Struct wyrm::Variable
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pub struct Variable<T> where
T: Node, { /* fields omitted */ }Handle to a node in the computation graph. The underlying nodes are reference counted, so the handles can be freely cloned to use the nodes multiple times in the same graph.
Methods
impl<T> Variable<T> where
T: Node, [src]
T: Node,
pub fn value(&self) -> Bor<T::Value>[src]
Get the value of the node.
pub fn forward(&self)[src]
Run the forward pass through the subgraph terminating at this node, recursing through the ancestor nodes.
pub fn zero_gradient(&self)[src]
Zero the gradients. Must be called after a backward step or whenever inputs change.
pub fn needs_gradient(&self) -> bool[src]
pub fn parameters(&self) -> Vec<Variable<ParameterNode>>[src]
Return the parameters of the graph.
impl<T> Variable<T> where
T: Node<Value = Arr, InputGradient = Arr>, [src]
T: Node<Value = Arr, InputGradient = Arr>,
pub fn boxed(&self) -> Variable<Rc<Node<Value = Arr, InputGradient = Arr>>>[src]
Box the variable, erasing its specific type. Use to manage the complexity of variable types in deep computation graphs.
pub fn backward(&mut self, weight: f32)[src]
Run the backward pass through the subgraph terminating at this node. The weight parameter scales the gradients.
pub fn clip(&self, min: f32, max: f32)[src]
Clip the value. Useful for clipping losses.
pub fn square(&self) -> Variable<SquareNode<T>>[src]
Square this variable.
pub fn scalar_sum(&self) -> Variable<SumNode<T>>[src]
Sum this variable.
pub fn ln(&self) -> Variable<LogNode<T>>[src]
Take the natural logarithm of this variable.
pub fn tanh(&self) -> Variable<TanhNode<T>>[src]
Take the tanh of this variable.
pub fn t(&self) -> Variable<TransposeNode<T>>[src]
Transpose this variable.
pub fn exp(&self) -> Variable<ExpNode<T>>[src]
Exponentiate this variable.
pub fn softmax(&self) -> Variable<SoftmaxNode<T>>[src]
Compute the softmax of this variable.
pub fn log_softmax(&self) -> Variable<LogSoftmaxNode<T>>[src]
Compute the log-softmax of this variable.
pub fn sigmoid(&self) -> Variable<SigmoidNode<T>>[src]
Compute the sigmoid of this variable.
pub fn relu(&self) -> Variable<ReluNode<T>>[src]
Compute the ReLU of this variable.
pub fn vector_dot<S>(
&self,
other: &Variable<S>
) -> Variable<VectorDotNode<T, S>> where
S: Node<Value = Arr, InputGradient = Arr>, [src]
&self,
other: &Variable<S>
) -> Variable<VectorDotNode<T, S>> where
S: Node<Value = Arr, InputGradient = Arr>,
Compute the row-wise vector dot product of LHS and RHS.
pub fn dot<S>(&self, other: &Variable<S>) -> Variable<DotNode<T, S>> where
S: Node<Value = Arr, InputGradient = Arr>, [src]
S: Node<Value = Arr, InputGradient = Arr>,
Compute the matrix multiplication of LHS and RHS.
pub fn stack<S>(
&self,
other: &Variable<S>,
axis: Axis
) -> Variable<ConcatenateNode<T, S>> where
S: Node<Value = Arr, InputGradient = Arr>, [src]
&self,
other: &Variable<S>,
axis: Axis
) -> Variable<ConcatenateNode<T, S>> where
S: Node<Value = Arr, InputGradient = Arr>,
Stack/concatenate LHS and RHS, either row-wise (ndarray::Axis(0)) or
column-wise (ndarray::Axis(1)).
impl Variable<ParameterNode>[src]
pub fn dense_gradient(&self) -> Option<Arr>[src]
Return the (dense) gradient value of this node.
pub fn index(
&self,
index: &Variable<IndexInputNode>
) -> Variable<IndexNode<ParameterNode>>[src]
&self,
index: &Variable<IndexInputNode>
) -> Variable<IndexNode<ParameterNode>>
Row-wise indexing of this parameter node. Primiarily used to implement embedding layers.
impl<T> Variable<SparseCategoricalCrossentropyNode<T>> where
T: Node<Value = Arr, InputGradient = Arr>, [src]
T: Node<Value = Arr, InputGradient = Arr>,
pub fn predictions(&self) -> Bor<Arr>[src]
Return the log-softmax predictions from a sparse categorical cross-entropy node.
Calling .value() on the node returns the value of the loss;
this function allows getting the predictins with low overhead.
