tfl.kronecker_factored_lattice_lib.evaluate_with_hypercube_interpolation

Evaluates a Kronecker-Factored Lattice using hypercube interpolation.

tfl.kronecker_factored_lattice_lib.evaluate_with_hypercube_interpolation(
    inputs, scale, bias, kernel, units, num_terms, lattice_sizes, clip_inputs
)

Kronecker-Factored Lattice function is the product of the piece-wise linear interpolation weights for each dimension of the input.

inputs Tensor representing points to apply lattice interpolation to. If units = 1, tensor should be of shape: (batch_size, ..., dims) or list of dims tensors of same shape (batch_size, ..., 1). If units > 1, tensor should be of shape: (batch_size, ..., units, dims) or list of dims tensors of same shape (batch_size, ..., units, 1). A typical shape is (batch_size, dims).
scale Kronecker-Factored Lattice scale of shape (units, num_terms).
bias Kronecker-Factored Lattice bias of shape (units).
kernel Kronecker-Factored Lattice kernel of shape (1, lattice_sizes, units * dims, num_terms).
units Output dimension of the Kronecker-Factored Lattice.
num_terms Number of independently trained submodels per unit, the outputs of which are averaged to get the final output.
lattice_sizes Number of vertices per dimension.
clip_inputs If inputs should be clipped to the input range of the Kronecker-Factored Lattice.

Tensor of shape: (batch_size, ..., units).