TensorFLow Classification Example

Simple tensorflow classification example codes. Works on small image datasets.

Supporting network achitectures includes standard networks under tensorflow.contrib.slim.nets

    @@alexnet_v2
    @@inception_v1
    @@inception_v1_base
    @@inception_v2
    @@inception_v2_base
    @@inception_v3
    @@inception_v3_base
    @@overfeat
    @@vgg_a
    @@vgg_16

TFRecords: data are pre-processed into TFRecords format

• convert_to_records.py: create TFRecords data file from raw images

• example data: logo apperance binary classification training dataset: around 800 images validation dataset: around 200 images

• train.py train a VGG network by default

• test.py restore checkpoint and test network on validation dataset

• train2.py train and do validation periodically.

Picpac: data are pre-processed into picpac format

• train_picpac.py

TensorBoard

Keep a training log by specify --log_dir before starting train.py. While training, visualise learning on TensorBoard by command

$ tensorboard --logdir=path/to/log-directory

Docker image

Networks can be trained on GPU with tensorflow installed, or in a docker container on CPU. To build this docker image, use Dockerfile provided in tensorflow-docker repository.

$ docker build -f /path/to/a/Dockerfile .