Python parameters.Parameters方法代碼示例

本文整理匯總了Python中parameters.Parameters方法的典型用法代碼示例。如果您正苦於以下問題：Python parameters.Parameters方法的具體用法？Python parameters.Parameters怎麽用？Python parameters.Parameters使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類parameters的用法示例。

在下文中一共展示了parameters.Parameters方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: master

# 需要導入模塊: import parameters [as 別名]
# 或者: from parameters import Parameters [as 別名]
def master(train_data, dev_data, utility):
  #creates TF graph and calls trainer or evaluator
  batch_size = utility.FLAGS.batch_size 
  model_dir = utility.FLAGS.output_dir + "/model" + utility.FLAGS.job_id + "/"
  #create all paramters of the model
  param_class = parameters.Parameters(utility)
  params, global_step, init = param_class.parameters(utility)
  key = "test" if (FLAGS.evaluator_job) else "train"
  graph = model.Graph(utility, batch_size, utility.FLAGS.max_passes, mode=key)
  graph.create_graph(params, global_step)
  prev_dev_error = 0.0
  final_loss = 0.0
  final_accuracy = 0.0
  #start session
  with tf.Session() as sess:
    sess.run(init.name)
    sess.run(graph.init_op.name)
    to_save = params.copy()
    saver = tf.train.Saver(to_save, max_to_keep=500)
    if (FLAGS.evaluator_job):
      while True:
        selected_models = {}
        file_list = tf.gfile.ListDirectory(model_dir)
        for model_file in file_list:
          if ("checkpoint" in model_file or "index" in model_file or
              "meta" in model_file):
            continue
          if ("data" in model_file):
            model_file = model_file.split(".")[0]
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          selected_models[model_step] = model_file
        file_list = sorted(selected_models.items(), key=lambda x: x[0])
        if (len(file_list) > 0):
          file_list = file_list[0:len(file_list) - 1]
	print "list of models: ", file_list
        for model_file in file_list:
          model_file = model_file[1]
          print "restoring: ", model_file
          saver.restore(sess, model_dir + "/" + model_file)
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          print "evaluating on dev ", model_file, model_step
          evaluate(sess, dev_data, batch_size, graph, model_step)
    else:
      ckpt = tf.train.get_checkpoint_state(model_dir)
      print "model dir: ", model_dir
      if (not (tf.gfile.IsDirectory(utility.FLAGS.output_dir))):
        print "create dir: ", utility.FLAGS.output_dir
        tf.gfile.MkDir(utility.FLAGS.output_dir)
      if (not (tf.gfile.IsDirectory(model_dir))):
        print "create dir: ", model_dir
        tf.gfile.MkDir(model_dir)
      Train(graph, utility, batch_size, train_data, sess, model_dir,
            saver)

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:57，代碼來源:neural_programmer.py

