text: # MIT License
  # 
  # Copyright (c) 2016 Chong Chan
  # 
  # 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.
  
  # pylint: disable=missing-docstring
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import models.network as network
  
  def inference(images, keep_probability, phase_train=True, weight_decay=0.0):
      """ Define an inference network for face recognition based 
             on inception modules using batch normalization
      
      Args:
        images: The images to run inference on, dimensions batch_size x height x width x channels
        phase_train: True if batch normalization should operate in training mode
      """
      endpoints = {}
      net = network.conv(images, 3, 64, 7, 7, 2, 2, 'SAME', 'conv1_7x7', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['conv1'] = net
      net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool1')
      endpoints['pool1'] = net
      net = network.conv(net,  64, 64, 1, 1, 1, 1, 'SAME', 'conv2_1x1', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['conv2_1x1'] = net
      net = network.conv(net,  64, 192, 3, 3, 1, 1, 'SAME', 'conv3_3x3', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['conv3_3x3'] = net
      net = network.mpool(net,  3, 3, 2, 2, 'SAME', 'pool3')
      endpoints['pool3'] = net
    
      net = network.inception(net, 192, 1, 64, 96, 128, 16, 32, 3, 32, 1, 'MAX', 'incept3a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept3a'] = net
      net = network.inception(net, 256, 1, 64, 96, 128, 32, 64, 3, 64, 1, 'MAX', 'incept3b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept3b'] = net
      net = network.inception(net, 320, 2, 0, 128, 256, 32, 64, 3, 0, 2, 'MAX', 'incept3c', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept3c'] = net
      
      net = network.inception(net, 640, 1, 256, 96, 192, 32, 64, 3, 128, 1, 'MAX', 'incept4a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept4a'] = net
      net = network.inception(net, 640, 2, 0, 160, 256, 64, 128, 3, 0, 2, 'MAX', 'incept4e', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept4e'] = net
      
      net = network.inception(net, 1024, 1, 256, 96,  384, 0, 0, 3, 96,  1, 'MAX', 'incept5a', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept5a'] = net
      net = network.inception(net, 736, 1, 256, 96, 384, 0, 0, 3, 96, 1, 'MAX', 'incept5b', phase_train=phase_train, use_batch_norm=True, weight_decay=weight_decay)
      endpoints['incept5b'] = net
      net = network.apool(net,  3, 3, 1, 1, 'VALID', 'pool6')
      endpoints['pool6'] = net
      net = tf.reshape(net, [-1, 736])
      endpoints['prelogits'] = net
      net = tf.nn.dropout(net, keep_probability)
      endpoints['dropout'] = net
      
      return net, endpoints
    