mindspore.ops.ApplyProximalAdagrad

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

Updates relevant entries according to the proximal adagrad algorithm. The proximal adagrad algorithm was proposed in Efficient Learning using Forward-Backward Splitting.

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

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, the lower priority data type will be converted to the relatively highest priority data type.

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 have the same shape and dtype as var.

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

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

  • l2 (Union[Number, Tensor]) - l2 regularization strength, must be a 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.

  • RuntimeError – If the data type of var, accum and grad conversion of Parameter is not supported.

Supported Platforms:

Ascend GPU CPU

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]]))