flax.linen.GRUCell#

class flax.linen.GRUCell(features, gate_fn=<PjitFunction of <function sigmoid>>, activation_fn=<PjitFunction 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'>, carry_init=<function zeros>, parent=<flax.linen.module._Sentinel object>, name=None)[source]#

GRU cell.

The mathematical definition of the cell is as follows

\[\begin{split}\begin{array}{ll} r = \sigma(W_{ir} x + b_{ir} + W_{hr} h) \\ z = \sigma(W_{iz} x + b_{iz} + W_{hz} h) \\ n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' = (1 - z) * n + z * h \\ \end{array}\end{split}\]

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

Example usage:

>>> import flax.linen as nn
>>> import jax, jax.numpy as jnp

>>> x = jax.random.normal(jax.random.key(0), (2, 3))
>>> layer = nn.GRUCell(features=4)
>>> carry = layer.initialize_carry(jax.random.key(1), x.shape)
>>> variables = layer.init(jax.random.key(2), carry, x)
>>> new_carry, out = layer.apply(variables, carry, x)
features#

number of output features.

Type

int

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

Union[jax.nn.initializers.Initializer, Callable[[…], Any]]

recurrent_kernel_init#

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

Type

Union[jax.nn.initializers.Initializer, Callable[[…], Any]]

bias_init#

initializer for the bias parameters (default: initializers.zeros_init())

Type

Union[jax.nn.initializers.Initializer, Callable[[…], Any]]

dtype#

the dtype of the computation (default: None).

Type

Optional[Union[str, type[Any], numpy.dtype, jax._src.typing.SupportsDType, Any]]

param_dtype#

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

Type

Union[str, type[Any], numpy.dtype, jax._src.typing.SupportsDType, Any]

__call__(carry, inputs)[source]#

Gated recurrent unit (GRU) cell.

Parameters
  • carry – the hidden state of the GRU cell, initialized using GRUCell.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.

initialize_carry(rng, input_shape)[source]#

Initialize the RNN cell carry.

Parameters
  • rng – random number generator passed to the init_fn.

  • input_shape – a tuple providing the shape of the input to the cell.

Returns

An initialized carry for the given RNN cell.

Methods

initialize_carry(rng, input_shape)

Initialize the RNN cell carry.