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 Flower Photos dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The Flower Photos dataset is a collection of 3,670 flower photos in five different species. This dataset is part of the TensorFlow standard dataset collection.
From iteration Take1, we constructed and tuned a machine learning model using a simple three-layer MLP network. We also observed the best result that we could obtain using the validation dataset.
In this Take2 iteration, we will construct and tune a machine learning model using the VGG-16 architecture. We will also observe the best result that we can obtain using the validation dataset.
ANALYSIS: From iteration Take1, the baseline model’s performance achieved an accuracy score of 80.24% after 25 epochs using the training dataset. After tuning the model, the model also processed the validation dataset with an accuracy score of 74.69%.
In this Take2 iteration, the VGG-16 model’s performance achieved an accuracy score of 73.71% after 25 epochs using the training dataset. After tuning the model, the model also processed the validation dataset with an accuracy score of 65.53%.
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: Flower Photos Dataset
Dataset ML Model: Multi-class image classification with numerical attributes
Dataset Reference: https://www.tensorflow.org/datasets/catalog/tf_flowers
One potential source of performance benchmarks: https://www.tensorflow.org/tutorials/images/classification
The HTML formatted report can be found here on GitHub.