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<center>
<img src="banner-small.png">
</center>
<br>
<h1 align='center'>Tutorial: Implementing Layer-Wise Relevance Propagation</h1>
<br>
<p><em>
first version: Jul 14, 2016<br>
last update: Sep 17, 2019
</em></p>
<p>This tutorial explains how to implement layer-wise relevance propagation
(LRP) easily and efficiently, as described in the overview paper:
<blockquote style='background-color:#EEEEEE; padding: 3px; border: 1px dashed
#999999'>
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, K.-R. Müller<br><a
href="https://doi.org/10.1007/978-3-030-28954-6_10">Layer-wise Relevance
Propagation: An Overview</a><br><font color="#008800">in Explainable AI:
Interpreting, Explaining and Visualizing Deep Learning, Springer LNCS, vol.
11700,
2019</font>
</blockquote>
We consider two models: (1) a simple plain deep rectifier network trained on the
MNIST handwritten digits data, (2) the VGG-16 network trained on ImageNet and
applicable to general image classification.</p>
<p><em>Note:</em> If you are instead looking for ready to use software, have a
look at the <a href="http://www.heatmapping.org/index.html#software">software
section</a> of this website. If you want to try relevance propagation without
installing software, check our <a
href="http://www.heatmapping.org/index.html#demos">interactive demos</a>. For
the original paper on LRP, see instead:</p>
<blockquote style='background-color:#EEEEEE; padding: 3px; border: 1px dashed
#999999'>
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, W. Samek<br><a
href="https://doi.org/10.1371/journal.pone.0130140">On pixel-wise explanations
for non-linear classifier decisions by layer-wise relevance
propagation</a><br><font color="#008800">PloS ONE 10 (7), e0130140,
2015</font>
</blockquote>
<h3>Prerequisites</h3>
<p>For this tutorial, you need to install Python, Numpy, PyTorch, Matplotlib,
and OpenCV. Then, you should download <a href="tutorial.zip">tutorial.zip</a>
and extract it in your working directory. It contains data, model parameters,
and some additional functions (in the file <code>utils.py</code>).
<h2>1 &nbsp; Numpy Implementation for a Fully-Connected Network</h2>
<p>We first load 12 examplary MNIST test digits.</p>
import utils
X,T = utils.loaddata()
%matplotlib inline
utils.digit(X.reshape(1,12,28,28).transpose(0,2,1,3).reshape(28,12*28),9,0.75)
![png](index_files/index_2_0.png)
Each digit is stored as a 784-dimensional vector of pixel values, where "-1.0"
corresponds to black and "+1.0" corresponds to white.
<h3>1.1 &nbsp; Predicting the class of MNIST digits</h3>
<p>These digits are fed to a fully connected neural network with layer sizes
784-300-100-10 with ReLU activations for each hidden layer. The architecture is
depicted in the figure below.</p>
<center>
<img src="mlp.png" width=350>
</center>
<p>The network we consider achieves an error of 1.6% which is a typical
performance for a neural network without particular structure or regularization.
The function <code>utils.loadparams()</code> retrieves its parameters for
us.</p>
W,B = utils.loadparams()
L = len(W)
<p>From these parameters, the forward pass can be computed as a sequence of
matrix multiplications and nonlinearities.</p>
import numpy
A = [X]+[None]*L
for l in range(L):
A[l+1] = numpy.maximum(0,A[l].dot(W[l])+B[l])
Note that this code has added an additional top-layer ReLU activation compared
to the original neural network. This however doesn't affect computations when
looking at positive output scores. The top layer activations are scores
measuring the evidence the network has found for each class. In the following,
we show the first three digits and the scores produced for each class at the
output:
for i in range(3):
utils.digit(X[i].reshape(28,28),0.75,0.75)
p = A[L][i]
print(" ".join(['[%1d] %.1f'%(d,p[d]) for d in range(10)]))
![png](index_files/index_8_0.png)
[0] 0.0 [1] 3.6 [2] 49.1 [3] 8.9 [4] 0.0 [5] 0.0 [6] 0.0 [7] 1.4 [8] 1.6 [9] 0.0
![png](index_files/index_8_2.png)
[0] 0.0 [1] 27.0 [2] 0.0 [3] 0.0 [4] 5.3 [5] 0.0 [6] 0.0 [7] 13.0 [8] 8.1 [9] 2.3
![png](index_files/index_8_4.png)
[0] 49.1 [1] 0.0 [2] 10.6 [3] 0.0 [4] 0.0 [5] 0.2 [6] 0.0 [7] 3.0 [8] 0.0 [9] 9.2
<p>As expected, the highest score systematically corresponds to the correct
digit.</p>
<h3>1.2 &nbsp; Explaining the predictions with LRP</h3>
