IDLmloptQuickProp
Optimization algorithms are used by neural networks to help minimize an error function by modifying the model’s internal learnable parameters. The IDLmloptQuickProp optimizer follows a quadratic approximation of the previous gradient step and the current gradient, which is expected to be close to the minimum of the loss function.
Example
Compile_opt idl2
Optimizer = IDLmloptQuickProp(0.1)
Print, Optimizer(0.1, 0.1, 0.1)
Note: Though the above is how an optimizer can be used as a standalone object, the common scenario is to pass it to the IDLmlFeedForwardNeuralNetwork::Train() method via the OPTIMIZER keyword.
Syntax
Optimizer = IDLmloptQuickProp()
Result = Optimizer(LearningRate)
Arguments
LearningRate
Specify the initial learning rate value to use as a starting point. Note that if this parameter is too small, it may lead to slow convergence, and if it is too large, it can miss the optimal solution.