NeuroFlow is an experimental deep learning framework written in Julia.
It implements auto-gradient functionality with an atomic level dynamic computational graph and provides api in Pytorch style.
import Pkg
Pkg.add("NeuroFlow")We start with a simple linear example:
using NeuroFlow
import Distributions: Uniform, Normal, mean
using Plots
# generate some fake data obeyed the linear model
N = 1000
x = rand(Uniform(-10, 10), N) |> sort
ϵ = rand(Normal(0, 1), N)
# parameters setting
a, b = 2.5, 1.5
y = a .* x .+ b .+ ϵ
# declare parameters which needs to be optimize
â,b̂ = Param(1.), Param(1.)
# define the linear model with parameters
lm(x) = â * x + b̂
# use SGD optimizer
optimizer = SGD([â;b̂]; η=1e-2)
loss_records = []
# train for 100 epochs
for epoch in 1:100
ŷ = lm.(x)
loss = mean((y.-ŷ).^2)
# this three steps are just like pytorch
zero_grad!(optimizer)
backward!(loss)
step!(optimizer)
push!(loss_records, loss.val)
if epoch % 5 == 0
println("epoch=$epoch,loss=$(loss.val)")
end
endMore detail about this example can be seen in examples/LinearRegression.jl
More examples can be found in examples