Neural Network Framework Implementation in Python

Introduction This is a Python-based neural network framework for creating and training artificial neural networks. It provides the flexibility to define, configure, and train custom neural network architectures.

Features Modular Design: The framework is designed with modularity in mind, allowing you to easily assemble and configure neural network layers.

Activation Functions: A variety of activation functions are available, and you can even define custom ones.

Data Normalization: It includes data normalization methods to ensure stable training.

Training and Testing: The framework supports training your model and testing its accuracy against a test dataset.

Installation To use this framework, make sure you have Python installed. Clone this repository and install the required dependencies:

bash Copy code git clone <repository_url> cd neural-network-framework pip install -r requirements.txt Usage Data Preprocessing:

The framework assumes a CSV dataset (e.g., "futebol.csv"). You can adapt the dataset by changing the file path in the code. Model Configuration:

Define the neural network architecture using the provided layers, activation functions, and their configurations. Data Normalization:

If necessary, normalize the data to ensure stable training. The code includes an example of data normalization. Training:

Train your model using the provided training data. You can configure the number of epochs and learning rate. python Copy code model.fit(x_train, y_train, num_epochs, learning_rate) Evaluation:

Evaluate the model's performance on the training and test datasets to assess accuracy. Prediction:

Use the trained model for making predictions. python Copy code predicted = model.predict(inputs) Example A complete example using the provided dataset is available in the code. You can follow the code structure and customize it for your own data and network architecture.

License This framework is released under the MIT License. You can find more details in the LICENSE file.

Credits This framework was created by Carlos Calgaro.

Issues and Contributions If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on GitHub.