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):

            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)
Create model variables (no previous unsupervised pretraining).

        :param n_features: number of features
        :return: self
        
yadlt/models/autoencoders/stacked_denoising_autoencoder.py