Struct Tensor 

pub struct Tensor(/* private fields */);

A PyTorch-like differentiable tensor type

Implementations

impl Tensor

pub fn new(array: Array2<f64>) -> Tensor

Create a new tensor from an Array2

pub fn shape(&self) -> (usize, usize)

Find the shape of a tensor

pub fn rand(shape: [usize; 2]) -> Tensor

Create a tensor filled with random values

Examples found in repository

examples/random.rs (line 8)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    let rand = Tensor::rand([3, 1]) * 2.0 - 1.0;
    println!("{:?}", rand);
}

examples/arange.rs (line 6)

fn main() {
    let x = Tensor::arange(-5..5, [10, 1]);
    let func = &x * -5.0;
    let y = &func + &(Tensor::rand([10, 1]) * 0.4);
    println!("{:?}", x);
    println!("{:?}", y);
}

examples/nn_full.rs (line 8)

fn main() {
    Logger::new().init().unwrap();
    let x = Tensor::linspace(-5.0, 5.0, 50);
    let func = &x * -5.0;
    let y = &func + &(Tensor::rand([50, 1]) * 0.4);

    let mut model = Model::new();
    model.add_layer(Linear::new(1, 10, Activations::None));
    model.add_layer(Linear::new(10, 10, Activations::None));
    model.add_layer(Linear::new(10, 1, Activations::None));

    model.compile(Optimizers::SGD);
    model.fit(&x, &y, 500, 0.00001, true);

    let x_pred = scalar!(1.0);
    let y_pred = model.predict(&x_pred);
    println!("Predict result (should be -5): {:?}", y_pred);
}

examples/tiny_nn.rs (line 29)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    #[rustfmt::skip]
    let train_data = tensor![
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [0.0, 1.0, 1.0]];
    #[rustfmt::skip]
    let train_labels = tensor![
        [0.0],
        [1.0],
        [1.0],
        [0.0]
    ].reshape([4, 1]);
    let weights = Tensor::rand([3, 1]);
    let biases = Tensor::rand([4, 1]);
    println!("Weights before training:\n{:?}", weights);
    let now = Instant::now();
    for epoch in 0..(EPOCHS + 1) {
        let output = forward_pass(&train_data, &weights, &biases);
        let loss = elara_math::mse(&output, &train_labels);
        println!("Epoch {}, loss {:?}", epoch, loss);
        loss.backward();
        weights.update(LR);
        weights.zero_grad();
        biases.update(LR);
        biases.zero_grad();
    }
    println!("{:?}", now.elapsed());
    let pred_data = tensor![[1.0, 0.0, 0.0]];
    let pred = forward_pass(&pred_data, &weights, &biases);
    println!("Weights after training:\n{:?}", weights);
    println!("Prediction [1, 0, 0] -> {:?}", pred);
}

pub fn from_f64(val: f64) -> Tensor

Create a tensor from a f64

pub fn ones(shape: [usize; 2]) -> Tensor

Create a tensor of shape filled with ones

pub fn zeros(shape: [usize; 2]) -> Tensor

Create a tensor of shape filled with zeros

pub fn update(&self, lr: f64)

Update tensor value given its derivative and a learning rate; useful for machine learning applications

Examples found in repository

examples/tiny_nn.rs (line 38)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    #[rustfmt::skip]
    let train_data = tensor![
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [0.0, 1.0, 1.0]];
    #[rustfmt::skip]
    let train_labels = tensor![
        [0.0],
        [1.0],
        [1.0],
        [0.0]
    ].reshape([4, 1]);
    let weights = Tensor::rand([3, 1]);
    let biases = Tensor::rand([4, 1]);
    println!("Weights before training:\n{:?}", weights);
    let now = Instant::now();
    for epoch in 0..(EPOCHS + 1) {
        let output = forward_pass(&train_data, &weights, &biases);
        let loss = elara_math::mse(&output, &train_labels);
        println!("Epoch {}, loss {:?}", epoch, loss);
        loss.backward();
        weights.update(LR);
        weights.zero_grad();
        biases.update(LR);
        biases.zero_grad();
    }
    println!("{:?}", now.elapsed());
    let pred_data = tensor![[1.0, 0.0, 0.0]];
    let pred = forward_pass(&pred_data, &weights, &biases);
    println!("Weights after training:\n{:?}", weights);
    println!("Prediction [1, 0, 0] -> {:?}", pred);
}

