GlenCrawford / tensorflow_mnist_fashion_recognition

Clothing classification with a Tensorflow 2 and Keras neural network.

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MNIST fashion image recognition with a Tensorflow 2 and Keras neural network

Tensorflow/Keras neural network to train on the MNIST Fashion dataset and classify inputs with 87% accuracy.

The dataset is a collection of images (60,000 for training and 10,000 for testing), each one being an image of 28 by 28 pixels, each pixel being a greyscale value from 0 to 255. Each image has an associated label, which is an integer between 0 and 9, mapping as follows:

Label Garment type
0 T-shirt/top
1 Trouser
2 Pullover
3 Dress
4 Coat
5 Sandal
6 Shirt
7 Sneaker
8 Bag
9 Ankle boot

Adapted/modified/annotated starting from a tutorial by @TechWithTimm.

Requirements

Python version: 3.7.4

See dependencies.txt for packages and versions (and below to install).

Architecture of the neural network

Each image input is a 2D array, representing a 28 by 28 pixel image, with each value being a decimal from 0 to 1 (shrunk down from greyscale 0 to 255 values).

Input layer: 784 neurons (28 * 28), one for each pixel.

One hidden layer: 128 neurons.

Output layer: 10 neurons, one for each of the 10 labels/classes, as integers from 0 to 9. The total of all of the values of this layer's neurons will equal 1. The network's prediction is the neuron with the highest value.

Setup

Clone the Git repo.

Install the dependencies:

pip install -r dependencies.txt

Run

python main.py

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Clothing classification with a Tensorflow 2 and Keras neural network.


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