Posit AI Weblog: lime v0.4: The Kitten Image Version

Introduction

I’m blissful to report a brand new main launch of lime has landed on CRAN. lime is
an R port of the Python library of the identical title by Marco Ribeiro that permits
the person to pry open black field machine studying fashions and clarify their
outcomes on a per-observation foundation. It really works by modelling the result of the
black field within the native neighborhood across the commentary to elucidate and utilizing
this native mannequin to elucidate why (not how) the black field did what it did. For
extra details about the speculation of lime I’ll direct you to the article
introducing the methodology.

New options

The meat of this launch facilities round two new options which can be considerably
linked: Native help for keras fashions and help for explaining picture fashions.

keras and pictures

J.J. Allaire was sort sufficient to namedrop lime throughout his keynote introduction
of the tensorflow and keras packages and I felt compelled to help them
natively. As keras is by far the preferred approach to interface with tensorflow
it’s first in line for build-in help. The addition of keras implies that
lime now instantly helps fashions from the next packages:

Should you’re engaged on one thing too obscure or leading edge to not be capable to use
these packages it’s nonetheless doable to make your mannequin lime compliant by
offering predict_model() and model_type() strategies for it.

keras fashions are used identical to every other mannequin, by passing it into the lime()
operate together with the coaching knowledge in an effort to create an explainer object.
As a result of we’re quickly going to speak about picture fashions, we’ll be utilizing one of many
pre-trained ImageNet fashions that’s obtainable from keras itself:

Mannequin
______________________________________________________________________________________________
Layer (kind)                              Output Form                         Param #        
==============================================================================================
input_1 (InputLayer)                      (None, 224, 224, 3)                  0              
______________________________________________________________________________________________
block1_conv1 (Conv2D)                     (None, 224, 224, 64)                 1792           
______________________________________________________________________________________________
block1_conv2 (Conv2D)                     (None, 224, 224, 64)                 36928          
______________________________________________________________________________________________
block1_pool (MaxPooling2D)                (None, 112, 112, 64)                 0              
______________________________________________________________________________________________
block2_conv1 (Conv2D)                     (None, 112, 112, 128)                73856          
______________________________________________________________________________________________
block2_conv2 (Conv2D)                     (None, 112, 112, 128)                147584         
______________________________________________________________________________________________
block2_pool (MaxPooling2D)                (None, 56, 56, 128)                  0              
______________________________________________________________________________________________
block3_conv1 (Conv2D)                     (None, 56, 56, 256)                  295168         
______________________________________________________________________________________________
block3_conv2 (Conv2D)                     (None, 56, 56, 256)                  590080         
______________________________________________________________________________________________
block3_conv3 (Conv2D)                     (None, 56, 56, 256)                  590080         
______________________________________________________________________________________________
block3_pool (MaxPooling2D)                (None, 28, 28, 256)                  0              
______________________________________________________________________________________________
block4_conv1 (Conv2D)                     (None, 28, 28, 512)                  1180160        
______________________________________________________________________________________________
block4_conv2 (Conv2D)                     (None, 28, 28, 512)                  2359808        
______________________________________________________________________________________________
block4_conv3 (Conv2D)                     (None, 28, 28, 512)                  2359808        
______________________________________________________________________________________________
block4_pool (MaxPooling2D)                (None, 14, 14, 512)                  0              
______________________________________________________________________________________________
block5_conv1 (Conv2D)                     (None, 14, 14, 512)                  2359808        
______________________________________________________________________________________________
block5_conv2 (Conv2D)                     (None, 14, 14, 512)                  2359808        
______________________________________________________________________________________________
block5_conv3 (Conv2D)                     (None, 14, 14, 512)                  2359808        
______________________________________________________________________________________________
block5_pool (MaxPooling2D)                (None, 7, 7, 512)                    0              
______________________________________________________________________________________________
flatten (Flatten)                         (None, 25088)                        0              
______________________________________________________________________________________________
fc1 (Dense)                               (None, 4096)                         102764544      
______________________________________________________________________________________________
fc2 (Dense)                               (None, 4096)                         16781312       
______________________________________________________________________________________________
predictions (Dense)                       (None, 1000)                         4097000        
==============================================================================================
Complete params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
______________________________________________________________________________________________

The vgg16 mannequin is a picture classification mannequin that has been construct as a part of
the ImageNet competitors the place the objective is to categorise footage into 1000
classes with the best accuracy. As we are able to see it’s pretty difficult.

