bm可以看做是hopfield的一个特例。rbm又是bm的一个特例。下面的代码，看了很久才恍然大悟，好还前面看过bm的理论文章。
def sample_h_given_v(self, v0_sample):
''' This function infers state of hidden units given visible units '''
# compute the activation of the hidden units given a sample of the visibles
pre_sigmoid_h1, h1_mean = self.propup(v0_sample)
# get a sample of the hiddens given their activation
# Note that theano_rng.binomial returns a symbolic sample of dtype
# int64 by default. If we want to keep our computations in floatX
# for the GPU we need to specify to return the dtype floatX
h1_sample = self.theano_rng.binomial(size = h1_mean.shape, n = 1, p = h1_mean,
dtype = theano.config.floatX)
return [pre_sigmoid_h1, h1_mean, h1_sample]
这里对h1_sample采用2项分布，根据概率p做采用，是融合了模拟退火的思想的。
大家可以用sigmoid参数一个近似的概率[0,1],然后用binomial做个简单实验。
def propup(self, vis):
''' This function propagates the visible units activation upwards to
the hidden units
Note that we return also the pre-sigmoid activation of the layer. As
it will turn out later, due to how Theano deals with optimizations,
this symbolic variable will be needed to write down a more
stable computational graph (see details in the reconstruction cost function)
'''
pre_sigmoid_activation = T.dot(vis, self.W) + self.hbias
return [pre_sigmoid_activation, T.nnet.sigmoid(pre_sigmoid_activation)]
这个函数和上面的函数一起实现Gibbs 采样。不过文章中对一个样本各个分量的采样不是一起做的。这里用wx+b来一起实现。还得再看下文章。