def _smooth_l1(y_true, y_pred):
        """ Compute the smooth L1 loss of y_pred w.r.t. y_true.

        Args
            y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
            y_pred: Tensor from the network of shape (B, N, 4).

        Returns
            The smooth L1 loss of y_pred w.r.t. y_true.
        """
        # separate target and state
        regression        = y_pred
        regression_target = y_true[:, :, :-1]
        anchor_state      = y_true[:, :, -1]

        # filter out "ignore" anchors
        indices           = tensorflow.where(keras.backend.equal(anchor_state, 1))
        regression        = tensorflow.gather_nd(regression, indices)
        regression_target = tensorflow.gather_nd(regression_target, indices)

        # compute smooth L1 loss
        # f(x) = 0.5 * (sigma * x)^2          if |x| < 1 / sigma / sigma
        #        |x| - 0.5 / sigma / sigma    otherwise
        regression_diff = regression - regression_target
        regression_diff = keras.backend.abs(regression_diff)
        regression_loss = tensorflow.where(
            keras.backend.less(regression_diff, 1.0 / sigma_squared),
            0.5 * sigma_squared * keras.backend.pow(regression_diff, 2),
            regression_diff - 0.5 / sigma_squared
        )

        # compute the normalizer: the number of positive anchors
        normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])
        normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())
        return keras.backend.sum(regression_loss) / normalizer
 Compute the smooth L1 loss of y_pred w.r.t. y_true.

        Args
            y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
            y_pred: Tensor from the network of shape (B, N, 4).

        Returns
            The smooth L1 loss of y_pred w.r.t. y_true.
        
keras_retinanet/losses.py