#!/usr/bin/env python

# -*- coding: utf-8 -*-

# Import useful packages

import tensorflow as tf

def DNN(Input, keep_prob, weights_1, biases_1, weights_2, biases_2):

    '''

    Args:

        Input: The input EEG signals

        keep_prob: The Keep probability of Dropout

        weights_1: The Weights of first fully-connected layer

        biases_1: The biases of first fully-connected layer

        weights_2: The Weights of second fully-connected layer

        biases_2: The biases of second fully-connected layer

    Returns:

        FC_2: Final prediction of DNN Model

        FC_1: Extracted features from the first fully connected layer

    '''

    # First fully-connected layer

    FC_1 = tf.matmul(Input, weights_1) + biases_1

    FC_1 = tf.layers.batch_normalization(FC_1, training=True)

    FC_1 = tf.nn.softplus(FC_1)

    FC_1 = tf.nn.dropout(FC_1, keep_prob)

    # Second fully-connected layer

    FC_2 = tf.matmul(FC_1, weights_2) + biases_2

    FC_2 = tf.nn.softmax(FC_2)

    return FC_2, FC_1