Method __init__

numpy_ml/neural_nets/layers/layers.py:2353–2419

r""" A sparse Erdos-Renyi layer with evolutionary rewiring via the sparse evolutionary training (SET) algorithm. Notes ----- .. math:: Y = f( (\mathbf{W} \odot \mathbf{W}_{mask}) \mathbf{X} + \mathbf{b} ) where :math:`\odot` is the eleme

(
        self,
        n_out,
        zeta=0.3,
        epsilon=20,
        act_fn=None,
        init="glorot_uniform",
        optimizer=None,
    )

class SparseEvolution(LayerBase):
    def __init__(
        self,
        n_out,
        zeta=0.3,
        epsilon=20,
        act_fn=None,
        init="glorot_uniform",
        optimizer=None,
    ):
        r"""
        A sparse Erdos-Renyi layer with evolutionary rewiring via the sparse
        evolutionary training (SET) algorithm.

        Notes
        -----
        .. math::

            Y = f( (\mathbf{W} \odot \mathbf{W}_{mask}) \mathbf{X} + \mathbf{b} )

        where :math:`\odot` is the elementwise multiplication operation, `f` is
        the layer activation function, and :math:`\mathbf{W}_{mask}` is an
        evolved binary mask.

        Parameters
        ----------
        n_out : int
            The dimensionality of the layer output
        zeta : float
            Proportion of the positive and negative weights closest to zero to
            drop after each training update. Default is 0.3.
        epsilon : float
            Layer sparsity parameter. Default is 20.
        act_fn : str, :doc:`Activation <numpy_ml.neural_nets.activations>` object, or None
            The element-wise output nonlinearity used in computing `Y`. If None,
            use the identity function :math:`f(X) = X`. Default is None.
        init : {'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'}
            The weight initialization strategy. Default is `'glorot_uniform'`.
        optimizer : str, :doc:`Optimizer <numpy_ml.neural_nets.optimizers>` object, or None
            The optimization strategy to use when performing gradient updates
            within the :meth:`update` method.  If None, use the :class:`SGD
            <numpy_ml.neural_nets.optimizers.SGD>` optimizer with default
            parameters. Default is None.

        Attributes
        ----------
        X : list
            Running list of inputs to the :meth:`forward <numpy_ml.neural_nets.LayerBase.forward>` method since the last call to :meth:`update <numpy_ml.neural_nets.LayerBase.update>`. Only updated if the `retain_derived` argument was set to True.
        gradients : dict
            Dictionary of loss gradients with regard to the layer parameters
        parameters : dict
            Dictionary of layer parameters
        hyperparameters : dict
            Dictionary of layer hyperparameters
        derived_variables : dict
            Dictionary of any intermediate values computed during
            forward/backward propagation.
        """  # noqa: E501
        super().__init__(optimizer)