SGD class
Implements the Stochastic Gradient Descent (SGD) optimizer.
This is the most fundamental optimization algorithm. At each step, it updates
every parameter by moving it in the direction of the negative gradient,
scaled by the learningRate.
The update rule is: parameter = parameter - learningRate * gradient.
While simple and effective for many problems, it can be slower to converge
than more advanced adaptive optimizers like Adam or RMSprop.
Example
Optimizer optimizer = SGD(model.parameters, learningRate: 0.01);
Properties
- hashCode → int
-
The hash code for this object.
no setterinherited
- learningRate → double
-
The step size for the gradient updates.
finalinherited
-
parameters
→ List<
Tensor> -
The list of model parameters (weights and biases) that this optimizer will update.
finalinherited
- runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
Methods
-
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
step(
) → void -
Performs a single optimization step using the basic gradient descent rule.
override
-
toString(
) → String -
A string representation of this object.
inherited
-
zeroGrad(
) → void -
Resets the gradients of all parameters to zero.
inherited
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited