parameters: (feats, anchors, num_classes, input_shape, image_shape)
return_statement: return boxes, box_scores
docstring: Process Conv layer output
docstring_summary: Process Conv layer output
docstring_tokens: Process Conv layer output
function: def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
      """Process Conv layer output"""
      box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats,
          anchors, num_classes, input_shape)
      boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
      boxes = K.reshape(boxes, [-1, 4])
      box_scores = box_confidence * box_class_probs
      box_scores = K.reshape(box_scores, [-1, num_classes])
      return boxes, box_scores
function_tokens: def yolo_boxes_and_scores ( feats , anchors , num_classes , input_shape , image_shape ) : box_xy , box_wh , box_confidence , box_class_probs = yolo_head ( feats , anchors , num_classes , input_shape ) boxes = yolo_correct_boxes ( box_xy , box_wh , input_shape , image_shape ) boxes = K . reshape ( boxes , [ - 1 , 4 ] ) box_scores = box_confidence * box_class_probs box_scores = K . reshape ( box_scores , [ - 1 , num_classes ] ) return boxes , box_scores