The Multilayer Perceptron project is a C++ desktop application that implements an artificial neural network for recognizing handwritten letters of the Latin alphabet (A-Z). The system provides a Qt6-based graphical interface that allows users to train neural networks, test their performance, and classify handwritten letters from images or hand-drawn input. The application demonstrates two distinct implementation approaches—matrix-based and graph-based—for constructing and executing multilayer perceptrons, enabling users to compare performance and architectural trade-offs.
make run
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GUI implementation, based on QT6
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Load train and test datasets from a csv file.
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Choose the network topology with 2-5 hidden layers.
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Training with using the backpropagation method and sigmoid activation.
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Matrix form: all layers are represented as weight matrices.
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Graph form: each neuron is represented as some node object connected to other nodes by refs.
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Perform experiments on a selected portion of the test sample, defined by a floating-point number ranging from 0 to 1.
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Load BMP images (image size can be up to 512x512) with Latin letters and classify them.
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Draw two-color square images by hand and classify them.
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Real-time training process for a user-defined number of epochs with displaying the error values for each training epoch.
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Run the training process using cross-validation for a given number of groups k.
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Save to a file and load weights of perceptron from a file.
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