from tensorflow import flags
from model import DCGAN
import os
import scipy.misc
import numpy as np
from model import DCGAN
from utils import pp, visualize, to_json
flags = tf.app.flags
#flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
#flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
#flags.DEFINE_string("checkpoint_dir", "checkpoint_new2", "Directory name to save the checkpoints [checkpoint]")
FLAGS = flags.FLAGS
from PIL import Image
from tensorflow.contrib.framework.python.framework import checkpoint_utils
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
dcgan = DCGAN(sess,
batch_size=1024,
sample_size=1024,
num_iters=80000,
LEARNING_RATE=1.)
dcgan.reverse_GAN_batch_all_prec(FLAGS)
