'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).'

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()):
            (net, _) = inception.inception_v3_base(images, final_endpoint=mparams.final_endpoint)
        return net
