PReLU

parametricmatrixmodels.modules.PReLU

class PReLU(single_parameter=True, init_magnitude=0.25, real=True)

Bases: BaseModule

Element-wise Parametric Rectified Linear Unit (PReLU) activation function.

\[\begin{split}\text{PReLU}(x) = \begin{cases} x, & \text{ if } x \ge 0 \\ ax, & \text{ otherwise } \end{cases}\end{split}\]

where \(a\) is a learnable parameter that controls the slope of the negative part of the function. \(a\) can be either a single parameter shared across all input features, or a separate parameter for each input feature.

See also

torch.nn.PReLU

PyTorch implementation of PReLU activation function.

LeakyReLU

Non-parametric ReLU activation function with a fixed negative slope.

__init__(single_parameter=True, init_magnitude=0.25, real=True)

Create a new PReLU module.

Parameters:

• single_parameter (bool) – If True, use a single learnable parameter for all input features. If False, use a separate learnable parameter for each input feature. Default is True.

• init_magnitude (float) – Initial magnitude of the learnable parameter(s). Default is 0.25.

• real (bool) – If True, the learnable parameter(s) will be real-valued. If False, the learnable parameter(s) will be complex-valued. Default is True.

__call__	Call the module with the current parameters and given input, state, and rng.
_get_callable	Returns a jax.jit-able and jax.grad-able callable that represents the module's forward pass.
astype	Convenience wrapper to set_precision using the dtype argument, returns self.
compile	Compile the module to be used with the given input shape.
deserialize	Deserialize the module from a dictionary.
get_hyperparameters	Get the hyperparameters of the module.
get_num_trainable_floats	Returns the number of trainable floats in the module.
get_output_shape	Get the output shape of the module given the input shape.
get_params	Get the current trainable parameters of the module.
get_state	Get the current state of the module.
is_ready	Return True if the module is initialized and ready for training or inference.
name	Returns the name of the module, unless overridden, this is the class name.
serialize	Serialize the module to a dictionary.
set_hyperparameters	Set the hyperparameters of the module.
set_params	Set the trainable parameters of the module.
set_precision	Set the precision of the module parameters and state.
set_state	Set the state of the module.

__call__(input_NF, training=False, state=(), rng=None)

Call the module with the current parameters and given input, state, and rng.

Parameters:

• input_NF (Array) – Input array of shape (num_samples, num_features).

• training (bool) – Whether the module is in training mode, by default False.

• state (tuple[Array, ...]) – State of the module, by default ().

• rng (Any) – JAX random key, by default None.

Return type:

tuple[Array, tuple[Array, ...]]

Returns:

Output array of shape (num_samples, num_output_features) and new state.

Raises:

ValueError – If the module is not ready (i.e., compile() has not been called).

See also

_get_callable

Returns a callable that can be used to compute the output and new state given the parameters, input, training flag, state, and rng.

_get_callable()

Returns a jax.jit-able and jax.grad-able callable that represents the module’s forward pass.

This method must be implemented by all subclasses and must return a jax-jit-able and jax-grad-able callable in the form of

module_callable(
    params: tuple[np.ndarray, ...],
    input_NF: np.ndarray[num_samples, num_features],
    training: bool,
    state: tuple[np.ndarray, ...],
    rng: Any
) -> (
        output_NF: np.ndarray[num_samples, num_output_features],
        new_state: tuple[np.ndarray, ...]
    )

That is, all hyperparameters are traced out and the callable depends explicitly only on a tuple of parameter jax.numpy arrays, the input array, the training flag, a state tuple of jax.numpy arrays, and a JAX rng key.

The training flag will be traced out, so it doesn’t need to be jittable

Return type:

Callable

Returns:

A callable that takes the module’s parameters, input data, training flag, state, and rng key and returns the output data and new state.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

See also

__call__

Calls the module with the current parameters and given input, state, and rng.

astype(dtype)

Convenience wrapper to set_precision using the dtype argument, returns self.

Parameters:

dtype (dtype | str) – Precision to set for the module parameters. Valid options are: For 32-bit precision (all options are equivalent) np.float32, np.complex64, "float32", "complex64", "single", "f32", "c64", 32 For 64-bit precision (all options are equivalent) np.float64, np.complex128, "float64", "complex128", "double", "f64", "c128", 64

Return type:

BaseModule

Returns:

BaseModule – The module itself, with updated precision.

Raises:

• ValueError – If the precision is invalid or if 64-bit precision is requested but JAX_ENABLE_X64 is not set.

