概要

時間がないので，GANの仕組みなどはとりあえず割愛
generatorとdiscriminatorの設定を書く
味噌はgenerator，discriminatorともに活性化関数にLeakyReLuを使う
学習プロセス全体のソースコードはgithubにアップロード予定です（準備中です申し訳ありません）

モデルパラメータ達

Generator

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

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

出力結果

出力結果の行はクラスに対応しています．
DCGANはただ画像を生成するだけですが，元の画像で構築した学習モデルに生成画像を入力して予測されたラベルによってラベリングして，予測クラスごとに画像を出力させています．
generatorにLeakyReLUをいれることで，物体の対象がよりしっかりと生成できてる感がある感じがします．

結論

突貫でやったので，詳しく後日ちゃんと書きたいと思います．