<p>We now implement the layer-wise relevance propagation (LRP) procedure from
the top to the bottom of the network. As a first step, we create a list to store
relevance scores at each layer. The top layer relevance scores are set to the
top-layer activations, which we multiply by a label indicator in order to retain
only the evidence for the actual class.</p>
R = [None]*L + [A[L]*(T[:,None]==numpy.arange(10))]
<p> The LRP-0, LRP-$\epsilon$, and LRP-$\gamma$ rules described in the <a
href="">LRP overview paper</a> (Section 10.2.1) for propagating relevance on the
lower layers are special cases of the more general propagation rule</p>
<p>
$$
R_j = \sum_k \frac{a_j \rho(w_{jk})}{\epsilon + \sum_{0,j} a_j \rho(w_{jk})} R_k
$$
</p>
<p>(cf. Section 10.2.2), where $\rho$ is a function that transform the weights,
and $\epsilon$ is a small positive increment. We define below two helper
functions that perform the weight transformation and the incrementation. In
practice, we would like to apply different rules at different layers (cf.
Section 10.3). Therefore, we also give the layer index "<code>l</code>" as
argument to these functions.</p>
def rho(w,l): return w + [None,0.1,0.0,0.0][l] * numpy.maximum(0,w)
def incr(z,l): return z + [None,0.0,0.1,0.0][l] * (z**2).mean()**.5+1e-9
<p>In particular, these functions and the layer they receive as a parameter let
us reduce the general rule to LRP-0 for the top-layer, to LRP-$\epsilon$ with
$\epsilon = 0.1\,\text{std}$ for the layer just below, and to LRP-$\gamma$ with
$\gamma=0.1$ for the layer before. We now come to the practical implementation
of this general rule. It can be decomposed as a sequence of four
computations:</p>
<p>
\begin{align*}
\forall_k:&~z_k = {\textstyle \epsilon + \sum_{0,j}} a_j \rho(w_{jk}) &
(\text{step }1)\\
\forall_k:&~s_k = R_k / z_k \qquad & (\text{step }2)\\
\forall_j:&~c_j = {\textstyle \sum_k} \rho(w_{jk}) s_k \qquad & (\text{step
}3)\\
\forall_j:&~R_j = a_j \cdot c_j \qquad & (\text{step }4)
\end{align*}
</p>
<p>The layer-wise relevance propagation procedure then consists of iterating
over the layers in reverse order, starting from the top layer towards the first
layers, and at each layer, applying this sequence of computations.</p>
for l in range(1,L)[::-1]:
w = rho(W[l],l)
b = rho(B[l],l)
z = incr(A[l].dot(w)+b,l) # step 1
s = R[l+1] / z # step 2
c = s.dot(w.T) # step 3
R[l] = A[l]*c # step 4
<p>Note that the loop above stops one layer before reaching the pixels. To
propagate relevance scores until the pixels, we need to apply an alternate
propagation rule that properly handles pixel values received as input (cf.
Section 10.3.2). In particular, we apply for this layer the $z^\mathcal{B}$-rule
given by:</p>
$$
R_i = \sum_j \frac{a_i w_{ij} - l_i w_{ij}^+ - h_i w_{ij}^-}{\sum_{i} a_i w_{ij}
- l_i w_{ij}^+ - h_i w_{ij}^-} R_j
$$
<p>In this rule, $l_i$ and $h_i$ are the lower and upper bounds of pixel values,
i.e. "-1" and "+1", and $(\cdot)^+$ and $(\cdot)^-$ are shortcut notations for
$\max(0,\cdot)$ and $\min(0,\cdot)$. The $z^\mathcal{B}$-rule can again be
implemented with a four-step procedure similar to the one used in the layers
above. Here, we need to create two copies of the weights, and also create arrays
of pixel values set to $l_i$ and $h_i$ respectively:</p>
w = W[0]
wp = numpy.maximum(0,w)
wm = numpy.minimum(0,w)
lb = A[0]*0-1
hb = A[0]*0+1
z = A[0].dot(w)-lb.dot(wp)-hb.dot(wm)+1e-9 # step 1
s = R[1]/z # step 2
c,cp,cm = s.dot(w.T),s.dot(wp.T),s.dot(wm.T) # step 3
R[0] = A[0]*c-lb*cp-hb*cm # step 4
<p>We have now reached the bottom layer. The obtained pixel-wise relevance
scores can be rendered as a heatmap.</p>
utils.digit(X.reshape(1,12,28,28).transpose(0,2,1,3).reshape(28,12*28),9,0.75)
utils.heatmap(R[0].reshape(1,12,28,28).transpose(0,2,1,3).reshape(28,12*28),9,0.75)
![png](index_files/index_18_0.png)
![png](index_files/index_18_1.png)
Relevant pixels are highlighted in red. Pixels that contribute negatively to the
prediction, if any, are shown in blue. On most digits, we find that the digit
itself is highlighted, as well as some parts of the background. For example, we
observe two red horizontal bars next to the digit "3", highlighting the fact
that if those pixels would be different, the digit 3 would likely turn into a
"8". Same for the vertical bar above the digit "4" that supports the class "4"
instead of the class "9".
