Next up, we will build the second main component in the generative adversarial network, which is the discriminator. The discriminator is pretty much the same as the generator, but instead of using the tanh activation function, we will be using the sigmoid activation function; it will produce a binary output that will represent the judgment of the discriminator on the input image:

def discriminator(disc_input, num_hiddern_units=128, reuse_vars=False, leaky_relu_alpha=0.01):
    ''' Building the discriminator part of the network
    
        Function Arguments
        ---------
        disc_input : discrminator input tensor
        num_hiddern_units : Number of neurons/units in the hidden layer
        reuse_vars : Reuse variables with tf.variable_scope
        leaky_relu_alpha : leaky ReLU parameter
        
        Function Returns
        -------
        sigmoid_out, logits_layer: 
    '''
    with tf.variable_scope('discriminator', reuse=reuse_vars):
        
        # Defining the generator hidden layer
        hidden_layer_1 = tf.layers.dense(disc_input, num_hiddern_units, activation=None)
        
        # Feeding the output of hidden_layer_1 to leaky relu
        hidden_layer_1 = tf.maximum(hidden_layer_1, leaky_relu_alpha*hidden_layer_1)
        
        logits_layer = tf.layers.dense(hidden_layer_1, 1, activation=None)
        sigmoid_out = tf.nn.sigmoid(logits_layer)
        
        return sigmoid_out, logits_layer