9243 - val_loss: 0.3608 - val_accuracy: 0.8511
Epoch 224/300
6/6 - 0s - loss: 0.1865 - accuracy: 0.9027 - val_loss: 0.3623 - val_accuracy: 0.8511
Epoch 225/300
6/6 - 0s - loss: 0.1689 - accuracy: 0.9189 - val_loss: 0.3636 - val_accuracy: 0.8511
Epoch 226/300
6/6 - 0s - loss: 0.1567 - accuracy: 0.9568 - val_loss: 0.3666 - val_accuracy: 0.8511
Epoch 227/300
6/6 - 0s - loss: 0.1677 - accuracy: 0.9081 - val_loss: 0.3655 - val_accuracy: 0.8511
Epoch 228/300
6/6 - 0s - loss: 0.1957 - accuracy: 0.9189 - val_loss: 0.3652 - val_accuracy: 0.8511
Epoch 229/300
6/6 - 0s - loss: 0.1490 - accuracy: 0.9351 - val_loss: 0.3648 - val_accuracy: 0.8511
Epoch 230/300
6/6 - 0s - loss: 0.1948 - accuracy: 0.8973 - val_loss: 0.3639 - val_accuracy: 0.8511
Epoch 231/300
6/6 - 0s - loss: 0.1643 - accuracy: 0.9297 - val_loss: 0.3641 - val_accuracy: 0.8511
Epoch 232/300
6/6 - 0s - loss: 0.1604 - accuracy: 0.9459 - val_loss: 0.3633 - val_accuracy: 0.8511
Epoch 233/300
6/6 - 0s - loss: 0.1617 - accuracy: 0.9459 - val_loss: 0.3643 - val_accuracy: 0.8511
Epoch 234/300
6/6 - 0s - loss: 0.1948 - accuracy: 0.9081 - val_loss: 0.3648 - val_accuracy: 0.8511
Epoch 235/300
6/6 - 0s - loss: 0.1899 - accuracy: 0.9081 - val_loss: 0.3640 - val_accuracy: 0.8511
Epoch 236/300
6/6 - 0s - loss: 0.1760 - accuracy: 0.9135 - val_loss: 0.3643 - val_accuracy: 0.8511
Epoch 237/300
6/6 - 0s - loss: 0.1829 - accuracy: 0.9243 - val_loss: 0.3646 - val_accuracy: 0.8511
Epoch 238/300
6/6 - 0s - loss: 0.1683 - accuracy: 0.9135 - val_loss: 0.3646 - val_accuracy: 0.8511
Epoch 239/300
6/6 - 0s - loss: 0.1454 - accuracy: 0.9514 - val_loss: 0.3669 - val_accuracy: 0.8511
Epoch 240/300
6/6 - 0s - loss: 0.1836 - accuracy: 0.9189 - val_loss: 0.3672 - val_accuracy: 0.8511
Epoch 241/300
6/6 - 0s - loss: 0.1783 - accuracy: 0.9243 - val_loss: 0.3677 - val_accuracy: 0.8511
Epoch 242/300
6/6 - 0s - loss: 0.1916 - accuracy: 0.9351 - val_loss: 0.3664 - val_accuracy: 0.8511
Epoch 243/300
6/6 - 0s - loss: 0.1766 - accuracy: 0.9081 - val_loss: 0.3664 - val_accuracy: 0.8511
Epoch 244/300
6/6 - 0s - loss: 0.1536 - accuracy: 0.9351 - val_loss: 0.3664 - val_accuracy: 0.8511
Epoch 245/300
6/6 - 0s - loss: 0.1638 - accuracy: 0.9297 - val_loss: 0.3672 - val_accuracy: 0.8511
Epoch 246/300
6/6 - 0s - loss: 0.1882 - accuracy: 0.9297 - val_loss: 0.3659 - val_accuracy: 0.8511
Epoch 247/300
6/6 - 0s - loss: 0.1739 - accuracy: 0.9135 - val_loss: 0.3673 - val_accuracy: 0.8511
Epoch 248/300
6/6 - 0s - loss: 0.1894 - accuracy: 0.9243 - val_loss: 0.3690 - val_accuracy: 0.8511
Epoch 249/300
6/6 - 0s - loss: 0.1536 - accuracy: 0.9514 - val_loss: 0.3692 - val_accuracy: 0.8511
Epoch 250/300
6/6 - 0s - loss: 0.2050 - accuracy: 0.9027 - val_loss: 0.3703 - val_accuracy: 0.8511
Epoch 251/300
6/6 - 0s - loss: 0.1697 - accuracy: 0.9243 - val_loss: 0.3705 - val_accuracy: 0.8511
Epoch 252/300
6/6 - 0s - loss: 0.1623 - accuracy: 0.9243 - val_loss: 0.3714 - val_accuracy: 0.8511
Epoch 253/300
6/6 - 0s - loss: 0.1873 - accuracy: 0.9243 - val_loss: 0.3731 - val_accuracy: 0.8511
Epoch 254/300
6/6 - 0s - loss: 0.1758 - accuracy: 0.9189 - val_loss: 0.3721 - val_accuracy: 0.8511
Epoch 255/300
6/6 - 0s - loss: 0.1590 - accuracy: 0.9351 - val_loss: 0.3714 - val_accuracy: 0.8511
Epoch 256/300
6/6 - 0s - loss: 0.1778 - accuracy: 0.9243 - val_loss: 0.3698 - val_accuracy: 0.8511
Epoch 257/300
6/6 - 0s - loss: 0.1692 - accuracy: 0.9189 - val_loss: 0.3704 - val_accuracy: 0.8511
Epoch 258/300
6/6 - 0s - loss: 0.1701 - accuracy: 0.9297 - val_loss: 0.3707 - val_accuracy: 0.8511
Epoch 259/300
6/6 - 0s - loss: 0.1577 - accuracy: 0.9243 - val_loss: 0.3731 - val_accuracy: 0.8511
Epoch 260/300
6/6 - 0s - loss: 0.1621 - accuracy: 0.9351 - val_loss: 0.3741 - val_accuracy: 0.8511
Epoch 261/300
6/6 - 0s - loss: 0.1657 - accuracy: 0.9459 - val_loss: 0.3748 - val_accuracy: 0.8298
Epoch 262/300
