# Copyright (c) 2012, 2013 GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import unittest
import numpy as np
import GPy
[docs]class MappingGradChecker(GPy.core.Model):
"""
This class has everything we need to check the gradient of a mapping. It
implement a simple likelihood which is a weighted sum of the outputs of the
mapping. the gradients are checked against the parameters of the mapping
and the input.
"""
def __init__(self, mapping, X, name='map_grad_check'):
super(MappingGradChecker, self).__init__(name)
self.mapping = mapping
self.link_parameter(self.mapping)
self.X = GPy.core.Param('X',X)
self.link_parameter(self.X)
self.dL_dY = np.random.randn(self.X.shape[0], self.mapping.output_dim)
[docs] def log_likelihood(self):
return np.sum(self.mapping.f(self.X) * self.dL_dY)
[docs] def parameters_changed(self):
self.X.gradient = self.mapping.gradients_X(self.dL_dY, self.X)
self.mapping.update_gradients(self.dL_dY, self.X)
[docs]class MappingTests(unittest.TestCase):
[docs] def test_kernelmapping(self):
X = np.random.randn(100,3)
Z = np.random.randn(10,3)
mapping = GPy.mappings.Kernel(3, 2, Z, GPy.kern.RBF(3))
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
[docs] def test_linearmapping(self):
mapping = GPy.mappings.Linear(3, 2)
X = np.random.randn(100,3)
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
[docs] def test_mlpmapping(self):
mapping = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
X = np.random.randn(100,3)
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
[docs] def test_mlpextmapping(self):
np.random.seed(42)
X = np.random.randn(100,3)
for activation in ['tanh', 'relu', 'sigmoid']:
mapping = GPy.mappings.MLPext(input_dim=3, hidden_dims=[5,5], output_dim=2, activation=activation)
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
[docs] def test_addmapping(self):
m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
m2 = GPy.mappings.Linear(input_dim=3, output_dim=2)
mapping = GPy.mappings.Additive(m1, m2)
X = np.random.randn(100,3)
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
[docs] def test_compoundmapping(self):
m1 = GPy.mappings.MLP(input_dim=3, hidden_dim=5, output_dim=2)
Z = np.random.randn(10,2)
m2 = GPy.mappings.Kernel(2, 4, Z, GPy.kern.RBF(2))
mapping = GPy.mappings.Compound(m1, m2)
X = np.random.randn(100,3)
self.assertTrue(MappingGradChecker(mapping, X).checkgrad())
if __name__ == "__main__":
print("Running unit tests, please be (very) patient...")
unittest.main()