pub fn arange<I: Iterator<Item = i32>>(range: I, shape: [usize; 2]) -> Tensor

Create a tensor from a range

Examples found in repository

examples/relu.rs (line 4)

fn main() {
    let t1 = Tensor::arange(-10..10, [2, 5]);
    println!("{:?}", t1);
    let t2 = t1.relu();
    println!("{:?}", t2);
}

examples/arange.rs (line 4)

fn main() {
    let x = Tensor::arange(-5..5, [10, 1]);
    let func = &x * -5.0;
    let y = &func + &(Tensor::rand([10, 1]) * 0.4);
    println!("{:?}", x);
    println!("{:?}", y);
}

pub fn linspace(start: f64, end: f64, num: usize) -> Tensor

Create a tensor containing a linearly-spaced interval

Examples found in repository

examples/nn_full.rs (line 6)

fn main() {
    Logger::new().init().unwrap();
    let x = Tensor::linspace(-5.0, 5.0, 50);
    let func = &x * -5.0;
    let y = &func + &(Tensor::rand([50, 1]) * 0.4);

    let mut model = Model::new();
    model.add_layer(Linear::new(1, 10, Activations::None));
    model.add_layer(Linear::new(10, 10, Activations::None));
    model.add_layer(Linear::new(10, 1, Activations::None));

    model.compile(Optimizers::SGD);
    model.fit(&x, &y, 500, 0.00001, true);

    let x_pred = scalar!(1.0);
    let y_pred = model.predict(&x_pred);
    println!("Predict result (should be -5): {:?}", y_pred);
}

pub fn reshape(&mut self, shape: [usize; 2]) -> Tensor

Change the shape of a tensor and return a new tensor

Examples found in repository

examples/tiny_nn.rs (line 28)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    #[rustfmt::skip]
    let train_data = tensor![
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [0.0, 1.0, 1.0]];
    #[rustfmt::skip]
    let train_labels = tensor![
        [0.0],
        [1.0],
        [1.0],
        [0.0]
    ].reshape([4, 1]);
    let weights = Tensor::rand([3, 1]);
    let biases = Tensor::rand([4, 1]);
    println!("Weights before training:\n{:?}", weights);
    let now = Instant::now();
    for epoch in 0..(EPOCHS + 1) {
        let output = forward_pass(&train_data, &weights, &biases);
        let loss = elara_math::mse(&output, &train_labels);
        println!("Epoch {}, loss {:?}", epoch, loss);
        loss.backward();
        weights.update(LR);
        weights.zero_grad();
        biases.update(LR);
        biases.zero_grad();
    }
    println!("{:?}", now.elapsed());
    let pred_data = tensor![[1.0, 0.0, 0.0]];
    let pred = forward_pass(&pred_data, &weights, &biases);
    println!("Weights after training:\n{:?}", weights);
    println!("Prediction [1, 0, 0] -> {:?}", pred);
}

pub fn len(&self) -> usize

Get the number of elements in a tensor

pub fn sum(&self) -> Tensor

Find the sum of a tensor

pub fn mean(&self) -> Tensor

Find the mean of a tensor

pub fn exp(&self) -> Tensor

Exponential function for tensors

pub fn relu(&self) -> Tensor

ReLU function for tensors

Examples found in repository

examples/tiny_nn.rs (line 9)

fn forward_pass(data: &Tensor, weights: &Tensor, biases: &Tensor) -> Tensor {
    (&data.matmul(&weights) + biases).relu()
}

examples/relu.rs (line 6)

fn main() {
    let t1 = Tensor::arange(-10..10, [2, 5]);
    println!("{:?}", t1);
    let t2 = t1.relu();
    println!("{:?}", t2);
}

pub fn pow(&self, power: f64) -> Tensor

Power function for tensors (not recommended as it breaks easily)