6/6 - 0s - loss: 0.1686 - accuracy: 0.9243 - val_loss: 0.3765 - val_accuracy: 0.8298
Epoch 263/300
6/6 - 0s - loss: 0.1711 - accuracy: 0.9243 - val_loss: 0.3760 - val_accuracy: 0.8298
Epoch 264/300
6/6 - 0s - loss: 0.1881 - accuracy: 0.8973 - val_loss: 0.3783 - val_accuracy: 0.8298
Epoch 265/300
6/6 - 0s - loss: 0.1597 - accuracy: 0.9189 - val_loss: 0.3795 - val_accuracy: 0.8298
Epoch 266/300
6/6 - 0s - loss: 0.1646 - accuracy: 0.9243 - val_loss: 0.3794 - val_accuracy: 0.8298
Epoch 267/300
6/6 - 0s - loss: 0.1301 - accuracy: 0.9568 - val_loss: 0.3788 - val_accuracy: 0.8298
Epoch 268/300
6/6 - 0s - loss: 0.1593 - accuracy: 0.9243 - val_loss: 0.3801 - val_accuracy: 0.8298
Epoch 269/300
6/6 - 0s - loss: 0.1364 - accuracy: 0.9514 - val_loss: 0.3819 - val_accuracy: 0.8298
Epoch 270/300
6/6 - 0s - loss: 0.1727 - accuracy: 0.9135 - val_loss: 0.3827 - val_accuracy: 0.8298
Epoch 271/300
6/6 - 0s - loss: 0.1786 - accuracy: 0.9405 - val_loss: 0.3837 - val_accuracy: 0.8298
Epoch 272/300
6/6 - 0s - loss: 0.1600 - accuracy: 0.9351 - val_loss: 0.3835 - val_accuracy: 0.8298
Epoch 273/300
6/6 - 0s - loss: 0.1416 - accuracy: 0.9351 - val_loss: 0.3850 - val_accuracy: 0.8298
Epoch 274/300
6/6 - 0s - loss: 0.1424 - accuracy: 0.9459 - val_loss: 0.3859 - val_accuracy: 0.8298
Epoch 275/300
6/6 - 0s - loss: 0.1576 - accuracy: 0.9351 - val_loss: 0.3860 - val_accuracy: 0.8298
Epoch 276/300
6/6 - 0s - loss: 0.1266 - accuracy: 0.9676 - val_loss: 0.3881 - val_accuracy: 0.8298
Epoch 277/300
6/6 - 0s - loss: 0.1743 - accuracy: 0.9351 - val_loss: 0.3906 - val_accuracy: 0.8298
Epoch 278/300
6/6 - 0s - loss: 0.1529 - accuracy: 0.9351 - val_loss: 0.3901 - val_accuracy: 0.8298
Epoch 279/300
6/6 - 0s - loss: 0.1972 - accuracy: 0.9027 - val_loss: 0.3885 - val_accuracy: 0.8298
Epoch 280/300
6/6 - 0s - loss: 0.1456 - accuracy: 0.9405 - val_loss: 0.3901 - val_accuracy: 0.8298
Epoch 281/300
6/6 - 0s - loss: 0.1838 - accuracy: 0.9081 - val_loss: 0.3899 - val_accuracy: 0.8298
Epoch 282/300
6/6 - 0s - loss: 0.1432 - accuracy: 0.9405 - val_loss: 0.3890 - val_accuracy: 0.8298
Epoch 283/300
6/6 - 0s - loss: 0.1481 - accuracy: 0.9405 - val_loss: 0.3905 - val_accuracy: 0.8298
Epoch 284/300
6/6 - 0s - loss: 0.1612 - accuracy: 0.9081 - val_loss: 0.3892 - val_accuracy: 0.8298
Epoch 285/300
6/6 - 0s - loss: 0.1443 - accuracy: 0.9297 - val_loss: 0.3899 - val_accuracy: 0.8298
Epoch 286/300
6/6 - 0s - loss: 0.1723 - accuracy: 0.9189 - val_loss: 0.3899 - val_accuracy: 0.8298
Epoch 287/300
6/6 - 0s - loss: 0.1401 - accuracy: 0.9514 - val_loss: 0.3905 - val_accuracy: 0.8298
Epoch 288/300
6/6 - 0s - loss: 0.1196 - accuracy: 0.9622 - val_loss: 0.3914 - val_accuracy: 0.8298
Epoch 289/300
6/6 - 0s - loss: 0.1725 - accuracy: 0.9459 - val_loss: 0.3899 - val_accuracy: 0.8511
Epoch 290/300
6/6 - 0s - loss: 0.1651 - accuracy: 0.9189 - val_loss: 0.3912 - val_accuracy: 0.8511
Epoch 291/300
6/6 - 0s - loss: 0.1674 - accuracy: 0.9459 - val_loss: 0.3936 - val_accuracy: 0.8511
Epoch 292/300
6/6 - 0s - loss: 0.1475 - accuracy: 0.9297 - val_loss: 0.3970 - val_accuracy: 0.8511
Epoch 293/300
6/6 - 0s - loss: 0.1435 - accuracy: 0.9459 - val_loss: 0.3977 - val_accuracy: 0.8511
Epoch 294/300
6/6 - 0s - loss: 0.1572 - accuracy: 0.9135 - val_loss: 0.4011 - val_accuracy: 0.8298
Epoch 295/300
6/6 - 0s - loss: 0.1397 - accuracy: 0.9405 - val_loss: 0.4022 - val_accuracy: 0.8298
Epoch 296/300
6/6 - 0s - loss: 0.1453 - accuracy: 0.9568 - val_loss: 0.4016 - val_accuracy: 0.8085
Epoch 297/300
6/6 - 0s - loss: 0.1554 - accuracy: 0.9459 - val_loss: 0.4028 - val_accuracy: 0.8085
Epoch 298/300
6/6 - 0s - loss: 0.1659 - accuracy: 0.9351 - val_loss: 0.4029 - val_accuracy: 0.7872
Epoch 299/300
6/6 - 0s - loss: 0.1472 - accuracy: 0.9351 - val_loss: 0.4040 - val_accuracy: 0.8085
Epoch 300/300
6/6 - 0s - loss: 0.1587 - accuracy: 0.9351 - val_loss: 0.4016 - val_accuracy: 0.7872

