In this project you will discover how to effectively use the Keras library in your machine learning project by working through a binary classification project step-by-step.
The dataset we will use in this tutorial is the Sonar dataset.
This is a dataset that describes sonar chirp returns bouncing off different services. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders.
You can learn more about this dataset on the UCI Machine Learning repository: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)
It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.
In this post, you discovered the three API styles used in Keras Deep Learning library in Python.
You learned how you can work through a binary classification problem step-by-step with Keras, specifically:
- How to load and prepare data for use in Keras.
- How to create a baseline neural network model.
- How to evaluate a Keras model using scikit-learn and stratified k-fold cross validation.
- How data preparation schemes can lift the performance of your models.
- How experiments adjusting the network topology can lift model performance.
What things you need to install the software and how to install them
puthon IDE
jupyter notebook
- python - Programming Language
- tensorflow - TensorFlow is an open-source machine learning library for research and production
- keras - Keras is a high-level neural networks API
- sklearn - Scikit-learn is a free software machine learning library for the Python
- numpy - NumPy is the fundamental package for scientific computing
- pandas - Pandas is a software library used for data manipulation and analysis
- M.Junaid Fiaz - JD
This project is licensed under the APACHE License - see the LICENSE.md file for details