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!23879 add detail desciption of sequence length and fix some grammar errors. 

Merge pull request !23879 from 吕昱峰（Nate.River）/rnn_doc
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i-robot 2021-09-30 02:32:03 +00:00 committed by Gitee
parent 6f2f35c3a1 62280e9387
commit c3d8210658
1 changed files with 14 additions and 6 deletions
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20
mindspore/nn/layer/rnns.py
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@ -390,13 +390,17 @@ class RNN(_RNNBase):
shape (seq_len, batch_size, `input_size`) or (batch_size, seq_len, `input_size`).
- **hx** (Tensor) - Tensor of data type mindspore.float32 and
shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` must be the same as `x`.
- **seq_length** (Tensor) - The length of each batch.
- **seq_length** (Tensor) - The length of each sequence in a input batch.
Tensor of shape :math:`(\text{batch_size})`. Default: None.
This input indicates the real sequence length before padding to avoid padded elements
have been used to compute hidden state and affect the final output. It is recommend to
use this input when **x** has padding elements.
Outputs:
Tuple, a tuple contains (`output`, `h_n`).
- **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`).
- **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`) or
(batch_size, seq_len, num_directions * `hidden_size`).
- **hx_n** (Tensor) - Tensor of shape (num_directions * `num_layers`, batch_size, `hidden_size`).
Raises:
@ -479,13 +483,17 @@ class GRU(_RNNBase):
shape (seq_len, batch_size, `input_size`) or (batch_size, seq_len, `input_size`).
- **hx** (Tensor) - Tensor of data type mindspore.float32 and
shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` must be the same as `x`.
- **seq_length** (Tensor) - The length of each batch.
- **seq_length** (Tensor) - The length of each sequence in a input batch.
Tensor of shape :math:`(\text{batch_size})`. Default: None.
This input indicates the real sequence length before padding to avoid padded elements
have been used to compute hidden state and affect the final output. It is recommend to
use this input when **x** has padding elements.
Outputs:
Tuple, a tuple contains (`output`, `h_n`).
- **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`).
- **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`) or
(batch_size, seq_len, num_directions * `hidden_size`).
- **hx_n** (Tensor) - Tensor of shape (num_directions * `num_layers`, batch_size, `hidden_size`).
Raises:
@ -550,7 +558,7 @@ class RNNCell(_RNNCellBase):
input_size (int): Number of features of input.
hidden_size (int): Number of features of hidden layer.
has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True.
nonlinearity (str): The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
nonlinearity (str): The non-linearity to use. Can be either `tanh` or `relu`. Default: `tanh`.
Inputs:
- **x** (Tensor) - Tensor of shape (batch_size, `input_size`).
@ -561,7 +569,7 @@ class RNNCell(_RNNCellBase):
- **h'** (Tensor) - Tensor of shape (batch_size, `hidden_size`).
Raises:
TypeError: If `input_size`, `hidden_size` is not an int.
TypeError: If `input_size` or `hidden_size` is not an int or not great than 0.
TypeError: If `has_bias` is not a bool.
ValueError: If `nonlinearity` is not in ['tanh', 'relu'].