wangnan39@huawei.com
5 years ago
1 changed files with 43 additions and 29 deletions
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+ 43
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mindspore/ops/operations/nn_ops.py
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| @@ -2773,17 +2773,18 @@ class SparseApplyAdam(PrimitiveWithInfer): | |||
| If False, updates the gradients without using NAG. Default: False. | |||
| Inputs: | |||
| - **var** (Parameter) - Parameters to be updated. | |||
| - **m** (Parameter) - The 1st moment vector in the updating formula. Has the same type as `var`. | |||
| - **var** (Parameter) - Parameters to be updated. With float32 data type. | |||
| - **m** (Parameter) - The 1st moment vector in the updating formula. Has the same type as `var`. With | |||
| float32 data type. | |||
| - **v** (Parameter) - The 2nd moment vector in the updating formula. Mean square gradients, | |||
| has the same type as `var`. | |||
| - **beta1_power** (float) - :math:`beta_1^t` in the updating formula. | |||
| - **beta2_power** (float) - :math:`beta_2^t` in the updating formula. | |||
| - **lr** (float) - :math:`l` in the updating formula. | |||
| - **beta1** (float) - The exponential decay rate for the 1st moment estimates. | |||
| - **beta2** (float) - The exponential decay rate for the 2nd moment estimates. | |||
| - **epsilon** (float) - Term added to the denominator to improve numerical stability. | |||
| - **gradient** (Tensor) - Gradient value. | |||
| has the same type as `var`. With float32 data type. | |||
| - **beta1_power** (Tensor) - :math:`beta_1^t` in the updating formula. With float32 data type. | |||
| - **beta2_power** (Tensor) - :math:`beta_2^t` in the updating formula. With float32 data type. | |||
| - **lr** (Tensor) - :math:`l` in the updating formula. With float32 data type. | |||
| - **beta1** (Tensor) - The exponential decay rate for the 1st moment estimates. With float32 data type. | |||
| - **beta2** (Tensor) - The exponential decay rate for the 2nd moment estimates. With float32 data type. | |||
| - **epsilon** (Tensor) - Term added to the denominator to improve numerical stability. With float32 data type. | |||
| - **gradient** (Tensor) - Gradient value. With float32 data type. | |||
| - **indices** (Tensor) - Gradient indices. With int32 data type. | |||
| Outputs: | |||
| @@ -2803,17 +2804,23 @@ class SparseApplyAdam(PrimitiveWithInfer): | |||
| >>> def __init__(self): | |||
| >>> super(Net, self).__init__() | |||
| >>> self.sparse_apply_adam = P.SparseApplyAdam() | |||
| >>> self.var = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="var") | |||
| >>> self.m = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="m") | |||
| >>> self.v = Parameter(Tensor(np.ones([3, 3, 3]).astype(np.float32)), name="v") | |||
| >>> self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="var") | |||
| >>> self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="m") | |||
| >>> self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="v") | |||
| >>> def construct(self, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, indices): | |||
| >>> out = self.sparse_apply_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, | |||
| >>> epsilon, grad, indices) | |||
| >>> return out | |||
| >>> net = Net() | |||
| >>> gradient = Tensor(np.random.rand(3, 3, 3).astype(np.float32)) | |||
| >>> indices = Tensor([0, 1, 2], mstype.int32) | |||
| >>> result = net(0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient, indices) | |||
| >>> beta1_power = Tensor(0.9, mstype.float32) | |||
| >>> beta2_power = Tensor(0.999, mstype.float32) | |||
| >>> lr = Tensor(0.001, mstype.float32) | |||
| >>> beta1 = Tensor(0.9, mstype.float32) | |||
| >>> beta2 = Tensor(0.999, mstype.float32) | |||
| >>> epsilon = Tensor(1e-8, mstype.float32) | |||
| >>> gradient = Tensor(np.random.rand(2, 1, 2), mstype.float32) | |||
| >>> indices = Tensor([0, 1], mstype.int32) | |||
| >>> result = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices) | |||
| """ | |||
| __mindspore_signature__ = ( | |||
| ('var', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T), | |||
| @@ -2900,17 +2907,18 @@ class SparseApplyLazyAdam(PrimitiveWithInfer): | |||
| If False, updates the gradients without using NAG. Default: False. | |||
| Inputs: | |||
| - **var** (Parameter) - Weights to be updated. | |||
| - **m** (Parameter) - The 1st moment vector in the updating formula. Has the same type as `var`. | |||
| - **var** (Parameter) - Parameters to be updated. With float32 data type. | |||
| - **m** (Parameter) - The 1st moment vector in the updating formula. Has the same type as `var`. With | |||
| float32 data type. | |||
| - **v** (Parameter) - The 2nd moment vector in the updating formula. Mean square gradients, | |||
| has the same type as `var`. | |||
| - **beta1_power** (float) - :math:`beta_1^t` in the updating formula. | |||
| - **beta2_power** (float) - :math:`beta_2^t` in the updating formula. | |||
| - **lr** (float) - :math:`l` in the updating formula. | |||
| - **beta1** (float) - The exponential decay rate for the 1st moment estimates. | |||
| - **beta2** (float) - The exponential decay rate for the 2nd moment estimates. | |||
| - **epsilon** (float) - Term added to the denominator to improve numerical stability. | |||
| - **gradient** (Tensor) - Gradient value. | |||
| has the same type as `var`. With float32 data type. | |||
| - **beta1_power** (Tensor) - :math:`beta_1^t` in the updating formula. With float32 data type. | |||
| - **beta2_power** (Tensor) - :math:`beta_2^t` in the updating formula. With float32 data type. | |||
| - **lr** (Tensor) - :math:`l` in the updating formula. With float32 data type. | |||
| - **beta1** (Tensor) - The exponential decay rate for the 1st moment estimates. With float32 data type. | |||
| - **beta2** (Tensor) - The exponential decay rate for the 2nd moment estimates. With float32 data type. | |||
| - **epsilon** (Tensor) - Term added to the denominator to improve numerical stability. With float32 data type. | |||
| - **gradient** (Tensor) - Gradient value. With float32 data type. | |||
| - **indices** (Tensor) - Gradient indices. With int32 data type. | |||
| Outputs: | |||
| @@ -2938,9 +2946,15 @@ class SparseApplyLazyAdam(PrimitiveWithInfer): | |||
| >>> beta2, epsilon, grad, indices) | |||
| >>> return out | |||
| >>> net = Net() | |||
| >>> gradient = Tensor(np.random.rand(3, 3, 3).astype(np.float32)) | |||
| >>> indices = Tensor([0, 1, 2], mstype.int32) | |||
| >>> result = net(0.9, 0.999, 0.001, 0.9, 0.999, 1e-8, gradient, indices) | |||
| >>> beta1_power = Tensor(0.9, mstype.float32) | |||
| >>> beta2_power = Tensor(0.999, mstype.float32) | |||
| >>> lr = Tensor(0.001, mstype.float32) | |||
| >>> beta1 = Tensor(0.9, mstype.float32) | |||
| >>> beta2 = Tensor(0.999, mstype.float32) | |||
| >>> epsilon = Tensor(1e-8, mstype.float32) | |||
| >>> gradient = Tensor(np.random.rand(2, 1, 2), mstype.float32) | |||
| >>> indices = Tensor([0, 1], mstype.int32) | |||
| >>> result = net(beta1_power, beta2_power, lr, beta1, beta2, epsilon, gradient, indices) | |||
| """ | |||
| __mindspore_signature__ = ( | |||
| ('var', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T), | |||