Update '*var' according to the Adam algorithm.
tf.raw_ops.ResourceApplyAdamWithAmsgrad(
var,
m,
v,
vhat,
beta1_power,
beta2_power,
lr,
beta1,
beta2,
epsilon,
grad,
use_locking=False,
name=None
)
\[\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)\]
\[m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g\]
\[v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g\]
\[\hat{v}_t := max{\hat{v}_{t-1}, v_t}\]
\[\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)\]
Args |
var
|
A Tensor of type resource . Should be from a Variable().
|
m
|
A Tensor of type resource . Should be from a Variable().
|
v
|
A Tensor of type resource . Should be from a Variable().
|
vhat
|
A Tensor of type resource . Should be from a Variable().
|
beta1_power
|
A 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 .
Must be a scalar.
|
beta2_power
|
A Tensor . Must have the same type as beta1_power .
Must be a scalar.
|
lr
|
A Tensor . Must have the same type as beta1_power .
Scaling factor. Must be a scalar.
|
beta1
|
A Tensor . Must have the same type as beta1_power .
Momentum factor. Must be a scalar.
|
beta2
|
A Tensor . Must have the same type as beta1_power .
Momentum factor. Must be a scalar.
|
epsilon
|
A Tensor . Must have the same type as beta1_power .
Ridge term. Must be a scalar.
|
grad
|
A Tensor . Must have the same type as beta1_power . 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.
|
name
|
A name for the operation (optional).
|
Returns |
The created Operation.
|