Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The CycleGAN Horse vs. Zebra dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains datasets that consist of images from two classes A and B (for example, apple vs. orange, horses vs. zebras, and so on). The researchers used the images to train machine learning models for research work in the area of General Adversarial Networks.

This Take1 iteration will construct and tune machine learning models for this dataset using TensorFlow with a simple VGG-1 network. We will also observe the best result that we can obtain using the test dataset. The final output from this iteration will become our baseline performance level for future iterations.

ANALYSIS: The baseline model’s (one layer with eight convolutional filters) performance achieved an accuracy score of 88.85% after 15 epochs using the unseen test dataset. After experimenting with different layer configurations, the best model (one layer with 64 convolutional filters) processed the validation dataset with 91.15% accuracy.

CONCLUSION: In this iteration, the best TensorFlow model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Horse vs. Zebra Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://people.eecs.berkeley.edu/%7Etaesung_park/CycleGAN/datasets/

One potential source of performance benchmarks: https://arxiv.org/abs/1703.10593 or https://junyanz.github.io/CycleGAN/

The HTML formatted report can be found here on GitHub.