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[gpu] upate gradient

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This commit is contained in:
Zhenwen Dai 2014-04-01 17:38:52 +01:00
parent 98816659dd
commit af56b9951c
3 changed files with 171 additions and 20 deletions
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17
GPy/kern/_src/rbf.py
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@ -11,6 +11,9 @@ from ...core.parameterization import variational
from psi_comp import ssrbf_psi_comp
from psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF
import pycuda.gpuarray as gpuarray
import pycuda.autoinit
class RBF(Stationary):
"""
Radial Basis Function kernel, aka squared-exponential, exponentiated quadratic or Gaussian kernel:
@ -26,8 +29,8 @@ class RBF(Stationary):
self.weave_options = {}
self.group_spike_prob = False
# if self.useGPU:
# self.psicomp = PSICOMP_SSRBF()
if self.useGPU:
self.psicomp = PSICOMP_SSRBF()
def K_of_r(self, r):
@ -70,6 +73,13 @@ class RBF(Stationary):
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
dL_dpsi0_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi0))
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
self.psicomp.update_gradients_expectations(dL_dpsi0_gpu, dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
vg = self.variance.gradient.copy()
lg = self.lengthscale.gradient.copy()
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -89,6 +99,9 @@ class RBF(Stationary):
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
else:
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum()
print np.abs(vg-self.variance.gradient)
print np.abs(lg-self.lengthscale.gradient)
elif isinstance(variational_posterior, variational.NormalPosterior):
l2 = self.lengthscale**2