text: # MIT License
  #
  # Copyright (c) 2018
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from tfmtcnn.networks.NetworkFactory import NetworkFactory
  from tfmtcnn.datasets.TensorFlowDataset import TensorFlowDataset
  
  
  class AbstractNetworkTrainer(object):
      def __init__(self, network_name):
          self._network = NetworkFactory.network(network_name)
          self._number_of_samples = 0
          self._batch_size = 384
  
      def network_name(self):
          return (self._network.network_name())
  
      def network_size(self):
          return (self._network.network_size())
  
      def dataset_dir(self, dataset_root_dir):
          dataset_dir = os.path.join(dataset_root_dir, self.network_name())
          tensorflow_dir = os.path.join(dataset_dir, 'tensorflow')
          return (tensorflow_dir)
  
      def network_train_dir(self, train_root_dir):
          network_train_dir = os.path.join(train_root_dir, self.network_name())
          return (network_train_dir)
  
      def _positive_file_name(self, dataset_dir):
          positive_file_name = TensorFlowDataset.tensorflow_file_name(
              dataset_dir, 'positive')
          return (positive_file_name)
  
      def _part_file_name(self, dataset_dir):
          part_file_name = TensorFlowDataset.tensorflow_file_name(
              dataset_dir, 'part')
          return (part_file_name)
  
      def _negative_file_name(self, dataset_dir):
          negative_file_name = TensorFlowDataset.tensorflow_file_name(
              dataset_dir, 'negative')
          return (negative_file_name)
  
      def _image_list_file_name(self, dataset_dir):
          image_list_file_name = TensorFlowDataset.tensorflow_file_name(
              dataset_dir, 'image_list')
          return (image_list_file_name)
  
      def train(self, network_name, dataset_root_dir, train_root_dir,
                base_learning_rate, max_number_of_epoch, log_every_n_steps):
          raise NotImplementedError('Must be implemented by the subclass.')
  