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[GPU] update gradients rest

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This commit is contained in:
Zhenwen Dai 2014-04-02 11:43:32 +01:00
parent 73f690a4c9
commit b90a867232
2 changed files with 106 additions and 38 deletions
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68
GPy/kern/_src/rbf.py
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@ -73,36 +73,33 @@ 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)
#contributions from psi0:
self.variance.gradient = np.sum(dL_dpsi0)
#from psi1
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
if self.ARD:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
if self.useGPU:
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)
else:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum()
#from psi2
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
if self.ARD:
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)
_, _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)
#contributions from psi0:
self.variance.gradient = np.sum(dL_dpsi0)
#from psi1
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
if self.ARD:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
else:
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum()
#from psi2
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
if self.ARD:
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()
elif isinstance(variational_posterior, variational.NormalPosterior):
l2 = self.lengthscale**2
if l2.size != self.input_dim:
@ -141,6 +138,12 @@ class RBF(Stationary):
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
gZ = self.psicomp.gradients_Z_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -150,6 +153,8 @@ class RBF(Stationary):
#psi2
grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1)
print np.abs(gZ - grad).max()
return grad
elif isinstance(variational_posterior, variational.NormalPosterior):
@ -174,6 +179,11 @@ class RBF(Stationary):
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
# Spike-and-Slab GPLVM
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
dL_dpsi1_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi1))
dL_dpsi2_gpu = gpuarray.to_gpu(np.asfortranarray(dL_dpsi2))
gmu,gS,gg = self.psicomp.gradients_qX_expectations(dL_dpsi1_gpu, dL_dpsi2_gpu, self.variance, self.lengthscale, Z, variational_posterior)
ndata = variational_posterior.mean.shape[0]
_, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
@ -191,6 +201,8 @@ class RBF(Stationary):
if self.group_spike_prob:
grad_gamma[:] = grad_gamma.mean(axis=0)
print np.abs(gmu-grad_mu).max(),np.abs(gS-grad_S).max(),np.abs(gg-grad_gamma).max()
return grad_mu, grad_S, grad_gamma