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
)
Args |
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.
|
Returns |
List of assetion ops in graph mode or directly executes assertions in eager
mode.
|
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Last updated 2024-08-02 UTC.
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