As a way to create an explainer we might want to move within the coaching knowledge as
effectively. For picture knowledge the coaching knowledge is actually solely used to inform lime that we
are coping with a picture mannequin, so any picture will suffice. The format for the
coaching knowledge is solely the trail to the photographs, and since the web runs on
kitten footage we’ll use one in all these:

img <- image_read('https://www.data-imaginist.com/belongings/photographs/kitten.jpg')
img_path <- file.path(tempdir(), 'kitten.jpg')
image_write(img, img_path)
plot(as.raster(img))

As with textual content fashions the explainer might want to know learn how to put together the enter
knowledge for the mannequin. For keras fashions this implies formatting the picture knowledge as
tensors. Fortunately keras comes with a whole lot of instruments for reshaping picture knowledge:

image_prep <- operate(x) {
  arrays <- lapply(x, operate(path) {
    img <- image_load(path, target_size = c(224,224))
    x <- image_to_array(img)
    x <- array_reshape(x, c(1, dim(x)))
    x <- imagenet_preprocess_input(x)
  })
  do.name(abind::abind, c(arrays, checklist(alongside = 1)))
}
explainer <- lime(img_path, mannequin, image_prep)

We now have an explainer mannequin for understanding how the vgg16 neural community
makes its predictions. Earlier than we go alongside, lets see what the mannequin consider our
kitten:

res <- predict(mannequin, image_prep(img_path))
imagenet_decode_predictions(res)

[[1]]
  class_name class_description      rating
1  n02124075      Egyptian_cat 0.48913878
2  n02123045             tabby 0.15177219
3  n02123159         tiger_cat 0.10270492
4  n02127052              lynx 0.02638111
5  n03793489             mouse 0.00852214

So, it’s fairly certain about the entire cat factor. The rationale we have to use
imagenet_decode_predictions() is that the output of a keras mannequin is all the time
only a anonymous tensor:

[1]    1 1000

NULL

We’re used to classifiers realizing the category labels, however this isn’t the case
for keras. Motivated by this, lime now have a approach to outline/overwrite the
class labels of a mannequin, utilizing the as_classifier() operate. Let’s redo our
explainer:

model_labels <- readRDS(system.file('extdata', 'imagenet_labels.rds', package deal = 'lime'))
explainer <- lime(img_path, as_classifier(mannequin, model_labels), image_prep)

There may be additionally an as_regressor() operate which tells lime, indubitably,
that the mannequin is a regression mannequin. Most fashions might be introspected to see
which sort of mannequin they’re, however neural networks doesn’t actually care. lime
guesses the mannequin kind from the activation used within the final layer (linear
activation == regression), but when that heuristic fails then
as_regressor()/as_classifier() can be utilized.

We are actually able to poke into the mannequin and discover out what makes it suppose our
picture is of an Egyptian cat. However… first I’ll have to speak about one more
idea: superpixels (I promise I’ll get to the reason half in a bit).

