discriminator.py

def get_disc_normal(image_shape=(64,64,3)):
    dropout_prob = 0.4
    kernel_init = 'glorot_uniform'
    dis_input = Input(shape = image_shape)
    
    # Conv layer 1:
    discriminator = Conv2D(filters = 64, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(dis_input)
    discriminator = LeakyReLU(0.2)(discriminator)
    # Conv layer 2:
    discriminator = Conv2D(filters = 128, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
    discriminator = BatchNormalization(momentum = 0.5)(discriminator)
    discriminator = LeakyReLU(0.2)(discriminator)
    # Conv layer 3:   
    discriminator = Conv2D(filters = 256, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
    discriminator = BatchNormalization(momentum = 0.5)(discriminator)
    discriminator = LeakyReLU(0.2)(discriminator)
    # Conv layer 4:
    discriminator = Conv2D(filters = 512, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(discriminator)
    discriminator = BatchNormalization(momentum = 0.5)(discriminator)
    discriminator = LeakyReLU(0.2)(discriminator)#discriminator = MaxPooling2D(pool_size=(2, 2))(discriminator)
    # Flatten
    discriminator = Flatten()(discriminator)
    # Dense Layer
    discriminator = Dense(1)(discriminator)
    # Sigmoid Activation
    discriminator = Activation('sigmoid')(discriminator)
    # Optimizer and Compiling model
    dis_opt = Adam(lr=0.0002, beta_1=0.5)
    discriminator_model = Model(input = dis_input, output = discriminator)
    discriminator_model.compile(loss='binary_crossentropy', optimizer=dis_opt, metrics=['accuracy'])
    discriminator_model.summary()
    return discriminator_model