Contents

require 'backprop'

include BackProp

# inputs x1, x2
x1 = Value.new(2, label: :x1)
x2 = Value.new(0, label: :x2)

# weights w1, w2
w1 = Value.new(-3, label: :w1)
w2 = Value.new(1, label: :w2)

# neuron bias
b = Value.new(6.8813735870195432, label: :b)

xw1 = x1*w1; xw1.label = :xw1
xw2 = x2*w2; xw2.label = :xw2

sum = xw1 + xw2; sum.label = :sum
n = sum + b; n.label = :n

o = n.tanh; o.label = :o

puts "Calculate gradient by hand:"
o.gradient = 1

# do/dn
# d/dx tanh x = 1 - tanh(x)^2

# 1 - o**2

n.gradient = 1 - o.value ** 2

# n = sum + b
sum.gradient = n.gradient
b.gradient = n.gradient

# sum = xw1 + xw2
xw1.gradient = sum.gradient
xw2.gradient = sum.gradient

# xw1 = x1 * w1
x1.gradient = xw1.gradient * w1.value
w1.gradient = xw1.gradient * x1.value

# xw2 = x2 * w2
x2.gradient = xw2.gradient * w2.value
w2.gradient = xw2.gradient * x2.value

p o
puts

puts "Reset gradient:"
o.reset_gradient
p o
puts

puts "Calculate gradient via backprop:"
o.backward
p o
puts

Version data entries

2 entries across 2 versions & 1 rubygems

Version	Path
backprop-0.0.1.1	demo/neuron.rb
backprop-0.0.0.1	demo/neuron.rb