keras/keras_core/regularizers/regularizers.py

import math

from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.utils.numerical_utils import normalize


@keras_core_export(
    ["keras_core.Regularizer", "keras_core.regularizers.Regularizer"]
)
class Regularizer:
    """Regularizer base class.

    Regularizers allow you to apply penalties on layer parameters or layer
    activity during optimization. These penalties are summed into the loss
    function that the network optimizes.

    Regularization penalties are applied on a per-layer basis. The exact API
    will depend on the layer, but many layers (e.g. `Dense`, `Conv1D`, `Conv2D`
    and `Conv3D`) have a unified API.

    These layers expose 3 keyword arguments:

    - `kernel_regularizer`: Regularizer to apply a penalty on the layer's kernel
    - `bias_regularizer`: Regularizer to apply a penalty on the layer's bias
    - `activity_regularizer`: Regularizer to apply a penalty on the layer's
        output

    All layers (including custom layers) expose `activity_regularizer` as a
    settable property, whether or not it is in the constructor arguments.

    The value returned by the `activity_regularizer` is divided by the input
    batch size so that the relative weighting between the weight regularizers
    and the activity regularizers does not change with the batch size.

    You can access a layer's regularization penalties by calling `layer.losses`
    after calling the layer on inputs.

    ## Example

    >>> layer = Dense(
    ...     5, input_dim=5,
    ...     kernel_initializer='ones',
    ...     kernel_regularizer=L1(0.01),
    ...     activity_regularizer=L2(0.01))
    >>> tensor = ops.ones(shape=(5, 5)) * 2.0
    >>> out = layer(tensor)

    >>> # The kernel regularization term is 0.25
    >>> # The activity regularization term (after dividing by the batch size)
    >>> # is 5
    >>> ops.sum(layer.losses)
    5.25

    ## Available penalties

    ```python
    L1(0.3)  # L1 Regularization Penalty
    L2(0.1)  # L2 Regularization Penalty
    L1L2(l1=0.01, l2=0.01)  # L1 + L2 penalties
    ```

    ## Directly calling a regularizer

    Compute a regularization loss on a tensor by directly calling a regularizer
    as if it is a one-argument function.

    E.g.

    >>> regularizer = L2(2.)
    >>> tensor = ops.ones(shape=(5, 5))
    >>> regularizer(tensor)
    50.0

    ## Developing new regularizers

    Any function that takes in a weight matrix and returns a scalar
    tensor can be used as a regularizer, e.g.:

    >>> def l1_reg(weight_matrix):
    ...    return 0.01 * ops.sum(ops.absolute(weight_matrix))
    ...
    >>> layer = Dense(5, input_dim=5,
    ...     kernel_initializer='ones', kernel_regularizer=l1_reg)
    >>> tensor = ops.ones(shape=(5, 5))
    >>> out = layer(tensor)
    >>> layer.losses
    0.25

    Alternatively, you can write your custom regularizers in an
    object-oriented way by extending this regularizer base class, e.g.:

    >>> class L2Regularizer(Regularizer):
    ...   def __init__(self, l2=0.):
    ...     self.l2 = l2
    ...
    ...   def __call__(self, x):
    ...     return self.l2 * ops.sum(ops.square(x))
    ...
    ...   def get_config(self):
    ...     return {'l2': float(self.l2)}
    ...
    >>> layer = Dense(
    ...   5, input_dim=5, kernel_initializer='ones',
    ...   kernel_regularizer=L2Regularizer(l2=0.5))

    >>> tensor = ops.ones(shape=(5, 5))
    >>> out = layer(tensor)
    >>> layer.losses
    12.5

    ### A note on serialization and deserialization:

    Registering the regularizers as serializable is optional if you are just
    training and executing models, exporting to and from SavedModels, or saving
    and loading weight checkpoints.

    Registration is required for saving and
    loading models to HDF5 format, Keras model cloning, some visualization
    utilities, and exporting models to and from JSON. If using this
    functionality, you must make sure any python process running your model has
    also defined and registered your custom regularizer.
    """

    def __call__(self, x):
        """Compute a regularization penalty from an input tensor."""
        return 0.0

    @classmethod
    def from_config(cls, config):
        """Creates a regularizer from its config.

        This method is the reverse of `get_config`,
        capable of instantiating the same regularizer from the config
        dictionary.

        This method is used by Keras `model_to_estimator`, saving and
        loading models to HDF5 formats, Keras model cloning, some visualization
        utilities, and exporting models to and from JSON.

        Args:
            config: A Python dictionary, typically the output of get_config.

        Returns:
            A regularizer instance.
        """
        return cls(**config)

    def get_config(self):
        """Returns the config of the regularizer.

        An regularizer config is a Python dictionary (serializable)
        containing all configuration parameters of the regularizer.
        The same regularizer can be reinstantiated later
        (without any saved state) from this configuration.

        This method is optional if you are just training and executing models,
        exporting to and from SavedModels, or using weight checkpoints.

        This method is required for Keras `model_to_estimator`, saving and
        loading models to HDF5 formats, Keras model cloning, some visualization
        utilities, and exporting models to and from JSON.

        Returns:
            Python dictionary.
        """
        raise NotImplementedError(f"{self} does not implement get_config()")


@keras_core_export(
    ["keras_core.regularizers.L1L2", "keras_core.regularizers.l1_l2"]
)
class L1L2(Regularizer):
    """A regularizer that applies both L1 and L2 regularization penalties.

    The L1 regularization penalty is computed as:
    `loss = l1 * reduce_sum(abs(x))`

    The L2 regularization penalty is computed as
    `loss = l2 * reduce_sum(square(x))`

    L1L2 may be passed to a layer as a string identifier:

    >>> dense = Dense(3, kernel_regularizer='l1_l2')

    In this case, the default values used are `l1=0.01` and `l2=0.01`.

