def _define_head(self):
    head_model = tf.keras.models.Sequential(name='DiscriminatorHead')
    head_model.add(layers.Dense(units=2048, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=2048, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=1024, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=512, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=args.num_classes, activation=None, name='Logits'))
    return head_model
def _define_head(self):
    head_model = tf.keras.models.Sequential(name='DiscriminatorHead')
    head_model.add(layers.Dense(units=2048, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=2048, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=1024, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=512, activation='relu'))
    head_model.add(layers.Dropout(rate=0.5))
    head_model.add(layers.Dense(units=args.num_classes, activation=None, name='Logits'))
    return head_model