Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

PR

Just a small problem.

I can fix it online!

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindspore.mint.optim.Adam

class mindspore.mint.optim.Adam(params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0, amsgrad=False, *, maximize=False)

Implements Adaptive Moment Estimation (Adam) algorithm.

The updating formulas are as follows:

$$\begin{array}{r}\begin{array}{rl}& \mathbf{\text{input}}:\gamma \text{ (lr)},{\beta }_1,{\beta }_2\text{ (betas)},{\theta }_0\text{ (params)},f(\theta )\text{ (objective)}\\ & \lambda \text{ (weight decay)},\mathit{\text{amsgrad}},\mathit{\text{maximize}}\\ & \mathbf{\text{initialize}}:m_0\leftarrow 0\text{ ( first moment)},v_0\leftarrow 0\text{ (second moment)},{\widehat{v_0}}^{max}\leftarrow 0\\ & \mathbf{\text{for}}t=1\mathbf{\text{to}}\dots \mathbf{\text{do}}\\ & \mathbf{\text{if}}\mathit{\text{maximize}}:\\ & g_t\leftarrow -{\mathrm{\nabla }}_{\theta }{f}_t({\theta }_{t-1})\\ & \mathbf{\text{else}}\\ & g_t\leftarrow {\mathrm{\nabla }}_{\theta }{f}_t({\theta }_{t-1})\\ & \mathbf{\text{if}}\lambda \ne 0\\ & g_t\leftarrow g_t+\lambda {\theta }_{t-1}\\ & m_t\leftarrow {\beta }_1{m}_{t-1}+(1-{\beta }_1)g_t\\ & v_t\leftarrow {\beta }_2{v}_{t-1}+(1-{\beta }_2)g_t^2\\ & \widehat{m_t}\leftarrow m_t/(1-{\beta }_1^t)\\ & \widehat{v_t}\leftarrow v_t/(1-{\beta }_2^t)\\ & \mathbf{\text{if}}amsgrad\\ & {\widehat{v_t}}^{max}\leftarrow \max ({\widehat{v_t}}^{max},\widehat{v_t})\\ & {\theta }_t\leftarrow {\theta }_{t-1}-\gamma \widehat{m_t}/(\sqrt{{\widehat{v_t}}^{max}}+ϵ)\\ & \mathbf{\text{else}}\\ & {\theta }_t\leftarrow {\theta }_{t-1}-\gamma \widehat{m_t}/(\sqrt{\widehat{v_t}}+ϵ)\\ & \mathbf{r}\mathbf{e}\mathbf{t}\mathbf{u}\mathbf{r}\mathbf{n}{\theta }_{\mathbf{t}}\end{array}\end{array}$$

Warning

This is an experimental API that is subject to change or deletion.

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-3.

• betas (Tuple[float, float], optional) – The exponential decay rate for the moment estimations. Should be in range (0.0, 1.0). Default: (0.9, 0.999).

• eps (float, optional) – term added to the denominator to improve numerical stability. Should be greater than 0. Default: 1e-8.

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

• amsgrad (bool, optional) – whether to use the AMSGrad algorithm. Default: False.

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 lr is not int, float or Tensor.

• ValueError – If the lr is less than 0.

• ValueError – If the eps is less than 0.0.

• ValueError – If the betas is not in the range of [0, 1).

• ValueError – If the weight_decay is less than 0.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import mint
>>> from mindspore import mint
>>> # 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 = mint.optim.Adam(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