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mindspore.ops.ApplyRMSProp

class mindspore.ops.ApplyRMSProp(use_locking=False)

Optimizer that implements the Root Mean Square prop(RMSProp) algorithm. Please refer to the usage in source code of nn.RMSProp.

The updating formulas of ApplyRMSProp algorithm are as follows,

$$\begin{array}{r}\begin{array}{l}\\ s_{t+1}=\rho s_t+(1-\rho )(\mathrm{\nabla }{Q}_i(w){)}^2\\ m_{t+1}=\beta m_t+\frac{\eta }{\sqrt{s_{t+1}+ϵ}}\mathrm{\nabla }{Q}_i(w)\\ w=w-m_{t+1}\end{array}\end{array}$$

where $w$ represents var, which will be updated. $s_{t+1}$ represents mean_square, $s_t$ is the last momentent of $s_{t+1}$, $m_{t+1}$ represents moment, $m_t$ is the last momentent of $m_{t+1}$. $\rho$ represents decay. $\beta$ is the momentum term, represents momentum. $ϵ$ is a smoothing term to avoid division by zero, represents epsilon. $\eta$ represents learning_rate. $\mathrm{\nabla }{Q}_i(w)$ represents grad.

Warning

Note that in dense implementation of this algorithm, “mean_square” and “moment” will update even if “grad” is 0, but in this sparse implementation, “mean_square” and “moment” will not update in iterations during which “grad” is 0.

Parameters

use_locking (bool) – Whether to enable a lock to protect the variable and accumlation tensors from being updated. Default: False.

Inputs:

• var (Tensor) - Weights to be update.

• mean_square (Tensor) - Mean square gradients, must have the same type as var.

• moment (Tensor) - Delta of var, must have the same type as var.

• learning_rate (Union[Number, Tensor]) - Learning rate. Must be a float number or a scalar tensor with float16 or float32 data type.

• grad (Tensor) - Gradient, must have the same type as var.

• decay (float) - Decay rate. Only constant value is allowed.

• momentum (float) - Momentum. Only constant value is allowed.

• epsilon (float) - Ridge term. Only constant value is allowed.

Outputs:

Tensor, parameters to be update.

Raises

• TypeError – If use_locking is not a bool.

• TypeError – If var, mean_square, moment or decay is not a Tensor.

• TypeError – If learning_rate is neither a Number nor a Tensor.

• TypeError – If dtype of decay, momentum or epsilon is not float.

• TypeError – If dtype of learning_rate is neither float16 nor float32.

• ValueError – If decay, momentum or epsilon is not a constant value.

Supported Platforms:

Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.apply_rms_prop = ops.ApplyRMSProp()
...         self.var = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="var")
...
...     def construct(self, mean_square, moment, grad, decay, momentum, epsilon, lr):
...         out = self.apply_rms_prop(self.var, mean_square, moment, lr, grad, decay, momentum, epsilon)
...         return out
...
>>> net = Net()
>>> mean_square = Tensor(np.ones([2, 2]).astype(np.float32))
>>> moment = Tensor(np.ones([2, 2]).astype(np.float32))
>>> grad = Tensor(np.ones([2, 2]).astype(np.float32))
>>> output = net(mean_square, moment, grad, 0.0, 1e-10, 0.001, 0.01)
>>> print(net.var.asnumpy())
[[0.990005  0.990005]
 [0.990005  0.990005]]