rubygrad

A port of Andrej Karpathy's micrograd to Ruby

Objectives

Learn neural networks, perceptrons, gradient descent, activation functions, backpropagation, etc.
Learn Ruby and compare it with Python
Evolve the library to add more features not necessarily present in micrograd for example the ability to specify a different activation function for each layer
(Work in Progress) Use it to build a perceptron to recognize handwritten digits (see my other project mnist-ready)

Installation

gem install rubygrad

Usage

require 'rubygrad'

Binary Classification Example

# Build a Machine Learning Perceptron with 4 layers
# First  Layer (Layer 0) => Input Layer => 3 Neurons => 3 Inputs
# Second Layer (Layer 1) => Hidden Layer => 4 Neurons
# Third  Layer (Layer 2) => Hidden Layer => 4 Neurons
# Fourth Layer (Layer 3) => Output Layer => 1 Neuron => 1 Output
nn = MLP.new(3, 4, 4, 1, :tanh)

# 4 input samples
x_inputs = [
    [2.0, 3.0, -1.0],
    [3.0, -1.0, 0.5],
    [0.5, 1.0, 1.0],
    [1.0, 1.0, -1.0]
]

# expected output for each of the 4 inputs above
y_expected = [1.0, -1.0, -1.0, 1.0]

passes = 2000
learning_rate = 0.2

(0...passes).each do |pass| 

    # forward pass (calculate output)
    y_calculated = x_inputs.map { |x| nn.calc(x) }

    # loss function (check how good the neural net is)
    loss = 0.0
    y_expected.each_index { |i| loss += (y_calculated[i] - y_expected[i]) ** 2 }

    # backward pass (calculate gradients)
    nn.zero_grad
    loss.backward

    # improve neural net (update weights and biases)
    nn.parameters.each { |p| p.value -= learning_rate * p.grad }
    
    # print some info about our progress from time to time
    nn.print_pass(learning_rate, loss, pass, passes) if (pass + 1) % (passes / 20) == 0 or pass == 0

    break if loss.value == 0 # just for fun and just in case
end

y_calculated = x_inputs.map { |x| nn.calc(x) }
puts
puts "Final NN results:"
y_calculated.each_with_index { |y_c, i| puts "Output: #{y_c} => Expected: #{y_expected[i]}" }

Output

Pass    1 => Learning rate: 0.20 => Loss: 6.4414947529
Pass  100 => Learning rate: 0.20 => Loss: 0.0014086315
Pass  200 => Learning rate: 0.20 => Loss: 0.0007669303
Pass  300 => Learning rate: 0.20 => Loss: 0.0005244376
Pass  400 => Learning rate: 0.20 => Loss: 0.0003974497
Pass  500 => Learning rate: 0.20 => Loss: 0.0003194754
Pass  600 => Learning rate: 0.20 => Loss: 0.0002667991
Pass  700 => Learning rate: 0.20 => Loss: 0.0002288631
Pass  800 => Learning rate: 0.20 => Loss: 0.0002002596
Pass  900 => Learning rate: 0.20 => Loss: 0.0001779336
Pass 1000 => Learning rate: 0.20 => Loss: 0.0001600304
Pass 1100 => Learning rate: 0.20 => Loss: 0.0001453591
Pass 1200 => Learning rate: 0.20 => Loss: 0.0001331204
Pass 1300 => Learning rate: 0.20 => Loss: 0.0001227580
Pass 1400 => Learning rate: 0.20 => Loss: 0.0001138729
Pass 1500 => Learning rate: 0.20 => Loss: 0.0001061715
Pass 1600 => Learning rate: 0.20 => Loss: 0.0000994331
Pass 1700 => Learning rate: 0.20 => Loss: 0.0000934883
Pass 1800 => Learning rate: 0.20 => Loss: 0.0000882054
Pass 1900 => Learning rate: 0.20 => Loss: 0.0000834802
Pass 2000 => Learning rate: 0.20 => Loss: 0.0000792291

Final NN results:
Output: 0.9946071757394565 => Expected: 1.0
Output: -0.9970474246057773 => Expected: -1.0
Output: -0.9960119927936328 => Expected: -1.0
Output: 0.9949518028977203 => Expected: 1.0

