apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-10 04:22:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[GPU] psi varDTC ready

Browse source
This commit is contained in:
Zhenwen Dai 2014-04-07 10:18:03 +01:00
parent 5e01b94d37
commit f776db45df
3 changed files with 8 additions and 6 deletions
Download patch file Download diff file Expand all files Collapse all files

9
GPy/inference/latent_function_inference/var_dtc_parallel.py
View file

@ -302,18 +302,19 @@ def update_gradients(model):
while not isEnd:
isEnd, n_range, grad_dict = model.inference_method.inference_minibatch(model.kern, model.X, model.Z, model.likelihood, model.Y)
if isinstance(model.X, VariationalPosterior):
X_slice = model.X[n_range[0]:n_range[1]]
#gradients w.r.t. kernel
model.kern.update_gradients_expectations(variational_posterior=model.X[n_range[0]:n_range[1]], Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
model.kern.update_gradients_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
kern_grad += model.kern.gradient
#gradients w.r.t. Z
model.Z.gradient[:,model.kern.active_dims] += model.kern.gradients_Z_expectations(
grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=model.X[n_range[0]:n_range[1]])
grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=model.Z, variational_posterior=X_slice)
#gradients w.r.t. posterior parameters of X
X_grad = model.kern.gradients_qX_expectations(variational_posterior=model.X[n_range[0]:n_range[1]], Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
model.set_X_gradients(model.X[n_range[0]:n_range[1]], X_grad)
X_grad = model.kern.gradients_qX_expectations(variational_posterior=X_slice, Z=model.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
model.set_X_gradients(X_slice, X_grad)
if het_noise:
dL_dthetaL[n_range[0]:n_range[1]] = grad_dict['dL_dthetaL']