class brainpy.dyn.LSTMCell(num_in, num_out, Wi_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[2459466628 2755113083]), Wh_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[2459466628 2755113083]), b_initializer=ZeroInit, state_initializer=ZeroInit, activation='tanh', mode=None, train_state=False, name=None)[source]#

Long short-term memory (LSTM) RNN core.

The implementation is based on (zaremba, et al., 2014) [1]. Given \(x_t\) and the previous state \((h_{t-1}, c_{t-1})\) the core computes

\[\begin{split}\begin{array}{ll} i_t = \sigma(W_{ii} x_t + W_{hi} h_{t-1} + b_i) \\ f_t = \sigma(W_{if} x_t + W_{hf} h_{t-1} + b_f) \\ g_t = \tanh(W_{ig} x_t + W_{hg} h_{t-1} + b_g) \\ o_t = \sigma(W_{io} x_t + W_{ho} h_{t-1} + b_o) \\ c_t = f_t c_{t-1} + i_t g_t \\ h_t = o_t \tanh(c_t) \end{array}\end{split}\]

where \(i_t\), \(f_t\), \(o_t\) are input, forget and output gate activations, and \(g_t\) is a vector of cell updates.

The output is equal to the new hidden, \(h_t\).


Forget gate initialization: Following (Jozefowicz, et al., 2015) [2] we add 1.0 to \(b_f\) after initialization in order to reduce the scale of forgetting in the beginning of the training.

  • num_in (int) – The dimension of the input vector

  • num_out (int) – The number of hidden unit in the node.

  • state_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The state initializer.

  • Wi_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The input weight initializer.

  • Wh_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The hidden weight initializer.

  • b_initializer (optional, callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The bias weight initializer.

  • activation (str, callable) – The activation function. It can be a string or a callable function. See brainpy.math.activations for more details.


property c#

Memory cell.

property h#

Hidden state.


The function to specify the updating rule.