def CSL(h_samples, x_test, model):
'''
Conservative Sampling-based Log-likelihood (CSL)
"Bounding the Test Log-Likelihood of Generative Models"
Yoshua Bengio, Li Yao, Kyunghyun Cho
http://arxiv.org/pdf/1311.6184.pdf
'''
log.maybeLog(None, "Starting CSL estimate...")
t = time.time()
x_indices = xrange(x_test.shape[0])
h_indices = xrange(len(h_samples))
LL = 0
times = []
for x_i in x_indices:
_t = time.time()
x = x_test[x_i:x_i+1]
r = T.zeros_like(x, dtype='float32')
for h_i in h_indices:
h = h_samples[h_i][0].eval()
r += model.pxh(x,h)
fs = r / len(h_samples)
LL += T.log(fs.mean(1))
times.append(time.time()-_t)
if x_i % 1 == 0:
log.maybeLog(None, make_time_units_string(numpy.average(times)*(x_test.shape[0]-x_i))+" remaining")
log.maybeLog(None, "CSL took "+make_time_units_string(time.time()-t))
return (LL / len(x_test)).eval()
def get_ll(x, parzen, batch_size=10):
inds = range(x.shape[0])
n_batches = int(numpy.ceil(float(len(inds)) / batch_size))
times = []
lls = []
for i in range(n_batches):
begin = time.time()
ll = parzen(x[inds[i::n_batches]])
end = time.time()
times.append(end-begin)
lls.extend(ll)
if i % 10 == 0:
log.maybeLog(None, [i, make_time_units_string(numpy.mean(times)), numpy.mean(lls)])
return lls
