class_docstr: Restricted  Boltzmann Machine
class_docstr_tok: ["Restricted","Boltzmann","Machine"]
docstr: Initialize the parameters of the model.
  
  This is a single layer model with two biases. So we have a rectangular matrix w_{ij} and
  two bias vectors to initialize.
  
  Args:
      n_visible (int): number of visible units (input layer)
      n_hidden (int): number of hidden units (latent variables of the model)
  
  Returns:
      tf.Tensor, tf.Tensor, tf.Tensor:
      - `w` of size (n_visible, n_hidden): correlation matrix initialized by sampling from a normal distribution with zero mean and given variance init_stdv.
      - `bv` of size (1, n_visible): visible units' bias, initialized to zero.
      - `bh` of size (1, n_hidden): hidden units' bias, initiliazed to zero.
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code: def init_parameters(self):
      """Initialize the parameters of the model.
  
      This is a single layer model with two biases. So we have a rectangular matrix w_{ij} and
      two bias vectors to initialize.
  
      Args:
          n_visible (int): number of visible units (input layer)
          n_hidden (int): number of hidden units (latent variables of the model)
  
      Returns:
          tf.Tensor, tf.Tensor, tf.Tensor:
          - `w` of size (n_visible, n_hidden): correlation matrix initialized by sampling from a normal distribution with zero mean and given variance init_stdv.
          - `bv` of size (1, n_visible): visible units' bias, initialized to zero.
          - `bh` of size (1, n_hidden): hidden units' bias, initiliazed to zero.
      """
      with tf.compat.v1.variable_scope("Network_parameters"):
          self.w = tf.compat.v1.get_variable(
              "weight",
              [self.n_visible, self.n_hidden],
              initializer=tf.compat.v1.random_normal_initializer(
                  stddev=self.stdv, seed=self.seed
              ),
              dtype="float32",
          )
  
          self.bv = tf.compat.v1.get_variable(
              "v_bias",
              [1, self.n_visible],
              initializer=tf.compat.v1.zeros_initializer(),
              dtype="float32",
          )
  
          self.bh = tf.compat.v1.get_variable(
              "h_bias",
              [1, self.n_hidden],
              initializer=tf.compat.v1.zeros_initializer(),
              dtype="float32",
          )
  
imports: import numpy
  import tensorflow
  import logging
  import os
  from pathlib import Path
label_desc: Use image node_id 7 for calling the RBM obj's underlying member method code with example usage: obj.init_parameters() without return types