Trait Implementations
impl<T: Debug> Debug for Variable<T> where
T: Node, [src]
T: Node,
fn fmt(&self, __arg_0: &mut Formatter) -> Result[src]
Formats the value using the given formatter. Read more
impl<T: Node> Clone for Variable<T>[src]
fn clone(&self) -> Self[src]
Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)1.0.0[src]
Performs copy-assignment from source. Read more
impl<'value> DataInput<&'value Arr> for Variable<ParameterNode>[src]
impl<'value> DataInput<&'value Arr> for Variable<InputNode>[src]
impl DataInput<f32> for Variable<InputNode>[src]
impl<'value> DataInput<&'value [usize]> for Variable<IndexInputNode>[src]
impl DataInput<usize> for Variable<IndexInputNode>[src]
impl<LHS, RHS> Add<Variable<RHS>> for Variable<LHS> where
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>,
type Output = Variable<AddNode<LHS, RHS>>
The resulting type after applying the + operator.
fn add(self, other: Variable<RHS>) -> Self::Output[src]
Performs the + operation.
impl<LHS> Add<f32> for Variable<LHS> where
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
LHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the LHS.
type Output = Variable<AddNode<LHS, InputNode>>
The resulting type after applying the + operator.
fn add(self, other: f32) -> Self::Output[src]
Performs the + operation.
impl<RHS> Add<Variable<RHS>> for f32 where
RHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the RHS.
type Output = Variable<AddNode<InputNode, RHS>>
The resulting type after applying the + operator.
fn add(self, other: Variable<RHS>) -> Self::Output[src]
Performs the + operation.
impl<LHS, RHS> Sub<Variable<RHS>> for Variable<LHS> where
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>,
type Output = Variable<SubNode<LHS, RHS>>
The resulting type after applying the - operator.
fn sub(self, other: Variable<RHS>) -> Self::Output[src]
Performs the - operation.
impl<LHS> Sub<f32> for Variable<LHS> where
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
LHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the LHS.
type Output = Variable<SubNode<LHS, InputNode>>
The resulting type after applying the - operator.
fn sub(self, other: f32) -> Self::Output[src]
Performs the - operation.
impl<RHS> Sub<Variable<RHS>> for f32 where
RHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the RHS.
type Output = Variable<SubNode<InputNode, RHS>>
The resulting type after applying the - operator.
fn sub(self, other: Variable<RHS>) -> Self::Output[src]
Performs the - operation.
impl<LHS, RHS> Mul<Variable<RHS>> for Variable<LHS> where
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>,
type Output = Variable<MulNode<LHS, RHS>>
The resulting type after applying the * operator.
fn mul(self, other: Variable<RHS>) -> Self::Output[src]
Performs the * operation.
impl<LHS> Mul<f32> for Variable<LHS> where
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
LHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the LHS.
type Output = Variable<MulNode<LHS, InputNode>>
The resulting type after applying the * operator.
fn mul(self, other: f32) -> Self::Output[src]
Performs the * operation.
impl<RHS> Mul<Variable<RHS>> for f32 where
RHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the RHS.
type Output = Variable<MulNode<InputNode, RHS>>
The resulting type after applying the * operator.
fn mul(self, other: Variable<RHS>) -> Self::Output[src]
Performs the * operation.
impl<LHS, RHS> Div<Variable<RHS>> for Variable<LHS> where
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
LHS: Node<Value = Arr, InputGradient = Arr>,
type Output = Variable<DivNode<LHS, RHS>>
The resulting type after applying the / operator.
fn div(self, other: Variable<RHS>) -> Self::Output[src]
Performs the / operation.
impl<LHS> Div<f32> for Variable<LHS> where
LHS: Node<Value = Arr, InputGradient = Arr>, [src]
LHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the LHS.
type Output = Variable<DivNode<LHS, InputNode>>
The resulting type after applying the / operator.
fn div(self, other: f32) -> Self::Output[src]
Performs the / operation.
impl<RHS> Div<Variable<RHS>> for f32 where
RHS: Node<Value = Arr, InputGradient = Arr>, [src]
RHS: Node<Value = Arr, InputGradient = Arr>,
The constant will be broadcast to have the same shape as the RHS.
type Output = Variable<DivNode<InputNode, RHS>>
The resulting type after applying the / operator.
fn div(self, other: Variable<RHS>) -> Self::Output[src]
Performs the / operation.
impl<T> Neg for Variable<T> where
T: Node<Value = Arr, InputGradient = Arr>, [src]
T: Node<Value = Arr, InputGradient = Arr>,