How to visualize CNTK network in C#

When building deep learning models, it is often required to check the model for consistency and proper parameters definition. In ANNdotNET, ml network models are designed using Visual Network Designer (VND), so it is easy to see the network configuration. Beside VND, in ANNdotNET there are several visualization features on different level: network preparation, model training phase, post training evaluation, performance analysis, and export results. In this blog post we will learn how to use those features when working with deep learning models

Visualization during network preparation and model training

When preparing network and training parameters, we need information about data sets, input format and output type. This information is relevant for selecting what type of network model to configure, what types of layers we will use, and what learner to select. For example the flowing image shows  network configuration containing of 2 embedding layers, 3 dense layers and 2 dropout layers. This network configuration is used to train CNTK model for mushroom data set. As can be seen network layers are arranged as listbox items, and the user has possibility to see, on the highest level, how neural networks looks like, which layers are included in the network, and how many dimensions each layer is defined. This is very helpful, since it provides the way of building network very quickly and accurately, and it requires much less times in comparisons to use traditional way of coding the network in python, or other programming language.

Image 1: ANNdotNET Network Settings 

ANNdotNET Network Settings page provides pretty much information about the network, input and output layers, what data set are defined, as well as whole network configuration arranged in layers. Beside network related information, the Network Settings tab page also provides the learning parameters for the network training. More about Visual Network Designer the ready can find on one of the previous blog post.

Since ANNdotNET implements MLEngine which is based on CNTK, so all CNTK related visualization features could be used. The CNTK  library provides rich set of visualizations. For example you can use Tensorboard in CNTK  for visualization not just computational graph, but also training history, model evaluation etc. Beside Tensorboard, CNTK provides logger module which uses Graphviz tool for visualizing network graph. The bad news of this is that all above features cannot be run on C#, since those implementation are available only in python.

This is one of the main reason why ANNdotNET provides rich set of visualizations for .NET platform. This includes: training history, model evaluation for training and validation data set, as well as model performance analysis. The following image show some of the visualization features: the training history (loss and evaluation) of minibatches during training of mushroom model:

Moreover, the following image shows evaluation of training and validation set for each iteration during training:

Those graphs are generated during training phase, so the user can see what is happening with the model.  This is of tremendous help, when deciding when to stop the training process, or are training parameters produce good model at all, or this can be helpful in case when can stop and change parameters values. In case we need to stop the training process immediately, ANNdotNET provides Stop command which stops training process at any time.

Model performance visualization

Once the model is trained, ANNdotNET provides performance analysis tool for all three types of ML problems: regression, binary and multi class classification.

Since the mushrooms project is binary ML problem the following image shows the performance of the trained model:

Using Graphviz to visualize CNTK network graph in C#

We have seen that ANNdotNET provides all types of visualizations CNTK models, and those features are provided by mouse click through the GUI interfaces. One more feature are coming to ANNdotNET v1.1 which uses Grpahviz to visualize CNTK network graph. The feature is implemented based on original CNTK python implementation with some modification and style.

In order to use Graphviz to visualize network computation graph the following requirements must be met:

  • Install Graphviz on you machine.
  • Register Graphviz path as system variable. (See image below)

Now that you have install Graphviz tool, you can generate nice image of your network model directly in ANNdotNET just by click on Graph button above the Visual Network Designer (see image 1).

Here is some of nice graphs which can be generate from ANNdotNET preclaculated models.

Graphviz generated graph of mushrooms model implemented in ANNdotNET

In case you like this nice visualization features go to, download the latest version from release section or just download the source code and try it with Visual Studio, but don’t forget to give a star.

Star ANNdotNET project if you found it useful.

In the next blog post I will show you how visualization of CNTK computational graph is implemented, so you will be able to use it in your custom solutions.


ANNdotNET v1.1 has been release


ANNdotNET –  is an open source project for deep learning written in C# for developing and training deep learning models. The project is based on Microsoft CNTK (CogNitive ToolKit) Microsoft open source library for deep learning. It is supposed to be higher API for deep learning in .NET, but also provides, data preparation and transformation from
rawDataSet  into mlready dataset, monitoring the training process with additional evaluation functions, capability of early stopping during training, model evaluation and validation, exporting and deployment options.

