* Replace len(inducing_variable) with inducing_variable.num inducing property (#1594). Adds support for inducing variables with dynamically changing shape. Change usage from `len(inducing_variable)` to `inducing_variable.num_inducing` instead. Resolves #1578. * HeteroskedasticTFPConditional should construct tensors at class-construction, not at module-import time (#1598)
To reference or cite the objects present in the Software Heritage archive, permalinks based on SoftWare Heritage persistent IDentifiers (SWHIDs) must be used.
Select below a type of object currently browsed in order to display its associated SWHID and permalink.
# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf from ..base import TensorLike from ..inducing_variables import InducingPatches, InducingPoints, Multiscale from ..kernels import Convolutional, Kernel, SquaredExponential from .dispatch import Kuf @Kuf.register(InducingPoints, Kernel, TensorLike) def Kuf_kernel_inducingpoints(inducing_variable: InducingPoints, kernel: Kernel, Xnew): return kernel(inducing_variable.Z, Xnew) @Kuf.register(Multiscale, SquaredExponential, TensorLike) def Kuf_sqexp_multiscale(inducing_variable: Multiscale, kernel: SquaredExponential, Xnew): Xnew, _ = kernel.slice(Xnew, None) Zmu, Zlen = kernel.slice(inducing_variable.Z, inducing_variable.scales) idlengthscales = kernel.lengthscales + Zlen d = inducing_variable._cust_square_dist(Xnew, Zmu, idlengthscales) lengthscales = tf.reduce_prod(kernel.lengthscales / idlengthscales, 1) lengthscales = tf.reshape(lengthscales, (1, -1)) return tf.transpose(kernel.variance * tf.exp(-0.5 * d) * lengthscales) @Kuf.register(InducingPatches, Convolutional, object) def Kuf_conv_patch(inducing_variable, kernel, Xnew): Xp = kernel.get_patches(Xnew) # [N, num_patches, patch_len] bigKzx = kernel.base_kernel.K( inducing_variable.Z, Xp ) # [M, N, P] -- thanks to broadcasting of kernels Kzx = tf.reduce_sum(bigKzx * kernel.weights if hasattr(kernel, "weights") else bigKzx, ) return Kzx / kernel.num_patches
Computing file changes ...