def sample_h_given_x(self, x):
h_pre = K.dot(x, self.Wrbm) + self.bh
h_sigm = self.activation(self.scaling_h_given_x * h_pre)
# drop out noise
#if(0.0 < self.p < 1.0):
# noise_shape = self._get_noise_shape(h_sigm)
# h_sigm = K.in_train_phase(K.dropout(h_sigm, self.p, noise_shape), h_sigm)
if (self.hidden_unit_type == 'binary'):
h_samp = K.random_binomial(shape=h_sigm.shape, p=h_sigm)
# random sample
# \hat{h} = 1, if p(h=1|x) > uniform(0, 1)
# 0, otherwise
elif (self.hidden_unit_type == 'nrlu'):
h_samp = nrlu(h_pre)
else:
h_samp = h_sigm
if (0.0 < self.p < 1.0):
noise_shape = self._get_noise_shape(h_samp)
h_samp = K.in_train_phase(K.dropout(h_samp, self.p, noise_shape),
h_samp)
return h_samp, h_pre, h_sigm
def sample_h_given_x(self, x):
h_pre = K.dot(x, self.W) + self.bh
h_sigm = K.maximum(self.scaling_h_given_x * h_pre, 0)
#std = K.mean(K.sigmoid(self.scaling_h_given_x * h_pre))
#eta = random_normal(shape=h_pre.shape, std=std)
#h_samp = K.maximum(h_pre + eta, 0)
h_samp = nrlu(h_pre)
return h_samp, h_pre, h_sigm
def call(self, x, mask=None):
if self.mode == 'maximum_likelihood':
# draw maximum likelihood sample from Bernoulli distribution
# x* = argmax_x p(x) = 1 if p(x=1) >= 0.5
# 0 otherwise
return T.round(x, mode='half_away_from_zero')
elif self.mode == 'random':
# draw random sample from Bernoulli distribution
# x* = x ~ p(x) = 1 if p(x=1) > uniform(0, 1)
# 0 otherwise
return self.srng.binomial(size=x.shape, n=1, p=x, dtype=theano.config.floatX)
elif self.mode == 'mean_field':
# draw mean-field approximation sample from Bernoulli distribution
# x* = E[p(x)] = E[Bern(x; p)] = p
return x
elif self.mode == 'nrlu':
return nrlu(x)
else:
raise NotImplementedError('Unknown sample mode!')