mindspore.experimental.optim.Adagrad

class mindspore.experimental.optim.Adagrad(params, lr=0.01, lr_decay=0.0, weight_decay=0.0, initial_accumulator_value=0.0, eps=1e-10, *, maximize=False)[source]

Implements Adagrad algorithm.

\[\begin{split}\begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned}\end{split}\]

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