(self, images, is_training=True, reuse=None)
Computes convolutional features using the InceptionV3 model.

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      is_training: whether is training or not.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A tensor of shape [batch_size, OH, OW, N], where OWxOH is resolution of
      output feature map and N is number of output features (depends on the
      network architecture).
Computes convolutional features using the InceptionV3 model.
Computes convolutional features using the InceptionV3 model .
def conv_tower_fn(self, images, is_training=True, reuse=None):
    """Computes convolutional features using the InceptionV3 model.

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      is_training: whether is training or not.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A tensor of shape [batch_size, OH, OW, N], where OWxOH is resolution of
      output feature map and N is number of output features (depends on the
      network architecture).
    """
    mparams = self._mparams['conv_tower_fn']
    logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
    with tf.variable_scope('conv_tower_fn/INCE'):
      if reuse:
        tf.get_variable_scope().reuse_variables()
      with slim.arg_scope(inception.inception_v3_arg_scope()):
        with slim.arg_scope([slim.batch_norm, slim.dropout],
                            is_training=is_training):
          net, _ = inception.inception_v3_base(
            images, final_endpoint=mparams.final_endpoint)
      return net
def conv_tower_fn ( self , images , is_training = True , reuse = None ) : mparams = self . _mparams [ 'conv_tower_fn' ] logging . debug ( 'Using final_endpoint=%s' , mparams . final_endpoint ) with tf . variable_scope ( 'conv_tower_fn/INCE' ) : if reuse : tf . get_variable_scope ( ) . reuse_variables ( ) with slim . arg_scope ( inception . inception_v3_arg_scope ( ) ) : with slim . arg_scope ( [ slim . batch_norm , slim . dropout ] , is_training = is_training ) : net , _ = inception . inception_v3_base ( images , final_endpoint = mparams . final_endpoint ) return net