apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-08 19:42:39 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

fixed SGD to work with new interface

Browse source
This commit is contained in:
Nicolo Fusi 2012-12-10 17:25:54 +00:00
parent c9358c6aec
commit 8c4606355d
1 changed files with 6 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

15
GPy/inference/SGD.py
View file

@ -16,12 +16,9 @@ class opt_SGD(Optimizer):
"""
def __init__(self, start, f_fp, f, fp, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, **kwargs):
def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, **kwargs):
self.opt_name = "Stochastic Gradient Descent"
self.f = f
self.fp = fp
self.f_fp = f_fp
self.model = model
self.iterations = iterations
self.momentum = momentum
@ -115,7 +112,7 @@ class opt_SGD(Optimizer):
else:
raise NotImplementedError
def step_with_missing_data(self, X, Y, step, shapes):
def step_with_missing_data(self, f_fp, X, Y, step, shapes):
N, Q = X.shape
samples = self.non_null_samples(self.model.Y)
j = self.subset_parameter_vector(self.x_opt, samples, shapes)
@ -129,7 +126,7 @@ class opt_SGD(Optimizer):
b,p = self.shift_constraints(j)
momentum_term = self.momentum * step[j]
f, fp = self.f_fp(self.x_opt[j])
f, fp = f_fp(self.x_opt[j])
step[j] = self.learning_rate[j] * fp
self.x_opt[j] -= step[j] + momentum_term
@ -137,7 +134,7 @@ class opt_SGD(Optimizer):
return f, step, self.model.N
def opt(self):
def opt(self, f_fp=None, f=None, fp=None):
self.x_opt = self.model.get_param()
X, Y = self.model.X.copy(), self.model.Y.copy()
N, Q = self.model.X.shape
@ -165,11 +162,11 @@ class opt_SGD(Optimizer):
if missing_data:
shapes = self.get_param_shapes(N, Q)
f, step, Nj = self.step_with_missing_data(X, Y, step, shapes)
f, step, Nj = self.step_with_missing_data(f_fp, X, Y, step, shapes)
else:
Nj = N
momentum_term = self.momentum * step # compute momentum using update(t-1)
f, fp = self.f_fp(self.x_opt)
f, fp = f_fp(self.x_opt)
step = self.learning_rate * fp # compute update(t)
self.x_opt -= step + momentum_term