Accuracy Loss Graph

Deep CC

[INF
Reading [keras model] 'model.h5'
[SUCCESS]
Saved 'model_deepC/model.onnx'
[INFO]
Reading [onnx model] 'model_deepC/model.onnx'
[INFO]
Model info:
ir_vesion : 4
doc :
[WARNING]
[ONNX]: terminal (input/output) dense_2_input's shape is less than 1. Changing it to 1.
[WARNING]
[ONNX]: terminal (input/output) dense_3's shape is less than 1. Changing it to 1.
WARN (GRAPH): found operator node with the same name (dense_3) as io node.
[INFO]
Running DNNC graph sanity check ...
[SUCCESS]
Passed sanity check.
[INFO]
Writing C++ file 'model_deepC/model.cpp'
[INFO]
deepSea model files are ready in 'model_deepC/'
[RUNNING COMMAND]
g++ -std=c++11 -O3 -fno-rtti -fno-exceptions -I. -I/opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/include -isystem /opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/packages/eigen-eigen-323c052e1731 "model_deepC/model.cpp" -D_AITS_MAIN -o "model_deepC/model.exe"
[RUNNING COMMAND]
size "model_deepC/model.exe"
text data bss dec hex filename
121323 2968 760 125051 1e87b model_deepC/model.exe
[SUCCESS]
Saved model as executable "model_deepC/model.exe"

Notebook Link- Click Here

Credits- Siddharth Ganjoo

Simplifying AI development for EDGE devices