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 CycleGAN Cezanne vs. Photo dataset is a binary classification situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: The CycleGAN dataset collection contains 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 General Adversarial Networks (GAN).

In this iteration, we will construct a CNN model based on the DenseNet121 architecture to make predictions.

ANALYSIS: In this iteration, the DenseNet121 model’s performance achieved an accuracy score of 99.80% after ten epochs using the training dataset. The same model processed the validation dataset with an accuracy measurement of 98.90%. Finally, the final model processed the test dataset with an accuracy score of 99.87%.

CONCLUSION: In this iteration, the DenseNet121-based CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.

Dataset Used: CycleGAN Cezanne vs. Photo 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.

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