A neural network, and tensor dynamic automatic differentiation implementation for Rust.
let l1 = Dense::new(input_size, hidden_size, &initializer, Some(&relu));
let l2 = Dense::new(hidden_size, output_size, &initializer, Some(&softmax));
let mut model = Model::new(vec![&mut l1, &mut l2], &gradient_descent, &cross_entropy);
for _ in 0..iterations {
// array operations are never in-place for corgi, so values never change
let input = Array::from((vec![batch_size, input_size], vec![...]));
let target = Array::from((vec![batch_size, output_size], vec![...]));
let _result = model.forward(input);
let loss = model.backward(target);
// update the parameters, and clear gradients (backward pass only sets gradients)
model.update();
println!("loss: {}", loss);
}- Array operations are never in-place, meaning array values are never modified.
- Eager execution.
- Dynamic-as-possible computational graph.
for _ in 0..10 {
c = &c + &(&a * &b);
if c[0] > 50.0 {
c = &c * &a;
}
}
c.backward(None);- The Array is responsible differentiates operations done on it for the backward pass.
- No graph structure for ergonomics - an
Arraycontains only its children. - Arrays do note store consumers (at the moment). They store consumer counts instead.
- The
openblas, ornetlibfeatures can be enabled. - Versions prior to 0.9.7 of Corgi did not prioritise optimisation, and will be slow.
- Arrays are untracked by default, so if gradients are required,
tracked(), orstart_tracking()must be used (see the documentation for details). - Tracked arrays are arrays which require gradients to be computed, and stored.
- For more information, see the documentation for
tracked(), anduntracked()inarray.rs.
- Fully-connected neural network (full version):
let initializer = initializer::he();
let relu = activation::relu();
let softmax = activation::softmax();
let ce = cost::cross_entropy();
let gd = GradientDescent::new(learning_rate);
let l1 = Dense::new(input_size, hidden_size, &initializer, Some(&relu));
let l2 = Dense::new(hidden_size, output_size, &initializer, Some(&softmax));
let mut model = Model::new(vec![&mut l1, &mut l2], &gd, &ce);
for _ in 0..iterations {
let mut input = vec![0.0; input_size * batch_size];
let mut target = vec![0.0; output_size * batch_size];
// set inputs, and targets
// arrays in corgi should not be mutated after creation, so we initialise the values first
let input = Array::from((vec![batch_size, input_size], input));
let target = Array::from((vec![batch_size, output_size], target));
let _result = model.forward(input);
let loss = model.backward(target);
// update the parameters, and clear gradients (backward pass only sets gradients)
model.update();
println!("loss: {}", loss);
}- Dynamic computational graph:
let a = arr![5.0].tracked();
let b = arr![2.0].tracked();
let mut c = arr![0.0].tracked();
for _ in 0..10 {
c = &c + &(&a * &b);
if c[0] > 50.0 {
c = &c * &a;
}
}
assert_eq!(c, arr![195300.0]);
c.backward(None);
assert_eq!(c.gradient(), arr![1.0]);
assert_eq!(b.gradient(), arr![97650.0]);
assert_eq!(a.gradient(), arr![232420.0]);- Custom operation (still needs some work).
- Shields are from shields.io.
- MIT 6.034 on OpenCourseWare for a primer on Backward Propagation.
- CS231n YouTube recordings for a primer on Convolutional Neural Networks.
A lot of the library was built around being as dynamic as possible, meaning if chosen well, some design choices might be similar to other dynamic computational graph libraries.
Third-party libraries were used, and can be found in Cargo.toml.
- MIT
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