Neural Network Console Windows Version 2.00 Released

Monday, December 21, 2020


Posted by Shinichiro Takahashi

We have updated the Neural Network Console Windows today.
This post will introduce the major updates.

・ Custom unit function
・ Import as unit function of pre-trained model
・ Addition of XAI related plug-ins


1. Custom unit function

A function has been added that allows you to register your own defined unit as a component and add it to the network with a simple operation.

The usage of the custom unit function is as follows.

1. Edit the network structure you want to reuse as a unit and save it as a project file.

2. From the right-click menu “Register Custom Unit” in the component list, select the saved project file.

The selected project will be registered in the Custom Unit category at the bottom of the component list.

3. Add the registered custom unit to the network in the same way as adding a normal layer.

You can add the project saved in step 1 to the current network as a unit.

The custom unit function makes it easier to reuse your frequently used network architecture.


2. Import as Unit function of pre-trained model

A function to import pre-trained models such as nnp, onnx, and pb files as a unit has been added.

From the right-click menu on the Edit tab, select Import, “Import, nntxt, nnp, ONNX as Unit” and select the pre-trained model file.
The selected pre-trained model will be imported as a unit into the current project.

This feature makes it easier to perform transfer learning using a pre-trained model.


3. Addition of XAI related plugins

The following four plug-ins have been added as Explainable AI (XAI) related plug-ins.

SGD Influence
Using a method called SGD Influence [1], the influence of the input images on recognition result are evaluated. The dataset and the scores are shown in the influential order, which can be referred for data cleansing.

Face Evaluation
Measure skin color of human face in input images, calculating a score called Individual Typology Angle (ITA) [2].

LIME (batch)
Using a method called LIME [3], the areas of the input image that affect the classification result are made visible in the model, which performs image classification. LIME(batch) plug-in processes all images in the specified dataset, while LIME plug-in processes a single image.

LIME (tabular)
Using a method called LIME [3], a classification result is explained with the contribution of the features in input table data. Each feature is explained with a set of inequality and degree of contribution, which enables to interpret the classifier judgement [4].


We will continue to update Neural Network Console.
We look forward to getting feedbacks from the users for futher improvements!

Neural Network Console Windows


S.Hara, A. Nitanday, T. Maehara
“Data Cleansing for Models Trained with SGD” (2019)

Diversity in Faces
Michele Merler, Nalini Ratha, Rogerio S. Feris, John R. Smith

Why Should I Trust You ?: Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin

Note that this plugin does not support regression model or classification model with categorical features.