architecture

get_hidden_sizes(_L_in, l1, n=10)

Generates a list of hidden sizes for a neural network with a given input size and l1 regularization. The list is generated by dividing the input size by 2 until the minimum size is reached.

Parameters:

Name	Type	Description	Default
_L_in	int	input size.	required
l1	int	l1 regularization.	required
n	int	number of hidden sizes to generate.	10

Returns:

Type	Description
list	list of hidden sizes.

Examples:

>>> from spotpython.hyperparameters.architecture import get_hidden_sizes
    _L_in = 10
    l1 = 10
    n = 10
    get_hidden_sizes(_L_in, l1, n)
    [10, 5, 2, 1, 1, 1, 1, 1, 1, 1]

Source code in spotpython/hyperparameters/architecture.py

def get_hidden_sizes(_L_in, l1, n=10) -> list:
    """
    Generates a list of hidden sizes for a neural network with a given input size and l1 regularization.
    The list is generated by dividing the input size by 2 until the minimum size is reached.

    Args:
        _L_in (int):
            input size.
        l1 (int):
            l1 regularization.
        n (int):
            number of hidden sizes to generate.

    Returns:
        (list):
            list of hidden sizes.

    Examples:
        >>> from spotpython.hyperparameters.architecture import get_hidden_sizes
            _L_in = 10
            l1 = 10
            n = 10
            get_hidden_sizes(_L_in, l1, n)
            [10, 5, 2, 1, 1, 1, 1, 1, 1, 1]
    """
    if l1 < 4:
        raise ValueError("l1 must be at least 4")
    n_low = _L_in // 4
    n_high = max(l1, 2 * n_low)
    hidden_sizes = generate_div2_list(n_high, n_low)
    # keep only the first n values of hidden_sizes list
    if len(hidden_sizes) > n:
        hidden_sizes = hidden_sizes[:n]
    return hidden_sizes