mlpclassifier 0.1.1

An implementation of an MLP classifier (with an interface of that of scikit-learn's MLPClassifier class.

Neural-Network-From-Scratch-COSC-221-CSB

Neural Network to classify handwritten digits (a rite of passage project at this point lol).

We will try to re-implement a stripped down version of the MLPClassifier class from scikit-learn from first principles. With this, we can then train a general classifier using the Multi-Layered Perceptron model.

To run

So since we've re-implemented an MLP using scikit-learn's MLPClassifier as a template, the API should be familiar.

To import

Dataset

Download the dataset from Kaggle

curl -L https://www.kaggle.com/api/v1/datasets/download/hojjatk/mnist-dataset -o ./dataset.zip

Then just unzip it into a directory called ./dataset

unzip -d dataset ./dataset.zip

Optional, but clean redundancy:

rm -r *-idx*-ubyte

I've removed some duplicates, so currently I have:

$ ls ./dataset/
 t10k-images.idx3-ubyte   train-images.idx3-ubyte
 t10k-labels.idx1-ubyte   train-labels.idx1-ubyte

So it seems like by convention:

• we divide our dataset into training data, and then testing data
• currently, it seems like we have 60k training examples and 10k testing examples
• they do this to see how well the model has generalized

Reference model

For now, we'll use a reference model through scikit-learn.

TODO

• debug all the row vector stuff
• [] package it in pip
• [] document the API

Forward propagation

• variable L for layer
• a list $n^{[l]}$ for the size at each layer
• [] initialize using He's initalization
• forward propagation step using that forward propagation formula

Backward propagation

• He's initialization
• back propagation
• scoring
• saving
• [] make the learn rate $\alpha$ more adjustable