def _create_variables_no_pretrain(self, n_features):
        """Create model variables (no previous unsupervised pretraining).

        :param n_features: number of features
        :return: self
        """
        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)))
Create model variables (no previous unsupervised pretraining).

        :param n_features: number of features
        :return: self
        
yadlt/models/boltzmann/deep_autoencoder.py