Trait ha_ndarray::NDArrayMath
source · pub trait NDArrayMath: NDArray + Sized {
// Provided methods
fn add<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn checked_div<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn div<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn mul<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn rem<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn sub<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>
where O: NDArray<DType = Self::DType> + Sized { ... }
fn log<O>(
self,
base: O
) -> Result<ArrayOp<ArrayDualFloat<Self::DType, Self, O>>, Error>
where O: NDArray<DType = <Self::DType as CDatatype>::Float> + Sized { ... }
fn pow<O>(
self,
exp: O
) -> Result<ArrayOp<ArrayDualFloat<Self::DType, Self, O>>, Error>
where O: NDArray<DType = <Self::DType as CDatatype>::Float> + Sized { ... }
}Provided Methods§
sourcefn add<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where
O: NDArray<DType = Self::DType> + Sized,
fn add<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
Examples found in repository?
examples/backprop.rs (line 42)
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fn main() -> Result<(), Error> {
let context = Context::default()?;
let weights = RandomNormal::with_context(context.clone(), 2)?;
let weights = ArrayOp::new(vec![2, 1], weights) - 0.5;
let mut weights = ArrayBase::<Arc<RwLock<Buffer<f32>>>>::copy(&weights)?;
let inputs = RandomUniform::with_context(context, vec![NUM_EXAMPLES, 2])?;
let inputs = ArrayOp::new(vec![NUM_EXAMPLES, 2], inputs) * 2.;
let inputs = ArrayBase::<Arc<Buffer<f32>>>::copy(&inputs)?;
let inputs_bool = inputs.clone().lt_scalar(1.0)?;
let inputs_left = inputs_bool
.clone()
.slice(vec![(0..NUM_EXAMPLES).into(), 0.into()])?;
let inputs_right = inputs_bool.slice(vec![(0..NUM_EXAMPLES).into(), 1.into()])?;
let labels = inputs_left
.and(inputs_right)?
.expand_dims(vec![1])?
.cast()?;
let labels = ArrayBase::<Buffer<f32>>::copy(&labels)?;
let output = inputs.matmul(weights.clone())?;
let error = labels.sub(output)?;
let loss = error.clone().pow_scalar(2.)?;
let d_loss = error * 2.;
let weights_t = weights.clone().transpose(None)?;
let gradient = d_loss.matmul(weights_t)?;
let deltas = gradient.sum(vec![0], false)?.expand_dims(vec![1])?;
let new_weights = weights.clone().add(deltas * LEARNING_RATE)?;
let mut i = 0;
loop {
let loss = ArrayBase::<Buffer<f32>>::copy(&loss)?;
if loss.clone().lt_scalar(1.0)?.all()? {
return Ok(());
}
if i % 100 == 0 {
println!(
"loss: {} (max {})",
loss.clone().sum_all()?,
loss.clone().max_all()?
);
}
assert!(!loss.clone().is_inf()?.any()?, "divergence at iteration {i}");
assert!(!loss.is_nan()?.any()?, "unstable by iteration {i}");
weights.write(&new_weights)?;
i += 1;
}
}fn checked_div<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
fn div<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
fn mul<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
fn rem<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
sourcefn sub<O>(
self,
rhs: O
) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where
O: NDArray<DType = Self::DType> + Sized,
fn sub<O>( self, rhs: O ) -> Result<ArrayOp<ArrayDual<Self::DType, Self, O>>, Error>where O: NDArray<DType = Self::DType> + Sized,
Examples found in repository?
examples/backprop.rs (line 35)
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fn main() -> Result<(), Error> {
let context = Context::default()?;
let weights = RandomNormal::with_context(context.clone(), 2)?;
let weights = ArrayOp::new(vec![2, 1], weights) - 0.5;
let mut weights = ArrayBase::<Arc<RwLock<Buffer<f32>>>>::copy(&weights)?;
let inputs = RandomUniform::with_context(context, vec![NUM_EXAMPLES, 2])?;
let inputs = ArrayOp::new(vec![NUM_EXAMPLES, 2], inputs) * 2.;
let inputs = ArrayBase::<Arc<Buffer<f32>>>::copy(&inputs)?;
let inputs_bool = inputs.clone().lt_scalar(1.0)?;
let inputs_left = inputs_bool
.clone()
.slice(vec![(0..NUM_EXAMPLES).into(), 0.into()])?;
let inputs_right = inputs_bool.slice(vec![(0..NUM_EXAMPLES).into(), 1.into()])?;
let labels = inputs_left
.and(inputs_right)?
.expand_dims(vec![1])?
.cast()?;
let labels = ArrayBase::<Buffer<f32>>::copy(&labels)?;
let output = inputs.matmul(weights.clone())?;
let error = labels.sub(output)?;
let loss = error.clone().pow_scalar(2.)?;
let d_loss = error * 2.;
let weights_t = weights.clone().transpose(None)?;
let gradient = d_loss.matmul(weights_t)?;
let deltas = gradient.sum(vec![0], false)?.expand_dims(vec![1])?;
let new_weights = weights.clone().add(deltas * LEARNING_RATE)?;
let mut i = 0;
loop {
let loss = ArrayBase::<Buffer<f32>>::copy(&loss)?;
if loss.clone().lt_scalar(1.0)?.all()? {
return Ok(());
}
if i % 100 == 0 {
println!(
"loss: {} (max {})",
loss.clone().sum_all()?,
loss.clone().max_all()?
);
}
assert!(!loss.clone().is_inf()?.any()?, "divergence at iteration {i}");
assert!(!loss.is_nan()?.any()?, "unstable by iteration {i}");
weights.write(&new_weights)?;
i += 1;
}
}