flax.linen.LSTMCell#

class flax.linen.LSTMCell(gate_fn=<CompiledFunction of <function sigmoid>>, activation_fn=<CompiledFunction of <function jax.numpy.tanh>>, kernel_init=<function variance_scaling.<locals>.init>, recurrent_kernel_init=<function orthogonal.<locals>.init>, bias_init=<function zeros>, dtype=None, param_dtype=<class 'jax.numpy.float32'>, parent=<flax.linen.module._Sentinel object>, name=None)[source]#

LSTM cell.

The mathematical definition of the cell is as follows

\[\begin{split}\begin{array}{ll} i = \sigma(W_{ii} x + W_{hi} h + b_{hi}) \\ f = \sigma(W_{if} x + W_{hf} h + b_{hf}) \\ g = \tanh(W_{ig} x + W_{hg} h + b_{hg}) \\ o = \sigma(W_{io} x + W_{ho} h + b_{ho}) \\ c' = f * c + i * g \\ h' = o * \tanh(c') \\ \end{array}\end{split}\]

where x is the input, h is the output of the previous time step, and c is the memory.

gate_fn#

activation function used for gates (default: sigmoid)

Type

Callable[[…], Any]

activation_fn#

activation function used for output and memory update (default: tanh).

Type

Callable[[…], Any]

kernel_init#

initializer function for the kernels that transform the input (default: lecun_normal).

Type

Callable[[Any, Tuple[int, …], Any], Any]

recurrent_kernel_init#

initializer function for the kernels that transform the hidden state (default: orthogonal).

Type

Callable[[Any, Tuple[int, …], Any], Any]

bias_init#

initializer for the bias parameters (default: zeros)

Type

Callable[[Any, Tuple[int, …], Any], Any]

dtype#

the dtype of the computation (default: infer from inputs and params).

Type

Optional[Any]

param_dtype#

the dtype passed to parameter initializers (default: float32).

Type

Any

__call__(carry, inputs)[source]#

A long short-term memory (LSTM) cell.

Parameters
  • carry – the hidden state of the LSTM cell, initialized using LSTMCell.initialize_carry.

  • inputs – an ndarray with the input for the current time step. All dimensions except the final are considered batch dimensions.

Returns

A tuple with the new carry and the output.

Methods

initialize_carry(rng, batch_dims, size[, ...])

Initialize the RNN cell carry.