This repository contains two hidden layers with 8 and 4 neurons, respectively. Both models use the rectified linear unit (ReLU) activation function in the first layer and the sigmoid activation function in the output layer. The models are compiled using binary cross-entropy as the loss function and the Adam optimizer.
The code defines the neural networks, trains them on four input/output pairs using the fit() method, and evaluates the trained models using the evaluate() method. The predictions made by the models on the same input pairs are also printed to the console.
The inputs and outputs are defined as numpy arrays, which allows for easy integration with other Python libraries.
The code requires Keras and numpy to be installed. Users can clone the repository and run the code in a Python environment with these dependencies installed.
Users can use this code as a template for building and training their own neural networks in Keras. They can modify the number of hidden layers, the number of neurons in each layer, the activation functions, and the loss and optimization functions to suit their own needs.
Contributions to this repository are welcome. Users can fork the repository, make changes, and submit pull requests.
This code is licensed under the MIT License, which allows for free use, modification, and distribution.