mindspore.experimental.optim.Adadelta

class mindspore.experimental.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0.0, *, maximize=False)

Implements Adadelta algorithm.

$$\begin{array}{r}\begin{array}{rl}& \\ & \mathbf{\text{input}}:\gamma \text{ (lr)},{\theta }_0\text{ (params)},f(\theta )\text{ (objective)},\rho \text{ (decay)},\lambda \text{ (weight decay)}\\ & \mathbf{\text{initialize}}:v_0\leftarrow 0\text{ (square avg)},u_0\leftarrow 0\text{ (accumulate variables)}\\ & \\ & \mathbf{\text{for}}t=1\mathbf{\text{to}}\dots \mathbf{\text{do}}\\ & g_t\leftarrow {\mathrm{\nabla }}_{\theta }{f}_t({\theta }_{t-1})\\ & if\lambda \ne 0\\ & g_t\leftarrow g_t+\lambda {\theta }_{t-1}\\ & v_t\leftarrow v_{t-1}\rho +g_t^2(1-\rho )\\ & \mathrm{\Delta }{x}_t\leftarrow \frac{\sqrt{u_{t-1}+ϵ}}{\sqrt{v_t+ϵ}}{g}_t\\ & u_t\leftarrow u_{t-1}\rho +\mathrm{\Delta }{x}_t^2(1-\rho )\\ & {\theta }_t\leftarrow {\theta }_{t-1}-\gamma \mathrm{\Delta }{x}_t\\ & \\ & \mathbf{r}\mathbf{e}\mathbf{t}\mathbf{u}\mathbf{r}\mathbf{n}{\theta }_{\mathbf{t}}\\ & \end{array}\end{array}$$

Warning

This is an experimental optimizer API that is subject to change. This module must be used with lr scheduler module in LRScheduler Class .

Parameters

• params (Union[list(Parameter), list(dict)]) – list of parameters to optimize or dicts defining parameter groups.

• lr (Union[int, float, Tensor], optional) – learning rate. Default: 1.0.

• rho (float, optional) – coefficient used for computing a running average of squared gradients. $\rho$ in the formula above. Default: 0.9.

• eps (float, optional) – term added to the denominator to improve numerical stability. $ϵ$ in the formula above. Default: 1e-6.

• weight_decay (float, optional) – weight decay (L2 penalty). Default: 0..

Keyword Arguments

maximize (bool, optional) – maximize the params based on the objective, instead of minimizing. Default: False.

Inputs:

• gradients (tuple[Tensor]) - The gradients of params.

Raises

• ValueError – If the learning rate is not int, float or Tensor.

• ValueError – If the learning rate is less than 0.

• ValueError – If the eps is less than or equal to 0.0.

• ValueError – If the rho is not in the range of [0, 1].

• ValueError – If the weight_decay is less than 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import nn
>>> from mindspore.experimental import optim
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
>>> optimizer = optim.Adadelta(net.trainable_params(), lr=0.1)
>>> def forward_fn(data, label):
...     logits = net(data)
...     loss = loss_fn(logits, label)
...     return loss, logits
>>> grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
>>> def train_step(data, label):
...     (loss, _), grads = grad_fn(data, label)
...     optimizer(grads)
...     return loss