NeuroFlow is fast neural networks (deep learning) Rust crate. It relies on three pillars: speed, reliability, and speed again.
Hello, everyone! Work on the crate is currently suspended because I am a little busy to do it :( Thanks you all
How to use
Let's try to approximate very simple function 0.5*sin(e^x) - cos(e^(-x)).
extern crate neuroflow;
use neuroflow::FeedForward;
use neuroflow::data::DataSet;
use neuroflow::activators::Type::Tanh;
fn main(){
/*
Define neural network with 1 neuron in input layers. Network contains 4 hidden layers.
And, such as our function returns single value, it is reasonable to have 1 neuron in the output layer.
*/
let mut nn = FeedForward::new(&[1, 7, 8, 8, 7, 1]);
/*
Define DataSet.
DataSet is the Type that significantly simplifies work with neural network.
Majority of its functionality is still under development :(
*/
let mut data: DataSet = DataSet::new();
let mut i = -3.0;
// Push the data to DataSet (method push accepts two slices: input data and expected output)
while i <= 2.5 {
data.push(&[i], &[0.5*(i.exp().sin()) - (-i.exp()).cos()]);
i += 0.05;
}
// Here, we set necessary parameters and train neural network by our DataSet with 50 000 iterations
nn.activation(Tanh)
.learning_rate(0.01)
.train(&data, 50_000);
let mut res;
// Let's check the result
i = 0.0;
while i <= 0.3{
res = nn.calc(&[i])[0];
println!("for [{:.3}], [{:.3}] -> [{:.3}]", i, 0.5*(i.exp().sin()) - (-i.exp()).cos(), res);
i += 0.07;
}
}Expected output
for [0.000], [-0.120] -> [-0.119]
for [0.070], [-0.039] -> [-0.037]
for [0.140], [0.048] -> [0.050]
for [0.210], [0.141] -> [0.141]
for [0.280], [0.240] -> [0.236]
But we don't want to lose our trained network so easily. So, there is functionality to save and restore neural networks from files.
/*
In order to save neural network into file call function save from neuroflow::io module.
First argument is link on the saving neural network;
Second argument is path to the file.
*/
neuroflow::io::save(&mut nn, "test.flow").unwrap();
/*
After we have saved the neural network to the file we can restore it by calling
of load function from neuroflow::io module.
We must specify the type of new_nn variable.
The only argument of load function is the path to file containing
the neural network
*/
let mut new_nn: FeedForward = neuroflow::io::load("test.flow").unwrap();Classic XOR problem (with no classic input of data)
Let's create file named TerribleTom.csv in the root of project. This file should have following innards:
0,0,-,0
0,1,-,1
1,0,-,1
1,1,-,0
where - is the delimiter that separates input vector from its desired output vector.
extern crate neuroflow;
use neuroflow::FeedForward;
use neuroflow::data::DataSet;
use neuroflow::activators::Type::Tanh;
fn main(){
/*
Define neural network with 2 neurons in input layers,
1 hidden layer (with 2 neurons),
1 neuron in output layer
*/
let mut nn = FeedForward::new(&[2, 2, 1]);
// Here we load data for XOR from the file `TerribleTom.csv`
let mut data = DataSet::from_csv("TerribleTom.csv");
// Set parameters and train the network
nn.activation(Tanh)
.learning_rate(0.1)
.momentum(0.15)
.train(&data, 20_000);
let mut res;
let mut d;
for i in 0..data.len(){
res = nn.calc(data.get(i).0)[0];
d = data.get(i);
println!("for [{:.3}, {:.3}], [{:.3}] -> [{:.3}]", d.0[0], d.0[1], d.1[0], res);
}
}Expected output
for [0.000, 0.000], [0.000] -> [0.000]
for [1.000, 0.000], [1.000] -> [1.000]
for [0.000, 1.000], [1.000] -> [1.000]
for [1.000, 1.000], [0.000] -> [0.000]
Installation
Insert into your project's cargo.toml block next line
[dependencies]
neuroflow = "0.1.3"Then in project root file
extern crate neuroflow;License
MIT License
Attribution
The origami bird from logo is made by Freepik