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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:$$\begin{aligned} (1)\qquad f_t &=\sigma(W_f[h_{t-1}, x_t] + b_f) \\ (2)\qquad i_t &=\sigma(W_i[h_{t-1}, x_t] + b_i) \\ (3)\qquad o_t &=\sigma(W_o[h_{t-1}, x_t] + b_o) \\ (4)\qquad \tilde{c}_t &=tanh(W_c[h_{t-1}, x_t] + b_c) \\ (5)\qquad c_t &=f_t ⊙ c_{t-1} + i_t ⊙ \tilde{c}_t \\ (6)\qquad c_{o}^{t} &=tanh(c_t) \\ (7)\qquad h_t &=o_t ⊙ c_{o}^{t} \\ \end{aligned}$$
  • $x_t ∈ R^{d}$: 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, [object Object] is called to obtain the input parameters and compute the required workspace size based on the process. Then, [object Object] 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 validation. The following error codes may be returned.

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• Parameters

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• Returns

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

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• Deterministic computation:
  • The aclnnLSTM is implemented in deterministic mode by default.
• All inputs and outputs support the FLOAT16 and FLOAT32 types, and their data types must be the same.

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