Python model.DCGAN Examples

The following are 4 code examples of model.DCGAN(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module model , or try the search function .
Example #1
Source File: main.py    From flow-gan with MIT License 4 votes vote down vote up 
def main(_):
  np.random.seed(0)
  tf.set_random_seed(0)
  pp.pprint(flags.FLAGS.__flags)

  if FLAGS.input_width is None:
    FLAGS.input_width = FLAGS.input_height

  if not os.path.exists(FLAGS.checkpoint_dir):
    os.makedirs(FLAGS.checkpoint_dir)
  if not os.path.exists(FLAGS.sample_dir):
    os.makedirs(FLAGS.sample_dir)

  run_config = tf.ConfigProto()
  run_config.gpu_options.allow_growth=True
  run_config.allow_soft_placement=True
  sess = None
  with tf.Session(config=run_config) as sess:
    dcgan = DCGAN(
        sess,
        input_width=FLAGS.input_width,
        input_height=FLAGS.input_height,
        batch_size=FLAGS.batch_size,
        sample_num=FLAGS.batch_size,
        c_dim=FLAGS.c_dim,
        z_dim=FLAGS.c_dim * FLAGS.input_height * FLAGS.input_width,
        dataset_name=FLAGS.dataset,
        checkpoint_dir=FLAGS.checkpoint_dir,
        f_div=FLAGS.f_div,
        prior=FLAGS.prior,
        lr_decay=FLAGS.lr_decay,
        min_lr=FLAGS.min_lr,
        model_type=FLAGS.model_type,
        log_dir=FLAGS.log_dir,
        alpha=FLAGS.alpha,
        batch_norm_adaptive=FLAGS.batch_norm_adaptive,
        init_type=FLAGS.init_type,
        reg=FLAGS.reg,
        n_critic=FLAGS.n_critic,
        hidden_layers=FLAGS.hidden_layers,
        no_of_layers=FLAGS.no_of_layers,
        like_reg=FLAGS.like_reg,
        df_dim=FLAGS.df_dim)

  dcgan.train(FLAGS)
Example #2
Source File: pacgan_task.py    From PacGAN with MIT License 4 votes vote down vote up 
def main(self):
        FLAGS = Struct(**self._config)
        if FLAGS.input_width is None:
            FLAGS.input_width = FLAGS.input_height
        if FLAGS.output_width is None:
            FLAGS.output_width = FLAGS.output_height
        
        FLAGS.checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
        if not os.path.exists(FLAGS.checkpoint_dir):
            os.makedirs(FLAGS.checkpoint_dir)
        FLAGS.sample_dir = os.path.join(self._work_dir, "samples")
        if not os.path.exists(FLAGS.sample_dir):
            os.makedirs(FLAGS.sample_dir)
            
        FLAGS.work_dir = self._work_dir

        #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
        run_config = tf.ConfigProto()
        run_config.gpu_options.allow_growth=True
        
        if FLAGS.random:
            seed = random.randint(1, 100000)        
            np.random.seed(seed)
            with open(os.path.join(self._work_dir, "seed.txt"), "w") as f:
                f.write("{}".format(seed))
                
        t_num_test_samples = int(ceil(float(FLAGS.num_test_sample) / float(FLAGS.batch_size))) * FLAGS.batch_size
                
        test_samples = np.random.uniform(-1, 1, size = (t_num_test_samples, FLAGS.z_dim))

        with tf.Session(config=run_config) as sess:
            dcgan = DCGAN(
                sess,
                input_width=FLAGS.input_width,
                input_height=FLAGS.input_height,
                output_width=FLAGS.output_width,
                output_height=FLAGS.output_height,
                batch_size=FLAGS.batch_size,
                sample_num=FLAGS.batch_size,
                dataset_name=FLAGS.dataset,
                input_fname_pattern=FLAGS.input_fname_pattern,
                crop=FLAGS.crop,
                checkpoint_dir=FLAGS.checkpoint_dir,
                sample_dir=FLAGS.sample_dir,
                packing_num=FLAGS.packing_num,
                num_training_sample=FLAGS.num_training_sample,
                num_test_sample=FLAGS.num_test_sample,
                z_dim=FLAGS.z_dim,
                test_samples=test_samples)

            show_all_variables()

            dcgan.train(FLAGS)
       
