mindspore.nn.LSTMCell

class mindspore.nn.LSTMCell(input_size: int, hidden_size: int, has_bias: bool = True)

A LSTM (Long Short-Term Memory) cell.

$$\begin{array}{r}\begin{array}{l}\\ i_t=\sigma (W_{ix}{x}_t+b_{ix}+W_{ih}{h}_{(t-1)}+b_{ih})\\ f_t=\sigma (W_{fx}{x}_t+b_{fx}+W_{fh}{h}_{(t-1)}+b_{fh})\\ {\tilde{c}}_t=\tanh (W_{cx}{x}_t+b_{cx}+W_{ch}{h}_{(t-1)}+b_{ch})\\ o_t=\sigma (W_{ox}{x}_t+b_{ox}+W_{oh}{h}_{(t-1)}+b_{oh})\\ c_t=f_t\ast c_{(t-1)}+i_t\ast {\tilde{c}}_t\\ h_t=o_t\ast \tanh (c_t)\end{array}\end{array}$$

Here $\sigma$ is the sigmoid function, and $\ast$ is the Hadamard product. $W,b$ are learnable weights between the output and the input in the formula. For instance, $W_{ix},b_{ix}$ are the weight and bias used to transform from input $x$ to $i$. Details can be found in paper LONG SHORT-TERM MEMORY and Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling.

The encapsulated LSTMCell can be simplified to the following formula:

$$h^{{}^{\prime }},c^{{}^{\prime }}=LSTMCell(x,(h_0,c_0))$$

Parameters

• 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.

Inputs:

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

• hx (tuple) - A tuple of two Tensors (h_0, c_0) both of data type mindspore.float32 and shape $(batch\mathrm{\_}size,hidden\mathrm{\_}size)$. The data type of hx must be the same as x.

Outputs:

• hx’ (Tensor) - A tuple of two Tensors (h’, c’) both of data shape $(batch\mathrm{\_}size,hidden\mathrm{\_}size)$.

Raises

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

• TypeError – If has_bias is not a bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = nn.LSTMCell(10, 16)
>>> x = Tensor(np.ones([5, 3, 10]).astype(np.float32))
>>> h = Tensor(np.ones([3, 16]).astype(np.float32))
>>> c = Tensor(np.ones([3, 16]).astype(np.float32))
>>> output = []
>>> for i in range(5):
...     hx = net(x[i], (h, c))
...     output.append(hx)
>>> print(output[0][0].shape)
(3, 16)