Perceptron
Package for training Multiclass Perceptron models.
Installation
You can clone the package
using Pkg
Pkg.clone("https://github.com/davidbp/MulticlassPerceptron.jl") Or use add to install the package. Remember to be in pkg> mode inside Julia (type ]).
(v1.1) pkg> add "https://github.com/davidbp/MulticlassPerceptron.jl"
Test the code
In the examples folder there are some code examples to test the package.
Executing julia --project=. ./examples/basic_usage_train.jl you should get
Loading data
MNIST Dataset Loading...
MNIST Dataset Loaded, it took 0.69 seconds
Start Learning
Epoch: 50 Accuracy: 0.899
Learning took 9.635 seconds
Results:
Train accuracy:0.93595
Test accuracy:0.9263
If this works then you can already use MulticlassPerceptron models!
Core model
The following code shows how to instantiate a struct MulticlassPerceptronCore and how to train it.
using CategoricalArrays
using MulticlassPerceptron
using Statistics
n, p, n_classes, sparse, f_average_weights = 100, 2, 2, false, true
X, y,_ = MulticlassPerceptron.make_blobs(n; centers=n_classes, random_seed=4, return_centers=true)
X = copy(X')
perceptron_core = MulticlassPerceptronCore(Float64, n_classes, p, sparse)
fit!(perceptron_core, X, y; n_epochs=200, f_average_weights=f_average_weights, verbosity=2)
ŷ = MulticlassPerceptron.predict(perceptron_core, X)
println("length(y)=$(length(y))")
println("size(X)=$(size(X))")
println("length(ŷ)=$(length(ŷ))")
println("Accuracy $(mean(ŷ .== y))")Basic usage
This code snippet loads the MNIST Dataset and saves the classes as a CategoricalArray
using MulticlassPerceptron
using MLDatasets
using CategoricalArrays
## Load data
train_imgs = MNIST.images(:train) # size(train_imgs) -> (60000,)
test_imgs = MNIST.images(:test) # size(test_imgs) -> (10000,)
## Prepare data
train_x = Float32.(hcat(reshape.(train_imgs, :)...)) # size(train_x) -> (784, 60000)
test_x = Float32.(hcat(reshape.(test_imgs, :)...)) # size(test_x) -> (784, 60000)
train_y = MNIST.labels(:train) .+ 1;
test_y = MNIST.labels(:test) .+ 1;
## Encode targets as CategoricalArray objects
train_y = CategoricalArray(train_y)
test_y = CategoricalArray(test_y)We can create a MulticlassPerceptronClassifer as follows :
using MulticlassPerceptron
n_features = size(train_x, 1);
n_classes = length(unique(train_y));
perceptron = MulticlassPerceptron.MulticlassPerceptronClassifier(n_epochs=50; f_average_weights=true)The function fit is used to train the model. The result containing the trained model is kept inside fitresult.
fitresult, _ , _ = MLJBase.fit(perceptron, 1, train_x, train_y)