Struct gad::net::Then [−][src]
pub struct Then<N1, N2>(_, _);
The result of Net::then
Trait Implementations
impl<'de, N1, N2> Deserialize<'de> for Then<N1, N2> where
N1: Deserialize<'de>,
N2: Deserialize<'de>,
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impl<'de, N1, N2> Deserialize<'de> for Then<N1, N2> where
N1: Deserialize<'de>,
N2: Deserialize<'de>,
[src]fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl<Algebra, N1, N2> Net<Algebra> for Then<N1, N2> where
Algebra: HasGradientReader,
N1: Net<Algebra>,
N2: Net<Algebra, Input = N1::Output>,
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impl<Algebra, N1, N2> Net<Algebra> for Then<N1, N2> where
Algebra: HasGradientReader,
N1: Net<Algebra>,
N2: Net<Algebra, Input = N1::Output>,
[src]type Input = N1::Input
Input of the network.
type Output = N2::Output
Output of the network.
type Weights = Then<N1::Weights, N2::Weights>
External representation for the weights of the network.
type GradientInfo = Then<N1::GradientInfo, N2::GradientInfo>
How to read the gradients of the weights after a backward pass.
fn eval_with_gradient_info(
&self,
graph: &mut Algebra,
input: Self::Input
) -> Result<(Self::Output, Self::GradientInfo)>
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&self,
graph: &mut Algebra,
input: Self::Input
) -> Result<(Self::Output, Self::GradientInfo)>
fn get_weights(&self) -> Self::Weights
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fn set_weights(&mut self, weights: Self::Weights) -> Result<()>
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fn update_weights(&mut self, delta: Self::Weights) -> Result<()>
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fn read_weight_gradients(
&self,
info: Self::GradientInfo,
reader: &Algebra::GradientReader
) -> Result<Self::Weights>
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&self,
info: Self::GradientInfo,
reader: &Algebra::GradientReader
) -> Result<Self::Weights>
fn eval(&self, graph: &mut Algebra, input: Self::Input) -> Result<Self::Output>
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fn map<F, O>(self, f: F) -> Map<Self, F> where
Self: Sized,
F: Fn(&mut Algebra, Self::Output) -> Result<O>,
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Self: Sized,
F: Fn(&mut Algebra, Self::Output) -> Result<O>,
fn using<N>(self, net: N) -> Using<Self, N> where
Self: Sized,
N: Net<Algebra, Input = ()>,
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Self: Sized,
N: Net<Algebra, Input = ()>,
fn then<N>(self, net: N) -> Then<Self, N> where
Self: Sized,
N: Net<Algebra, Input = Self::Output>,
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Self: Sized,
N: Net<Algebra, Input = Self::Output>,
fn and<N>(self, net: N) -> (Self, N) where
Self: Sized,
N: Net<Algebra>,
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Self: Sized,
N: Net<Algebra>,
Auto Trait Implementations
impl<N1, N2> RefUnwindSafe for Then<N1, N2> where
N1: RefUnwindSafe,
N2: RefUnwindSafe,
impl<N1, N2> RefUnwindSafe for Then<N1, N2> where
N1: RefUnwindSafe,
N2: RefUnwindSafe,
impl<N1, N2> UnwindSafe for Then<N1, N2> where
N1: UnwindSafe,
N2: UnwindSafe,
impl<N1, N2> UnwindSafe for Then<N1, N2> where
N1: UnwindSafe,
N2: UnwindSafe,
Blanket Implementations
impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
[src]impl<Data, Algebra, N> SingleOutputNet<Data, Algebra> for N where
Algebra: HasGradientReader + CoreAlgebra<Data, Value = <N as Net<Algebra>>::Output>,
N: Net<Algebra>,
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impl<Data, Algebra, N> SingleOutputNet<Data, Algebra> for N where
Algebra: HasGradientReader + CoreAlgebra<Data, Value = <N as Net<Algebra>>::Output>,
N: Net<Algebra>,
[src]fn add_square_loss(self) -> SquareLoss<Self, Data> where
Self: Sized,
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Self: Sized,