The process of creating, training, evaluating and exporting models is provided from the GUI Application and does not require knowledge for supported programming languages.

The ANNdotNET is ideal in several scenarios when user want:

  • more focus on neural network development and training process using on classic desktop approach, instead of focusing on coding,
  • less time spending on debugging source code and peripheral tasks like installing and updating packages, debugging errors in the code, and more focusing on different configuration and parameter variants,
  • to model and is not familiar with supported programming languages,

In case the problem requires more advanced custom models, or training process, ANNdotNET CMD provides high level of API for such implementation. All ml configurations developed with GUI tool, can be handled with CMD tool and vice versa.

To get quick introduction to the tool, there are dozens of pre-calculated projects included in the installer which can be opened from the Start page as well as from CMD tool. The projects are based on famous datasets freely distributed on repositories from several categories: regression, binary and multi-class classification problems, image classifications, times series, etc.

This version brings upgrade of Machine Learning Engine and set of minor bug fixes identified in the application.

The following enhancements has been made in this release

  • The ANNdotNET MLEngine now relies on CNTK 2.6.
  • Information about data sets has been added to Network Page
  • Chart controls on Training and Evaluation pages are simplified and improved visibility.
  • Refresh button has been removed and added automatic model evaluation.

Bug Fixes

  • Test Tab Page had bug which add new rows whenever the user press Evaluate button.

ANNdotNET v1.1 can be downloaded from the Github page at For full list of features you can see release note file at:

Export options in ANNdotNET

ANNdotNET v1.0 has been release a few weeks ago, and the feedback is very positive. Also up to now there is no any blocking or serious bug in the release which makes me very happy. For this blog post we are going through Export options in ANNdotNET.

The ANNdotNET supposed to be an application which can offer whole life-cycle for  machine learning project: from the defining raw data set, cleaning and features engineering, to training and evaluation of the model. Also with different mlconfig files within the same project, the user has ability to create as many ml configurations as wants. Once the user select the best ml configuration, and the training and evaluation process completes, the next step in ML project life-cycle is the model deployment/export.

Currently, ANNdotNET defines three export options:

  • Export model result to CSV file,
  • Export model and model result to Excel, and
  • Export model in CNTK file format.

With those three export option, we can achieve many ML scenarios.

Export to CSV

Export to CSV provides exporting actual and predicted values of testing data set to comma separated txt file. In case the testing data set is not provided, the result of validation data set will exported. In case nor testing nor validation dataset are not provided the export process is terminated.

The export process starts by selecting appropriate mlconfig file. The network model must be trained prior to be exported.

2018-10-22_9-35-07.pngOnce the export process completes, the csv file is created on disk. We can import the exported result in Excel, and similar content will be shows as image below:


Exported result is shows in two columns. The actual and predicted values. In case the classification result is exported, in the header the information about class values are exported.

Export to Excel

Export to Excel option is more than just exporting the result. In fact, it is deployment of the model into Excel environment. Beside exporting all defined data sets (training, Validation, and Test) the model is also exported. Predicted values are calculated by using ANNdotNET Excel Add-in, which the model evaluation looks like calling ordinary Excel formula.  More information how it works can be found here.


Exported xlsx file can be opened, and the further analysis for the model and related data sets can be continued. The following image shows exported model for Concrete Slum Test example. Since only two data sets are defined (training and validation) those data sets are exported. As can be seen the predicted column is not filled, only the row is filled with the formula that must be evaluated by inserting equal sign “=” in front of the formula.


Once the formula is evaluated for the first row, we can use Excel trick to copy it on other rows.

The same situation is for other data sets separated in Excel Worksheets.

Export to CNTK

The last option allows to export CNTK trained model in CNTK format. Also ONNX format will be supported as soon as being available on CNTK for C# library. This option is handy in situation where trained CNTK model being evaluated in other solutions.

For this blog post, there is a short video which the reader can see all three options in actions.