Multi-Class Deep Learning Model for Fashion-MNIST Using Keras Take 2

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

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Fashion MNIST dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Fashion-MNIST is an MNIST-like fashion product dataset from Zalando Research. It consists of a training set of 60,000 clothing article examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.

For the Take1 iteration, we constructed a simple linear/dense deep-learning model to classify the images. This model serves as the baseline for future iterations.

For this iteration, we will tune this simple model further to examine whether we can improve its performance.

ANALYSIS: For the Take1 iteration, the baseline performance of the model achieved an accuracy score of 88.58% after training for three epochs. The same baseline model processed the test dataset with an accuracy of 87.72%. If we were to train the model much further than three epochs, we could run into the over-fitting issue.

For this iteration, the performance of the Take2 model with the default parameters achieved an accuracy score of 91.68% after training for ten epochs. After tuning the hyperparameters, the Take2 model with the best hyperparameters processed the training dataset with an accuracy of 91.82%. The same Take2 model processed the test dataset with an accuracy of 89.20%.

For this iteration, tuning the hyperparameters does not appear to have a noticeable effect on the model accuracy. The reason could be that, for image recognition, a linear model can only do so much.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

Dataset Used: Fashion-MNIST Dataset

Dataset ML Model: Multi-class classification with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.