#![allow(clippy::unnecessary_wraps)]
use gad::prelude::*;
#[test]
fn test_zero() -> Result<()> {
let mut g = Graph1::new();
let a = g.variable(3i32);
let b = g.zeros(&a);
assert_eq!(*b.data(), 0);
assert_eq!(b.id(), None);
Ok(())
}
#[test]
fn test_one() -> Result<()> {
let mut g = Graph1::new();
let a = g.variable(3i32);
let b = g.ones(&a);
assert_eq!(*b.data(), 1);
assert_eq!(b.id(), None);
Ok(())
}
#[test]
fn test_neg() -> Result<()> {
let mut g = Graph1::new();
let a = g.variable(3i32);
let b = g.neg(&a);
assert_eq!(*b.data(), -3);
let gradients = g.evaluate_gradients_once(b.gid()?, 1)?;
assert_eq!(*gradients.get(a.gid()?).unwrap(), -1);
Ok(())
}
#[test]
fn test_sub() -> Result<()> {
let mut g = Graph1::new();
let a = g.variable(1i32);
let b = g.variable(2i32);
let c = g.sub(&a, &b)?;
assert_eq!(*c.data(), -1);
let gradients = g.evaluate_gradients_once(c.gid()?, 1)?;
assert_eq!(*gradients.get(a.gid()?).unwrap(), 1);
assert_eq!(*gradients.get(b.gid()?).unwrap(), -1);
Ok(())
}
#[test]
fn test_mul() -> Result<()> {
let mut g = Graph1::new();
let a = g.variable(1i32);
let b = g.variable(2i32);
let c = g.mul(&a, &b)?;
assert_eq!(*c.data(), 2);
let gradients = g.evaluate_gradients_once(c.gid()?, 1)?;
assert_eq!(*gradients.get(a.gid()?).unwrap(), 2);
assert_eq!(*gradients.get(b.gid()?).unwrap(), 1);
Ok(())
}
#[cfg(feature = "arrayfire")]
mod af_arith_test {
use super::*;
use arrayfire as af;
#[test]
fn test_neg() -> Result<()> {
let dims = af::Dim4::new(&[4, 3, 1, 1]);
let mut g = Graph1::new();
let a = g.variable(af::randu::<f32>(dims));
let b = g.neg(&a);
let direction = af::constant(1f32, dims);
let gradients = g.evaluate_gradients_once(b.gid()?, direction.clone())?;
let grad = gradients.get(a.gid()?).unwrap();
let est = testing::estimate_gradient(a.data(), &direction, 0.001f32, |x| 0.0f32 - x);
testing::assert_almost_all_equal(&grad, &est, 0.001);
Ok(())
}
#[test]
fn test_sub() -> Result<()> {
let dims = af::Dim4::new(&[4, 3, 1, 1]);
let mut g = Graph1::new();
let a = g.variable(af::randu::<f32>(dims));
let b = g.variable(af::randu::<f32>(dims));
let c = g.sub(&a, &b)?;
let direction = af::constant(1f32, dims);
let gradients = g.evaluate_gradients_once(c.gid()?, direction.clone())?;
{
let grad = gradients.get(a.gid()?).unwrap();
let est = testing::estimate_gradient(a.data(), &direction, 0.001f32, |x| x - b.data());
testing::assert_almost_all_equal(&grad, &est, 0.001);
}
{
let grad = gradients.get(b.gid()?).unwrap();
let est = testing::estimate_gradient(b.data(), &direction, 0.001f32, |x| a.data() - x);
testing::assert_almost_all_equal(&grad, &est, 0.001);
}
Ok(())
}
#[test]
fn test_mul() -> Result<()> {
let dims = af::Dim4::new(&[4, 3, 1, 1]);
let mut g = Graph1::new();
let a = g.variable(af::randu::<f32>(dims));
let b = g.variable(af::randu::<f32>(dims));
let c = g.mul(&a, &b)?;
let direction = af::constant(1f32, dims);
let gradients = g.evaluate_gradients_once(c.gid()?, direction.clone())?;
{
let grad = gradients.get(a.gid()?).unwrap();
let est = testing::estimate_gradient(a.data(), &direction, 0.001f32, |x| x * b.data());
testing::assert_almost_all_equal(&grad, &est, 0.001);
}
{
let grad = gradients.get(b.gid()?).unwrap();
let est = testing::estimate_gradient(b.data(), &direction, 0.001f32, |x| a.data() * x);
testing::assert_almost_all_equal(&grad, &est, 0.001);
}
Ok(())
}
}