File Structure in ANNdotNET
The basic file of ANNdotNET is machine learning configuration file, shortly named mlconfig. The mlconfig file holds the information about: features, labels, learning and training parameters, neural network configuration and set of paths required for running machine learning solution. Whenever an user presses Run button from ANNdotNET GUI or Run command/method from ANNdotNET CMD, the training process start by loading the mlconfig file into application memory.
Furthermore, the ANNdotNET GUI Tool uses annproject file (*.ann) that holds information of one or more mlconfig files. The user start by creating a new mlconfig file by pressing New->Default button. Then the user must load the raw dataset. In case the New->Image Classifier command is pressed, the user has to provide folder path for each image labels.
Regardless of the created project, the mlconfig should be created. As an example following image shows Breast Cancer annproject created based on the Breast Cancer data set. The project consists of two mlconfig files. Each ml config
file holds different ml configuration. This is very handy in case you want to find best possible neural network model for different ml configuration. This is natural way of doing data science.
On the other hand, ANNdotNET CMD is based on mlconifg file only. It can handle only one filer per session. Any mlconfig file created with GUI tool can be run with ANNdotNET CMD.
There are two ways how to create mlconfig and annproject files:
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Using ANNdotNET GUI Tool, or
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Manually by using any txt-based editor.
Press New button and select Default or Image Classifier commands in ANNdotNET GUI in order to start creating a new project. Once the project is created, user must load raw dataset or define image paths for all defined labels in order to create ml config file.
To create mlconfig file manually, please see section about manually create mlconfig file.
Once the user presses the New button, the empty project is created. While creating a new project, the project file and project folder are created on disk. We can illustrate file and folder structure as follows:
Say we create a new project called Project01. The folder named Project01 is created, as well as project file named Projct01.ann. Those two items are shown on the following image:
Once the project (annproject) is created, we can load raw dataset file. The raw dataset file (rawDataSet) is the file contains data of the problem we are going to solve using the tool. The structure of the rawDataSet is classic table-based
textual data. For example we can load https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data file
directly into ANNdotNET and start processing the data in order to implement ml solution.
Once we load the rawDataSet, the project file structure now looks like on the following image.
Notice, that rawDataSet file is created in the project folder and renamed according to the ANNdotNET naming convention. So, loaded rawDataSet is renamed to [ProjectName]_rawdata.txt, but it can be of any valid file name.
Now that we have annproject and rawDataSet, we can create ml configuration (mlconfig) file. The mlconfig file is created when the user press Create MLConfig button from the application’s ribbon control. Within the same project, we can create as many ml configuration as we want, with different forms, structure and size of training and validation dataset, also with different network, learning and training parameters.
The following image shows the ANNdotNET project with created 4 ml configurations: Model0, Model1, Model2 and Model3.
For each ml configuration separate folder is created, which offers clean and easy way to follow file structures. Each ml configuration contains folders and files arranged within the folder. The following image shows files and folder structure of ANNdotNET ml configuration.
Depending of stage of completeness, the ml configuration can consist of the following folders:
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data– contains training, validation and testing ml ready data set, -
log– contains files of training history information, -
models– files of cntk format created during various phase of training, -
temp_models– folder holding temporary model files during training. All content from the folder is deleted once the trainer is stopped.
Files which can be found at the ml configuration folder are:
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mlconfig– ml configuration file, it contains the information for training, validation and evaluating model. -
model checkpoint state files– files stored current state of the trainer. This file is needed in case the user want to continue with training based on the previous trainer state.
The process of creating mlconfig file, starts by data transformation where the rawdataset transforms into training and validation mlready dataset. The mlready dataset is the data set which is created from the rawDataSet, with format, features and label definition which the CNTK library can recognize.