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autograd

build Crates.io version docs.rs

Tensors and differentiable operations backed by ndarray.

Cargo.toml

If you use basic linalg operations, especially matrix multiplications, blas feature would be important to speed them up.

[dependencies]
autograd = {"<version>", features = ["blas", "<blas-implementation-choice>"] }

<blas-implementation-choice> must be one of the following (See also blas-src)

  • accelerate macOS only
  • intel-mkl Intel/AMD CPU only. Includes Vector Mathematics (VM) ops
  • openblas

Features

Reverse-mode automatic differentiation using lazy tensors

Here we are just computing partial derivatives of z = 2x^2 + 3y + 1.

use autograd as ag;
use ag::tensor_ops::*;

ag::run(|ctx: &mut ag::Context<_>| {
   let x = ctx.placeholder("x", &[]);
   let y = ctx.placeholder("y", &[]);
   let z = 2.*x*x + 3.*y + 1.;

   // dz/dy
   let gy = &grad(&[z], &[y])[0];
   println!("{:?}", gy.eval(ctx));   // => Ok(3.)

   // dz/dx (requires to fill the placeholder `x`)
   let gx = &grad(&[z], &[x])[0];
   let feed = ag::ndarray::arr0(2.);
   println!("{:?}", ctx.evaluator().push(gx).feed(x, feed.view()).run()[0]);  // => Ok(8.)

   // ddz/dx (differentiates `z` again)
   let ggx = &grad(&[gx], &[x])[0];
   println!("{:?}", ggx.eval(ctx));  // => Ok(4.)
});

Neural networks

This crate has various low-level features inspired by tensorflow/theano to train neural networks. Since computation graphs require only bare minimum of heap allocations, the overhead is small, even for complex networks.

// MNIST digits classification with multi-layer-perceptron
use autograd as ag;
use ag::optimizers::adam::Adam;
use ag::tensor_ops::*;
use ag::prelude::*;

let mut env = ag::VariableEnvironment::new();

let rng = ag::ndarray_ext::ArrayRng::<f32>::default();

// Register variables in this env.
env.name("w").set(rng.glorot_uniform(&[28 * 28, 10]));
env.name("b").set(ag::ndarray_ext::zeros(&[1, 10]));

let adam = Adam::default("my_adam", env.default_namespace().current_var_ids(), &mut env);

for epoch in 0..3 {  // 0.11 sec/epoch on 2.7GHz Intel Core i5
   env.run(|ctx| {
       let x = ctx.placeholder("x", &[-1, 28*28]);
       let y = ctx.placeholder("y", &[-1]);
       let w = ctx.variable("w");
       let b = ctx.variable("b");
       let z = matmul(x, w) + b;
       let mean_loss = reduce_mean(sparse_softmax_cross_entropy(z, &y), &[0], false);
       let grads = &grad(&[mean_loss], &[w, b]);

       // let mut feeder = ag::Feeder::new();
       // feeder.push(x, x_batch).push(y, y_batch);
       // adam.update(&[w, b], grads, ctx, feeder);
   });
}

Abstractions

use autograd as ag;
use ag::tensor_ops::*;
use ag::ndarray;

// `Tensor::map()`
ag::run(|ctx| {
    let x = ones(&[2, 3], ctx);
    // apply ndarray's methods
    let y = x.map(|x| x.fold_axis(ndarray::Axis(0), 0.0, |acc, x| acc + x));
    let z = x.map(|x| ag::ndarray_ext::zeros(x.shape()));
});

// Hooks
ag::run(|ctx| {
    let x: ag::Tensor<f32> = ones(&[2, 3], ctx).show_shape();
    let y: ag::Tensor<f32> = ones(&[2, 3], ctx).raw_hook(|x| println!("{}", x));
});

For detailed, see documentation or examples