mindspore.nn.LSTMCell

class mindspore.nn.LSTMCell(*args, **kwargs)[source]

A LSTM (Long Short-Term Memory) cell.

\[\begin{split}\begin{array}{ll} \\ i_t = \sigma(W_{ix} x_t + b_{ix} + W_{ih} h_{(t-1)} + b_{ih}) \\ f_t = \sigma(W_{fx} x_t + b_{fx} + W_{fh} h_{(t-1)} + b_{fh}) \\ \tilde{c}_t = \tanh(W_{cx} x_t + b_{cx} + W_{ch} h_{(t-1)} + b_{ch}) \\ o_t = \sigma(W_{ox} x_t + b_{ox} + W_{oh} h_{(t-1)} + b_{oh}) \\ c_t = f_t * c_{(t-1)} + i_t * \tilde{c}_t \\ h_t = o_t * \tanh(c_t) \\ \end{array}\end{split}\]

Here \(\sigma\) is the sigmoid function, and \(*\) 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:

\[h^{'},c^{'} = LSTMCell(x, (h_0, c_0))\]

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_size, input_size).
hx (tuple) - A tuple of two Tensors (h_0, c_0) both of data type mindspore.float32 and shape (batch_size, hidden_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_size, hidden_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)