class_docstr: Restricted  Boltzmann Machine
class_docstr_tok: ["Restricted","Boltzmann","Machine"]
docstr: Call the different RBM modules to generate the computational graph
docstr_tok: ["Call","the","different","RBM","modules","to","generate","the","computational","graph"]
code: def generate_graph(self):
      """Call the different RBM modules to generate the computational graph"""
  
      log.info("Creating the computational graph")
  
      self.placeholder()  # create the visible units placeholder
      self.data_pipeline()  # data_pipeline
      self.init_parameters()  # initialize Network parameters
  
      # --------------Initialize protocol for Gibbs sampling------------------
      log.info("Initialize Gibbs protocol")
      self.k = 1  # initialize the G_sampling step
      # initialize epoch_sample index
      self.l = 0  # noqa: E741 ambiguous variable name 'l'
      self.gibbs_sampling()  # returns the sampled value of the visible units
  
      # ---Instantiate loss function and optimizer----------------------------
      obj = self.losses(self.v)  # objective function
  
      rate = (
          self.learning_rate / self.minibatch
      )  # learning rate rescaled by the batch size
  
      self.opt = tf.compat.v1.train.AdamOptimizer(
          learning_rate=rate
      ).minimize(loss=obj)
  
imports: import numpy
  import tensorflow
  import logging
  import os
  from pathlib import Path
label_desc: Use image node_id 15 for calling the RBM obj's underlying member method code with example usage: obj.generate_graph() without return types