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
)
\[\text{lr}_t := \mathrm{lr} \cdot \frac{\sqrt{1 - \beta_2^t} }{1 - \beta_1^t}\]
\[m_t := \beta_1 \cdot m_{t-1} + (1 - \beta_1) \cdot g\]
\[v_t := \beta_2 \cdot v_{t-1} + (1 - \beta_2) \cdot g^2\]
\[\text{var} := \begin{cases} \text{var} - (m_t \beta_1 + g \cdot (1 - \beta_1))\cdot\text{lr}_t/(\sqrt{v_t} + \epsilon), &\text{if use_nesterov}\\\\ \text{var} - m_t \cdot \text{lr}_t /(\sqrt{v_t} + \epsilon), &\text{otherwise} \end{cases}\]
Args |
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).
|
Returns |
A mutable Tensor . Has the same type as var .
|