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
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