tf.raw_ops.ApplyAdam

Update '*var' according to the Adam algorithm.

tf.raw_ops.ApplyAdam(
    var,
    m,
    v,
    beta1_power,
    beta2_power,
    lr,
    beta1,
    beta2,
    epsilon,
    grad,
    use_locking=False,
    use_nesterov=False,
    name=None
)

\[\text{lr}_t := \mathrm{lr} \cdot \frac{\sqrt{1 - \beta_2^t} }{1 - \beta_1^t}\]

\[m_t := \beta_1 \cdot m_{t-1} + (1 - \beta_1) \cdot g\]

\[v_t := \beta_2 \cdot v_{t-1} + (1 - \beta_2) \cdot g^2\]

\[\text{var} := \begin{cases} \text{var} - (m_t \beta_1 + g \cdot (1 - \beta_1))\cdot\text{lr}_t/(\sqrt{v_t} + \epsilon), &\text{if use_nesterov}\\\\ \text{var} - m_t \cdot \text{lr}_t /(\sqrt{v_t} + \epsilon), &\text{otherwise} \end{cases}\]

var A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64. Should be from a Variable(). m A mutable Tensor. Must have the same type as var. Should be from a Variable(). v A mutable Tensor. Must have the same type as var. Should be from a Variable(). beta1_power A Tensor. Must have the same type as var. Must be a scalar. beta2_power A Tensor. Must have the same type as var. Must be a scalar. lr A Tensor. Must have the same type as var. Scaling factor. Must be a scalar. beta1 A Tensor. Must have the same type as var. Momentum factor. Must be a scalar. beta2 A Tensor. Must have the same type as var. Momentum factor. Must be a scalar. epsilon A Tensor. Must have the same type as var. Ridge term. Must be a scalar. grad A Tensor. Must have the same type as var. The gradient. use_locking An optional bool. Defaults to False. If True, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov An optional bool. Defaults to False. If True, uses the nesterov update. name A name for the operation (optional).