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mindspore.experimental.optim.Adagrad

class mindspore.experimental.optim.Adagrad(params, lr=1e-2, lr_decay=0.0, weight_decay=0.0, initial_accumulator_value=0.0, eps=1e-10, *, maximize=False)

Implements Adagrad algorithm.

$$\begin{array}{r}\begin{array}{rl}& \\ & \mathbf{\text{input}}:\gamma \text{ (lr)},{\theta }_0\text{ (params)},f(\theta )\text{ (objective)},\lambda \text{ (weight decay)},\\ & \tau \text{ (initial accumulator value)},\eta \text{ (lr decay)}\\ & \mathbf{\text{initialize}}:state\mathrm{\_}su{m}_0\leftarrow 0\\ & \\ & \mathbf{\text{for}}t=1\mathbf{\text{to}}\dots \mathbf{\text{do}}\\ & g_t\leftarrow {\mathrm{\nabla }}_{\theta }{f}_t({\theta }_{t-1})\\ & \tilde{\gamma }\leftarrow \gamma /(1+(t-1)\eta )\\ & \mathbf{\text{if}}\lambda \ne 0\\ & g_t\leftarrow g_t+\lambda {\theta }_{t-1}\\ & state\mathrm{\_}su{m}_t\leftarrow state\mathrm{\_}su{m}_{t-1}+g_t^2\\ & {\theta }_t\leftarrow {\theta }_{t-1}-\tilde{\gamma }\frac{g_t}{\sqrt{state\mathrm{\_}su{m}_t}+ϵ}\\ & \\ & \mathbf{r}\mathbf{e}\mathbf{t}\mathbf{u}\mathbf{r}\mathbf{n}{\theta }_{\mathbf{t}}\\ & \end{array}\end{array}$$

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: 1e-2.

• lr_decay (float, optional) – learning rate decay. Default: 0..

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

• initial_accumulator_value (float, optional) – the initial accumulator value. Default: 0..

• eps (float, optional) – term added to the denominator to improve numerical stability. Default: 1e-10.

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 learning rate decay is less than 0.

• ValueError – If the weight_decay is less than 0.

• ValueError – If the initial_accumulator_value is less than 0.0.

• ValueError – If the eps is less than 0.0.

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.Adagrad(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