개요

모델 파라미터들

Generator

def _build_generator(self) -> Model:
    start_pix_x = 4
    start_pix_y = 4
    kernel_ini = RandomNormal(mean=0.0, stddev=0.02)

    inputs = Input(shape=self.noise_shape)
    x = Dense(
        units=256*start_pix_x*start_pix_y,
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(inputs)
    x = LeakyReLU(alpha=0.2)(x)
    x = Reshape((start_pix_x, start_pix_y, 256))(x)
    x = Conv2DTranspose(
        filters=128,
        kernel_size=4,
        strides=2,
        padding='same',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    # x = BatchNormalization(axis=3)(x)
    x = Conv2DTranspose(
        filters=128,
        kernel_size=4,
        strides=2,
        padding='same',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    # x = BatchNormalization(axis=3)(x)
    x = Conv2DTranspose(
        filters=128,
        kernel_size=4,
        strides=2,
        padding='same',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    x = Conv2D(
        filters=3,
        kernel_size=3,
        padding='same',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)

    y = Activation('tanh')(x)

    model = Model(inputs, y)
    if self.verbose:
        model.summary()

    return model

Discriminator

def _build_discriminator(self) -> Model:
    kernel_ini = RandomNormal(mean=0.0, stddev=0.02)
    inputs = Input(shape=self.shape)
    x = GaussianNoise(stddev=0.05)(inputs)  # prevent d from overfitting.
    x = Conv2D(
        filters=64,
        kernel_size=3,
        padding='SAME',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    # x = Dropout(0.5)(x)
    x = Conv2D(
        filters=128,
        kernel_size=3,
        strides=2,
        padding='SAME',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    # x = Dropout(0.5)(x)
    # x = BatchNormalization(axis=3)(x)
    x = Conv2D(
        filters=128,
        kernel_size=3,
        strides=2,
        padding='SAME',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)
    # x = Dropout(0.5)(x)
    # x = BatchNormalization(axis=3)(x)
    x = Conv2D(
        filters=256,
        kernel_size=3,
        strides=2,
        padding='SAME',
        kernel_initializer=kernel_ini,
        bias_initializer='zeros')(x)
    x = LeakyReLU(alpha=0.2)(x)

    x = Flatten()(x)
    features = Dropout(0.4)(x)

    validity = Dense(1, activation='sigmoid')(features)

    model4d = Model(inputs, validity)
    model4g = Model(inputs, validity)
    if self.verbose:
        model4d.summary()

    return model4d, model4g

출력 결과

결론