def _create_ladder_prior(self, ladder_len):
param = np.zeros((2, ladder_len, ladder_len))
mean, var = param
lateral_mean = logit(1e-4)
var[:,0] = 1e-3
for i in xrange(1, ladder_len):
mean[i,1:i+1] = lateral_mean
var[i,1:i+1] = 1e-3
return param
def _create_top_prior(self, layer_len, ladder_len):
param = np.zeros((2, layer_len, ladder_len))
mean, var = param
# Make top-level units mutually exclusive and equally probable.
probs = np.reciprocal(np.arange(layer_len, 0, -1, dtype=float))
probs[-1] -= 1e-3
bias_mean = logit(probs)
lateral_mean = logit(1e-6)
# Make mutual exclusivity high probable but individual probabilities
# fairly variable.
mean[:,0] = bias_mean
var[:,0] = 1e-3
for i in xrange(1, layer_len):
mean[i,1:i+1] = lateral_mean
var[i,1:i+1] = 1e-3
return param
