generator.py

def get_gen_normal(noise_shape):
    ''' This function takes as input shape of the noise vector and creates the Keras generator    architecture.
    '''
    kernel_init = 'glorot_uniform'    
    gen_input = Input(shape = noise_shape) 
    
    # Transpose 2D conv layer 1. 
    generator = Conv2DTranspose(filters = 512, kernel_size = (4,4), strides = (1,1), padding = "valid", data_format = "channels_last", kernel_initializer = kernel_init)(gen_input)
    generator = BatchNormalization(momentum = 0.5)(generator)
    generator = LeakyReLU(0.2)(generator)
    
    # Transpose 2D conv layer 2.
    generator = Conv2DTranspose(filters = 256, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(generator)
    generator = BatchNormalization(momentum = 0.5)(generator)
    generator = LeakyReLU(0.2)(generator)
    
    # Transpose 2D conv layer 3.
    generator = Conv2DTranspose(filters = 128, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(generator)
    generator = BatchNormalization(momentum = 0.5)(generator)
    generator = LeakyReLU(0.2)(generator)
    
    # Transpose 2D conv layer 4.
    generator = Conv2DTranspose(filters = 64, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(generator)
    generator = BatchNormalization(momentum = 0.5)(generator)
    generator = LeakyReLU(0.2)(generator)
    
    # conv 2D layer 1.
    generator = Conv2D(filters = 64, kernel_size = (3,3), strides = (1,1), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(generator)
    generator = BatchNormalization(momentum = 0.5)(generator)
    generator = LeakyReLU(0.2)(generator)
    
    # Final Transpose 2D conv layer 5 to generate final image. Filter size 3 for 3 image channel
    generator = Conv2DTranspose(filters = 3, kernel_size = (4,4), strides = (2,2), padding = "same", data_format = "channels_last", kernel_initializer = kernel_init)(generator)
    
    # Tanh activation to get final normalized image
    generator = Activation('tanh')(generator)
    
    # defining the optimizer and compiling the generator model.
    gen_opt = Adam(lr=0.00015, beta_1=0.5)
    generator_model = Model(input = gen_input, output = generator)
    generator_model.compile(loss='binary_crossentropy', optimizer=gen_opt, metrics=['accuracy'])
    generator_model.summary()
    return generator_model