code: def _create_variables_no_pretrain(self, n_features):
          
          self.encoding_w_ = []
          self.encoding_b_ = []
  
          for l, layer in enumerate(self.layers):
  
              w_name = 'enc-w-{}'.format(l)
              b_name = 'enc-b-{}'.format(l)
  
              if l == 0:
                  w_shape = [n_features, self.layers[l]]
              else:
                  w_shape = [self.layers[l - 1], self.layers[l]]
  
              w_init = tf.truncated_normal(shape=w_shape, stddev=0.1)
              W = tf.Variable(w_init, name=w_name)
              tf.summary.histogram(w_name, W)
              self.encoding_w_.append(W)
  
              b_init = tf.constant(0.1, shape=[self.layers[l]])
              b = tf.Variable(b_init, name=b_name)
              tf.summary.histogram(b_name, b)
              self.encoding_b_.append(b)
docs: Create model variables (no previous unsupervised pretraining).
  
          :param n_features: number of features
          :return: self