<h2>2 &nbsp; PyTorch Implementation for the VGG-16 Network</h2>
<p>In the example above, LRP rules could be easily expressed in terms of matrix-
vector operations. In practice, state-of-the-art neural networks such as VGG-16
make use of more complex layers such as convolutions and pooling. In this case,
LRP rules are more conveniently implemented by casting the operations of the
four-step procedure above as <em>forward</em> and <em>gradient</em> evaluations
on these layers. These operations are readily available in neural network
frameworks such as PyTorch and TensorFlow, and can therefore be reused for the
purpose of implementing LRP. Here, we take the VGG-16 pretrained network for
image classification. For this network, we consider the task of explaining the
evidence for the class "castle" it has found in the following image:</p>
<center>
<img src="castle.jpg" width=224 style='margin: 0.5em'>
</center>
<p>The image is first loaded in the notebook.</p>
import cv2
img = numpy.array(cv2.imread('castle.jpg'))[...,::-1]/255.0
<p>It is then converted to a torch tensor of appropriate dimensions and
normalized to be given as input to the VGG-16 network.</p>
import torch
mean = torch.Tensor([0.485, 0.456, 0.406]).reshape(1,-1,1,1)
std = torch.Tensor([0.229, 0.224, 0.225]).reshape(1,-1,1,1)
X = (torch.FloatTensor(img[numpy.newaxis].transpose([0,3,1,2])*1) - mean) / std
<p>The VGG-16 network is then loaded and its top-level dense layers are
converted into equivalent 1x1 convolutions.</p>
import torchvision
model = torchvision.models.vgg16(pretrained=True); model.eval()
layers = list(model._modules['features']) + utils.toconv(list(model._modules['classifier']))
L = len(layers)
<h3>2.1 &nbsp; Predicting the class of an image</h3>
<p>The input can then be propagated in the network and the activations at each
layer are collected:</p>
A = [X]+[None]*L
for l in range(L): A[l+1] = layers[l].forward(A[l])
<p>Activations in the top layer are the scores the neural network predicts for
each class. We show below the 10 classes with highest score:</p>
scores = numpy.array(A[-1].data.view(-1))
ind = numpy.argsort(-scores)
for i in ind[:10]:
print('%20s (%3d): %6.3f'%(utils.imgclasses[i][:20],i,scores[i]))
castle (483): 11.029
church, church build (497): 9.522
monastery (663): 9.401
bell cote, bell cot (442): 9.047
cinema, movie theate (498): 8.016
analog clock (409): 7.108
street sign (919): 7.102
traffic light, traff (920): 7.058
thatch, thatched roo (853): 6.978
alp (970): 6.812
<p>We observe that the neuron castle (index 483) has the highest score. This is
expected due to the presence of a castle in the image. Note that other building-
related classes are also assigned a high score, as well as classes corresponding
to other objects present in the image (e.g. street sign and traffic light).</p>
<h3>2.2 &nbsp; Explaining the prediction with LRP</h3>
<p>The following code iterates from the top layer to the first layer in reverse
order and applies propagation rules at each layer. Top-layer activations are
first multiplied by the mask to retain only the predicted evidence for the class
"castle".</p>
T = torch.FloatTensor((1.0*(numpy.arange(1000)==483).reshape([1,1000,1,1])))
R = [None]*L + [(A[-1]*T).data]
<p>This evidence can then be propagated backward in the network by applying
propagation rules at each layer.</p>
<p><b>Convolution layers:</b> Observing that convolutions are special types of
linear layers, we can use the same propagation rules as in the MNIST example,
and a similar four-step procedure for applying these rules. Steps 2 and 4 are
simple element-wise computations. Step 1 can be implemented as a forward
computation in the layer, where we have preliminary transformed the layer
parameters, and where we apply the increment function afterwards. As shown in
the LRP overview paper, Step 3 can instead be computed as a gradient in the
space of input activations:</p>
<p>
$$
c_j = \big[\nabla~\big({\textstyle \sum_k}~z_k(\boldsymbol{a}) \cdot
s_k\big)\big]_j
$$
</p>
<p>where $s_k$ is treated as constant.</p>
<p><b>Pooling layers:</b> It is suggested in Section 10.3.2 of the paper to
treat max-pooling layers as average pooling layers in the backward pass.
