CIFAR10 Image Classifier Explanations¶
We will use a Tensorflow classifier built on CIFAR10 image dataset which is a 10 class image dataset to show the example of explanation on image data.
Create the InferenceService with Alibi Explainer¶
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
name: "cifar10"
spec:
predictor:
tensorflow:
storageUri: "gs://seldon-models/tfserving/cifar10/resnet32"
resources:
requests:
cpu: 0.1
memory: 5Gi
limits:
memory: 10Gi
explainer:
alibi:
type: AnchorImages
storageUri: "gs://seldon-models/tfserving/cifar10/explainer-py36-0.5.2"
config:
batch_size: "40"
stop_on_first: "True"
resources:
requests:
cpu: 0.1
memory: 5Gi
limits:
memory: 10Gi
Note
The InferenceService resource describes:
- A pretrained tensorflow model stored on a Google bucket
- An AnchorImage Seldon Alibi Explainer, see the Alibi Docs for further details.
Test on notebook¶
Run this example using the Jupyter notebook.
Once created you will be able to test the predictions:
And then get an explanation for it: