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Complex Machine Learning Model Overview:

This project features a complex machine learning model designed for binary classification tasks. The model leverages a combination of convolutional, recurrent, and dense layers to achieve high accuracy and generalization.

Model Architecture Convolutional Layers:

Purpose: Extract spatial features from sequential data.

Configuration:

  • Number of layers: 1 to 6
  • Filters: 32 to 128
  • Kernel sizes: 3 or 5

Purpose: Capture temporal and sequential dependencies.

Configuration:

  • Number of layers: 2 to 7
  • Units per layer: 50 to 150
  • Type: LSTM or GRU
  • Dropout rate: 0.3 to 0.5

Purpose: Process features and perform final classification.

Configuration:

  • Number of layers: 7 to 10
  • Units per layer: 64 to 256
  • L2 Regularization: 0.01 to 0.1
  • Dropout rate: 0.3 to 0.5

Activation: sigmoid for binary classification.

Requirements:

  • numpy
  • tensorflow
  • scikit-learn
  • keras-tuner

How to install:

  1. Import the Project:

    • Go to the Reblit page, click on the "Import from GitHub" option and enter the URL: https://github.com/TelmoFari/model-complex

  2. Install the dependencies

    • Run the following command to install the dependencies:

      pip install -r requirements.txt

  3. Run the Model:

    • Replit will probably prompt you for a run command. Use the command below:

      python model-complex-ia.py

Model Evaluation and Saving:

During training, the model is evaluated and automatically saved in the model-complex-ia folder.

Configuration Hyperparameters:

  • Adjust the hyperparameters: directly in the create_model function.
  • Training Intervals: The model training loop runs every minute. Modify the time.sleep(60) interval in model-complex-ia.py if needed.

License:

This project is licensed under the MIT License.

Acknowledgments:

  • TensorFlow and Keras: For providing the deep learning framework that made the development of this model possible.
  • Scikit-learn: For its indispensable machine learning tools.

Feel free to adapt this project to your needs. Making necessary changes to files.