Python input.ld_cifar10方法代碼示例

本文整理匯總了Python中differential_privacy.multiple_teachers.input.ld_cifar10方法的典型用法代碼示例。如果您正苦於以下問題：Python input.ld_cifar10方法的具體用法？Python input.ld_cifar10怎麽用？Python input.ld_cifar10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differential_privacy.multiple_teachers.input的用法示例。

在下文中一共展示了input.ld_cifar10方法的2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: train_teacher

# 需要導入模塊: from differential_privacy.multiple_teachers import input [as 別名]
# 或者: from differential_privacy.multiple_teachers.input import ld_cifar10 [as 別名]
def train_teacher(dataset, nb_teachers, teacher_id):
  """
  This function trains a teacher (teacher id) among an ensemble of nb_teachers
  models for the dataset specified.
  :param dataset: string corresponding to dataset (svhn, cifar10)
  :param nb_teachers: total number of teachers in the ensemble
  :param teacher_id: id of the teacher being trained
  :return: True if everything went well
  """
  # If working directories do not exist, create them
  assert input.create_dir_if_needed(FLAGS.data_dir)
  assert input.create_dir_if_needed(FLAGS.train_dir)

  # Load the dataset
  if dataset == 'svhn':
    train_data,train_labels,test_data,test_labels = input.ld_svhn(extended=True)
  elif dataset == 'cifar10':
    train_data, train_labels, test_data, test_labels = input.ld_cifar10()
  elif dataset == 'mnist':
    train_data, train_labels, test_data, test_labels = input.ld_mnist()
  else:
    print("Check value of dataset flag")
    return False

  # Retrieve subset of data for this teacher
  data, labels = input.partition_dataset(train_data,
                                         train_labels,
                                         nb_teachers,
                                         teacher_id)

  print("Length of training data: " + str(len(labels)))

  # Define teacher checkpoint filename and full path
  if FLAGS.deeper:
    filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt'
  else:
    filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt'
  ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename

  # Perform teacher training
  assert deep_cnn.train(data, labels, ckpt_path)

  # Append final step value to checkpoint for evaluation
  ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)

  # Retrieve teacher probability estimates on the test data
  teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)

  # Compute teacher accuracy
  precision = metrics.accuracy(teacher_preds, test_labels)
  print('Precision of teacher after training: ' + str(precision))

  return True

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:55，代碼來源:train_teachers.py

示例2: train_teacher

# 需要導入模塊: from differential_privacy.multiple_teachers import input [as 別名]
# 或者: from differential_privacy.multiple_teachers.input import ld_cifar10 [as 別名]
def train_teacher(dataset, nb_teachers, teacher_id):
  """
  This function trains a teacher (teacher id) among an ensemble of nb_teachers
  models for the dataset specified.
  :param dataset: string corresponding to dataset (svhn, cifar10)
  :param nb_teachers: total number of teachers in the ensemble
  :param teacher_id: id of the teacher being trained
  :return: True if everything went well
  """
  # If working directories do not exist, create them
  assert input.create_dir_if_needed(FLAGS.data_dir)
  assert input.create_dir_if_needed(FLAGS.train_dir)

  # Load the dataset
  if dataset == 'svhn':
    train_data,train_labels,test_data,test_labels = input.ld_svhn(extended=True)
  elif dataset == 'cifar10':
    train_data, train_labels, test_data, test_labels = input.ld_cifar10()
  elif dataset == 'mnist':
    train_data, train_labels, test_data, test_labels = input.ld_mnist()
  else:
    print("Check value of dataset flag")
    return False
    
  # Retrieve subset of data for this teacher
  data, labels = input.partition_dataset(train_data, 
                                         train_labels, 
                                         nb_teachers, 
                                         teacher_id)

  print("Length of training data: " + str(len(labels)))

  # Define teacher checkpoint filename and full path
  if FLAGS.deeper:
    filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt'
  else:
    filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt'
  ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename

  # Perform teacher training
  assert deep_cnn.train(data, labels, ckpt_path)

  # Append final step value to checkpoint for evaluation
  ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)

  # Retrieve teacher probability estimates on the test data
  teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)

  # Compute teacher accuracy
  precision = metrics.accuracy(teacher_preds, test_labels)
  print('Precision of teacher after training: ' + str(precision))

  return True

開發者ID:coderSkyChen，項目名稱:Action_Recognition_Zoo，代碼行數:55，代碼來源:train_teachers.py

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