https://github.com/GPflow/GPflow
Tip revision: f56e5a2e9c20b744a699e975917fd11f33d84c90 authored by ST John on 13 April 2021, 15:46:17 UTC
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Tip revision: f56e5a2
test_inducing_variables.py
# Copyright 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 numpy as np
import tensorflow as tf
import gpflow
def test_inducing_points_with_variable_shape():
N, M1, D, P = 50, 13, 3, 1
X, Y = np.random.randn(N, D), np.random.randn(N, P)
Z1 = np.random.randn(M1, D)
# use explicit tf.Variable with None shape:
iv = gpflow.inducing_variables.InducingPoints(
tf.Variable(Z1, trainable=False, dtype=gpflow.default_float(), shape=(None, D))
)
# Note that we cannot have Z be trainable if we want to be able to change its shape;
# TensorFlow optimizers expect shape to be known at construction time.
m = gpflow.models.SGPR(data=(X, Y), kernel=gpflow.kernels.Matern32(), inducing_variable=iv)
# Check 1: that we can still optimize with None shape
opt = tf.optimizers.Adam()
@tf.function
def optimization_step():
opt.minimize(m.training_loss, m.trainable_variables)
optimization_step()
# Check 2: that we can successfully assign a new Z with different number of inducing points!
Z2 = np.random.randn(M1 + 1, D)
m.inducing_variable.Z.assign(Z2)
# Check 3: that we can also optimize with changed Z tensor
optimization_step()