https://github.com/katarinamolotova/multilayer-perceptron

https://github.com/katarinamolotova/multilayer-perceptron

cpp17 gui machine-learning multilayer-perceptron qt6

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/katarinamolotova/multilayer-perceptron
• Owner: katarinamolotova
• Created: 2022-10-14T12:28:28.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2023-07-30T12:26:12.000Z (about 2 years ago)
• Last Synced: 2025-01-06T17:29:55.674Z (9 months ago)
• Topics: cpp17, gui, machine-learning, multilayer-perceptron, qt6
• Language: C++
• Homepage:
• Size: 35.2 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# multilayer_perceptron

We needed to implement the MultilayerPerceptron project:

- The program must be developed in C++ language of C++17 standard.

- The program contains standard set of targets: all, install, uninstall, clean, dvi, dist, tests. Installation directory is build.

- GUI implementation, based on any Qt6 library with API for C++17.

- The program is implemented using the MVC pattern.

- The program provides the ability to form and train neural network models to classify handwritten Latin letters

- The perceptron can:

    - classify images with handwritten letters of the Latin alphabet

    - have from 2 to 5 hidden layers

    - use a sigmoid activation function for each hidden layer

    - be able to learn on an open dataset (e.g. EMNIST-letters presented in the datasets directory). The ratio of the test sample to the training one should be no more than 2:8, i.e. the test sample makes no more than 20% of the initial dataset

    - show accuracy on a test sample over 70 percent

    - be trained using the backpropagation method

- Implement the perceptron in two ways:

    - in matrix form (all layers are represented as weight matrices)

    - in graph form (each neuron is represented as some node object connected to other nodes by refs)

- The interface of the program provides the ability to:

    - run the experiment on the test sample or on a part of it, given by a floating point number between 0 and 1 (where 0 is the empty sample - the degenerate situation, and 1 is the whole test sample). After the experiment, there should be an average accuracy, precision, recall, f-measure and total time spent on the experiment displayed on the screen

    - load BMP images (image size can be up to 512x512) with Latin letters and classify them

    - draw two-color square images by hand in a separate window

    - start the real-time training process for a user-defined number of epochs with displaying the error control values for each training epoch. Make a report as a graph of the error change calculated on the test sample for each training epoch

    - run the training process using cross-validation for a given number of groups k

    - switch perceptron implementation (matrix or graph)

    - switch the number of perceptron hidden layers (from 2 to 5)

    - save to a file and load weights of perceptron from a file