Examples found in repository

examples/pow.rs (line 5)

fn main() {
    let x = scalar!(5.0);
    let y = x.pow(2.0);
    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
    x.zero_grad();
    let z = x.pow(-2.0);
    z.backward();
    println!("dz/dx: {:?}", x.grad().clone());
}

pub fn sigmoid(&self) -> Tensor

Sigmoid function for tensors

pub fn matmul(&self, rhs: &Tensor) -> Tensor

Tensor matrix multiplication

Examples found in repository

examples/tiny_nn.rs (line 9)

fn forward_pass(data: &Tensor, weights: &Tensor, biases: &Tensor) -> Tensor {
    (&data.matmul(&weights) + biases).relu()
}

examples/matmul.rs (line 9)

fn main() {
    let t1 = tensor![[1.0, 2.0], [3.0, 4.0]];
    let t2 = tensor![[5.0, 6.0], [7.0, 8.0]];
    println!("{:?}", t1);
    println!("{:?}", t2);
    // Expected: [[19, 22], [43, 50]]
    println!("{:?}", t1.matmul(&t2));
}

pub fn inner(&self) -> Ref<'_, TensorData>

Get the underlying TensorData of a tensor

pub fn inner_mut(&self) -> RefMut<'_, TensorData>

Get the underlying TensorData of a tensor as mutable

pub fn data(&self) -> impl Deref<Target = Array2<f64>> + '_

Get the underlying data NdArray of a tensor

pub fn data_mut(&self) -> impl DerefMut<Target = Array2<f64>> + '_

Get the underlying data NdArray of a tensor as mutable

pub fn grad(&self) -> impl Deref<Target = Array2<f64>> + '_

Find the gradient of a tensor Remember to call backward() first!

Examples found in repository

examples/autograd.rs (line 12)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    let x = tensor![[3.0]];
    let y = &x * &x;

    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
}

examples/pow.rs (line 7)

fn main() {
    let x = scalar!(5.0);
    let y = x.pow(2.0);
    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
    x.zero_grad();
    let z = x.pow(-2.0);
    z.backward();
    println!("dz/dx: {:?}", x.grad().clone());
}

pub fn grad_mut(&self) -> impl DerefMut<Target = Array2<f64>> + '_

Get the gradient of a tensor as mutable Remember to call backward() first!

pub fn zero_grad(&self)

Zero the gradient of a tensor

Examples found in repository

examples/pow.rs (line 8)

fn main() {
    let x = scalar!(5.0);
    let y = x.pow(2.0);
    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
    x.zero_grad();
    let z = x.pow(-2.0);
    z.backward();
    println!("dz/dx: {:?}", x.grad().clone());
}

examples/tiny_nn.rs (line 39)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    #[rustfmt::skip]
    let train_data = tensor![
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [0.0, 1.0, 1.0]];
    #[rustfmt::skip]
    let train_labels = tensor![
        [0.0],
        [1.0],
        [1.0],
        [0.0]
    ].reshape([4, 1]);
    let weights = Tensor::rand([3, 1]);
    let biases = Tensor::rand([4, 1]);
    println!("Weights before training:\n{:?}", weights);
    let now = Instant::now();
    for epoch in 0..(EPOCHS + 1) {
        let output = forward_pass(&train_data, &weights, &biases);
        let loss = elara_math::mse(&output, &train_labels);
        println!("Epoch {}, loss {:?}", epoch, loss);
        loss.backward();
        weights.update(LR);
        weights.zero_grad();
        biases.update(LR);
        biases.zero_grad();
    }
    println!("{:?}", now.elapsed());
    let pred_data = tensor![[1.0, 0.0, 0.0]];
    let pred = forward_pass(&pred_data, &weights, &biases);
    println!("Weights after training:\n{:?}", weights);
    println!("Prediction [1, 0, 0] -> {:?}", pred);
}

pub fn backward(&self)

