//  [python]
def custom_loss(self, y_true, y_pred):
        mask_shape = tf.shape(y_true)[:4]
        
        cell_x = tf.to_float(tf.reshape(tf.tile(tf.range(self.grid_w), [self.grid_h]), (1, self.grid_h, self.grid_w, 1, 1)))
        cell_y = tf.transpose(cell_x, (0,2,1,3,4))

        cell_grid = tf.tile(tf.concat([cell_x,cell_y], -1), [self.batch_size, 1, 1, self.nb_box, 1])
        
        coord_mask = tf.zeros(mask_shape)
        conf_mask  = tf.zeros(mask_shape)
        class_mask = tf.zeros(mask_shape)
        
        seen = tf.Variable(0.)
        total_recall = tf.Variable(0.)
        
        """
        Adjust prediction
        """
        ### adjust x and y      
        pred_box_xy = tf.sigmoid(y_pred[..., :2]) + cell_grid
        
        ### adjust w and h
        pred_box_wh = tf.exp(y_pred[..., 2:4]) * np.reshape(self.anchors, [1,1,1,self.nb_box,2])
        
        ### adjust confidence
        pred_box_conf = tf.sigmoid(y_pred[..., 4])
        
        ### adjust class probabilities
        pred_box_class = y_pred[..., 5:]
        
        """
        Adjust ground truth
        """
        ### adjust x and y
        true_box_xy = y_true[..., 0:2] # relative position to the containing cell
        
        ### adjust w and h
        true_box_wh = y_true[..., 2:4] # number of cells accross, horizontally and vertically
        
        ### adjust confidence
        true_wh_half = true_box_wh / 2.
        true_mins    = true_box_xy - true_wh_half
        true_maxes   = true_box_xy + true_wh_half
        
        pred_wh_half = pred_box_wh / 2.
        pred_mins    = pred_box_xy - pred_wh_half
        pred_maxes   = pred_box_xy + pred_wh_half       
        
        intersect_mins  = tf.maximum(pred_mins,  true_mins)
        intersect_maxes = tf.minimum(pred_maxes, true_maxes)
        intersect_wh    = tf.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
        
        true_areas = true_box_wh[..., 0] * true_box_wh[..., 1]
        pred_areas = pred_box_wh[..., 0] * pred_box_wh[..., 1]

        union_areas = pred_areas + true_areas - intersect_areas
        iou_scores  = tf.truediv(intersect_areas, union_areas)
        
        true_box_conf = iou_scores * y_true[..., 4]
        
        ### adjust class probabilities
        true_box_class = tf.argmax(y_true[..., 5:], -1)
        
        """
        Determine the masks
        """
        ### coordinate mask: simply the position of the ground truth boxes (the predictors)
        coord_mask = tf.expand_dims(y_true[..., 4], axis=-1) * self.coord_scale
        
        ### confidence mask: penelize predictors + penalize boxes with low IOU
        # penalize the confidence of the boxes, which have IOU with some ground truth box < 0.6
        true_xy = self.true_boxes[..., 0:2]
        true_wh = self.true_boxes[..., 2:4]
        
        true_wh_half = true_wh / 2.
        true_mins    = true_xy - true_wh_half
        true_maxes   = true_xy + true_wh_half
        
        pred_xy = tf.expand_dims(pred_box_xy, 4)
        pred_wh = tf.expand_dims(pred_box_wh, 4)
        
        pred_wh_half = pred_wh / 2.
        pred_mins    = pred_xy - pred_wh_half
        pred_maxes   = pred_xy + pred_wh_half    
        
        intersect_mins  = tf.maximum(pred_mins,  true_mins)
        intersect_maxes = tf.minimum(pred_maxes, true_maxes)
        intersect_wh    = tf.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
        
        true_areas = true_wh[..., 0] * true_wh[..., 1]
        pred_areas = pred_wh[..., 0] * pred_wh[..., 1]

        union_areas = pred_areas + true_areas - intersect_areas
        iou_scores  = tf.truediv(intersect_areas, union_areas)

        best_ious = tf.reduce_max(iou_scores, axis=4)
        conf_mask = conf_mask + tf.to_float(best_ious < 0.6) * (1 - y_true[..., 4]) * self.no_object_scale
        
        # penalize the confidence of the boxes, which are reponsible for corresponding ground truth box
        conf_mask = conf_mask + y_true[..., 4] * self.object_scale
        
        ### class mask: simply the position of the ground truth boxes (the predictors)
        class_mask = y_true[..., 4] * tf.gather(self.class_wt, true_box_class) * self.class_scale       
        
        """
        Warm-up training
        """
        no_boxes_mask = tf.to_float(coord_mask < self.coord_scale/2.)
        seen = tf.assign_add(seen, 1.)
        
        true_box_xy, true_box_wh, coord_mask = tf.cond(tf.less(seen, self.warmup_batches+1), 
                              lambda: [true_box_xy + (0.5 + cell_grid) * no_boxes_mask, 
                                       true_box_wh + tf.ones_like(true_box_wh) * \
                                       np.reshape(self.anchors, [1,1,1,self.nb_box,2]) * \
                                       no_boxes_mask, 
                                       tf.ones_like(coord_mask)],
                              lambda: [true_box_xy, 
                                       true_box_wh,
                                       coord_mask])
        
        """
        Finalize the loss
        """
        nb_coord_box = tf.reduce_sum(tf.to_float(coord_mask > 0.0))
        nb_conf_box  = tf.reduce_sum(tf.to_float(conf_mask  > 0.0))
        nb_class_box = tf.reduce_sum(tf.to_float(class_mask > 0.0))
        
        loss_xy    = tf.reduce_sum(tf.square(true_box_xy-pred_box_xy)     * coord_mask) / (nb_coord_box + 1e-6) / 2.
        loss_wh    = tf.reduce_sum(tf.square(true_box_wh-pred_box_wh)     * coord_mask) / (nb_coord_box + 1e-6) / 2.
        loss_conf  = tf.reduce_sum(tf.square(true_box_conf-pred_box_conf) * conf_mask)  / (nb_conf_box  + 1e-6) / 2.
        loss_class = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=true_box_class, logits=pred_box_class)
        loss_class = tf.reduce_sum(loss_class * class_mask) / (nb_class_box + 1e-6)
        
        loss = tf.cond(tf.less(seen, self.warmup_batches+1), 
                      lambda: loss_xy + loss_wh + loss_conf + loss_class + 10,
                      lambda: loss_xy + loss_wh + loss_conf + loss_class)
        
        if self.debug:
            nb_true_box = tf.reduce_sum(y_true[..., 4])
            nb_pred_box = tf.reduce_sum(tf.to_float(true_box_conf > 0.5) * tf.to_float(pred_box_conf > 0.3))
            
            current_recall = nb_pred_box/(nb_true_box + 1e-6)
            total_recall = tf.assign_add(total_recall, current_recall) 

            loss = tf.Print(loss, [loss_xy], message='Loss XY \t', summarize=1000)
            loss = tf.Print(loss, [loss_wh], message='Loss WH \t', summarize=1000)
            loss = tf.Print(loss, [loss_conf], message='Loss Conf \t', summarize=1000)
            loss = tf.Print(loss, [loss_class], message='Loss Class \t', summarize=1000)
            loss = tf.Print(loss, [loss], message='Total Loss \t', summarize=1000)
            loss = tf.Print(loss, [current_recall], message='Current Recall \t', summarize=1000)
            loss = tf.Print(loss, [total_recall/seen], message='Average Recall \t', summarize=1000)
        
        return loss