Struct autograd::context::Context

pub struct Context {
    pub variables: HashMap<Tensor, NdArray>,
    pub outputs: HashMap<Tensor, Result<NdArray, OpComputeErrorStatus>>,
}

What is necessary to run computation graphs.

Context object is used: - to create shared variable tensors - to create constant tensors - to run computation graphs actually

When a computation graph is evaluated, all the variables/constants in the graph must be what derives from the same context; otherwise will panic.

extern crate ndarray;
extern crate autograd as ag;

// new Context
let mut ctx = ag::Context::new();

let ref x = ag::placeholder(&[2]);
// make shared variable in the context
let ref v = ag::variable(ndarray::arr1(&[2., 2.]), &mut ctx);
let ref b = ag::ones(&[2]);
let ref z = x + v + b;

// fills placeholder
ctx.feed_input(x, ndarray::arr1(&[1., 1.]));

// eval
assert_eq!(z.eval(&mut ctx).as_slice().unwrap(), &[4., 4.]);

Fields

variables: HashMap<Tensor, NdArray>

outputs: HashMap<Tensor, Result<NdArray, OpComputeErrorStatus>>

Methods

impl Context

fn new() -> Context

Creates new context object.

fn list_vars(&self) -> Vec<&Tensor>

Returns all variables in this context.

fn feed_input_unchecked<T>(&mut self, placeholder: &Tensor, arr: Array<f32, T>) where
    T: Dimension, 

Skips arr's shape checking.

fn feed_input<T>(&mut self, placeholder: &Tensor, arr: Array<f32, T>) where
    T: Dimension, 

Trait Implementations

impl Clone for Context

fn clone(&self) -> Context

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.