scirs2_autograd::testing::gradient_checking

Struct ParameterGradientChecker

pub struct ParameterGradientChecker<F: Float> { /* private fields */ }

Expand description

Parameter gradient checker for neural networks

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impl<F: Float> ParameterGradientChecker<F>

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pub fn new() -> Self

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pub fn check_parameter_gradients<'a, Func>( &self, loss_function: Func, parameters: &'a HashMap<String, Tensor<'a, F>>, analytical_gradients: &'a HashMap<String, Tensor<'a, F>>, ) -> Result<ParameterCheckResult<'a, F>, StabilityError>
where Func: for<'b> Fn(&'b HashMap<String, Tensor<'b, F>>) -> Result<Tensor<'b, F>, StabilityError>,

Check gradients with respect to model parameters

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impl<F: Float> Default for ParameterGradientChecker<F>

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fn default() -> Self

Returns the “default value” for a type. Read more

Auto Trait Implementations§

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impl<F> UnwindSafe for ParameterGradientChecker<F>
where F: UnwindSafe,

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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more

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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more

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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more

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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T, U> Into for T
where U: From<T>,

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> IntoEither for T

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fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more

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fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more

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