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万万没想到 2020-03-23 15:33:01 +08:00 committed by chang zherui
parent d42954652c
commit 95f02396bd
1 changed files with 9 additions and 9 deletions
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18
mindspore/ops/operations/nn_ops.py
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@ -1219,14 +1219,14 @@ class ApplyMomentum(PrimitiveWithInfer):
gradient_scale (float): The scale of the gradient. Default: 1.0.
Inputs:
- **variable** (Tensor) - Weights to be update.
- **variable** (Tensor) - Weights to be updated.
- **accumulation** (Tensor) - Accumulated gradient value by moment weight.
- **learning_rate** (float) - Learning rate.
- **gradient** (Tensor) - Gradients.
- **momentum** (float) - Momentum.
Outputs:
Tensor, parameters to be update.
Tensor, parameters to be updated.
Examples:
>>> net = ResNet50()
@ -1318,15 +1318,15 @@ class SGD(PrimitiveWithInfer):
nesterov (bool): Enable Nesterov momentum. Default: False.
Inputs:
- **parameters** (Tensor) - Parameters to be update.
- **parameters** (Tensor) - Parameters to be updated.
- **gradient** (Tensor) - Gradients.
- **learning_rate** (Tensor) - Learning rate. e.g. Tensor(0.1, mindspore.float32).
- **accum** (Tensor) - Accum(velocity) to be update.
- **accum** (Tensor) - Accum(velocity) to be updated.
- **momentum** (Tensor) - Momentum. e.g. Tensor(0.1, mindspore.float32).
- **stat** (Tensor) - States to be updated with the same shape as gradient.
Outputs:
Tensor, parameters to be update.
Tensor, parameters to be updated.
"""
@prim_attr_register
@ -2141,7 +2141,7 @@ class Adam(PrimitiveWithInfer):
If False, updates the gradients without using NAG. Default: False.
Inputs:
- **var** (Tensor) - Weights to be update.
- **var** (Tensor) - Weights to be updated.
- **m** (Tensor) - The 1st moment vector in the updating formula.
- **v** (Tensor) - the 2nd moment vector in the updating formula.
- **beta1_power** (float) - :math:`beta_1^t` in the updating formula.
@ -2251,8 +2251,8 @@ class SparseApplyAdagrad(PrimitiveWithInfer):
use_locking (bool): If True, updating of the var and accum tensors will be protected. Default: False.
Inputs:
- **var** (Tensor) - Variable to be update. The type must be float32.
- **accum** (Tensor) - Accum to be update. The shape must be the same as `var`'s shape,
- **var** (Tensor) - Variable to be updated. The type must be float32.
- **accum** (Tensor) - Accum to be updated. The shape must be the same as `var`'s shape,
the type must be float32.
- **grad** (Tensor) - Gradient. The shape must be the same as `var`'s shape
except first dimension, the type must be float32.
@ -2372,7 +2372,7 @@ class LARSUpdate(PrimitiveWithInfer):
use_clip (bool): Whether to use clip operation for calculating the local learning rate. Default: False.
Inputs:
- **weight** (Tensor) - The weight to be update.
- **weight** (Tensor) - The weight to be updated.
- **gradient** (Tensor) - The gradient of weight, which has the same shape and dtype with weight.
- **norm_weight** (Tensor) - A scalar tensor, representing the square sum of weight.
- **norm_gradient** (Tensor) - A scalar tensor, representing the square sum of gradient.