def get_cost_updates(self, lr=0.1, persistent=None, k=1):

        # compute positive phase
        pre_sigmoid_ph, ph_mean, ph_sample = self.sample_h_given_v(self.input)

        # decide how to initialize persistent chain:
        # for CD, we use the newly generate hidden sample
        # for PCD, we initialize from the old state of the chain
        if persistent is None:
            chain_start = ph_sample
        else:
            chain_start = persistent
        # end-snippet-2
        # perform actual negative phase
        # in order to implement CD-k/PCD-k we need to scan over the
        # function that implements one gibbs step k times.
        # Read Theano tutorial on scan for more information :
        # http://deeplearning.net/software/theano/library/scan.html
        # the scan will return the entire Gibbs chain
        (
            [
                pre_sigmoid_nvs,
                nv_means,
                nv_samples,
                pre_sigmoid_nhs,
                nh_means,
                nh_samples
            ],
            updates
        ) = theano.scan(
            self.gibbs_hvh,
            # the None are place holders, saying that
            # chain_start is the initial state corresponding to the
            # 6th output
            outputs_info=[None, None, None, None, None, chain_start],
            n_steps=k,
            name="gibbs_hvh"
        )
        # start-snippet-3
        # determine gradients on RBM parameters
        # note that we only need the sample at the end of the chain
        chain_end = nv_samples[-1]

        cost = T.mean(self.free_energy(self.input)) - T.mean(
            self.free_energy(chain_end))
        # We must not compute the gradient through the gibbs sampling
        gparams = T.grad(cost, self.params, consider_constant=[chain_end])
        # end-snippet-3 start-snippet-4
        # constructs the update dictionary
        for gparam, param in zip(gparams, self.params):
            # make sure that the learning rate is of the right dtype
            updates[param] = param - gparam * T.cast(
                lr,
                dtype=theano.config.floatX
            )
        if persistent:
            # Note that this works only if persistent is a shared variable
            updates[persistent] = nh_samples[-1]
            # pseudo-likelihood is a better proxy for PCD
            monitoring_cost = self.get_pseudo_likelihood_cost(updates)
        else:
            # reconstruction cross-entropy is a better proxy for CD
            monitoring_cost = self.get_reconstruction_cost(updates,
                                                           pre_sigmoid_nvs[-1])

        return monitoring_cost, updates
        # end-snippet-4
This functions implements one step of CD-k or PCD-k
dataset/ETHPy150Open lisa-lab/DeepLearningTutorials/code/rbm.py/RBM.get_cost_updates