tfl.linear_lib.assert_constraints

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Asserts that weights satisfy constraints.

tfl.linear_lib.assert_constraints(
    weights,
    monotonicities,
    monotonic_dominances,
    range_dominances,
    input_min,
    input_max,
    normalization_order,
    eps=0.0001
)

weights Weights of Linear layer.
monotonicities List or tuple of same length as number of elements in 'weights' of {-1, 0, 1} which represent monotonicity constraints per dimension. -1 stands for decreasing, 0 for no constraints, 1 for increasing.
monotonic_dominances List of two-element tuple. First element is the index of the dominant feature. Second element is the index of the weak feature.
range_dominances List of two-element tuples. First element is the index of the dominant feature. Second element is the index of the weak feature.
input_min List or tuple of length same length as number of elements in 'weights' of either None or float which specifies the minimum value to clip by.
input_max List or tuple of length same length as number of elements in 'weights' of either None or float which specifies the maximum value to clip by.
normalization_order Whether weights have to have norm 1. Norm will be computed by: tf.norm(tensor, ord=normalization_order).
eps Allowed constraints violation.

List of assetion ops in graph mode or directly executes assertions in eager mode.