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mindspore.nn.LSTM

class mindspore.nn.LSTM(input_size, hidden_size, num_layers=1, has_bias=True, batch_first=False, dropout=0, bidirectional=False)

Stacked LSTM (Long Short-Term Memory) layers.

Apply LSTM layer to the input.

There are two pipelines connecting two consecutive cells in a LSTM model; one is cell state pipeline and the other is hidden state pipeline. Denote two consecutive time nodes as $t-1$ and $t$. Given an input $x_t$ at time $t$, an hidden state $h_{t-1}$ and an cell state $c_{t-1}$ of the layer at time $t-1$, the cell state and hidden state at time $t$ is computed using an gating mechanism. Input gate $i_t$ is designed to protect the cell from perturbation by irrelevant inputs. Forget gate $f_t$ affords protection of the cell by forgetting some information in the past, which is stored in $h_{t-1}$. Output gate $o_t$ protects other units from perturbation by currently irrelevant memory contents. Candidate cell state ${\tilde{c}}_t$ is calculated with the current input, on which the input gate will be applied. Finally, current cell state $c_t$ and hidden state $h_t$ are computed with the calculated gates and cell states. The complete formulation is as follows.

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

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.

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 . Default: 1.

• 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 x is batch_size. Default: False.

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

• bidirectional (bool) – Specifies whether it is a bidirectional LSTM. Default: False.

Inputs:

• x (Tensor) - Tensor of shape (seq_len, batch_size, input_size) or (batch_size, seq_len, input_size).

• hx (tuple) - A tuple of two Tensors (h_0, c_0) both of data type mindspore.float32 or mindspore.float16 and shape (num_directions * num_layers, batch_size, hidden_size). Data type of hx must be the same as x.

Outputs:

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

• output (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * hidden_size).

• hx_n (tuple) - A tuple of two Tensor (h_n, c_n) both of shape (num_directions * num_layers, batch_size, hidden_size).

Raises

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

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

• TypeError – If dropout is neither a float nor an int.

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

Supported Platforms:

Ascend GPU

Examples

>>> net = nn.LSTM(10, 16, 2, has_bias=True, batch_first=True, bidirectional=False)
>>> x = Tensor(np.ones([3, 5, 10]).astype(np.float32))
>>> h0 = Tensor(np.ones([1 * 2, 3, 16]).astype(np.float32))
>>> c0 = Tensor(np.ones([1 * 2, 3, 16]).astype(np.float32))
>>> output, (hn, cn) = net(x, (h0, c0))
>>> print(output.shape)
(3, 5, 16)