コード例 #1
0
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

    if FLAGS.teacher_data_share:
        train_data = train_data[:FLAGS.teacher_data_share]
        train_labels = train_labels[:FLAGS.teacher_data_share]
    # 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
コード例 #2
0
ファイル: train_teachers.py プロジェクト: 812864539/models
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
コード例 #3
0
def train_student(dataset, nb_teachers):
    """
  This function trains a student using predictions made by an ensemble of
  teachers. The student and teacher models are trained using the same
  neural network architecture.
  :param dataset: string corresponding to mnist, cifar10, or svhn
  :param nb_teachers: number of teachers (in the ensemble) to learn from
  :return: True if student training went well
  """
    assert input.create_dir_if_needed(FLAGS.train_dir)

    # Call helper function to prepare student data using teacher predictions
    stdnt_dataset = prepare_student_data(dataset, nb_teachers, save=True)

    # Unpack the student dataset
    stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels = stdnt_dataset

    # Prepare checkpoint filename and path
    if FLAGS.deeper:
        ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(
            nb_teachers) + '_student_deeper.ckpt'  #NOLINT(long-line)
    else:
        ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(
            nb_teachers) + '_student.ckpt'  # NOLINT(long-line)

    # Start student training
    assert deep_cnn.train(stdnt_data, stdnt_labels, ckpt_path)

    # Compute final checkpoint name for student (with max number of steps)
    ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)

    # Compute student label predictions on remaining chunk of test set
    student_preds = deep_cnn.softmax_preds(stdnt_test_data, ckpt_path_final)

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

    return True
コード例 #4
0
def train_student(dataset, nb_teachers):
  """
  This function trains a student using predictions made by an ensemble of
  teachers. The student and teacher models are trained using the same
  neural network architecture.
  :param dataset: string corresponding to mnist, cifar10, or svhn  与mnist、cifar10或svhn相对应的字符串
  :param nb_teachers: number of teachers (in the ensemble) to learn from
  :return: True if student training went well
  """
  assert input.create_dir_if_needed(FLAGS.train_dir)

  # Call helper function to prepare student data using teacher predictions调用助手函数,使用教师预测来准备学生数据
  stdnt_dataset = prepare_student_data(dataset, nb_teachers, save=True)

  # Unpack the student dataset 打开学生的数据集
  stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels = stdnt_dataset

  # Prepare checkpoint filename and path 准备检查点文件名和路径
  if FLAGS.deeper:
    ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_student_deeper.ckpt' #NOLINT(long-line)
  else:
    ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_student.ckpt'  # NOLINT(long-line)

  # Start student training
  assert deep_cnn.train(stdnt_data, stdnt_labels, ckpt_path)

  # Compute final checkpoint name for student (with max number of steps) 计算学生的最终检查点名称(最大步数)
  ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)

  # Compute student label predictions on remaining chunk of test set 在剩余的测试集上计算学生标签预测
  student_preds = deep_cnn.softmax_preds(stdnt_test_data, ckpt_path_final)

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

  return True