Method __init__

numpy_ml/neural_nets/layers/layers.py:3182–3211

A single two-dimensional pooling layer. Parameters ---------- kernel_shape : 2-tuple The dimension of a single 2D filter/kernel in the current layer stride : int The stride/hop of the convolution kernels as they move over the

(self, kernel_shape, stride=1, pad=0, mode="max", optimizer=None)

class Pool2D(LayerBase):
    def __init__(self, kernel_shape, stride=1, pad=0, mode="max", optimizer=None):
        """
        A single two-dimensional pooling layer.

        Parameters
        ----------
        kernel_shape : 2-tuple
            The dimension of a single 2D filter/kernel in the current layer
        stride : int
            The stride/hop of the convolution kernels as they move over the
            input volume. Default is 1.
        pad : int, tuple, or 'same'
            The number of rows/columns of 0's to pad the input. Default is 0.
        mode : {"max", "average"}
            The pooling function to apply.
        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.
        """  # noqa: E501
        super().__init__(optimizer)

        self.pad = pad
        self.mode = mode
        self.in_ch = None
        self.out_ch = None
        self.stride = stride
        self.kernel_shape = kernel_shape
        self.is_initialized = False

    def _init_params(self):
        self.derived_variables = {"out_rows": [], "out_cols": []}