New Neural Networks Rust crate
Let's try to approximate simple sin(x) function.
```rust /* Define neural network with 1 neuron in input layers (we have only 1 argument in sin(x), so it should be 1 neuron in the input layer). Network contains 2 hidden layers (that have 8 and 6 neurons respectively). And, such as sin(x) returns single value, it is reasonable to have 1 neuron in the output layer. */ let mut nn = FeedForward::new(&[1, 8, 6, 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 <= 3.0 {
data.push(&[i], &[i.sin()]);
i += 0.1;
}
// Here, we set necessary parameters and train neural network by our DataSet with 30 000 iterations
nn.activation(Tanh)
.learning_rate(0.05)
.train(&data, 30_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, i.sin(), res);
i += 0.05;
}
```
Expected output
for [0.000], [0.000] -> [0.003]
for [0.050], [0.050] -> [0.048]
for [0.100], [0.100] -> [0.098]
for [0.150], [0.149] -> [0.149]
for [0.200], [0.199] -> [0.199]
for [0.250], [0.247] -> [0.248]
for [0.300], [0.296] -> [0.297]
But we don't want to lose our trained network so easily. So, there is functionality to save and restore neural networks from files.
```rust
/*
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(&nn, "test.flow");
/*
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 = load("test.flow");
```
classic XOR problem ```rust /* 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]); let mut data = DataSet::new();
data.push(&[0f64, 0f64], &[0f64]);
data.push(&[1f64, 0f64], &[1f64]);
data.push(&[0f64, 1f64], &[1f64]);
data.push(&[1f64, 1f64], &[0f64]);
nn.activation(activators::Type::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]
```
csv, xlsx, etc)Previously the library was created only for educational purposes. Saying about now there is, also, sport interest :)
Insert into cargo.toml [dependencies] block next line
neuroflow = { git = "https://github.com/MikhailKravets/neuroflow.git" }
Then in your code
rust
extern crate neuroflow;
MIT License
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