'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):
        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)))
