Update '*var' according to the centered RMSProp algorithm.
tf.raw_ops.ResourceApplyCenteredRMSProp(
var,
mg,
ms,
mom,
lr,
rho,
momentum,
epsilon,
grad,
use_locking=False,
name=None
)
The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory.
Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero.
mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient
Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2)
mg <- rho * mg{t-1} + (1-rho) * grad ms <- rho * ms{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom
Args |
|---|
var
Tensor of type resource. Should be from a Variable().
mg
Tensor of type resource. Should be from a Variable().
ms
Tensor of type resource. Should be from a Variable().
mom
Tensor of type resource. Should be from a Variable().
lr
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.
Scaling factor. Must be a scalar.
rho
Tensor. Must have the same type as lr.
Decay rate. Must be a scalar.
momentum
Tensor. Must have the same type as lr.
Momentum Scale. Must be a scalar.
epsilon
Tensor. Must have the same type as lr.
Ridge term. Must be a scalar.
grad
Tensor. Must have the same type as lr. The gradient.
use_locking
bool. Defaults to False.
If True, updating of the var, mg, ms, and mom tensors is
protected by a lock; otherwise the behavior is undefined, but may exhibit less
contention.
name
Returns | |
|---|---|
| The created Operation. |