Observing that average pooling is also a special linear layer, the same
propagation rules as for the convolutional layers become applicable.</p>
<p>In the following code, we iterate the propagation procedure from the top-
layer towards the lower layers. Whenever we meet a max-pooling layer, we convert
it into an average pooling layer. The function <code>rho</code> and
<code>incr</code> are set differently at each layer, following the strategy of
Section 10.3.</p>
for l in range(1,L)[::-1]:
A[l] = (A[l].data).requires_grad_(True)
if isinstance(layers[l],torch.nn.MaxPool2d): layers[l] = torch.nn.AvgPool2d(2)
if isinstance(layers[l],torch.nn.Conv2d) or isinstance(layers[l],torch.nn.AvgPool2d):
if l <= 16: rho = lambda p: p + 0.25*p.clamp(min=0); incr = lambda z: z+1e-9
if 17 <= l <= 30: rho = lambda p: p; incr = lambda z: z+1e-9+0.25*((z**2).mean()**.5).data
if l >= 31: rho = lambda p: p; incr = lambda z: z+1e-9
z = incr(utils.newlayer(layers[l],rho).forward(A[l])) # step 1
s = (R[l+1]/z).data # step 2
(z*s).sum().backward(); c = A[l].grad # step 3
R[l] = (A[l]*c).data # step 4
else:
R[l] = R[l+1]
As each layer is composed of a collection of two-dimensional feature maps,
relevance scores at each layer can be visualized as a two-dimensional map. Here,
relevance scores are pooled over all feature maps at a given layer. The two-
dimensional maps are shown for a selection of VGG-16 layers.
for i,l in enumerate([31,21,11,1]):
utils.heatmap(numpy.array(R[l][0]).sum(axis=0),0.5*i+1.5,0.5*i+1.5)
![png](index_files/index_35_0.png)
![png](index_files/index_35_1.png)
![png](index_files/index_35_2.png)
![png](index_files/index_35_3.png)
<p>We observe that the explanation becomes increasingly resolved spatially. Note
that, like for the MNIST example, we have stopped the propagation procedure one
layer before the pixels because the rule we have used is not applicable to pixel
layers. Like for the MNIST case, we need ot apply the pixel-specific
$z^\mathcal{B}$-rule for this last layer. This rule can again be implemented in
terms of forward passes and gradient computations.</p>
A[0] = (A[0].data).requires_grad_(True)
lb = (A[0].data*0+(0-mean)/std).requires_grad_(True)
hb = (A[0].data*0+(1-mean)/std).requires_grad_(True)
z = layers[0].forward(A[0]) + 1e-9 # step 1 (a)
z -= utils.newlayer(layers[0],lambda p: p.clamp(min=0)).forward(lb) # step 1 (b)
z -= utils.newlayer(layers[0],lambda p: p.clamp(max=0)).forward(hb) # step 1 (c)
s = (R[1]/z).data # step 2
(z*s).sum().backward(); c,cp,cm = A[0].grad,lb.grad,hb.grad # step 3
R[0] = (A[0]*c+lb*cp+hb*cm).data # step 4
The relevance scores obtained in the pixel layer can now be summed over the RGB
channels to indicate actual pixel-wise contributions.
utils.heatmap(numpy.array(R[0][0]).sum(axis=0),3.5,3.5)
![png](index_files/index_39_0.png)
We observe that the heatmap highlights the outline of the castle as evidence for
the corresponding class. Some elements such as the traffic sign or the roof on
the left are seen as having a negative effect on the neuron "castle" and are
consequently highlighted in blue.
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