• RuntimeError – If the module is not ready (i.e., compile() has not been called).

See also

set_precision

Sets the precision of the module parameters and state.

compile(rng, input_shape)

Compile the module to be used with the given input shape.

This method initializes the module’s parameters and state based on the input shape and random key.

This is needed since Model s are built before the input data is given, so before training or inference can be done, the module needs to be compiled and each module passes its output shape to the next module’s compile method.

The RNG key is used to initialize random parameters, if needed.

This is not used to trace or jit the module’s callable, that is done automatically later.

Parameters:

• rng (Any) – JAX random key.

• input_shape (tuple[int, ...]) – Shape of the input data, e.g. (num_features,).

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

None

deserialize(data)

Deserialize the module from a dictionary.

This method sets the module’s parameters and state based on the provided dictionary.

The default implementation expects the dictionary to contain the module’s name, trainable parameters, and state.

Parameters:

data (dict[str, Any]) – Dictionary containing the serialized module data.

Raises:

ValueError – If the serialized data does not contain the expected keys or if the version of the serialized data is not compatible with with the current package version.

Return type:

None

get_hyperparameters()

Get the hyperparameters of the module.

Hyperparameters are used to configure the module and are not trainable. They can be set via set_hyperparameters.

Return type:

dict[str, Any]

Returns:

Dictionary containing the hyperparameters of the module.

get_num_trainable_floats()

Returns the number of trainable floats in the module. If the module does not have trainable parameters, returns 0. If the module is not ready, returns None.

Return type:

int | None

Returns:

Number of trainable floats in the module, or None if the module is not ready.

get_output_shape(input_shape)

Get the output shape of the module given the input shape.

Parameters:

input_shape (tuple[int, ...]) – Shape of the input data, e.g. (num_features,).

Return type:

tuple[int, ...]

Returns:

Shape of the output data, e.g. (num_output_features,).

Raises:

NotImplementedError – If the method is not implemented in the subclass.

get_params()

Get the current trainable parameters of the module. If the module has no trainable parameters, this method should return an empty tuple.

Return type:

tuple[Array, ...]

Returns:

Tuple of numpy arrays representing the module’s parameters.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

get_state()

Get the current state of the module.

States are used to store “memory” or other information that is not passed between modules, is not trainable, but may be updated during either training or inference. e.g. batch normalization state.

The state is optional, in which case this method should return the empty tuple.

Return type:

tuple[Array, ...]

Returns:

Tuple of numpy arrays representing the module’s state.

is_ready()

Return True if the module is initialized and ready for training or inference.

Return type:

bool

Returns:

True if the module is ready, False otherwise.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

name()

Returns the name of the module, unless overridden, this is the class name.

Return type:

str

Returns:

Name of the module.

serialize()

Serialize the module to a dictionary.

This method returns a dictionary representation of the module, including its parameters and state.

The default implementation serializes the module’s name, hyperparameters, trainable parameters, and state via a simple dictionary.

This only works if the module’s hyperparameters are auto-serializable. This includes basic types as well as numpy arrays.

Return type:

dict[str, Any]

Returns:

Dictionary containing the serialized module data.

set_hyperparameters(hyperparams)

Set the hyperparameters of the module.

Hyperparameters are used to configure the module and are not trainable. They can be set via this method.

The default implementation uses setattr to set the hyperparameters as attributes of the class instance.

Parameters:

hyperparameters – Dictionary containing the hyperparameters to set.

Raises:

TypeError – If hyperparameters is not a dictionary.

Return type:

None

set_params(params)

Set the trainable parameters of the module.

Parameters:

params (tuple[Array, ...]) – Tuple of numpy arrays representing the new parameters.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Return type:

None

set_precision(prec)

Set the precision of the module parameters and state.

Parameters:

prec (dtype | str | int) – Precision to set for the module parameters. Valid options are: For 32-bit precision (all options are equivalent) np.float32, np.complex64, "float32", "complex64", "single", "f32", "c64", 32. For 64-bit precision (all options are equivalent) np.float64, np.complex128, "float64", "complex128", "double", "f64", "c128", 64.

Raises:

• ValueError – If the precision is invalid or if 64-bit precision is requested but JAX_ENABLE_X64 is not set.

• RuntimeError – If the module is not ready (i.e., compile() has not been called).

Return type:

None

See also

astype

Convenience wrapper to set_precision using the dtype argument, returns self.

set_state(state)

Set the state of the module.

This method is optional.

Parameters:

state (tuple[Array, ...]) – Tuple of numpy arrays representing the new state.

Return type:

None