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• Description: Implements the long short-term memory (LSTM) network, which is a special recurrent neural network (RNN) model. Computes the LSTM network, receives the input sequence and initial state, and returns the output sequence and final state.
• Formula:$$f_t =sigm(W_f[h_{t-1}, x_t] + b_f)\\ i_t =sigm(W_i[h_{t-1}, x_t] + b_i)\\ o_t =sigm(W_o[h_{t-1}, x_t] + b_o)\\ \tilde{c}_t =tanh(W_c[h_{t-1}, x_t] + b_c)\\ c_t =f_t ⊙ c_{t-1} + i_t ⊙ \tilde{c}_t\\ c_{o}^{t} =tanh(c_t)\\ h_t =o_t ⊙ c_{o}^{t}\\$$
  • $x_t ∈ R^{d}$: input vector to the LSTM unit.
  • $f_t ∈ (0, 1)^{h}$: activation vector of the forget gate.
  • $i_t ∈ (0, 1)^{h}$: activation vector of the input/update gate.
  • $o_t ∈ (0, 1)^{h}$: activation vector of the output gate.
  • $h_i ∈ (-1, 1)^{h}$: hidden state vector, also known as output vector of the LSTM unit.
  • $\tilde{c}_t ∈ (-1, 1)^{h}$: cell input activation vector.
  • $c_t ∈ R^{h}$: cell state vector.
  • $W ∈ R^{h×d}, (U ∈ R^{h×h})∩(b ∈ R^{h})$: weight matrices and bias vector parameters which need to be learned during training.

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Each operator has calls. First, aclnnBidirectionLSTMGetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnBidirectionLSTM is called to perform computation.

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• Parameters:

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• Returns:

[object Object]: status code. For details, see .

The first-phase API implements input parameter verification. The following errors may be thrown:

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• Parameters:

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• Returns:

  [object Object]: status code. For details, see .

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• Deterministic compute:
  • aclnnBidirectionLSTM defaults to a deterministic implementation.

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The following example is for reference only. For details, see .