exelban / Tensorflow Cifar 10
Licence: mit
Cifar-10 CNN implementation using TensorFlow library with 20% error.
Stars: ✭ 85
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python
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tensorflow-cifar-10
Cifar-10 convolutional network implementation example using TensorFlow library.
Requirement
| Library | Version |
|---|---|
| Python | ^3.6.5 |
| Tensorflow | ^1.6.0 |
| Numpy | ^1.14.2 |
| Pickle | ^4.0 |
Accuracy
Best accurancy what I receive was 79.12% on test data set. You must to understand that network cant always learn with the same accuracy. But almost always accuracy more than 78%.
This repository is just example of implemantation convolution neural network. Here I implement a simple neural network for image recognition with good accuracy.
If you want to get more that 80% accuracy, You need to implement more complicated nn models (such as ResNet, GoogleLeNet, mobileNetV2 ect).
Usage
Download code
git clone https://github.com/exelban/tensorflow-cifar-10
cd tensorflow-cifar-10
Check if you have nessessary packages
pip3 install numpy tensorflow pickle
Train network
By default network will be run 60 epoch (60 times on all training data set).
You can change that by editing _EPOCH in train.py file.
Also by default it process 128 files in each step.
If you training network on CPU or GPU (lowest that 1060 6GB) change _BATCH_SIZE in train.py to a smaller value.
python3 train.py
Simple output:
Epoch: 60/60
Global step: 23070 - [>-----------------------------] 0% - acc: 0.9531 - loss: 1.5081 - 7045.4 sample/sec
Global step: 23080 - [>-----------------------------] 3% - acc: 0.9453 - loss: 1.5159 - 7147.6 sample/sec
Global step: 23090 - [=>----------------------------] 5% - acc: 0.9844 - loss: 1.4764 - 7154.6 sample/sec
Global step: 23100 - [==>---------------------------] 8% - acc: 0.9297 - loss: 1.5307 - 7104.4 sample/sec
Global step: 23110 - [==>---------------------------] 10% - acc: 0.9141 - loss: 1.5462 - 7091.4 sample/sec
Global step: 23120 - [===>--------------------------] 13% - acc: 0.9297 - loss: 1.5314 - 7162.9 sample/sec
Global step: 23130 - [====>-------------------------] 15% - acc: 0.9297 - loss: 1.5307 - 7174.8 sample/sec
Global step: 23140 - [=====>------------------------] 18% - acc: 0.9375 - loss: 1.5231 - 7140.0 sample/sec
Global step: 23150 - [=====>------------------------] 20% - acc: 0.9297 - loss: 1.5301 - 7152.8 sample/sec
Global step: 23160 - [======>-----------------------] 23% - acc: 0.9531 - loss: 1.5080 - 7112.3 sample/sec
Global step: 23170 - [=======>----------------------] 26% - acc: 0.9609 - loss: 1.5000 - 7154.0 sample/sec
Global step: 23180 - [========>---------------------] 28% - acc: 0.9531 - loss: 1.5074 - 6862.2 sample/sec
Global step: 23190 - [========>---------------------] 31% - acc: 0.9609 - loss: 1.4993 - 7134.5 sample/sec
Global step: 23200 - [=========>--------------------] 33% - acc: 0.9609 - loss: 1.4995 - 7166.0 sample/sec
Global step: 23210 - [==========>-------------------] 36% - acc: 0.9375 - loss: 1.5231 - 7116.7 sample/sec
Global step: 23220 - [===========>------------------] 38% - acc: 0.9453 - loss: 1.5153 - 7134.1 sample/sec
Global step: 23230 - [===========>------------------] 41% - acc: 0.9375 - loss: 1.5233 - 7074.5 sample/sec
Global step: 23240 - [============>-----------------] 43% - acc: 0.9219 - loss: 1.5387 - 7176.9 sample/sec
Global step: 23250 - [=============>----------------] 46% - acc: 0.8828 - loss: 1.5769 - 7144.1 sample/sec
Global step: 23260 - [==============>---------------] 49% - acc: 0.9219 - loss: 1.5383 - 7059.7 sample/sec
