mindspore.ops.AdamWeightDecay

class mindspore.ops.AdamWeightDecay(use_locking=False)[source]

Updates gradients by the Adaptive Moment Estimation algorithm with weight decay (AdamWeightDecay).

The Adam algorithm is proposed in Adam: A Method for Stochastic Optimization. The AdamWeightDecay variant was proposed in Decoupled Weight Decay Regularization.

The updating formulas are as follows,

\[\begin{split}\begin{array}{ll} \\ m = \beta_1 * m + (1 - \beta_1) * g \\ v = \beta_2 * v + (1 - \beta_2) * g * g \\ update = \frac{m}{\sqrt{v} + \epsilon} \\ update = \begin{cases} update + weight\_decay * w & \text{ if } weight\_decay > 0 \\ update & \text{ otherwise } \end{cases} \\ w = w - lr * update \end{array}\end{split}\]

\(m\) represents the 1st moment vector, \(v\) represents the 2nd moment vector, \(g\) represents gradient, \(\beta_1, \beta_2\) represent beta1 and beta2, \(lr\) represents learning_rate, \(w\) represents var, \(decay\) represents weight_decay, \(\epsilon\) represents epsilon.

Parameters

use_locking (bool) – Whether to enable a lock to protect variable tensors from being updated. If True , updates of the var, m, and v tensors will be protected by a lock. If False , the result is unpredictable. Default: False .

Inputs:

  • var (Parameter) - Weights to be updated. The shape is \((N, *)\) where \(*\) means, any number of additional dimensions. The data type can be float16 or float32.

  • m (Parameter) - The 1st moment vector in the updating formula, it should have the the shape as var. The data type can be float16 or float32.

  • v (Parameter) - The 2nd moment vector in the updating formula, it should have the same shape as m.

  • lr (float) - \(lr\) in the updating formula. The paper suggested value is \(10^{-8}\), the data type should be float32.

  • beta1 (float) - The exponential decay rate for the 1st moment estimations, the data type should be float32. The paper suggested value is \(0.9\)

  • beta2 (float) - The exponential decay rate for the 2nd moment estimations, the data type should be float32. The paper suggested value is \(0.999\)

  • epsilon (float) - Term added to the denominator to improve numerical stability, the data type should be float32.

  • decay (float) - The weight decay value, must be a scalar tensor with float32 data type. Default: 0.0 .

  • gradient (Tensor) - Gradient, has the same shape as var.

Outputs:

Tuple of 3 Tensor, the updated parameters.

  • var (Tensor) - The same shape and data type as var.

  • m (Tensor) - The same shape and data type as m.

  • v (Tensor) - The same shape and data type as v.

Raises

  • TypeError – If use_locking is not a bool.

  • TypeError – If lr, beta1, beta2, epsilon or decay is not a float32.

  • TypeError – If var, m or v is not a Parameter with dtype float16 or float32.

  • TypeError – If gradient is not a Tensor.

  • ValueError – If epsilon <= 0.

  • ValueError – If beta1, beta2 is not in range (0.0,1.0).

  • ValueError – If decay < 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, Parameter, ops
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.adam_weight_decay = ops.AdamWeightDecay()
...         self.var = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="var")
...         self.m = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="m")
...         self.v = Parameter(Tensor(np.ones([2, 2]).astype(np.float32)), name="v")
...     def construct(self, lr, beta1, beta2, epsilon, decay, grad):
...         out = self.adam_weight_decay(self.var, self.m, self.v, lr, beta1, beta2,
...                               epsilon, decay, grad)
...         return out
>>> net = Net()
>>> gradient = Tensor(np.ones([2, 2]).astype(np.float32))
>>> output = net(0.001, 0.9, 0.999, 1e-8, 0.0, gradient)
>>> print(net.var.asnumpy())
[[0.999 0.999]
[0.999 0.999]]