Perform backpropagation on a tensor

Examples found in repository

examples/autograd.rs (line 11)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    let x = tensor![[3.0]];
    let y = &x * &x;

    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
}

examples/pow.rs (line 6)

fn main() {
    let x = scalar!(5.0);
    let y = x.pow(2.0);
    y.backward();
    println!("dy/dx: {:?}", x.grad().clone());
    x.zero_grad();
    let z = x.pow(-2.0);
    z.backward();
    println!("dz/dx: {:?}", x.grad().clone());
}

examples/tiny_nn.rs (line 37)

fn main() {
    // Initialize logging library
    Logger::new().init().unwrap();

    #[rustfmt::skip]
    let train_data = tensor![
        [0.0, 0.0, 1.0],
        [1.0, 1.0, 1.0],
        [1.0, 0.0, 1.0],
        [0.0, 1.0, 1.0]];
    #[rustfmt::skip]
    let train_labels = tensor![
        [0.0],
        [1.0],
        [1.0],
        [0.0]
    ].reshape([4, 1]);
    let weights = Tensor::rand([3, 1]);
    let biases = Tensor::rand([4, 1]);
    println!("Weights before training:\n{:?}", weights);
    let now = Instant::now();
    for epoch in 0..(EPOCHS + 1) {
        let output = forward_pass(&train_data, &weights, &biases);
        let loss = elara_math::mse(&output, &train_labels);
        println!("Epoch {}, loss {:?}", epoch, loss);
        loss.backward();
        weights.update(LR);
        weights.zero_grad();
        biases.update(LR);
        biases.zero_grad();
    }
    println!("{:?}", now.elapsed());
    let pred_data = tensor![[1.0, 0.0, 0.0]];
    let pred = forward_pass(&pred_data, &weights, &biases);
    println!("Weights after training:\n{:?}", weights);
    println!("Prediction [1, 0, 0] -> {:?}", pred);
}

pub fn iter(&self) -> impl Iterator<Item = Tensor> + '_

Iterate over elements of a tensor

Examples found in repository

examples/iter.rs (line 5)

fn main() {
    let a = tensor![[1., 2.], [3., 4.]];
    for el in a.iter() {
        println!("{:?}", el);
    }
}

Trait Implementations

impl Add<&Tensor> for f64

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: &Tensor) -> Self::Output

Performs the + operation.

impl Add<Tensor> for f64

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: Tensor) -> Self::Output

Performs the + operation.

impl Add<f64> for &Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: f64) -> Self::Output

Performs the + operation.

impl Add<f64> for Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: f64) -> Self::Output

Performs the + operation.

impl Add for &Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: &Tensor) -> Self::Output

Performs the + operation.

impl Add for Tensor

type Output = Tensor

The resulting type after applying the + operator.

fn add(self, rhs: Tensor) -> Self::Output

Performs the + operation.

impl AddAssign<f64> for Tensor

fn add_assign(&mut self, rhs: f64)

Performs the += operation.

impl AddAssign for Tensor

fn add_assign(&mut self, rhs: Tensor)

Performs the += operation.

impl Clone for Tensor

fn clone(&self) -> Tensor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Tensor

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Deref for Tensor

type Target = Rc<RefCell<TensorData>>

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl DerefMut for Tensor

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

impl Div<&Tensor> for f64

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: &Tensor) -> Self::Output

Performs the / operation.

impl Div<Tensor> for f64

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: Tensor) -> Self::Output

Performs the / operation.

impl Div<f64> for &Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: f64) -> Self::Output

Performs the / operation.

impl Div<f64> for Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: f64) -> Self::Output

Performs the / operation.

impl Div for &Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: &Tensor) -> Self::Output

Performs the / operation.

impl Div for Tensor

type Output = Tensor

The resulting type after applying the / operator.

fn div(self, rhs: Tensor) -> Self::Output

Performs the / operation.