Specifying activation functions per each layer

# All layers will use tanh
nn = MLP.new(3, 4, 4, 1, :tanh)
# or (equivalent)
nn = MLP.new(3, 4, 4, 1, [:tanh])
# or (equivalent)
nn = MLP.new(3, 4, 4, 1, [:tanh, :tanh, :tanh])

# Layer 0 => That's the input layer, so there is no activation function
# Layer 1 => sigmoid
# Layer 2 => sigmoid
# Layer 3 => tanh
nn = MLP.new(3, 4, 4, 1, [:sigmoid, :sigmoid, :tanh])

Showing weights and biases of the whole neural network

nn = MLP.new(3, 4, 4, 1, [:sigmoid, :sigmoid, :tanh])

# show the neural net weights and biases as code
# you can easily save it for later and reload through the set_params method
nn.show_params

puts

# show the neural net weights and biases in words
nn.show_params(in_words = true)

Output

[
	[ 0.10716264032615364, -0.7071423208639602, 0.1163000965851324, 0.2701015638306199 ],
	[ 0.8748943671200455, -0.25715713613718627, -0.41685923836065575, -0.6043133638242268 ],
	[ 0.40159152554133537, -0.047327861996705245, 0.4876614339885963, 0.793143569774184 ],
	[ -0.28319370189054593, 0.6766873487030267, 0.8277741325778085, 0.3888260240294714 ]
],
[
	[ 0.8722769278933873, -0.9830828544066998, 0.715385878486424, -0.774099974211657, 0.7248963978186993 ],
	[ 0.3755762966825087, 0.03728211287511729, -0.04986344620919225, 0.3554754443267991, 0.7024771797583509 ],
	[ 0.46623333807978273, -0.053757185381019035, 0.03867611560991291, -0.11331576420042122, 0.31697978685612327 ],
	[ -0.23411955094766856, -0.23315628697626067, 0.11458612388308653, -0.20959685480548074, 0.372382068051728 ]
],
[
	[ 0.19761021225267417, -0.7834214164087676, 0.43077256716665757, -0.8111682816187338, -0.31730643380838086 ]
]

Layer 0: (3 inputs)
Layer 1: (4 neurons, :sigmoid activation)
	Neuron 1: (3 weights)
		Bias: 0.10716264032615364
		Weights: -0.7071423208639602, 0.1163000965851324, 0.2701015638306199
	Neuron 2: (3 weights)
		Bias: 0.8748943671200455
		Weights: -0.25715713613718627, -0.41685923836065575, -0.6043133638242268
	Neuron 3: (3 weights)
		Bias: 0.40159152554133537
		Weights: -0.047327861996705245, 0.4876614339885963, 0.793143569774184
	Neuron 4: (3 weights)
		Bias: -0.28319370189054593
		Weights: 0.6766873487030267, 0.8277741325778085, 0.3888260240294714
Layer 2: (4 neurons, :sigmoid activation)
	Neuron 1: (4 weights)
		Bias: 0.8722769278933873
		Weights: -0.9830828544066998, 0.715385878486424, -0.774099974211657, 0.7248963978186993
	Neuron 2: (4 weights)
		Bias: 0.3755762966825087
		Weights: 0.03728211287511729, -0.04986344620919225, 0.3554754443267991, 0.7024771797583509
	Neuron 3: (4 weights)
		Bias: 0.46623333807978273
		Weights: -0.053757185381019035, 0.03867611560991291, -0.11331576420042122, 0.31697978685612327
	Neuron 4: (4 weights)
		Bias: -0.23411955094766856
		Weights: -0.23315628697626067, 0.11458612388308653, -0.20959685480548074, 0.372382068051728
Layer 3: (1 neuron, :tanh activation)
	Neuron 1: (4 weights)
		Bias: 0.19761021225267417
		Weights: -0.7834214164087676, 0.43077256716665757, -0.8111682816187338, -0.31730643380838086

