LSTM¶

class
torch.nn.
LSTM
(*args, **kwargs)[source]¶ Applies a multilayer long shortterm memory (LSTM) RNN to an input sequence.
For each element in the input sequence, each layer computes the following function:
$\begin{array}{ll} \\ i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{t1} + b_{hi}) \\ f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{t1} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{t1} + b_{hg}) \\ o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{t1} + b_{ho}) \\ c_t = f_t \odot c_{t1} + i_t \odot g_t \\ h_t = o_t \odot \tanh(c_t) \\ \end{array}$where $h_t$ is the hidden state at time t, $c_t$ is the cell state at time t, $x_t$ is the input at time t, $h_{t1}$ is the hidden state of the layer at time t1 or the initial hidden state at time 0, and $i_t$, $f_t$, $g_t$, $o_t$ are the input, forget, cell, and output gates, respectively. $\sigma$ is the sigmoid function, and $\odot$ is the Hadamard product.
In a multilayer LSTM, the input $x^{(l)}_t$ of the $l$ th layer ($l >= 2$) is the hidden state $h^{(l1)}_t$ of the previous layer multiplied by dropout $\delta^{(l1)}_t$ where each $\delta^{(l1)}_t$ is a Bernoulli random variable which is $0$ with probability
dropout
.If
proj_size > 0
is specified, LSTM with projections will be used. This changes the LSTM cell in the following way. First, the dimension of $h_t$ will be changed fromhidden_size
toproj_size
(dimensions of $W_{hi}$ will be changed accordingly). Second, the output hidden state of each layer will be multiplied by a learnable projection matrix: $h_t = W_{hr}h_t$. Note that as a consequence of this, the output of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128. Parameters
input_size – The number of expected features in the input x
hidden_size – The number of features in the hidden state h
num_layers – Number of recurrent layers. E.g., setting
num_layers=2
would mean stacking two LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM and computing the final results. Default: 1bias – If
False
, then the layer does not use bias weights b_ih and b_hh. Default:True
batch_first – If
True
, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. Default:False
dropout – If nonzero, introduces a Dropout layer on the outputs of each LSTM layer except the last layer, with dropout probability equal to
dropout
. Default: 0bidirectional – If
True
, becomes a bidirectional LSTM. Default:False
proj_size – If
> 0
, will use LSTM with projections of corresponding size. Default: 0
 Inputs: input, (h_0, c_0)
input: tensor of shape $(L, N, H_{in})$ when
batch_first=False
or $(N, L, H_{in})$ whenbatch_first=True
containing the features of the input sequence. The input can also be a packed variable length sequence. Seetorch.nn.utils.rnn.pack_padded_sequence()
ortorch.nn.utils.rnn.pack_sequence()
for details.h_0: tensor of shape $(D * \text{num\_layers}, N, H_{out})$ containing the initial hidden state for each element in the batch. Defaults to zeros if (h_0, c_0) is not provided.
c_0: tensor of shape $(D * \text{num\_layers}, N, H_{cell})$ containing the initial cell state for each element in the batch. Defaults to zeros if (h_0, c_0) is not provided.
where:
$\begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ H_{in} ={} & \text{input\_size} \\ H_{cell} ={} & \text{hidden\_size} \\ H_{out} ={} & \text{proj\_size if } \text{proj\_size}>0 \text{ otherwise hidden\_size} \\ \end{aligned}$ Outputs: output, (h_n, c_n)
output: tensor of shape $(L, N, D * H_{out})$ when
batch_first=False
or $(N, L, D * H_{out})$ whenbatch_first=True
containing the output features (h_t) from the last layer of the LSTM, for each t. If atorch.nn.utils.rnn.PackedSequence
has been given as the input, the output will also be a packed sequence.h_n: tensor of shape $(D * \text{num\_layers}, N, H_{out})$ containing the final hidden state for each element in the batch.
c_n: tensor of shape $(D * \text{num\_layers}, N, H_{cell})$ containing the final cell state for each element in the batch.
 Variables
~LSTM.weight_ih_l[k] – the learnable inputhidden weights of the $\text{k}^{th}$ layer (W_iiW_ifW_igW_io), of shape (4*hidden_size, input_size) for k = 0. Otherwise, the shape is (4*hidden_size, num_directions * hidden_size)
~LSTM.weight_hh_l[k] – the learnable hiddenhidden weights of the $\text{k}^{th}$ layer (W_hiW_hfW_hgW_ho), of shape (4*hidden_size, hidden_size). If
proj_size > 0
was specified, the shape will be (4*hidden_size, proj_size).~LSTM.bias_ih_l[k] – the learnable inputhidden bias of the $\text{k}^{th}$ layer (b_iib_ifb_igb_io), of shape (4*hidden_size)
~LSTM.bias_hh_l[k] – the learnable hiddenhidden bias of the $\text{k}^{th}$ layer (b_hib_hfb_hgb_ho), of shape (4*hidden_size)
~LSTM.weight_hr_l[k] – the learnable projection weights of the $\text{k}^{th}$ layer of shape (proj_size, hidden_size). Only present when
proj_size > 0
was specified.
Note
All the weights and biases are initialized from $\mathcal{U}(\sqrt{k}, \sqrt{k})$ where $k = \frac{1}{\text{hidden\_size}}$
Note
For bidirectional LSTMs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when
batch_first=False
:output.view(seq_len, batch, num_directions, hidden_size)
.Warning
There are known nondeterminism issues for RNN functions on some versions of cuDNN and CUDA. You can enforce deterministic behavior by setting the following environment variables:
On CUDA 10.1, set environment variable
CUDA_LAUNCH_BLOCKING=1
. This may affect performance.On CUDA 10.2 or later, set environment variable (note the leading colon symbol)
CUBLAS_WORKSPACE_CONFIG=:16:8
orCUBLAS_WORKSPACE_CONFIG=:4096:2
.See the cuDNN 8 Release Notes for more information.
 Orphan
Note
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype
torch.float16
4) V100 GPU is used, 5) input data is not inPackedSequence
format persistent algorithm can be selected to improve performance.Examples:
>>> rnn = nn.LSTM(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = torch.randn(2, 3, 20) >>> c0 = torch.randn(2, 3, 20) >>> output, (hn, cn) = rnn(input, (h0, c0))