import tensorflow as tf

        output = self.activation(net_input) # output: p_s * b_s * o_s
        return output

    @property
    def regularization(self):
        return self.model_lam * (
            self.model_prob * tf.reduce_sum(tf.square(self.model_W)) +
            tf.reduce_sum(tf.square(self.model_b))
        )

# def generate_dropout_mask_placeholders(x_dim, hidden_sizes=(32,)):
#     dropout_mask_placeholders = []
#     for l, size in enumerate((x_dim, *hidden_sizes)):
#         dropout_mask_placeholders.append(tf.placeholder(dtype=tf.float32, shape=(size,),
