mindspore.ops.ApplyAdagrad

class mindspore.ops.ApplyAdagrad(*args, **kwargs)[source]

Updates relevant entries according to the adagrad scheme.

\[accum += grad * grad\]
\[var -= lr * grad * \frac{1}{\sqrt{accum}}\]

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

update_slots (bool) – If True, accum will be updated. Default: True.

Inputs:

  • var (Parameter) - Variable to be updated. With float32 or float16 data type.

  • accum (Parameter) - Accumulation to be updated. The shape and dtype must be the same as var. With float32 or float16 data type.

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

  • grad (Tensor) - A tensor for gradient. The shape and dtype must be the same as var. With float32 or float16 data type.

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 dtype of var, accum, lr or grad is neither float16 nor float32.

  • TypeError – If lr is neither a Number nor a Tensor.

Supported Platforms:

Ascend CPU GPU

Examples

>>> import numpy as np
>>> import mindspore.nn as nn
>>> from mindspore import Tensor
>>> from mindspore import Parameter
>>> from mindspore.ops import operations as ops
>>> import mindspore.common.dtype as mstype
>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.apply_adagrad = ops.ApplyAdagrad()
...         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")
...     def construct(self, lr, grad):
...         out = self.apply_adagrad(self.var, self.accum, lr, grad)
...         return out
...
>>> net = Net()
>>> lr = Tensor(0.001, mstype.float32)
>>> grad = Tensor(np.array([[0.3, 0.7], [0.1, 0.8]]).astype(np.float32))
>>> output = net(lr, grad)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Float32, value=
[[ 5.99638879e-01,  3.99296492e-01],
 [ 9.97817814e-02,  4.99281585e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
[[ 6.90000057e-01,  9.90000010e-01],
 [ 2.10000008e-01,  1.24000001e+00]]))