impl DivAssign<f64> for Tensor

fn div_assign(&mut self, rhs: f64)

Performs the /= operation.

impl DivAssign for Tensor

fn div_assign(&mut self, rhs: Tensor)

Performs the /= operation.

impl Hash for Tensor

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl Iterator for Tensor

type Item = Tensor

The type of the elements being iterated over.

fn next(&mut self) -> Option<Self::Item>

Advances the iterator and returns the next value.

fn next_chunk<const N: usize>( &mut self, ) -> Result<[Self::Item; N], IntoIter<Self::Item, N>>

where Self: Sized,

🔬This is a nightly-only experimental API. (iter_next_chunk)

Advances the iterator and returns an array containing the next N values.

fn size_hint(&self) -> (usize, Option<usize>)

Returns the bounds on the remaining length of the iterator.

fn count(self) -> usize

where Self: Sized,

Consumes the iterator, counting the number of iterations and returning it.

fn last(self) -> Option<Self::Item>

where Self: Sized,

Consumes the iterator, returning the last element.

fn advance_by(&mut self, n: usize) -> Result<(), NonZero<usize>>

🔬This is a nightly-only experimental API. (iter_advance_by)

Advances the iterator by n elements.

fn nth(&mut self, n: usize) -> Option<Self::Item>

Returns the nth element of the iterator.

fn step_by(self, step: usize) -> StepBy<Self>

where Self: Sized,

Creates an iterator starting at the same point, but stepping by the given amount at each iteration.

fn chain<U>(self, other: U) -> Chain<Self, <U as IntoIterator>::IntoIter>

where Self: Sized, U: IntoIterator<Item = Self::Item>,

Takes two iterators and creates a new iterator over both in sequence.

fn zip<U>(self, other: U) -> Zip<Self, <U as IntoIterator>::IntoIter>

where Self: Sized, U: IntoIterator,

‘Zips up’ two iterators into a single iterator of pairs.

fn intersperse(self, separator: Self::Item) -> Intersperse<Self>

where Self: Sized, Self::Item: Clone,

🔬This is a nightly-only experimental API. (iter_intersperse)

Creates a new iterator which places a copy of separator between adjacent items of the original iterator.

fn intersperse_with<G>(self, separator: G) -> IntersperseWith<Self, G>

where Self: Sized, G: FnMut() -> Self::Item,

🔬This is a nightly-only experimental API. (iter_intersperse)

Creates a new iterator which places an item generated by separator between adjacent items of the original iterator.

fn map<B, F>(self, f: F) -> Map<Self, F>

where Self: Sized, F: FnMut(Self::Item) -> B,

Takes a closure and creates an iterator which calls that closure on each element.

fn for_each<F>(self, f: F)

where Self: Sized, F: FnMut(Self::Item),

Calls a closure on each element of an iterator.

fn filter<P>(self, predicate: P) -> Filter<Self, P>

where Self: Sized, P: FnMut(&Self::Item) -> bool,

Creates an iterator which uses a closure to determine if an element should be yielded.

fn filter_map<B, F>(self, f: F) -> FilterMap<Self, F>

where Self: Sized, F: FnMut(Self::Item) -> Option<B>,

Creates an iterator that both filters and maps.

fn enumerate(self) -> Enumerate<Self>

where Self: Sized,

Creates an iterator which gives the current iteration count as well as the next value.

fn peekable(self) -> Peekable<Self>

where Self: Sized,

Creates an iterator which can use the peek and peek_mut methods to look at the next element of the iterator without consuming it. See their documentation for more information.

fn skip_while<P>(self, predicate: P) -> SkipWhile<Self, P>

where Self: Sized, P: FnMut(&Self::Item) -> bool,

Creates an iterator that skips elements based on a predicate.

fn take_while<P>(self, predicate: P) -> TakeWhile<Self, P>

where Self: Sized, P: FnMut(&Self::Item) -> bool,

Creates an iterator that yields elements based on a predicate.

