mindspore.nn.SGD

class mindspore.nn.SGD(*args, **kwargs)[source]

Implements stochastic gradient descent. Momentum is optional.

Introduction to SGD can be found at https://en.wikipedia.org/wiki/Stochastic_gradient_descent. Nesterov momentum is based on the formula from paper On the importance of initialization and momentum in deep learning.

\[v_{t+1} = u \ast v_{t} + gradient \ast (1-dampening)\]

If nesterov is True:

\[p_{t+1} = p_{t} - lr \ast (gradient + u \ast v_{t+1})\]

If nesterov is False:

\[p_{t+1} = p_{t} - lr \ast v_{t+1}\]

To be noticed, for the first step, \(v_{t+1} = gradient\)

Here : where p, v and u denote the parameters, accum, and momentum respectively.

Note

When separating parameter groups, if you want to centralize the gradient, set grad_centralization to True, but the gradient centralization can only be applied to the parameters of the convolution layer. If the parameters of the non convolution layer are set to True, an error will be reported.

To improve parameter groups performance, the customized order of parameters can be supported.

Parameters

  • params (Union[list[Parameter], list[dict]]) –

    When the params is a list of Parameter which will be updated, the element in params must be class Parameter. When the params is a list of dict, the “params”, “lr”, “weight_decay” and “order_params” are the keys can be parsed.

    • params: Required. The value must be a list of Parameter.

    • lr: Optional. If “lr” in the keys, the value of corresponding learning rate will be used. If not, the learning_rate in the API will be used.

    • order_params: Optional. If “order_params” in the keys, the value must be the order of parameters and the order will be followed in optimizer. There are no other keys in the dict and the parameters which in the value of ‘order_params’ must be in one of group parameters.

    • grad_centralization: Optional. The data type of “grad_centralization” is Bool. If “grad_centralization” is in the keys, the set value will be used. If not, the grad_centralization is False by default. This parameter only works on the convolution layer.

  • learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]) – A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of learning_rate is int, it will be converted to float. Default: 0.1.

  • momentum (float) – A floating point value the momentum. must be at least 0.0. Default: 0.0.

  • dampening (float) – A floating point value of dampening for momentum. must be at least 0.0. Default: 0.0.

  • weight_decay (float) – Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0.

  • nesterov (bool) – Enables the Nesterov momentum. If use nesterov, momentum must be positive, and dampening must equal to 0.0. Default: False.

  • loss_scale (float) – A floating point value for the loss scale, which must be larger than 0.0. In general, use the default value. Only when FixedLossScaleManager is used for training and the drop_overflow_update in FixedLossScaleManager is set to False, then this value needs to be the same as the loss_scale in FixedLossScaleManager. Refer to class mindspore.FixedLossScaleManager for more details. Default: 1.0.

Inputs:

  • gradients (tuple[Tensor]) - The gradients of params, the shape is the same as params.

Outputs:

Tensor[bool], the value is True.

Raises

ValueError – If the momentum, dampening or weight_decay value is less than 0.0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.SGD(params=net.trainable_params())
>>>
>>> #2) Use parameter groups and set different values
>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
>>> group_params = [{'params': conv_params,'grad_centralization':True},
...                 {'params': no_conv_params, 'lr': 0.01},
...                 {'order_params': net.trainable_params()}]
>>> optim = nn.SGD(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use default learning rate of 0.1 default weight decay of 0.0 and grad
>>> # centralization of True.
>>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0 and grad
>>> # centralization of False.
>>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
>>>
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)