LRUCellMML

class keras_mml.layers.recurrent.LRUCellMML

Cell class for the LRUMML layer.

This class processes one step within the whole time sequence input, whereas LRUMML processes the whole sequence.

units

Dimensionality of the output space.

state_dim

Dimensionality of the internal state space.

fully_mml

Whether to use matmul-free operations for all the layers.

r_min

Minimum modulus of the complex weights in \(\mathbf{\Lambda}\).

r_max

Maximum modulus of the complex weights in \(\mathbf{\Lambda}\).

max_phase

Maximum phase of the complex weights in \(\mathbf{\Lambda}\).

use_bias

Whether to use a bias vector for the layer.

state_size

Size of the recurrent state.

output_size

Size of the output vector.

__init__(units, state_dim, fully_mml=False, r_min=0, r_max=1, max_phase=6.283185307179586, use_bias=False, **kwargs)

Initializes a new instance of the layer.

Parameters:

• units (int) – Dimensionality of the output space.

• state_dim (int) – Dimensionality of the internal state space.

• fully_mml (bool, default: False) – Whether to use matmul-free operations for all the layers.

• r_min (float, default: 0) – Minimum modulus of the complex weights in \(\mathbf{\Lambda}\).

• r_max (float, default: 1) – Maximum modulus of the complex weights in \(\mathbf{\Lambda}\).

• max_phase (float, default: 6.283185307179586) – Maximum phase of the complex weights in \(\mathbf{\Lambda}\).

• use_bias (bool, default: False) – Whether to use a bias vector for the layer.

• **kwargs – Keyword arguments for keras.Layer.

Raises:

• ValueError – If the units provided is not a positive integer.

• ValueError – If the state dimensionality provided is not a positive integer.

build(input_shape)

Create layer weights.

Parameters:

input_shape (Tuple[int, int]) – Shape of the input.

call(inputs, states, training=False)

Calling method of the cell.

Parameters:

• inputs (Float[ndarray, 'batch_size features']) – Inputs into the layer.

• states (Float[ndarray, '*state_dims']) – State(s) from the previous timestep.

• training (default: False) – Whether the layer should behave in training mode or in inference mode.

Returns:

Float[ndarray, 'batch_size units'] – Transformed inputs.

get_config()

Gets the configuration for the layer.

Returns:

Layer configuration.

get_initial_state(batch_size=None)

Gets the initial states.

Parameters:

batch_size (Optional[int], default: None) – Batch size for the cell.

Returns:

List[ndarray] – Initial states.