main_mmd_fm.py 文件源码

def main(_):
    pp.pprint(flags.FLAGS.__flags)
    sample_dir_ = os.path.join(FLAGS.sample_dir, FLAGS.name)
    checkpoint_dir_ = os.path.join(FLAGS.checkpoint_dir, FLAGS.name)
    log_dir_ = os.path.join(FLAGS.log_dir, FLAGS.name)
    if not os.path.exists(checkpoint_dir_):
        os.makedirs(checkpoint_dir_)
    if not os.path.exists(sample_dir_):
        os.makedirs(sample_dir_)
    if not os.path.exists(log_dir_):
        os.makedirs(log_dir_)

    with tf.Session() as sess:
        if FLAGS.dataset == 'mnist':
            dcgan = DCGAN(sess, config=FLAGS, batch_size=FLAGS.batch_size, output_size=28, c_dim=1,
                    dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=checkpoint_dir_, sample_dir=sample_dir_, log_dir=log_dir_)
        else:
            dcgan = DCGAN(sess, image_size=FLAGS.image_size, batch_size=FLAGS.batch_size, output_size=FLAGS.output_size, c_dim=FLAGS.c_dim,
                    dataset_name=FLAGS.dataset, is_crop=FLAGS.is_crop, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir)

        if FLAGS.is_train:
            dcgan.train(FLAGS)
        else:
            dcgan.sampling(FLAGS)
            #dcgan.load(FLAGS.checkpoint_dir)

        if FLAGS.visualize:
            to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
                                          [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
                                          [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
                                          [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
                                          [dcgan.h4_w, dcgan.h4_b, None])

            # Below is codes for visualization
            OPTION = 2
            visualize(sess, dcgan, FLAGS, OPTION)