Global step: 23270 - [==============>---------------] 51% - acc: 0.8984 - loss: 1.5618 - 6638.6 sample/sec
Global step: 23280 - [===============>--------------] 54% - acc: 0.9453 - loss: 1.5151 - 7035.7 sample/sec
Global step: 23290 - [================>-------------] 56% - acc: 0.9609 - loss: 1.4996 - 7129.0 sample/sec
Global step: 23300 - [=================>------------] 59% - acc: 0.9609 - loss: 1.4997 - 7075.4 sample/sec
Global step: 23310 - [=================>------------] 61% - acc: 0.8750 - loss: 1.5842 - 7117.8 sample/sec
Global step: 23320 - [==================>-----------] 64% - acc: 0.9141 - loss: 1.5463 - 7157.2 sample/sec
Global step: 23330 - [===================>----------] 66% - acc: 0.9062 - loss: 1.5549 - 7169.3 sample/sec
Global step: 23340 - [====================>---------] 69% - acc: 0.9219 - loss: 1.5389 - 7164.4 sample/sec
Global step: 23350 - [====================>---------] 72% - acc: 0.9609 - loss: 1.5002 - 7135.4 sample/sec
Global step: 23360 - [=====================>--------] 74% - acc: 0.9766 - loss: 1.4842 - 7124.2 sample/sec
Global step: 23370 - [======================>-------] 77% - acc: 0.9375 - loss: 1.5231 - 7168.5 sample/sec
Global step: 23380 - [======================>-------] 79% - acc: 0.8906 - loss: 1.5695 - 7175.2 sample/sec
Global step: 23390 - [=======================>------] 82% - acc: 0.9375 - loss: 1.5225 - 7132.1 sample/sec
Global step: 23400 - [========================>-----] 84% - acc: 0.9844 - loss: 1.4768 - 7100.1 sample/sec
Global step: 23410 - [=========================>----] 87% - acc: 0.9766 - loss: 1.4840 - 7172.0 sample/sec
Global step: 23420 - [==========================>---] 90% - acc: 0.9062 - loss: 1.5542 - 7122.1 sample/sec
Global step: 23430 - [==========================>---] 92% - acc: 0.9297 - loss: 1.5313 - 7145.3 sample/sec
Global step: 23440 - [===========================>--] 95% - acc: 0.9297 - loss: 1.5301 - 7133.3 sample/sec
Global step: 23450 - [============================>-] 97% - acc: 0.9375 - loss: 1.5231 - 7135.7 sample/sec
Global step: 23460 - [=============================>] 100% - acc: 0.9250 - loss: 1.5362 - 10297.5 sample/sec
Epoch 60 - accuracy: 78.81% (7881/10000)
This epoch receive better accuracy: 78.81 > 78.78. Saving session...
###########################################################################################################
Run network on test data set
python3 predict.py
Simple output:
Trying to restore last checkpoint ...
Restored checkpoint from: ./tensorboard/cifar-10-v1.0.0/-23460
Accuracy on Test-Set: 78.81% (7881 / 10000)
Training time
Here you can see how much time takes 60 epoch:
| Device | Batch size | Time | Accuracy [%] |
|---|---|---|---|
| NVidia GTX 1070 | 128 | 8m 4s | 79.12 |
| Intel i7 7700HQ | 128 | 3h 30m | 78.91 |
Please send me (or open issue) your time and accuracy. I will add it to the list.
Model
What's new
v1.0.1
- Set random seed
- Added more information about elapsed time on epoch and full training
v1.0.0
- Removed all references to cifar 100
- Small fixes in data functions
- Almost fully rewrited train.py
- Simplyfy cnn model
- Changed optimizer to AdamOptimizer
- Changed Licence to MIT
- Removed confusion matrix (don't like to have unnecessary dependencies)
- Improved accuracy on testing data set (up to 79%)
- Small fixes in train.py
- Changed saver functions (now session will be saved only if accuracy in this session will be better than the last saved)
- Updated packages
v0.0.1
- Make tests on AWS instances
- Model fixes
- Remove cifar-100 dataset
v0.0.0
- First release
License
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