Method __init__

numpy_ml/neural_nets/layers/layers.py:860–893

Flatten a multidimensional input into a 2D matrix. Parameters ---------- keep_dim : {'first', 'last', -1} The dimension of the original input to retain. Typically used for retaining the minibatch dimension.. If -1, flatten all dimensions.

(self, keep_dim="first", optimizer=None)

class Flatten(LayerBase):
    def __init__(self, keep_dim="first", optimizer=None):
        """
        Flatten a multidimensional input into a 2D matrix.

        Parameters
        ----------
        keep_dim : {'first', 'last', -1}
            The dimension of the original input to retain. Typically used for
            retaining the minibatch dimension.. If -1, flatten all dimensions.
            Default is 'first'.
        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
            Unused
        gradients : dict
            Unused
        parameters : dict
            Unused
        hyperparameters : dict
            Dictionary of layer hyperparameters
        derived_variables : dict
            Dictionary of any intermediate values computed during
            forward/backward propagation.
        """  # noqa: E501
        super().__init__(optimizer)

        self.keep_dim = keep_dim
        self._init_params()

    def _init_params(self):
        self.gradients = {}