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 a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Yosemite Summer vs. Winter 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 a machine learning model for this dataset using TensorFlow with a simple VGG-3 network. We will also observe the best result that we can obtain using the test dataset. The final model from this iteration will become our baseline model for future iterations.
ANALYSIS: The baseline model’s performance achieved an accuracy score of 99.00% after 20 epochs using the training dataset. The final model also processed the test dataset with 74.95% accuracy.
CONCLUSION: In this iteration, the TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.
Dataset Used: Yosemite Summer vs. Winter Dataset
Dataset ML Model: Binary image 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.