fn map_while<B, P>(self, predicate: P) -> MapWhile<Self, P>

where Self: Sized, P: FnMut(Self::Item) -> Option<B>,

Creates an iterator that both yields elements based on a predicate and maps.

fn skip(self, n: usize) -> Skip<Self>

where Self: Sized,

Creates an iterator that skips the first n elements.

fn take(self, n: usize) -> Take<Self>

where Self: Sized,

Creates an iterator that yields the first n elements, or fewer if the underlying iterator ends sooner.

fn scan<St, B, F>(self, initial_state: St, f: F) -> Scan<Self, St, F>

where Self: Sized, F: FnMut(&mut St, Self::Item) -> Option<B>,

An iterator adapter which, like fold, holds internal state, but unlike fold, produces a new iterator.

fn flat_map<U, F>(self, f: F) -> FlatMap<Self, U, F>

where Self: Sized, U: IntoIterator, F: FnMut(Self::Item) -> U,

Creates an iterator that works like map, but flattens nested structure.

fn flatten(self) -> Flatten<Self>

where Self: Sized, Self::Item: IntoIterator,

Creates an iterator that flattens nested structure.

fn map_windows<F, R, const N: usize>(self, f: F) -> MapWindows<Self, F, N>

where Self: Sized, F: FnMut(&[Self::Item; N]) -> R,

🔬This is a nightly-only experimental API. (iter_map_windows)

Calls the given function f for each contiguous window of size N over self and returns an iterator over the outputs of f. Like slice::windows(), the windows during mapping overlap as well.

fn fuse(self) -> Fuse<Self>

where Self: Sized,

Creates an iterator which ends after the first None.

fn inspect<F>(self, f: F) -> Inspect<Self, F>

where Self: Sized, F: FnMut(&Self::Item),

Does something with each element of an iterator, passing the value on.

fn by_ref(&mut self) -> &mut Self

where Self: Sized,

Creates a “by reference” adapter for this instance of Iterator.

fn collect<B>(self) -> B

where B: FromIterator<Self::Item>, Self: Sized,

Transforms an iterator into a collection.

fn collect_into<E>(self, collection: &mut E) -> &mut E

where E: Extend<Self::Item>, Self: Sized,

🔬This is a nightly-only experimental API. (iter_collect_into)

Collects all the items from an iterator into a collection.

fn partition<B, F>(self, f: F) -> (B, B)

where Self: Sized, B: Default + Extend<Self::Item>, F: FnMut(&Self::Item) -> bool,

Consumes an iterator, creating two collections from it.

fn is_partitioned<P>(self, predicate: P) -> bool

where Self: Sized, P: FnMut(Self::Item) -> bool,

🔬This is a nightly-only experimental API. (iter_is_partitioned)

Checks if the elements of this iterator are partitioned according to the given predicate, such that all those that return true precede all those that return false.

fn try_fold<B, F, R>(&mut self, init: B, f: F) -> R

where Self: Sized, F: FnMut(B, Self::Item) -> R, R: Try<Output = B>,

An iterator method that applies a function as long as it returns successfully, producing a single, final value.

fn try_for_each<F, R>(&mut self, f: F) -> R

where Self: Sized, F: FnMut(Self::Item) -> R, R: Try<Output = ()>,

An iterator method that applies a fallible function to each item in the iterator, stopping at the first error and returning that error.

fn fold<B, F>(self, init: B, f: F) -> B

where Self: Sized, F: FnMut(B, Self::Item) -> B,

Folds every element into an accumulator by applying an operation, returning the final result.

fn reduce<F>(self, f: F) -> Option<Self::Item>

where Self: Sized, F: FnMut(Self::Item, Self::Item) -> Self::Item,

Reduces the elements to a single one, by repeatedly applying a reducing operation.

fn try_reduce<R>( &mut self, f: impl FnMut(Self::Item, Self::Item) -> R, ) -> <<R as Try>::Residual as Residual<Option<<R as Try>::Output>>>::TryType

where Self: Sized, R: Try<Output = Self::Item>, <R as Try>::Residual: Residual<Option<Self::Item>>,

