Python model.DCGAN属性代码示例

本文整理汇总了Python中model.DCGAN属性的典型用法代码示例。如果您正苦于以下问题：Python model.DCGAN属性的具体用法？Python model.DCGAN怎么用？Python model.DCGAN使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类model的用法示例。

在下文中一共展示了model.DCGAN属性的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: import model [as 别名]
# 或者: from model import DCGAN [as 别名]
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)

开发者ID:ermongroup，项目名称:flow-gan，代码行数:47，代码来源:main.py

示例2: main

# 需要导入模块: import model [as 别名]
# 或者: from model import DCGAN [as 别名]
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)

开发者ID:fjxmlzn，项目名称:PacGAN，代码行数:58，代码来源:pacgan_task.py

示例3: main

# 需要导入模块: import model [as 别名]
# 或者: from model import DCGAN [as 别名]
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)

开发者ID:fjxmlzn，项目名称:PacGAN，代码行数:53，代码来源:pacgan_task.py

示例4: main

# 需要导入模块: import model [as 别名]
# 或者: from model import DCGAN [as 别名]
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)

开发者ID:fjxmlzn，项目名称:PacGAN，代码行数:53，代码来源:pacgan_task.py

注：本文中的model.DCGAN属性示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。