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mindspore.ops.LSTM

class mindspore.ops.LSTM(input_size, hidden_size, num_layers, has_bias, bidirectional, dropout, proj_size=0)

Performs the Long Short-Term Memory (LSTM) on the input.

For more information, please refer to mindspore.nn.LSTM.

Parameters

• input_size (int) – Number of features of input.

• hidden_size (int) – Number of features of hidden layer.

• num_layers (int) – Number of layers of stacked LSTM, , which is only support 1 on CPU.

• has_bias (bool) – Whether the cell has bias b_ih and b_hh , which is only support False on CPU.

• bidirectional (bool) – Specifies whether it is a bidirectional LSTM, , which is only support False on CPU.

• dropout (float) – If not 0, append Dropout layer on the outputs of each LSTM layer except the last layer. The range of dropout is [0.0, 1.0].

• proj_size (int) – If proj_size > 0, a projection of the corresponding size will be used, which is only supported on CPU now. Default: 0 .

Inputs:

• input (Tensor) - Tensor of shape $(seq\mathrm{\_}len,batch\mathrm{\_}size,input\mathrm{\_}size)$ or $(batch\mathrm{\_}size,seq\mathrm{\_}len,input\mathrm{\_}size)$.

• h (Tensor) - Tensor of shape $(num\mathrm{\_}directions\ast num\mathrm{\_}layers,batch\mathrm{\_}size,real\mathrm{\_}hidden\mathrm{\_}size)$.

• c (Tensor) - Tensor of shape $(num\mathrm{\_}directions\ast num\mathrm{\_}layers,batch\mathrm{\_}size,hidden\mathrm{\_}size)$.

• w (Tensor) - A weight Tensor.

If $proj\mathrm{\_}size>0$ , $real\mathrm{\_}hidden\mathrm{\_}size=proj\mathrm{\_}size$ , otherwise $real\mathrm{\_}hidden\mathrm{\_}size=hidden\mathrm{\_}size$ .

Outputs:

Tuple, a tuple contains (output, h_n, c_n, reserve, state).

• output (Tensor) - Tensor of shape $(seq\mathrm{\_}len,batch\mathrm{\_}size,num\mathrm{\_}directions\ast real\mathrm{\_}hidden\mathrm{\_}size)$.

• h_n (Tensor) - Tensor of shape $(num\mathrm{\_}directions\ast num\mathrm{\_}layers,batch\mathrm{\_}size,real\mathrm{\_}hidden\mathrm{\_}size)$.

• c_n (Tensor) - Tensor of shape $(num\mathrm{\_}directions\ast num\mathrm{\_}layers,batch\mathrm{\_}size,hidden\mathrm{\_}size)$.

• reserve (Tensor) - Tensor of shape $(r,1)$.

• state (Tensor) - Random number generator state and its shape is $(s,1)$.

Raises

• TypeError – If input_size, hidden_size or num_layers is not an int.

• TypeError – If has_bias or bidirectional is not a bool.

• TypeError – If dropout is not a float.

• ValueError – If dropout is not in range [0.0, 1.0].

• ValueError – If proj_size is not in range [0, hidden_size).

Supported Platforms:

GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_size = 10
>>> hidden_size = 2
>>> num_layers = 1
>>> seq_len = 5
>>> batch_size = 2
>>>
>>> net = ops.LSTM(input_size, hidden_size, num_layers, True, False, 0.0)
>>> input_tensor = Tensor(np.ones([seq_len, batch_size, input_size]).astype(np.float32))
>>> h0 = Tensor(np.ones([num_layers, batch_size, hidden_size]).astype(np.float32))
>>> c0 = Tensor(np.ones([num_layers, batch_size, hidden_size]).astype(np.float32))
>>> w = Tensor(np.ones([112, 1, 1]).astype(np.float32))
>>> output, hn, cn, _, _ = net(input_tensor, h0, c0, w)
>>> print(output)
[[[0.9640267  0.9640267 ]
  [0.9640267  0.9640267 ]]
 [[0.9950539  0.9950539 ]
  [0.9950539  0.9950539 ]]
 [[0.99932843 0.99932843]
  [0.99932843 0.99932843]]
 [[0.9999084  0.9999084 ]
  [0.9999084  0.9999084 ]]
 [[0.9999869  0.9999869 ]
  [0.9999869  0.9999869 ]]]