示例2: master

# 需要導入模塊: import parameters [as 別名]
# 或者: from parameters import Parameters [as 別名]
def master(train_data, dev_data, utility):
  #creates TF graph and calls trainer or evaluator
  batch_size = utility.FLAGS.batch_size
  model_dir = utility.FLAGS.output_dir + "/model" + utility.FLAGS.job_id + "/"
  #create all paramters of the model
  param_class = parameters.Parameters(utility)
  params, global_step, init = param_class.parameters(utility)
  key = "test" if (FLAGS.evaluator_job) else "train"
  graph = model.Graph(utility, batch_size, utility.FLAGS.max_passes, mode=key)
  graph.create_graph(params, global_step)
  prev_dev_error = 0.0
  final_loss = 0.0
  final_accuracy = 0.0
  #start session
  with tf.Session() as sess:
    sess.run(init.name)
    sess.run(graph.init_op.name)
    to_save = params.copy()
    saver = tf.train.Saver(to_save, max_to_keep=500)
    if (FLAGS.evaluator_job):
      while True:
        selected_models = {}
        file_list = tf.gfile.ListDirectory(model_dir)
        for model_file in file_list:
          if ("checkpoint" in model_file or "index" in model_file or
              "meta" in model_file):
            continue
          if ("data" in model_file):
            model_file = model_file.split(".")[0]
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          selected_models[model_step] = model_file
        file_list = sorted(selected_models.items(), key=lambda x: x[0])
        if (len(file_list) > 0):
          file_list = file_list[0:len(file_list) - 1]
        print("list of models: ", file_list)
        for model_file in file_list:
          model_file = model_file[1]
          print("restoring: ", model_file)
          saver.restore(sess, model_dir + "/" + model_file)
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          print("evaluating on dev ", model_file, model_step)
          evaluate(sess, dev_data, batch_size, graph, model_step)
    else:
      ckpt = tf.train.get_checkpoint_state(model_dir)
      print("model dir: ", model_dir)
      if (not (tf.gfile.IsDirectory(utility.FLAGS.output_dir))):
        print("create dir: ", utility.FLAGS.output_dir)
        tf.gfile.MkDir(utility.FLAGS.output_dir)
      if (not (tf.gfile.IsDirectory(model_dir))):
        print("create dir: ", model_dir)
        tf.gfile.MkDir(model_dir)
      Train(graph, utility, batch_size, train_data, sess, model_dir,
            saver)

開發者ID:itsamitgoel，項目名稱:Gun-Detector，代碼行數:57，代碼來源:neural_programmer.py

示例3: master

# 需要導入模塊: import parameters [as 別名]
# 或者: from parameters import Parameters [as 別名]
def master(train_data, dev_data, utility):
  #creates TF graph and calls trainer or evaluator
  batch_size = utility.FLAGS.batch_size 
  model_dir = utility.FLAGS.output_dir + "/model" + utility.FLAGS.job_id + "/"
  #create all paramters of the model
  param_class = parameters.Parameters(utility)
  params, global_step, init = param_class.parameters(utility)
  key = "test" if (FLAGS.evaluator_job) else "train"
  graph = model.Graph(utility, batch_size, utility.FLAGS.max_passes, mode=key)
  graph.create_graph(params, global_step)
  prev_dev_error = 0.0
  final_loss = 0.0
  final_accuracy = 0.0
  #start session
  with tf.Session() as sess:
    sess.run(init.name)
    sess.run(graph.init_op.name)
    to_save = params.copy()
    saver = tf.train.Saver(to_save, max_to_keep=500)
    if (FLAGS.evaluator_job):
      while True:
        selected_models = {}
        file_list = tf.gfile.ListDirectory(model_dir)
        for model_file in file_list:
          if ("checkpoint" in model_file or "index" in model_file or
              "meta" in model_file):
            continue
          if ("data" in model_file):
            model_file = model_file.split(".")[0]
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          selected_models[model_step] = model_file
        file_list = sorted(selected_models.items(), key=lambda x: x[0])
        if (len(file_list) > 0):
          file_list = file_list[0:len(file_list) - 1]
	print "list of models: ", file_list
        for model_file in file_list:
          model_file = model_file[1]
          print "restoring: ", model_file
          saver.restore(sess, model_dir + "/" + model_file)
          model_step = int(
              model_file.split("_")[len(model_file.split("_")) - 1])
          print "evaluating on dev ", model_file, model_step
          evaluate(sess, dev_data, batch_size, graph, model_step)
    else:
      ckpt = tf.train.get_checkpoint_state(model_dir)
      print "model dir: ", model_dir
      if (not (tf.gfile.IsDirectory(model_dir))):
        print "create dir: ", model_dir
        tf.gfile.MkDir(model_dir)
      Train(graph, utility, batch_size, train_data, sess, model_dir,
            saver)

開發者ID:coderSkyChen，項目名稱:Action_Recognition_Zoo，代碼行數:54，代碼來源:neural_programmer.py

注：本文中的parameters.Parameters方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。