def _sample_laddered_layer(lateral, s):
s[...,:] += lateral[:,0]
s[...,0] = sample_indicator(sigmoid(s[...,0]))
for i in range(1, s.shape[-1]):
j = min(i, lateral.shape[1]-1)
s[...,i] += s[...,i-j:i].dot(lateral[i,j:0:-1])
s[...,i] = sample_indicator(sigmoid(s[...,i]))
return s
def sample_generative_dist(self, size = None,
all_layers = False, top_units = None):
""" Sample the generative distribution.
Parameters:
-----------
size : int, optional [default None]
The number of samples to draw. If None, returns a single sample.
all_layers : bool, optional [default False]
By default, an array of input unit samples is returned. If
'all_layers` is True, a list of sample arrays for *all* the layers,
in top-to-bottom order, is returned.
top_units : bit vector, optional
By default, the top-level units are sampled from the generative
biases. This parameter clamps the top-level units to specific
values.
Returns:
--------
A (list of) 2D sample array(s), where the first dimension indexes the
individual samples. See 'all_layers' parameter.
"""
d = self.G_top if top_units is None else top_units
if size is not None:
d = np.tile(d, (size,1))
if top_units is None:
d = sample_indicator(sigmoid(d))
samples = _sample_factorial_network(self.G, d)
return samples if all_layers else samples[-1]
def _sample_boltzmann_layer(group_size, s):
# Shortcut for degenerate case.
if group_size == 1:
return sample_indicator(sigmoid(s, out=s), out=s)
s.shape = s.shape[:-1] + (-1, group_size)
boltzmann_dist(s, axis=-1, out=s)
sample_exclusive_indicators(s, axis=-1, out=s)
s.shape = s.shape[:-2] + (-1,)
return s
def _sleep(G, G_top, R, rate):
# Generate a dream.
d = sample_indicator(sigmoid(G_top))
dreams = _sample_factorial_network(G, d)
dreams.reverse()
# Pass back up through the recognition network and adjust weights.
R_probs = _probs_for_factorial_network(R, dreams)
for R_weights, inputs, target, recognized, step \
in izip(R, dreams, dreams[1:], R_probs, rate[::-1]):
R_weights[:-1] += step * np.outer(inputs, target - recognized)
R_weights[-1] += step * (target - recognized)
def _sample_factorial_network(layers, s):
samples = [ s ]
for L in layers:
s = sample_indicator(sigmoid(s.dot(L[:-1]) + L[-1]))
samples.append(s)
return samples
