Cifar-10 Network in Network implementation example using TensorFlow library.
| Library | Version |
|---|---|
| Python | ^2.7 |
| Tensorflow | ^1.0.1 |
| Numpy | ^1.12.0 |
| Pickle | * |
git clone https://github.com/eugenelet/tensorflow-cifar-10-NiN
cd tensorflow-cifar-10-NiNBatch size: 128
Prediction made on per epoch basis.
161 epochs takes about 3h on GTX 1080.
python train.pypython predict.pyExample output:
Trying to restore last checkpoint ...
('Restored checkpoint from:', u'./tensorboard/aug-decay-RMS/-188692')
Accuracy on Test-Set: 86.18% (8618 / 10000)
[890 4 25 5 12 1 23 4 29 7] (0) airplane
[ 8 895 2 3 3 3 30 2 18 36] (1) automobile
[ 19 0 759 15 59 24 106 13 4 1] (2) bird
[ 13 1 28 696 46 86 99 22 5 4] (3) cat
[ 4 0 19 13 872 13 62 17 0 0] (4) deer
[ 2 1 18 71 40 807 38 21 0 2] (5) dog
[ 3 0 4 9 5 2 974 2 1 0] (6) frog
[ 3 0 14 14 29 19 15 905 0 1] (7) horse
[ 32 1 4 3 8 3 26 0 913 10] (8) ship
[ 12 20 3 5 1 1 21 3 27 907] (9) truck
(0) (1) (2) (3) (4) (5) (6) (7) (8) (9)tensorboard --logdir=./tensorboard| Convolution layer 1 |
|---|
| Conv_2d |
| ReLu |
| MLP |
| ReLu |
| MLP |
| ReLu |
| MaxPool |
| Convolution layer 2 |
| Conv_2d |
| ReLu |
| MLP |
| ReLu |
| MLP |
| ReLu |
| MaxPool |
| Convolution layer 3 |
| Conv_2d |
| ReLu |
| MLP |
| ReLu |
| MLP |
| ReLu |
| AvgPool |
| Softmax_linear |
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