            #OPTION = 0
            #visualize(sess, dcgan, FLAGS, OPTION)
Example #3
Source File: pacgan_task.py    From PacGAN with MIT License 4 votes vote down vote up 
def main(self):
        FLAGS = Struct(**self._config)
        if FLAGS.input_width is None:
            FLAGS.input_width = FLAGS.input_height
        if FLAGS.output_width is None:
            FLAGS.output_width = FLAGS.output_height
        
        FLAGS.checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
        if not os.path.exists(FLAGS.checkpoint_dir):
            os.makedirs(FLAGS.checkpoint_dir)
        FLAGS.sample_dir = os.path.join(self._work_dir, "samples")
        if not os.path.exists(FLAGS.sample_dir):
            os.makedirs(FLAGS.sample_dir)
            
        FLAGS.work_dir = self._work_dir

        #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
        run_config = tf.ConfigProto()
        run_config.gpu_options.allow_growth = True
        
        if FLAGS.random:
            seed = random.randint(1, 100000)        
            np.random.seed(seed)
            with open(os.path.join(self._work_dir, "seed.txt"), "w") as f:
                f.write("{}".format(seed))
                
        t_num_test_samples = int(ceil(float(FLAGS.num_test_sample) / float(FLAGS.batch_size))) * FLAGS.batch_size
                
        test_samples = np.random.normal(size = (t_num_test_samples, FLAGS.z_dim))

        with tf.Session(config=run_config) as sess:
            dcgan = DCGAN(
                sess,
                input_width = FLAGS.input_width,
                input_height = FLAGS.input_height,
                output_width = FLAGS.output_width,
                output_height = FLAGS.output_height,
                batch_size = FLAGS.batch_size,
                sample_num = FLAGS.batch_size,
                dataset_name = FLAGS.dataset,
                checkpoint_dir = FLAGS.checkpoint_dir,
                sample_dir = FLAGS.sample_dir,
                packing_num = FLAGS.packing_num,
                num_training_sample = FLAGS.num_training_sample,
                num_test_sample = FLAGS.num_test_sample,
                z_dim = FLAGS.z_dim,
                test_samples = test_samples)

            show_all_variables()
            print("Start training!")
            dcgan.train(FLAGS)
Example #4
Source File: pacgan_task.py    From PacGAN with MIT License 4 votes vote down vote up 
def main(self):
        FLAGS = Struct(**self._config)
        if FLAGS.input_width is None:
            FLAGS.input_width = FLAGS.input_height
        if FLAGS.output_width is None:
            FLAGS.output_width = FLAGS.output_height
        
        FLAGS.checkpoint_dir = os.path.join(self._work_dir, "checkpoint")
        if not os.path.exists(FLAGS.checkpoint_dir):
            os.makedirs(FLAGS.checkpoint_dir)
        FLAGS.sample_dir = os.path.join(self._work_dir, "samples")
        if not os.path.exists(FLAGS.sample_dir):
            os.makedirs(FLAGS.sample_dir)
            
        FLAGS.work_dir = self._work_dir

        #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
        run_config = tf.ConfigProto()
        run_config.gpu_options.allow_growth = True
        
        if FLAGS.random:
            seed = random.randint(1, 100000)        
            np.random.seed(seed)
            with open(os.path.join(self._work_dir, "seed.txt"), "w") as f:
                f.write("{}".format(seed))
                
        t_num_test_samples = int(ceil(float(FLAGS.num_test_sample) / float(FLAGS.batch_size))) * FLAGS.batch_size
                
        test_samples = np.random.normal(size = (t_num_test_samples, FLAGS.z_dim))

        with tf.Session(config=run_config) as sess:
            dcgan = DCGAN(
                sess,
                input_width=FLAGS.input_width,
                input_height=FLAGS.input_height,
                output_width=FLAGS.output_width,
                output_height=FLAGS.output_height,
                batch_size=FLAGS.batch_size,
                sample_num=FLAGS.batch_size,
                dataset_name=FLAGS.dataset,
                checkpoint_dir=FLAGS.checkpoint_dir,
                sample_dir=FLAGS.sample_dir,
                packing_num=FLAGS.packing_num,
                num_training_sample=FLAGS.num_training_sample,
                num_test_sample=FLAGS.num_test_sample,
                z_dim=FLAGS.z_dim,
                test_samples=test_samples)

            show_all_variables()
            print("Start training!")
            dcgan.train(FLAGS)