As a way to create significant permutations of our picture (keep in mind, that is the
central concept in lime), we’ve to outline how to take action. The permutations wants
to be substantial sufficient to have an effect on the picture, however not a lot that
the mannequin fully fails to recognise the content material in each case – additional,
they need to result in an interpretable outcome. The idea of superpixels lends
itself effectively to those constraints. Briefly, a superpixel is a patch of an space
with excessive homogeneity, and superpixel segmentation is a clustering of picture
pixels into various superpixels. By segmenting the picture to elucidate into
superpixels we are able to flip space of contextual similarity on and off in the course of the
permutations and discover out if that space is vital. It’s nonetheless essential to
experiment a bit because the optimum variety of superpixels rely on the content material of
the picture. Bear in mind, we’d like them to be giant sufficient to have an effect however not
so giant that the category likelihood turns into successfully binary. lime comes
with a operate to evaluate the superpixel segmentation earlier than starting the
clarification and it’s endorsed to play with it a bit — with time you’ll
possible get a really feel for the proper values:

# default
plot_superpixels(img_path)

# Altering some settings
plot_superpixels(img_path, n_superpixels = 200, weight = 40)

The default is ready to a fairly low variety of superpixels — if the topic of
curiosity is comparatively small it could be essential to extend the variety of
superpixels in order that the complete topic doesn’t find yourself in a single, or a number of
superpixels. The weight parameter will can help you make the segments extra
compact by weighting spatial distance greater than color distance. For this
instance we’ll keep on with the defaults.

Remember that explaining picture
fashions is way heavier than tabular or textual content knowledge. In impact it would create 1000
new photographs per clarification (default permutation dimension for photographs) and run these
via the mannequin. As picture classification fashions are sometimes fairly heavy, this
will end in computation time measured in minutes. The permutation is batched
(default to 10 permutations per batch), so that you shouldn’t be afraid of operating
out of RAM or hard-drive area.

clarification <- clarify(img_path, explainer, n_labels = 2, n_features = 20)

The output of a picture clarification is an information body of the identical format as that
from tabular and textual content knowledge. Every function can be a superpixel and the pixel
vary of the superpixel can be used as its description. Normally the reason
will solely make sense within the context of the picture itself, so the brand new model of
lime additionally comes with a plot_image_explanation() operate to do exactly that.
Let’s see what our clarification have to inform us:

plot_image_explanation(clarification)

We are able to see that the mannequin, for each the foremost predicted courses, focuses on the
cat, which is sweet since they’re each totally different cat breeds. The plot operate
received a number of totally different capabilities that can assist you tweak the visible, and it filters low
scoring superpixels away by default. Another view that places extra focus
on the related superpixels, however removes the context might be seen through the use of
show = 'block':

plot_image_explanation(clarification, show = 'block', threshold = 0.01)

Whereas not as widespread with picture explanations additionally it is doable to take a look at the
areas of a picture that contradicts the category:

plot_image_explanation(clarification, threshold = 0, show_negative = TRUE, fill_alpha = 0.6)

As every clarification takes longer time to create and must be tweaked on a
per-image foundation, picture explanations aren’t one thing that you just’ll create in
giant batches as you may do with tabular and textual content knowledge. Nonetheless, a number of
explanations may can help you perceive your mannequin higher and be used for
speaking the workings of your mannequin. Additional, because the time-limiting issue
in picture explanations are the picture classifier and never lime itself, it’s certain
to enhance as picture classifiers turns into extra performant.

Seize again

Aside from keras and picture help, a slew of different options and enhancements
have been added. Right here’s a fast overview:

• All clarification plots now embody the match of the ridge regression used to make
  the reason. This makes it simple to evaluate how good the assumptions about
  native linearity are stored.
• When explaining tabular knowledge the default distance measure is now 'gower'
  from the gower package deal. gower makes it doable to measure distances
  between heterogeneous knowledge with out changing all options to numeric and
  experimenting with totally different exponential kernels.
• When explaining tabular knowledge numerical options will not be sampled from
  a standard distribution throughout permutations, however from a kernel density outlined
  by the coaching knowledge. This could make sure that the permutations are extra
  consultant of the anticipated enter.

Wrapping up

This launch represents an vital milestone for lime in R. With the
addition of picture explanations the lime package deal is now on par or above its
Python relative, feature-wise. Additional improvement will give attention to enhancing the
efficiency of the mannequin, e.g. by including parallelisation or enhancing the native
mannequin definition, in addition to exploring various clarification sorts similar to
anchor.

Pleased Explaining!