Adam optimizer

    Adam optimizer as described in Adam - A Method for Stochastic Optimization.

    optimizer_adam(
      lr = 0.001,
      beta_1 = 0.9,
      beta_2 = 0.999,
      epsilon = NULL,
      decay = 0,
      amsgrad = FALSE,
      clipnorm = NULL,
      clipvalue = NULL
    )

    Arguments

    lr

    float >= 0. Learning rate.

    beta_1

    The exponential decay rate for the 1st moment estimates. float, 0 < beta < 1. Generally close to 1.

    beta_2

    The exponential decay rate for the 2nd moment estimates. float, 0 < beta < 1. Generally close to 1.

    epsilon

    float >= 0. Fuzz factor. If NULL, defaults to k_epsilon().

    decay

    float >= 0. Learning rate decay over each update.

    amsgrad

    Whether to apply the AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and Beyond".

    clipnorm

    Gradients will be clipped when their L2 norm exceeds this value.

    clipvalue

    Gradients will be clipped when their absolute value exceeds this value.

    Note

    Default parameters follow those provided in the original paper.

    References

    See also