🔬This is a nightly-only experimental API. (iterator_try_reduce)

Reduces the elements to a single one by repeatedly applying a reducing operation. If the closure returns a failure, the failure is propagated back to the caller immediately.

fn all<F>(&mut self, f: F) -> bool

where Self: Sized, F: FnMut(Self::Item) -> bool,

Tests if every element of the iterator matches a predicate.

fn any<F>(&mut self, f: F) -> bool

where Self: Sized, F: FnMut(Self::Item) -> bool,

Tests if any element of the iterator matches a predicate.

fn find<P>(&mut self, predicate: P) -> Option<Self::Item>

where Self: Sized, P: FnMut(&Self::Item) -> bool,

Searches for an element of an iterator that satisfies a predicate.

fn find_map<B, F>(&mut self, f: F) -> Option<B>

where Self: Sized, F: FnMut(Self::Item) -> Option<B>,

Applies function to the elements of iterator and returns the first non-none result.

fn try_find<R>( &mut self, f: impl FnMut(&Self::Item) -> R, ) -> <<R as Try>::Residual as Residual<Option<Self::Item>>>::TryType

where Self: Sized, R: Try<Output = bool>, <R as Try>::Residual: Residual<Option<Self::Item>>,

🔬This is a nightly-only experimental API. (try_find)

Applies function to the elements of iterator and returns the first true result or the first error.

fn position<P>(&mut self, predicate: P) -> Option<usize>

where Self: Sized, P: FnMut(Self::Item) -> bool,

Searches for an element in an iterator, returning its index.

fn max_by_key<B, F>(self, f: F) -> Option<Self::Item>

where B: Ord, Self: Sized, F: FnMut(&Self::Item) -> B,

Returns the element that gives the maximum value from the specified function.

fn max_by<F>(self, compare: F) -> Option<Self::Item>

where Self: Sized, F: FnMut(&Self::Item, &Self::Item) -> Ordering,

Returns the element that gives the maximum value with respect to the specified comparison function.

fn min_by_key<B, F>(self, f: F) -> Option<Self::Item>

where B: Ord, Self: Sized, F: FnMut(&Self::Item) -> B,

Returns the element that gives the minimum value from the specified function.

fn min_by<F>(self, compare: F) -> Option<Self::Item>

where Self: Sized, F: FnMut(&Self::Item, &Self::Item) -> Ordering,

Returns the element that gives the minimum value with respect to the specified comparison function.

fn unzip<A, B, FromA, FromB>(self) -> (FromA, FromB)

where FromA: Default + Extend<A>, FromB: Default + Extend<B>, Self: Sized + Iterator<Item = (A, B)>,

Converts an iterator of pairs into a pair of containers.

fn copied<'a, T>(self) -> Copied<Self>

where T: Copy + 'a, Self: Sized + Iterator<Item = &'a T>,

Creates an iterator which copies all of its elements.

fn cloned<'a, T>(self) -> Cloned<Self>

where T: Clone + 'a, Self: Sized + Iterator<Item = &'a T>,

Creates an iterator which clones all of its elements.

fn cycle(self) -> Cycle<Self>

where Self: Sized + Clone,

Repeats an iterator endlessly.

fn array_chunks<const N: usize>(self) -> ArrayChunks<Self, N>

where Self: Sized,

🔬This is a nightly-only experimental API. (iter_array_chunks)

Returns an iterator over N elements of the iterator at a time.

fn sum<S>(self) -> S

where Self: Sized, S: Sum<Self::Item>,

Sums the elements of an iterator.

fn product<P>(self) -> P

where Self: Sized, P: Product<Self::Item>,

Iterates over the entire iterator, multiplying all the elements

fn cmp_by<I, F>(self, other: I, cmp: F) -> Ordering

where Self: Sized, I: IntoIterator, F: FnMut(Self::Item, <I as IntoIterator>::Item) -> Ordering,

🔬This is a nightly-only experimental API. (iter_order_by)

