mindspore.ops.SparseApplyAdagrad

class mindspore.ops.SparseApplyAdagrad(lr, update_slots=True, use_locking=False)[source]

Updates relevant entries according to the adagrad scheme.

\[\begin{split}\begin{array}{ll} \\ accum += grad * grad \\ var -= lr * grad * (1 / sqrt(accum)) \end{array}\end{split}\]

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

  • lr (float) – Learning rate.

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

  • use_locking (bool) – If true, the var and accum 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. The shape and data type must be the same as var.

  • grad (Tensor) - Gradients has the same data type as var and grad.shape[1:] = var.shape[1:] if var.shape > 1.

  • indices (Tensor) - A vector of indices into the first dimension of var and accum. The type must be int32 and indices.shape[0] = grad.shape[0].

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 lr is not a float.

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

  • TypeError – If dtype of var, accum or grad is neither float16 nor float32.

  • TypeError – If dtype of indices is not int32.

Supported Platforms:

Ascend

Examples

>>> class Net(nn.Cell):
...     def __init__(self):
...         super(Net, self).__init__()
...         self.sparse_apply_adagrad = ops.SparseApplyAdagrad(lr=1e-8)
...         self.var = Parameter(Tensor(np.array([[[0.2]]]).astype(np.float32)), name="var")
...         self.accum = Parameter(Tensor(np.array([[[0.1]]]).astype(np.float32)), name="accum")
...     def construct(self, grad, indices):
...         out = self.sparse_apply_adagrad(self.var, self.accum, grad, indices)
...         return out
...
>>> net = Net()
>>> grad = Tensor(np.array([[[0.7]]]).astype(np.float32))
>>> indices = Tensor([0], mindspore.int32)
>>> output = net(grad, indices)
>>> print(output)
(Tensor(shape=[1, 1, 1], dtype=Float32, value=
[[[1.99999988e-01]]]), Tensor(shape=[1, 1, 1], dtype=Float32, value=
[[[1.00000001e-01]]]))