Fashion-MNIST Classification

In this Python program an Artificial Neural Network is trained in order to classify fashion item images (fashion-mnist dataset).

Usage

py classifyFashionMNIST.py

Example

py .\classifyFashionMNIST.py
    Found 1000 correct labels using the untrained model
    Found 9000 incorrect labels using the untrained model
    Train on 48000 samples, validate on 12000 samples
    Epoch 1/12
    .
    .
    .
    Epoch 12/12
    Training time: 2014.26
    Test loss: 0.25
    Test accuracy: 0.91
    Found 9106 correct labels
    Found 894 incorrect labels

Requirements

In order to run the code, install the following packages:

• numpy
• sklearn
• matplotlib
• tensorflow==1.5
• keras

Description

The data-set contains 60,000 images and it's split as:

• Training set (48,000 images)
• Validation set (12,000 images)
• Test set (10,000 images)

Network Architecture

Layer (type)	Output Shape	Param #
conv2d_1 (Conv2D)	(None, 28, 28, 128)	2176
max_pooling2d_1 (MaxPooling2)	(None, 14, 14, 128)	0
dropout_1 (Dropout)	(None, 14, 14, 128)	0
conv2d_2 (Conv2D)	(None, 14, 14, 128)	262272
max_pooling2d_2 (MaxPooling2)	(None, 7, 7, 128)	0
dropout_2 (Dropout)	(None, 7, 7, 128)	0
flatten_1 (Flatten)	(None, 6272)	0
dense_1 (Dense)	(None, 64)	401472
dropout_3 (Dropout)	(None, 64)	0
dense_2 (Dense)	(None, 256)	16640
dropout_4 (Dropout)	(None, 256)	0
dense_3 (Dense)	(None, 10)	2570

Results

Author

Giorgos Argyrides (g.aryrides@outlook.com)