Lexicographically compares the elements of this Iterator with those of another with respect to the specified comparison function.

fn partial_cmp<I>(self, other: I) -> Option<Ordering>

where I: IntoIterator, Self::Item: PartialOrd<<I as IntoIterator>::Item>, Self: Sized,

Lexicographically compares the PartialOrd elements of this Iterator with those of another. The comparison works like short-circuit evaluation, returning a result without comparing the remaining elements. As soon as an order can be determined, the evaluation stops and a result is returned.

fn partial_cmp_by<I, F>(self, other: I, partial_cmp: F) -> Option<Ordering>

where Self: Sized, I: IntoIterator, F: FnMut(Self::Item, <I as IntoIterator>::Item) -> Option<Ordering>,

🔬This is a nightly-only experimental API. (iter_order_by)

Lexicographically compares the elements of this Iterator with those of another with respect to the specified comparison function.

fn eq<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialEq<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are equal to those of another.

fn eq_by<I, F>(self, other: I, eq: F) -> bool

where Self: Sized, I: IntoIterator, F: FnMut(Self::Item, <I as IntoIterator>::Item) -> bool,

🔬This is a nightly-only experimental API. (iter_order_by)

Determines if the elements of this Iterator are equal to those of another with respect to the specified equality function.

fn ne<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialEq<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are not equal to those of another.

fn lt<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialOrd<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are lexicographically less than those of another.

fn le<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialOrd<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are lexicographically less or equal to those of another.

fn gt<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialOrd<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are lexicographically greater than those of another.

fn ge<I>(self, other: I) -> bool

where I: IntoIterator, Self::Item: PartialOrd<<I as IntoIterator>::Item>, Self: Sized,

Determines if the elements of this Iterator are lexicographically greater than or equal to those of another.

fn is_sorted_by<F>(self, compare: F) -> bool

where Self: Sized, F: FnMut(&Self::Item, &Self::Item) -> bool,

Checks if the elements of this iterator are sorted using the given comparator function.

fn is_sorted_by_key<F, K>(self, f: F) -> bool

where Self: Sized, F: FnMut(Self::Item) -> K, K: PartialOrd,

Checks if the elements of this iterator are sorted using the given key extraction function.

impl Mul<&Tensor> for f64

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: &Tensor) -> Self::Output

Performs the * operation.

impl Mul<Tensor> for f64

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: Tensor) -> Self::Output

Performs the * operation.

impl Mul<f64> for &Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: f64) -> Self::Output

Performs the * operation.

impl Mul<f64> for Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: f64) -> Self::Output

Performs the * operation.

impl Mul for &Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: &Tensor) -> Self::Output

Performs the * operation.

impl Mul for Tensor

type Output = Tensor

The resulting type after applying the * operator.

fn mul(self, rhs: Tensor) -> Self::Output

Performs the * operation.

impl MulAssign<f64> for Tensor

fn mul_assign(&mut self, rhs: f64)

Performs the *= operation.

impl MulAssign for Tensor

fn mul_assign(&mut self, rhs: Tensor)

Performs the *= operation.

impl PartialEq for Tensor

fn eq(&self, other: &Self) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Sub<&Tensor> for f64

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: &Tensor) -> Self::Output

Performs the - operation.

impl Sub<Tensor> for f64

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: Tensor) -> Self::Output

Performs the - operation.

impl Sub<f64> for &Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: f64) -> Self::Output

Performs the - operation.

impl Sub<f64> for Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: f64) -> Self::Output

Performs the - operation.

impl Sub for &Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: &Tensor) -> Self::Output

Performs the - operation.

impl Sub for Tensor

type Output = Tensor

The resulting type after applying the - operator.

fn sub(self, rhs: Tensor) -> Self::Output

Performs the - operation.

impl SubAssign<f64> for Tensor

fn sub_assign(&mut self, rhs: f64)

Performs the -= operation.

impl SubAssign for Tensor

fn sub_assign(&mut self, rhs: Tensor)

Performs the -= operation.

impl Eq for Tensor