code: def _create_variables_no_pretrain(self, n_features):
          
          self.encoding_w_ = []
          self.encoding_b_ = []
  
          for l, layer in enumerate(self.layers):
  
              if l == 0:
                  self.encoding_w_.append(tf.Variable(tf.truncated_normal(
                      shape=[n_features, self.layers[l]], stddev=0.1)))
                  self.encoding_b_.append(tf.Variable(tf.truncated_normal(
                      [self.layers[l]], stddev=0.1)))
              else:
                  self.encoding_w_.append(tf.Variable(tf.truncated_normal(
                      shape=[self.layers[l - 1], self.layers[l]], stddev=0.1)))
                  self.encoding_b_.append(tf.Variable(tf.truncated_normal(
                      [self.layers[l]], stddev=0.1)))
docs: Create model variables (no previous unsupervised pretraining).
  
          :param n_features: number of features
          :return: self