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!1843 fix ScatterAdd ScatterMax and BasicLSTMCell comments error 

Merge pull request !1843 from zhaozhenlong/fix-issues-scatter-and-lstm-comments
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mindspore-ci-bot 2020-06-04 17:23:55 +08:00 committed by Gitee
parent aef9c4d838 169ff48224
commit e7b7abc581
2 changed files with 10 additions and 8 deletions
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8
mindspore/ops/operations/array_ops.py
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@ -2241,7 +2241,8 @@ class ScatterMax(PrimitiveWithInfer):
"""
Update the value of the input tensor through the max operation.
Using given values to update tensor value through the max operation, along with the input indices,.
Using given values to update tensor value through the max operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
Args:
use_locking (bool): Whether protect the assignment by a lock. Default: True.
@ -2253,7 +2254,7 @@ class ScatterMax(PrimitiveWithInfer):
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, has the same shape and data type as `input_x`.
Parameter, the updated `input_x`.
Examples:
>>> input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32), name="input_x")
@ -2286,6 +2287,7 @@ class ScatterAdd(PrimitiveWithInfer):
Update the value of the input tensor through the add operation.
Using given values to update tensor value through the add operation, along with the input indices.
This operation outputs the `input_x` after the update is done, which makes it convenient to use the updated value.
Args:
use_locking (bool): Whether protect the assignment by a lock. Default: False.
@ -2297,7 +2299,7 @@ class ScatterAdd(PrimitiveWithInfer):
the data type is same as `input_x`, the shape is `indices_shape + x_shape[1:]`.
Outputs:
Tensor, has the same shape and data type as `input_x`.
Parameter, the updated `input_x`.
Examples:
>>> input_x = Parameter(Tensor(np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), mindspore.float32), name="x")

10
mindspore/ops/operations/nn_ops.py
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@ -3407,12 +3407,12 @@ class BasicLSTMCell(PrimitiveWithInfer):
Outputs:
- **ct** (Tensor) - Forward :math:`c_t` cache at moment `t`. Tensor of shape (`batch_size`, `hidden_size`).
- **ht** (Tensor) - Cell output. Tensor of shape (`batch_size`, `hidden_size`).
- **it** (Tensor) - Forward :math:`i_t` cache at moment `t`. Tensor of shape (`batch_size`, `4 x hidden_size`).
- **jt** (Tensor) - Forward :math:`j_t` cache at moment `t`. Tensor of shape (`batch_size`, `4 x hidden_size`).
- **ft** (Tensor) - Forward :math:`f_t` cache at moment `t`. Tensor of shape (`batch_size`, `4 x hidden_size`).
- **ot** (Tensor) - Forward :math:`o_t` cache at moment `t`. Tensor of shape (`batch_size`, `4 x hidden_size`).
- **it** (Tensor) - Forward :math:`i_t` cache at moment `t`. Tensor of shape (`batch_size`, `hidden_size`).
- **jt** (Tensor) - Forward :math:`j_t` cache at moment `t`. Tensor of shape (`batch_size`, `hidden_size`).
- **ft** (Tensor) - Forward :math:`f_t` cache at moment `t`. Tensor of shape (`batch_size`, `hidden_size`).
- **ot** (Tensor) - Forward :math:`o_t` cache at moment `t`. Tensor of shape (`batch_size`, `hidden_size`).
- **tanhct** (Tensor) - Forward :math:`tanh c_t` cache at moment `t`.
Tensor of shape (`batch_size`, `4 x hidden_size`).
Tensor of shape (`batch_size`, `hidden_size`).
Examples:
'block': P.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh'),