Setting all weights and biases manually

nn = MLP.new(3, 4, 4, 1, [:sigmoid, :sigmoid, :tanh])

puts "Random:"
nn.show_params

# Now set to whatever you want:
all = [
	[ 0.10716264032615364, -0.7071423208639602, 0.1163000965851324, 0.2701015638306199 ],
	[ 0.8748943671200455, -0.25715713613718627, -0.41685923836065575, -0.6043133638242268 ],
	[ 0.40159152554133537, -0.047327861996705245, 0.4876614339885963, 0.793143569774184 ],
	[ -0.28319370189054593, 0.6766873487030267, 0.8277741325778085, 0.3888260240294714 ]
],
[
	[ 0.8722769278933873, -0.9830828544066998, 0.715385878486424, -0.774099974211657, 0.7248963978186993 ],
	[ 0.3755762966825087, 0.03728211287511729, -0.04986344620919225, 0.3554754443267991, 0.7024771797583509 ],
	[ 0.46623333807978273, -0.053757185381019035, 0.03867611560991291, -0.11331576420042122, 0.31697978685612327 ],
	[ -0.23411955094766856, -0.23315628697626067, 0.11458612388308653, -0.20959685480548074, 0.372382068051728 ]
],
[
	[ 0.19761021225267417, -0.7834214164087676, 0.43077256716665757, -0.8111682816187338, -0.31730643380838086 ]
]

# Now change it
nn.set_params(all)

puts
puts "Manually changed to:"
nn.show_params

Output

Random:
[
	[ -0.13912293308540957, 0.30268799442308425, -0.048102743649764745, -0.962411703704696 ],
	[ -0.18594220779286608, 0.7714512137011857, -0.03133572981131927, 0.9173322198149367 ],
	[ -0.33333231737453084, 0.4417777450715037, -0.3164673982895738, -0.022523457918021128 ],
	[ -0.4833437064977759, 0.16276526923408197, -0.3352383125781533, -0.9459722548359815 ]
],
[
	[ 0.044017554817758375, 0.9404455938103717, 0.27848433588752086, 0.26209587268564327, -0.044640343587271536 ],
	[ 0.8732254287953087, -0.4878795211779561, -0.831005253289361, -0.9618004107162326, 0.8324107561903806 ],
	[ -0.9078279123217432, -0.4412340056261552, 0.2606014164539314, 0.9319403191251423, -0.06260506603018401 ],
	[ 0.2095400809859027, 0.6137215231647983, 0.6669886944164458, -0.3014712110858331, 0.4514830155708711 ]
],
[
	[ -0.5331404474464982, -0.8014351154541197, -0.3600371014778567, -0.8361159398334321, 0.08851349359521499 ]
]

Manually changed to:
[
	[ 0.10716264032615364, -0.7071423208639602, 0.1163000965851324, 0.2701015638306199 ],
	[ 0.8748943671200455, -0.25715713613718627, -0.41685923836065575, -0.6043133638242268 ],
	[ 0.40159152554133537, -0.047327861996705245, 0.4876614339885963, 0.793143569774184 ],
	[ -0.28319370189054593, 0.6766873487030267, 0.8277741325778085, 0.3888260240294714 ]
],
[
	[ 0.8722769278933873, -0.9830828544066998, 0.715385878486424, -0.774099974211657, 0.7248963978186993 ],
	[ 0.3755762966825087, 0.03728211287511729, -0.04986344620919225, 0.3554754443267991, 0.7024771797583509 ],
	[ 0.46623333807978273, -0.053757185381019035, 0.03867611560991291, -0.11331576420042122, 0.31697978685612327 ],
	[ -0.23411955094766856, -0.23315628697626067, 0.11458612388308653, -0.20959685480548074, 0.372382068051728 ]
],
[
	[ 0.19761021225267417, -0.7834214164087676, 0.43077256716665757, -0.8111682816187338, -0.31730643380838086 ]
]

Changing the activation functions manually

nn = MLP.new(3, 4, 4, 1, :sigmoid)

# All layers will be changed to tanh
nn.set_activation_function(:tanh)
# or (equivalent)
nn.set_activation_function([:tanh])
# or (equivalent)
nn.set_activation_function([:tanh, :tanh, :tanh])

# Change each of the three layers
# Remember that the first layer (input layer) does not have any activation function
nn.set_activation_function([:sigmoid, :sigmoid, :tanh])

saoj / rubygrad Goto Github PK

rubygrad's Introduction

rubygrad

Objectives

Installation

Usage

Binary Classification Example

Output

Specifying activation functions per each layer

Showing weights and biases of the whole neural network

Output

Setting all weights and biases manually

Output

Changing the activation functions manually

rubygrad's People

Contributors

Stargazers

Watchers

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent