#!/usr/bin/env python3
""" Description """


def update_variables_Adam(alpha, beta1, beta2, epsilon, var, grad, v, s, t):
    """updates a variable in place using the Adam optimization algorithm

    Args:
        alpha is the learning rate
        beta1 is the weight used for the first moment
        beta2 is the weight used for the second moment
        epsilon is a small number to avoid division by zero
        var is a numpy.ndarray containing the variable to be updated
        grad is a numpy.ndarray containing the gradient of var
        v is the previous first moment of var
        s is the previous second moment of var
        t is the time step used for bias correction
    """
    α = alpha
    β1 = beta1
    β2 = beta2
    ε = epsilon

    Vd = (β1 * v) + ((1 - β1) * grad)
    Sd = (β2 * s) + ((1 - β2) * grad * grad)

    Vd_ok = Vd / (1 - β1 ** t)
    Sd_ok = Sd / (1 - β2 ** t)

    w = var - α * (Vd_ok / ((Sd_ok ** (0.5)) + ε))
    return (w, Vd, Sd)

7bc649100dd5978c65b92727fdb1bcae49e4718a
<mask token>

<mask token>


def update_variables_Adam(alpha, beta1, beta2, epsilon, var, grad, v, s, t):
    """updates a variable in place using the Adam optimization algorithm

    Args:
        alpha is the learning rate
        beta1 is the weight used for the first moment
        beta2 is the weight used for the second moment
        epsilon is a small number to avoid division by zero
        var is a numpy.ndarray containing the variable to be updated
        grad is a numpy.ndarray containing the gradient of var
        v is the previous first moment of var
        s is the previous second moment of var
        t is the time step used for bias correction
    """
    α = alpha
    β1 = beta1
    β2 = beta2
    ε = epsilon
    Vd = β1 * v + (1 - β1) * grad
    Sd = β2 * s + (1 - β2) * grad * grad
    Vd_ok = Vd / (1 - β1 ** t)
    Sd_ok = Sd / (1 - β2 ** t)
    w = var - α * (Vd_ok / (Sd_ok ** 0.5 + ε))
    return w, Vd, Sd

#!/usr/bin/env python3
""" Description """


def update_variables_Adam(alpha, beta1, beta2, epsilon, var, grad, v, s, t):
    """updates a variable in place using the Adam optimization algorithm

    Args:
        alpha is the learning rate
        beta1 is the weight used for the first moment
        beta2 is the weight used for the second moment
        epsilon is a small number to avoid division by zero
        var is a numpy.ndarray containing the variable to be updated
        grad is a numpy.ndarray containing the gradient of var
        v is the previous first moment of var
        s is the previous second moment of var
        t is the time step used for bias correction
    """
    α = alpha
    β1 = beta1
    β2 = beta2
    ε = epsilon

    Vd = (β1 * v) + ((1 - β1) * grad)
    Sd = (β2 * s) + ((1 - β2) * grad * grad)

    Vd_ok = Vd / (1 - β1 ** t)
    Sd_ok = Sd / (1 - β2 ** t)

    w = var - α * (Vd_ok / ((Sd_ok ** (0.5)) + ε))
    return (w, Vd, Sd)
