LRP_linear_layer.py

'''
@author: Leila Arras
@maintainer: Leila Arras
@date: 21.06.2017
@version: 1.0+
@copyright: Copyright (c) 2017, Leila Arras, Gregoire Montavon, Klaus-Robert Mueller, Wojciech Samek
@license: see LICENSE file in repository root
'''

import numpy as np
from numpy import newaxis as na


def lrp_linear(hin, w, b, hout, Rout, bias_nb_units, eps, bias_factor=0.0, debug=False):
    """
    LRP for a linear layer with input dim D and output dim M.
    Args:
    - hin:            forward pass input, of shape (D,)
    - w:              connection weights, of shape (D, M)
    - b:              biases, of shape (M,)
    - hout:           forward pass output, of shape (M,) (unequal to np.dot(w.T,hin)+b if more than one incoming layer!)
    - Rout:           relevance at layer output, of shape (M,)
    - bias_nb_units:  total number of connected lower-layer units (onto which the bias/stabilizer contribution is redistributed for sanity check)
    - eps:            stabilizer (small positive number)
    - bias_factor:    set to 1.0 to check global relevance conservation, otherwise use 0.0 to ignore bias/stabilizer redistribution (recommended)
    Returns:
    - Rin:            relevance at layer input, of shape (D,)
    """
    sign_out = np.where(hout[na, :] >= 0, 1., -1.)  # shape (1, M)
    # print(sign_out)

    # 13 分子
    numer = (w * hin[:, na]) + (bias_factor * (b[na, :] * 1. + eps * sign_out * 1.) / bias_nb_units)  # shape (D, M)
    # Note: here we multiply the bias_factor with both the bias b and the stabilizer eps since in fact
    # using the term (b[na,:]*1. + eps*sign_out*1.) / bias_nb_units in the numerator is only useful for sanity check
    # (in the initial paper version we were using (bias_factor*b[na,:]*1. + eps*sign_out*1.) / bias_nb_units instead)

    # 13 分母
    denom = hout[na, :] + (eps * sign_out * 1.)  # shape (1, M)

    message = (numer / denom) * Rout[na, :]  # shape (D, M)

    Rin = message.sum(axis=1)  # shape (D,)

    if debug:
        print("local diff: ", Rout.sum() - Rin.sum())
    # Note: 
    # - local  layer   relevance conservation if bias_factor==1.0 and bias_nb_units==D (i.e. when only one incoming layer)
    # - global network relevance conservation if bias_factor==1.0 and bias_nb_units set accordingly to the total number of lower-layer connections 
    # -> can be used for sanity check

    return Rin