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fix: ✏️ Fix XOR to NXOR

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Louis Gallet 2024-11-02 16:02:27 +01:00
parent 5d66a2951e
commit 2655297f4e
Signed by: lgallet
GPG Key ID: 84D3DF1528A84511
5 changed files with 7 additions and 7 deletions
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main.pdf (Stored with Git LFS)
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sections/partie-technique/IA/IA.tex
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This section covers our research on artificial intelligence to develop an AI component for the project, particularly for the OCR.
Our first draft of the AI has not been integrated into the main project\footnote{You can find the code source of the project here: https://gitea.louisgallet.fr/lgallet/XOR-NeuralNetwork-C} because it is focused on our research on AI rather than the project itself.
Our first draft of the AI has not been integrated into the main project\footnote{You can find the code source of the project here: https://gitea.louisgallet.fr/OCRudoku/NXOR-NeuralNetwork-C} because it is focused on our research on AI rather than the project itself.
Below, you will find the research we conducted for the AI.

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sections/partie-technique/IA/entrainement/entrainement.tex
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@ -6,6 +6,6 @@ For each training example, the network performs a forward pass, then applies bac
The output results are displayed at each training step, allowing visualization of the final values of weights and biases.
\begin{figure}[H]
\caption{Example output of the training of the XOR neural network.}
\caption{Example output of the training of the NXOR neural network.}
\includegraphics[scale=0.5]{sections/partie-technique/IA/entrainement/ia-train-demo.png}
\end{figure}

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sections/partie-technique/IA/entrainement/ia-train-demo.png (Stored with Git LFS)
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sections/partie-technique/IA/structure-neuronne/structure-neuronne.tex
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The structure of this neural network consists of an input layer, a hidden layer, and an output layer. The network is configured to solve the XOR problem, with binary input values and a binary output. The hidden layer, represented by the array \texttt{hiddenLayer}, is connected to the inputs through weights \texttt{hiddenWeights}, while the output layer, \texttt{outputLayer}, is connected to the hidden layer via the weights \texttt{outputWeights}. The biases for each layer are initialized in \texttt{hiddenLayerBias} and \texttt{outputLayerBias}.
The structure of this neural network consists of an input layer, a hidden layer, and an output layer. The network is configured to solve the Non-XOR problem, with binary input values and a binary output. The hidden layer, represented by the array \texttt{hiddenLayer}, is connected to the inputs through weights \texttt{hiddenWeights}, while the output layer, \texttt{outputLayer}, is connected to the hidden layer via the weights \texttt{outputWeights}. The biases for each layer are initialized in \texttt{hiddenLayerBias} and \texttt{outputLayerBias}.