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update lstm doc

This commit is contained in:
caojian05 2020-09-24 23:25:46 +08:00
parent 16b77da7dd
commit 4deac20b80
1 changed files with 17 additions and 13 deletions
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mindspore/nn/layer/lstm.py
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@ -191,10 +191,11 @@ class LSTMCell(Cell):
`Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling
<https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43905.pdf>`_.
LSTMCell is a single-layer RNN, you can achieve multi-layer RNN by stacking LSTMCell.
Args:
input_size (int): Number of features of input.
hidden_size (int): Number of features of hidden layer.
layer_index (int): index of current layer of stacked LSTM . Default: 0.
has_bias (bool): Whether the cell has bias `b_ih` and `b_hh`. Default: True.
batch_first (bool): Specifies whether the first dimension of input is batch_size. Default: False.
dropout (float, int): If not 0, append `Dropout` layer on the outputs of each
@ -205,40 +206,43 @@ class LSTMCell(Cell):
Inputs:
- **input** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`).
- **h** - data type mindspore.float32 or
mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`).
mindspore.float16 and shape (num_directions, batch_size, `hidden_size`).
- **c** - data type mindspore.float32 or
mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`).
mindspore.float16 and shape (num_directions, batch_size, `hidden_size`).
Data type of `h' and 'c' must be the same of `input`.
- **w** - data type mindspore.float32 or
mindspore.float16 and shape (`weight_size`, 1, 1).
The value of `weight_size` depends on `input_size`, `hidden_size` and `bidirectional`
Outputs:
`output`, `h_n`, `c_n`, 'reserve', 'state'.
- **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`).
- **h** - A Tensor with shape (num_directions * `num_layers`, batch_size, `hidden_size`).
- **c** - A Tensor with shape (num_directions * `num_layers`, batch_size, `hidden_size`).
- **h** - A Tensor with shape (num_directions, batch_size, `hidden_size`).
- **c** - A Tensor with shape (num_directions, batch_size, `hidden_size`).
- **reserve** - reserved
- **state** - reserved
Examples:
>>> class LstmNet(nn.Cell):
>>> def __init__(self, input_size, hidden_size, layer_index, has_bias, batch_first, bidirectional):
>>> def __init__(self, input_size, hidden_size, has_bias, batch_first, bidirectional):
>>> super(LstmNet, self).__init__()
>>> self.lstm = nn.LSTMCell(input_size=input_size,
>>> hidden_size=hidden_size,
>>> layer_index=layer_index,
>>> has_bias=has_bias,
>>> batch_first=batch_first,
>>> bidirectional=bidirectional,
>>> dropout=0.0)
>>>
>>> def construct(self, inp, h0, c0):
>>> return self.lstm(inp, (h0, c0))
>>> def construct(self, inp, h, c, w):
>>> return self.lstm(inp, h, c, w)
>>>
>>> net = LstmNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False)
>>> net = LstmNet(10, 12, has_bias=True, batch_first=True, bidirectional=False)
>>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32))
>>> h0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
>>> c0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32))
>>> output, hn, cn, _, _ = net(input, h0, c0)
>>> h = Tensor(np.ones([1, 3, 12]).astype(np.float32))
>>> c = Tensor(np.ones([1, 3, 12]).astype(np.float32))
>>> w = Tensor(np.ones([1152, 1, 1]).astype(np.float32))
>>> output, h, c, _, _ = net(input, h, c, w)
"""
def __init__(self,