mindspore.ops.ApplyProximalAdagrad

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

Updates relevant entries according to the proximal adagrad algorithm.

\[\begin{split}\begin{array}{ll} \\ accum += grad * grad \\ \text{prox_v} = var - lr * grad * \frac{1}{\sqrt{accum}} \\ var = \frac{sign(\text{prox_v})}{1 + lr * l2} * \max(\left| \text{prox_v} \right| - lr * l1, 0) \end{array}\end{split}\]

Inputs of var, accum and grad comply with the implicit type conversion rules to make the data types consistent. If they have different data types, lower priority data type will be converted to relatively highest priority data type. RuntimeError exception will be thrown when the data type conversion of Parameter is required.

Parameters

use_locking (bool) – If true, the var and accumulation tensors will be protected from being updated. Default: False.

Inputs:

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

  • accum (Parameter) - Accumulation to be updated. Must has the same shape and dtype as var.

  • lr (Union[Number, Tensor]) - The learning rate value, must be scalar. The data type must be float16 or float32.

  • l1 (Union[Number, Tensor]) - l1 regularization strength, must be scalar. The data type must be float16 or float32.

  • l2 (Union[Number, Tensor]) - l2 regularization strength, must be scalar. The data type must be float16 or float32.

  • grad (Tensor) - Gradient with the same shape and dtype as var.

Outputs:

Tuple of 2 Tensors, the updated parameters.

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

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

Raises

  • TypeError – If use_blocking is not a bool.

  • TypeError – If dtype of var, lr, l1 or l2 is neither float16 nor float32.

  • TypeError – If lr, l1 or l2 is neither a Number nor a Tensor.

  • TypeError – If grad is not a Tensor.

Supported Platforms:

Ascend

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.apply_proximal_adagrad = ops.ApplyProximalAdagrad()
...         self.var = Parameter(Tensor(np.array([[0.6, 0.4],
...                                               [0.1, 0.5]]).astype(np.float32)), name="var")
...         self.accum = Parameter(Tensor(np.array([[0.6, 0.5],
...                                                 [0.2, 0.6]]).astype(np.float32)), name="accum")
...         self.lr = 0.01
...         self.l1 = 0.0
...         self.l2 = 0.0
...     def construct(self, grad):
...         out = self.apply_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2, grad)
...         return out
...
>>> net = Net()
>>> grad = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
>>> output = net(grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 5.96388459e-01,  3.92964751e-01],
 [ 9.78178233e-02,  4.92815793e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.90000057e-01,  9.90000010e-01],
 [ 2.10000008e-01,  1.24000001e+00]]))