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learning rate equal to vs momentum term equal to

Both phrases are correct, but they are used in different contexts. 'Learning rate equal to' is commonly used in machine learning when setting the learning rate for training a model, while 'momentum term equal to' is used in the context of optimization algorithms, particularly in deep learning, when setting the momentum parameter.

Last updated: March 17, 2024 • 503 views

learning rate equal to

This phrase is correct and commonly used in machine learning when setting the learning rate for training a model.

This phrase is used when specifying the value of the learning rate parameter in machine learning algorithms to control the step size during optimization.

Examples:

  • Set the learning rate equal to 0.01 for faster convergence.
  • The learning rate equal to 0.001 resulted in slower training.
  • Adjust the learning rate equal to the inverse of the square root of the iteration number.
  • The learning rate equal to 0.1 may lead to overshooting in some cases.
  • Choose a learning rate equal to 0.0001 for better stability.
  • ... threshold equal to 0 and learning rate equal to 0.5; the noise is equal to 0.5, which ... players with aspiration threshold equal to 2 and learning rate equal to 0.5.
  • ... policy learned by BayesiaLab over 1 000 time steps, with a discount factor equal to 0.99, a learning rate equal to 0.5 and an initial exploration rate equal to 1.
  • learning rate equal to the inverse of the largest eigenvalue of the Hessian. This result of course assume that these learning rates still fulfill criterions (4.33) and.
  • The weight update formula could also be rewritten as. Wk = Wk-l + kk RL!lffkXk,. ( E.12) where kk is a time and data dependent scalar learning rate equal to p-'.

Alternatives:

  • setting the learning rate to
  • assigning the learning rate as
  • specifying the learning rate as
  • defining the learning rate as
  • establishing the learning rate at

momentum term equal to

This phrase is correct and commonly used in the context of optimization algorithms, particularly in deep learning, when setting the momentum parameter.

This phrase is used when specifying the value of the momentum term in optimization algorithms to control the influence of previous gradients on the current update.

Examples:

  • Set the momentum term equal to 0.9 for faster convergence.
  • A higher momentum term equal to 0.99 can help escape local minima.
  • The momentum term equal to 0.5 balances between exploration and exploitation.
  • Adjust the momentum term equal to 0.95 for smoother optimization.
  • Choosing a momentum term equal to 0.8 is a common practice in deep learning.
  • 0.4 and a momentum term equal to 0.2. Once we had selected the best combination of topology, learning algorithm and parameters for the MLP, according to.
  • Oct 16, 2002 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with 3–32 inputs and 6 output neurons was able to reach a ...
  • Oct 16, 2002 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with 3–32 inputs and 6 output neurons was able to reach a ...
  • Dec 18, 2006 ... A MLP network (with learning rate equal to 0.2 and a momentum term equal to 0.3) with three 3–18 inputs, 2 hidden layers and 3 outputs ...

Alternatives:

  • setting the momentum term to
  • assigning the momentum term as
  • specifying the momentum term as
  • defining the momentum term as
  • establishing the momentum term at

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