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improve numerical stability of vardtc_parallel

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This commit is contained in:
Zhenwen Dai 2014-08-26 16:38:00 +01:00
parent 80adaed616
commit 77a96efeba
3 changed files with 8 additions and 7 deletions
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2
GPy/core/parameterization/transformations.py
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@ -54,7 +54,7 @@ class Transformation(object):
class Logexp(Transformation):
domain = _POSITIVE
def f(self, x):
return np.where(x>_lim_val, x, np.log(1. + np.exp(np.clip(x, -_lim_val, _lim_val)))) + epsilon
return np.where(x>_lim_val, x, np.log1p(np.exp(np.clip(x, -_lim_val, _lim_val)))) + epsilon
#raises overflow warning: return np.where(x>_lim_val, x, np.log(1. + np.exp(x)))
def finv(self, f):
return np.where(f>_lim_val, f, np.log(np.exp(f+1e-20) - 1.))

7
GPy/inference/latent_function_inference/var_dtc_parallel.py
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@ -169,14 +169,15 @@ class VarDTC_minibatch(LatentFunctionInference):
Kmm = kern.K(Z).copy()
diag.add(Kmm, self.const_jitter)
Lm = jitchol(Kmm)
Lambda = Kmm+psi2_full
LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT
LL = jitchol(Lambda)
LL = np.dot(Lm,LL)
b,_ = dtrtrs(LL, psi1Y_full.T)
bbt = np.square(b).sum()
v,_ = dtrtrs(LL.T,b,lower=False)
vvt = np.einsum('md,od->mo',v,v)
LmInvPsi2LmInvT = backsub_both_sides(Lm,psi2_full,transpose='right')
Psi2LLInvT = dtrtrs(LL,psi2_full)[0].T
LmInvPsi2LLInvT= dtrtrs(Lm,Psi2LLInvT)[0]

6
GPy/kern/_src/psi_comp/rbf_psi_gpucomp.py
View file

@ -378,7 +378,7 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
if self.GPU_direct:
dL_dpsi1_gpu = dL_dpsi1
dL_dpsi2_gpu = dL_dpsi2
dL_dpsi0_sum = gpuarray.sum(dL_dpsi0).get()
dL_dpsi0_sum = dL_dpsi0.get().sum() #gpuarray.sum(dL_dpsi0).get()
else:
dL_dpsi1_gpu = self.gpuCache['dL_dpsi1_gpu']
dL_dpsi2_gpu = self.gpuCache['dL_dpsi2_gpu']
@ -394,7 +394,7 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
self.g_psi1compDer.prepared_call((self.blocknum,1),(self.threadnum,1,1),dvar_gpu.gpudata,dl_gpu.gpudata,dZ_gpu.gpudata,dmu_gpu.gpudata,dS_gpu.gpudata,dL_dpsi1_gpu.gpudata,psi1_gpu.gpudata, np.float64(variance),l_gpu.gpudata,Z_gpu.gpudata,mu_gpu.gpudata,S_gpu.gpudata, np.int32(N), np.int32(M), np.int32(Q))
self.g_psi2compDer.prepared_call((self.blocknum,1),(self.threadnum,1,1),dvar_gpu.gpudata,dl_gpu.gpudata,dZ_gpu.gpudata,dmu_gpu.gpudata,dS_gpu.gpudata,dL_dpsi2_gpu.gpudata,psi2n_gpu.gpudata, np.float64(variance),l_gpu.gpudata,Z_gpu.gpudata,mu_gpu.gpudata,S_gpu.gpudata, np.int32(N), np.int32(M), np.int32(Q))
dL_dvar = dL_dpsi0_sum + gpuarray.sum(dvar_gpu).get()
dL_dvar = dL_dpsi0_sum + dvar_gpu.get().sum()#gpuarray.sum(dvar_gpu).get()
sum_axis(grad_mu_gpu,dmu_gpu,N*Q,self.blocknum)
dL_dmu = grad_mu_gpu.get()
sum_axis(grad_S_gpu,dS_gpu,N*Q,self.blocknum)
@ -404,7 +404,7 @@ class PSICOMP_RBF_GPU(PSICOMP_RBF):
sum_axis(grad_l_gpu,dl_gpu,Q,self.blocknum)
dL_dlengscale = grad_l_gpu.get()
else:
dL_dlengscale = gpuarray.sum(dl_gpu).get()
dL_dlengscale = dl_gpu.get().sum() #gpuarray.sum(dl_gpu).get()
return dL_dvar, dL_dlengscale, dL_dZ, dL_dmu, dL_dS