Kuzushiji MNIST character recognition

Background

Fitting the Kuzushiji-MNIST character recognition dataset with convolution networks using Keras.

The dataset is available on Kaggle and is described here: https://www.kaggle.com/anokas/kuzushiji . It is a very similar to the classical MNIST (70000 28x28 grayscale images, 10 classes), but larger (Kuzuhiji-49 - 270912 28x28 grayscale images, 49 classes).

Goal

• Multiclass classification : Get > 95% accuracy in classifying the images into 49 classes.

Challenges

• Size of dataset (270k images and 49 classes)

Software/Packages/Programs

Major

Name	Version
Python	3.6.6
Keras	2.2.4
hyperopt	0.2

Minor

Name	Version
matplotlib	3.0.3
NumPy	1.16.4
sklearn	0.21.2

Service

• Kaggle notebooks

Status

• CNN + batchnorm + hyperopt --> train = 0.99, test = 0.95

Todo

• Add some images to readme and the notebook

Overview of approach

Data and pre-processing

• Started with Kuzushiji dataset available in a convenient format.
• Train and test sets had similar class distributions and were balanced.
• Scaled features with SciPy's MinMaxScaler.
• Used keras.utils.to_categorical to one hot encode the labels.

Baseline

• Feedforward NN with 1 hidden layer, 32 ReLU units.
• Accuracy: train = 0.77, test = 0.65

Approach

• Convolution neural network.
• Used batchnorm greatly which greatly sped up learning.
• hyperopt for hyperparameter optimization to identify best kernel size, pool size and stride.
• Accuracy: train = 0.99, test = 0.94 --> overfitting

Fine tune

• Added another conv-pool-batchnorm layer to increase number of features.
• Accuracy: train = 0.99, test = 0.95