flax.linen.GRUCell#
- class flax.linen.GRUCell(*args, **kwds)[source]#
GRU cell.
The mathematical definition of the cell is as follows
\[\begin{split}\begin{array}{ll} r = \sigma(W_{ir} x + W_{hr} h + b_{hr}) \\ z = \sigma(W_{iz} x + W_{hz} h + b_{hz}) \\ n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' = (1 - z) * n + z * h \\ \end{array}\end{split}\]where x is the input and h, is the output of the previous time step.
- 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
jax.nn.initializers.Initializer
- recurrent_kernel_init#
initializer function for the kernels that transform the hidden state (default: initializers.orthogonal()).
- Type
jax.nn.initializers.Initializer
- bias_init#
initializer for the bias parameters (default: initializers.zeros_init())
- Type
jax.nn.initializers.Initializer
- dtype#
the dtype of the computation (default: None).
- Type
Optional[Any]
- param_dtype#
the dtype passed to parameter initializers (default: float32).
- Type
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.
Methods
initialize_carry(**kwargs)Initialize the RNN cell carry.