DCGAN-MNIST

About

Generative Adversarial Network implementation.

Dependencies

python, numpy, scipy, tensorflow (gpu version recommended).

Running

python main.py

arguments
--data_dir <directory for storing input data>
--use_mnist to use the MNIST data set, otherwise point to sketchy data set
--learning-rate <learning rate>
--decay-rate <decay rate>
--batch-size <batch size>
--epoch-size <epoch size>
--out <directory for storing output from generator>

Running trainer on GCloud ML-Engine

JOB_DIR='gs://uw-cs760-dcgan/job-dir/' # directoy on GCS
JOB_ID='dcgan_job_23' # unique job-id

gcloud ml-engine jobs submit training JOB_ID \
--package-path trainer/ \
--module-name trainer.task \
--job-dir JOB_DIR \
--staging-bucket 'gs://uw-cs760-dcgan/' \
--region 'us-central1' \
--scale-tier 'BASIC_GPU' \
-- \
--data-dir '/tmp/tensorflow/mnist/input_data/'

# launch tensorboard to view plots and images; and to download metrics
tensorboard --logdir=JOB_DIR --port 8088

Compatability

Only tested on windows with python 3.5.2, numpy+mkl 1.12.1, scipy 0.19.0, tensorflow-gpu 1.0.1.