Parameter Random Initialization Task

A A Parameter Random Initialization Task is Parameter Initialization Task that assigns random values to parameters.

• Context:
  • Task Input: random initializers.
  • Task Output: parameter's estimators.
• Example(s):
  • …
• Counter-Example(s):
  • Lipschitz Constrained Parameter Initialization Task,
  • Computer Programming Declaration Task,
  • Computer Programming Deinitialization Task,
  • Lazy Initialization Task.
• See: Machine Code, Computer Programming, Data Object, Programming Language, Run Time, Program Lifecycle, Object-Oriented Programming, Object-Oriented Programming Constructor.

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References

2021a

• (Swift Doc., 2021) ⇒ https://docs.swift.org/swift-book/LanguageGuide/Initialization.html Retrieved:2021-5-23.
  • QUOTE: Initialization is the process of preparing an instance of a class, structure, or enumeration for use. This process involves setting an initial value for each stored property on that instance and performing any other setup or initialization that’s required before the new instance is ready for use.

    You implement this initialization process by defining initializers, which are like special methods that can be called to create a new instance of a particular type. Unlike Objective-C initializers, Swift initializers don’t return a value. Their primary role is to ensure that new instances of a type are correctly initialized before they’re used for the first time.

    Instances of class types can also implement a deinitializer, which performs any custom cleanup just before an instance of that class is deallocated. For more information about deinitializers, see Deinitialization.

2021

• (Srihari, 2021) ⇒ Sargur N. Srihari (2021). “Parameter Initialization Strategies" Retrieved:2021-5-23.
  • QUOTE: Keras Initialization.
    • Initializations define the way to set the initial random weights of Keras layers.
    • The keyword arguments used for passing initializers to layers will depend on the layer.
    • Usually it is simply kernel_initializer and bias_initializer:

      model.add (Dense(64, kernel_initializer="randon_uniforn’, bias_initializer='zeros'))

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