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

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)
