Multi-Class Image Classification Deep Learning Model for Flower Photos Using TensorFlow Take 1

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.

In this Take1 iteration, we will construct and tune a machine learning model using a simple three-layer MLP network. We will also observe the best result that we can obtain using the validation dataset.

ANALYSIS: In this Take1 iteration, The baseline model’s performance achieved an accuracy score of 80.24% after 25 epochs using the training dataset. After tuning the model, the best model processed the validation dataset with an accuracy score of 74.69%.

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:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.