Tensorflow-preprocessing-training-testing

(non-distribution & distribution)

Use Tensorflow to do classification containing data preparation, training, testing.(single computer single GPU & single computer multi-GPU & multi-computer multi-GPU)

中文博客地址： http://blog.csdn.net/renhanchi/article/details/79570665

All parameters are in arg_parsing.py. So before you start this program, you should read it carefully!

STEPS:

1. Put all images in different diractories. Then run img2list.sh to create a txt file containing pathes and labels of all iamges.

   

2. Run list2bin.py to convert the images from rgb to tfrecords.

3. For single computer, one GPU or more, whatever. Just run:

    python main.py
   

4. For distribution, first you should modify PS_HOSTS and WORKER_HOSTS in arg_parsing.py. And then copy all dataset and codes to every server.

For ps host, run:

CUDA_VISIBLE_DEVICES='' python src/main.py --job_name=ps --task_index=0

CUDA_VISIBLE_DEVICES='' means using CPU to concat parameters.

For worker host, run:

python src/main.py --job_name=worker --task_index=0

Do remenber to increase task_index in every server.

5. All ckpt and event files will be saved in MODEL_DIR.

6. For testing, just run:

   python src/main.py --mode=testing
   

Notes

1. DO READ arg_parsing.py again and again to understand and control this program.

2. Use CUDA_VISIBLE_DEVICES=0,2 to choose GPUs.

3. For visualization, run:

   tensorboard --logdir=models/
   

4. More details, please see my blog above.