This notebook will cover the following two major topics :
- Understand the basic concepts of CNN model
- Implement CNN model in realtime using Fashion MNIST dataset
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 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. The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms.
- Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total.
- Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
- The training and test data sets have 785 columns.
- The first column consists of the class labels (see above), and represents the article of clothing.
- The rest of the columns contain the pixel-values of the associated image.
- To locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix. For example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.
I used a custom CNN using the Sequential model and optimized it with Adam's optimizer. Moreover, I manipulated the data using data augmentation and increased the size of the dataset. The model trained on three epochs and got over 80% accuracy, and you may also train with more epochs ( e.g., 30) and get over 95% accuracy. Loss and accuracy plots are shown below.
I used a custom function to show some of the errors when the model couldn't find the right answer and illustrated the model prediction and real value.