    Arguments:
        l1: float, L1 regularization factor.
        l2: float, L2 regularization factor.
    """

    def __init__(self, l1=0.0, l2=0.0):
        # The default value for l1 and l2 are different from the value in l1_l2
        # for backward compatibility reason. Eg, L1L2(l2=0.1) will only have l2
        # and no l1 penalty.
        l1 = 0.0 if l1 is None else l1
        l2 = 0.0 if l2 is None else l2
        validate_float_arg(l1, name="l1")
        validate_float_arg(l2, name="l2")

        self.l1 = l1
        self.l2 = l2

    def __call__(self, x):
        regularization = ops.convert_to_tensor(0.0, dtype=x.dtype)
        if self.l1:
            regularization += self.l1 * ops.sum(ops.absolute(x))
        if self.l2:
            regularization += self.l2 * ops.sum(ops.square(x))
        return regularization

    def get_config(self):
        return {"l1": float(self.l1), "l2": float(self.l2)}


@keras_core_export(["keras_core.regularizers.L1", "keras_core.regularizers.l1"])
class L1(Regularizer):
    """A regularizer that applies a L1 regularization penalty.

    The L1 regularization penalty is computed as:
    `loss = l1 * reduce_sum(abs(x))`

    L1 may be passed to a layer as a string identifier:

    >>> dense = Dense(3, kernel_regularizer='l1')

    In this case, the default value used is `l1=0.01`.

    Arguments:
        l1: float, L1 regularization factor.
    """

    def __init__(self, l1=0.01):
        l1 = 0.01 if l1 is None else l1
        validate_float_arg(l1, name="l1")
        self.l1 = ops.convert_to_tensor(l1)

    def __call__(self, x):
        return self.l1 * ops.sum(ops.absolute(x))

    def get_config(self):
        return {"l1": float(self.l1)}


@keras_core_export(["keras_core.regularizers.L2", "keras_core.regularizers.l2"])
class L2(Regularizer):
    """A regularizer that applies a L2 regularization penalty.

    The L2 regularization penalty is computed as:
    `loss = l2 * reduce_sum(square(x))`

    L2 may be passed to a layer as a string identifier:

    >>> dense = Dense(3, kernel_regularizer='l2')

    In this case, the default value used is `l2=0.01`.

    Arguments:
        l2: float, L2 regularization factor.
    """

    def __init__(self, l2=0.01):
        l2 = 0.01 if l2 is None else l2
        validate_float_arg(l2, name="l2")
        self.l2 = l2

    def __call__(self, x):
        return self.l2 * ops.sum(ops.square(x))

    def get_config(self):
        return {"l2": float(self.l2)}


@keras_core_export(
    [
        "keras_core.regularizers.OrthogonalRegularizer",
        "keras_core.regularizers.orthogonal_regularizer",
    ]
)
class OrthogonalRegularizer(Regularizer):
    """Regularizer that encourages input vectors to be orthogonal to each other.

    It can be applied to either the rows of a matrix (`mode="rows"`) or its
    columns (`mode="columns"`). When applied to a `Dense` kernel of shape
    `(input_dim, units)`, rows mode will seek to make the feature vectors
    (i.e. the basis of the output space) orthogonal to each other.

    Arguments:
        factor: Float. The regularization factor. The regularization penalty
            will be proportional to `factor` times the mean of the dot products
            between the L2-normalized rows (if `mode="rows"`, or columns if
            `mode="columns"`) of the inputs, excluding the product of each
            row/column with itself.  Defaults to 0.01.
        mode: String, one of `{"rows", "columns"}`. Defaults to `"rows"`. In
            rows mode, the regularization effect seeks to make the rows of the
            input orthogonal to each other. In columns mode, it seeks to make
            the columns of the input orthogonal to each other.

    Example:

    >>> regularizer = OrthogonalRegularizer(factor=0.01)
    >>> layer = Dense(units=4, kernel_regularizer=regularizer)
    """

    def __init__(self, factor=0.01, mode="rows"):
        validate_float_arg(factor, name="factor")
        self.factor = ops.convert_to_tensor(factor)
        if mode not in {"rows", "columns"}:
            raise ValueError(
                "Invalid value for argument `mode`. Expected one of "
                f'{{"rows", "columns"}}. Received: mode={mode}'
            )
        self.mode = mode

    def __call__(self, inputs):
        if len(inputs.shape) != 2:
            raise ValueError(
                "Inputs to OrthogonalRegularizer must have rank 2. Received: "
                f"inputs.shape={inputs.shape}"
            )
        if self.mode == "rows":
            inputs = normalize(inputs, axis=1)
            product = ops.matmul(inputs, ops.transpose(inputs))
            size = inputs.shape[0]
        else:
            inputs = normalize(inputs, axis=0)
            product = ops.matmul(ops.transpose(inputs), inputs)
            size = inputs.shape[1]
        product_no_diagonal = product * (
            1.0 - ops.eye(size, dtype=inputs.dtype)
        )
        num_pairs = size * (size - 1.0) / 2.0
        return (
            self.factor
            * 0.5
            * ops.sum(ops.absolute(product_no_diagonal))
            / num_pairs
        )

    def get_config(self):
        return {"factor": float(self.factor), "mode": self.mode}


def validate_float_arg(value, name):
    """check penalty number availability, raise ValueError if failed."""
    if not isinstance(value, (float, int)) or (
        math.isinf(value) or math.isnan(value)
    ):
        raise ValueError(
            f"Invalid value for argument {name}: expected a float. "
            f"Received: {name}={value}"
        )
    return float(value)