Introduction

It's the general project to walk through the proceses of using TensorFlow.

Most data is stored in CSV files and you can learn to convert them to TFRecords. This implements the neural network model which can extend to more complicated ones. It stores checkpoints for fault tolerance and inference. You can learn to use TensorBoard as well and the example data could be found in cancer-deep-learning-model.

Usage

dense data

The data format should be CSV and you can convert to TFRecords.

3,7,7,4,4,9,4,8,1,1
1,1,1,1,2,1,2,1,1,0
4,1,1,3,2,1,3,1,1,0
7,8,7,2,4,8,3,8,2,1
9,5,8,1,2,3,2,1,5,1

cd ./data/
python convert_cancer_to_tfrecords.py

sparse data

The data format should be LIBSVM and you can convert to TFRecords.

0 1:1 6:1 14:1 20:1 37:1 40:1 51:1 61:1 70:1 72:1 74:1 76:1 80:1 83:1
0 1:1 6:1 17:1 22:1 36:1 42:1 49:1 62:1 67:1 72:1 74:1 76:1 78:1
1 4:1 6:1 14:1 23:1 39:1 40:1 52:1 61:1 67:1 72:1 74:1 77:1 82:1 97:1
1 5:1 9:1 17:1 19:1 39:1 41:1 51:1 64:1 67:1 73:1 74:1 76:1 82:1 83:1
0 4:1 6:1 15:1 22:1 36:1 40:1 55:1 63:1 67:1 73:1 74:1 76:1 82:1 83:1
0 3:1 6:1 15:1 22:1 36:1 40:1 48:1 63:1 67:1 73:1 74:1 76:1 80:1 83:1

cd ./data/
python convert_a8a_to_tfrecords.py

Develop application

On dense data, we can use the cancer_classifier.py to train or implement your model. Refer to distributed for distributed implementation.

python cancer_classifier.py

You can also train the model from scrath and this takes time for better auc.

python cancer_classifier.py --mode=train_from_scratch

If we want to run inference or prediction, just run with parameters.

python cancer_classifier.py --mode=inference

You can specify the GPU to train.

CUDA_VISIBLE_DEVICES='0'

All above is the same for sparse data.

python a8a_classifier.py [parameters]

Use TensorBoard

The summary data is stored in tensorboard and we use TenorBoard for visualization.

tensorboard --logdir ./tensorboard/

Then go to http://127.0.0.1:6006 in the browser.