title: There's a problem with sigmoid use in multi-label image classification
body: <p>Multi-labeled image classification using 'inception resnet v2' model.</p>
  
  <p>I'm changing the required part to a sigmoid.</p>
  
  <p>Beyond a certain number of studies, it shows results such as softmax.</p>
  
  <p>The code is part of the 'inception resnet v2' in use.</p>
  
  <pre><code>def inception_resnet_v2(inputs, num_classes=16, is_training=True, dropout_keep_prob=0.8, reuse=None,scope='InceptionResnetV2', create_aux_logits=True, activation_fn=tf.nn.sigmoid):
  
  end_points = {}
  
  with tf.variable_scope(scope, 'InceptionResnetV2', [inputs],
                       reuse=reuse) as scope:
  with slim.arg_scope([slim.batch_norm, slim.dropout],
                      is_training=is_training):
  
    net, end_points = inception_resnet_v2_base(inputs, scope=scope,
                                               activation_fn=activation_fn)
  
    if create_aux_logits and num_classes:
      with tf.variable_scope('AuxLogits'):
        aux = end_points['PreAuxLogits']
        aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID',
                              scope='Conv2d_1a_3x3')
        aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1')
        aux = slim.conv2d(aux, 768, aux.get_shape()[1:3],
                          padding='VALID', scope='Conv2d_2a_5x5')
        aux = slim.flatten(aux)
        aux = slim.fully_connected(aux, num_classes, activation_fn=None,
                                   scope='Logits')
        end_points['AuxLogits'] = aux
  
    with tf.variable_scope('Logits'):
      # TODO(sguada,arnoegw): Consider adding a parameter global_pool which
      # can be set to False to disable pooling here (as in resnet_*()).
      kernel_size = net.get_shape()[1:3]
      if kernel_size.is_fully_defined():
        net = slim.avg_pool2d(net, kernel_size, padding='VALID',
                              scope='AvgPool_1a_8x8')
      else:
        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
      end_points['global_pool'] = net
      if not num_classes:
        return net, end_points
      net = slim.flatten(net)
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='Dropout')
      end_points['PreLogitsFlatten'] = net
      logits = slim.fully_connected(net, num_classes, activation_fn=None,
                                    scope='Logits')
      end_points['Logits'] = logits
  
      end_points['Predictions'] = tf.nn.sigmoid(logits, name='Predictions')
  
  return logits, end_points
  
  def inception_resnet_v2_arg_scope(
  weight_decay=0.00004,
  batch_norm_decay=0.9997,
  batch_norm_epsilon=0.001,
  activation_fn=tf.nn.sigmoid,
  batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS,
  batch_norm_scale=False):
  
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                    weights_regularizer=slim.l2_regularizer(weight_decay),
                    biases_regularizer=slim.l2_regularizer(weight_decay)):
  
  batch_norm_params = {
      'decay': batch_norm_decay,
      'epsilon': batch_norm_epsilon,
      'updates_collections': batch_norm_updates_collections,
      'fused': None,  # Use fused batch norm if possible.
      'scale': batch_norm_scale,
  }
  # Set activation_fn and parameters for batch_norm.
  with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,
                      normalizer_fn=slim.batch_norm,
                      normalizer_params=batch_norm_params) as scope:
    return scope
  </code></pre>
  
  <p>Is there a case like me?</p>
  
  <p>Or I wonder if I haven't used the sigmoid properly</p>