mindspore.nn.FTRL

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

Implements the FTRL algorithm with ApplyFtrl Operator.

FTRL is an online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions. Refer to paper Adaptive Bound Optimization for Online Convex Optimization. Refer to paper Ad Click Prediction: a View from the Trenches for engineering document.

The updating formulas are as follows,

\[\begin{split}\begin{array}{ll} \\ m_{t+1} = m_{t} + g^2 \\ u_{t+1} = u_{t} + g - \frac{m_{t+1}^\text{-p} - m_{t}^\text{-p}}{\alpha } * \omega_{t} \\ \omega_{t+1} = \begin{cases} \frac{(sign(u_{t+1}) * l1 - u_{t+1})}{\frac{m_{t+1}^\text{-p}}{\alpha } + 2 * l2 } & \text{ if } |u_{t+1}| > l1 \\ 0.0 & \text{ otherwise } \end{cases}\\ \end{array}\end{split}\]

\(m\) represents accum, \(g\) represents grads, \(t\) represents updating step, \(u\) represents linear, \(p\) represents lr_power, \(\alpha\) represents learning_rate, \(\omega\) represents params.

Note

When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the weight_decay in the API will be applied on all of the parameters.

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.

The sparse strategy is applied while the SparseGatherV2 operator being used for forward network. The sparse feature is under continuous development. If the sparse strategy wants to be executed on the host, set the target to the CPU.

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: Using different learning rate by separating parameters is currently not supported.

    • weight_decay: Optional. If “weight_decay” in the keys, the value of corresponding weight decay will be used. If not, the weight_decay 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.

  • initial_accum (float) – The starting value for accumulators, must be zero or positive values. Default: 0.1.

  • learning_rate (float) – The learning rate value, must be zero or positive, dynamic learning rate is currently not supported. Default: 0.001.

  • lr_power (float) – Learning rate power controls how the learning rate decreases during training, must be less than or equal to zero. Use fixed learning rate if lr_power is zero. Default: -0.5.

  • l1 (float) – l1 regularization strength, must be greater than or equal to zero. Default: 0.0.

  • l2 (float) – l2 regularization strength, must be greater than or equal to zero. Default: 0.0.

  • use_locking (bool) – If true, use locks for updating operation. Default: False.

  • loss_scale (float) – Value for the loss scale. It must be greater 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.

  • weight_decay (Union[float, int]) – Weight decay value to multiply weight, must be zero or positive value. Default: 0.0.

Inputs:

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

Outputs:

tuple[Parameter], the updated parameters, the shape is the same as params.

Raises

  • TypeError – If initial_accum, learning_rate, lr_power, l1, l2 or loss_scale is not a float.

  • TypeError – If element of parameters is neither Parameter nor dict.

  • TypeError – If weight_decay is neither float nor int.

  • TypeError – If use_nesterov is not a bool.

  • ValueError – If lr_power is greater than 0.

  • ValueError – If loss_scale is less than or equal to 0.

  • ValueError – If initial_accum, l1 or l2 is less than 0.

Supported Platforms:

Ascend GPU

Examples

>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.FTRL(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, 'weight_decay': 0.01, 'grad_centralization':True},
...                 {'params': no_conv_params},
...                 {'order_params': net.trainable_params()}]
>>> optim = nn.FTRL(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01 and grad
>>> # centralization of True.
>>> # The no_conv_params's parameters will use 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)
property target

The method is used to determine whether the parameter is updated on host or device. The input type is str and can only be ‘CPU’, ‘Ascend’ or ‘GPU’.