GAN_discriminator.py

def discriminator(x, is_reuse=False, alpha = 0.2):
    ''' Build the discriminator network.
    
        Arguments
        ---------
        x : Input tensor for the discriminator
        n_units: Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU
        
        Returns
        -------
        out, logits: 
    '''
    with tf.variable_scope("discriminator", reuse = is_reuse): 
        
        # Input layer 128*128*3 --> 64x64x64
        # Conv --> BatchNorm --> LeakyReLU   
        conv1 = tf.layers.conv2d(inputs = x,
                                filters = 64,
                                kernel_size = [5,5],
                                strides = [2,2],
                                padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name='conv1')
        
        batch_norm1 = tf.layers.batch_normalization(conv1,
                                                   training = True,
                                                   epsilon = 1e-5,
                                                     name = 'batch_norm1')

        conv1_out = tf.nn.leaky_relu(batch_norm1, alpha=alpha, name="conv1_out")
        
        
        # 64x64x64--> 32x32x128
        # Conv --> BatchNorm --> LeakyReLU   
        conv2 = tf.layers.conv2d(inputs = conv1_out,
                                filters = 128,
                                kernel_size = [5, 5],
                                strides = [2, 2],
                                padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name='conv2')
        
        batch_norm2 = tf.layers.batch_normalization(conv2,
                                                   training = True,
                                                   epsilon = 1e-5,
                                                     name = 'batch_norm2')
        
        conv2_out = tf.nn.leaky_relu(batch_norm2, alpha=alpha, name="conv2_out")

        
        
        # 32x32x128 --> 16x16x256
        # Conv --> BatchNorm --> LeakyReLU   
        conv3 = tf.layers.conv2d(inputs = conv2_out,
                                filters = 256,
                                kernel_size = [5, 5],
                                strides = [2, 2],
                                padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name='conv3')
        
        batch_norm3 = tf.layers.batch_normalization(conv3,
                                                   training = True,
                                                   epsilon = 1e-5,
                                                name = 'batch_norm3')
        
        conv3_out = tf.nn.leaky_relu(batch_norm3, alpha=alpha, name="conv3_out")

        
        
        # 16x16x256 --> 16x16x512
        # Conv --> BatchNorm --> LeakyReLU   
        conv4 = tf.layers.conv2d(inputs = conv3_out,
                                filters = 512,
                                kernel_size = [5, 5],
                                strides = [1, 1],
                                padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name='conv4')
        
        batch_norm4 = tf.layers.batch_normalization(conv4,
                                                   training = True,
                                                   epsilon = 1e-5,
                                                name = 'batch_norm4')
        
        conv4_out = tf.nn.leaky_relu(batch_norm4, alpha=alpha, name="conv4_out")

        
        
        # 16x16x512 --> 8x8x1024
        # Conv --> BatchNorm --> LeakyReLU   
        conv5 = tf.layers.conv2d(inputs = conv4_out,
                                filters = 1024,
                                kernel_size = [5, 5],
                                strides = [2, 2],
                                padding = "SAME",
                                kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
                                name='conv5')
        
        batch_norm5 = tf.layers.batch_normalization(conv5,
                                                   training = True,
                                                   epsilon = 1e-5,
                                                name = 'batch_norm5')
        
        conv5_out = tf.nn.leaky_relu(batch_norm5, alpha=alpha, name="conv5_out")

         
        # Flatten it
        flatten = tf.reshape(conv5_out, (-1, 8*8*1024))
        
        # Logits
        logits = tf.layers.dense(inputs = flatten,
                                units = 1,
                                activation = None)
        
        
        out = tf.sigmoid(logits)
        
        return out, logits