mindspore.ops.Adam

class mindspore.ops.Adam(use_locking=False, use_nesterov=False)

Updates gradients by the Adaptive Moment Estimation (Adam) algorithm.

The Adam algorithm is proposed in Adam: A Method for Stochastic Optimization.

For more details, please refer to nn.Adam.

The updating formulas are as follows,

$$\begin{array}{r}\begin{array}{l}\\ m={\beta }_1\ast m+(1-{\beta }_1)\ast g\\ v={\beta }_2\ast v+(1-{\beta }_2)\ast g\ast g\\ l=\alpha \ast \frac{\sqrt{1-{\beta }_2^t}}{1-{\beta }_1^t}\\ w=w-l\ast \frac{m}{\sqrt{v}+ϵ}\end{array}\end{array}$$

$m$ represents the 1st moment vector, $v$ represents the 2nd moment vector, $g$ represents gradient, $l$ represents scaling factor lr, ${\beta }_1,{\beta }_2$ represent beta1 and beta2, $t$ represents updating step while $bet{a}_1^t({\beta }_1^t)$ and $bet{a}_2^t({\beta }_2^t)$ represent beta1_power and beta2_power, $\alpha$ represents learning_rate, $w$ represents var, $ϵ$ 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.

• use_nesterov (bool) – Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients. If true, update the gradients using NAG. If false, update the gradients without using NAG. Default: False.

Inputs:

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

• m (Tensor) - The 1st moment vector in the updating formula, the shape and data type value should be the same as var.

• v (Tensor) - the 2nd moment vector in the updating formula, the shape and data type value should be the same as var. Mean square gradients with the same type as var.

• beta1_power (float) - $bet{a}_1^t({\beta }_1^t)$ in the updating formula, the data type value should be the same as var.

• beta2_power (float) - $bet{a}_2^t({\beta }_2^t)$ in the updating formula, the data type value should be the same as var.

• lr (float) - $l$ in the updating formula. The paper suggested value is ${10}^{-8}$, the data type value should be the same as var.

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

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

• epsilon (float) - Term added to the denominator to improve numerical stability.

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

Outputs:

Tuple of 3 Tensor, the updated parameters.

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

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

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

Raises

• TypeError – If neither use_locking nor use_nesterov is a bool.

• TypeError – If var, m or v is not a Tensor.

• TypeError – If beta1_power, beta2_power1, lr, beta1, beta2, epsilon or gradient is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.apply_adam = ops.Adam()
...         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, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad):
...         out = self.apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2,
...                               epsilon, grad)
...         return out
...
>>> net = Net()
>>> gradient = Tensor(np.ones([2, 2]).astype(np.float32))
>>> output = net(0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient)
>>> print(net.var.asnumpy())
[[0.9996838 0.9996838]
 [0.9996838 0.9996838]]