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장
오토인코더와 GAN을 사용한 표현 학습과 생성적 학습
conv_encoder = keras.models.Sequential([
keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
keras.layers.Conv2D(16, kernel_size=3, padding="same", activation="selu"),
keras.layers.MaxPool2D(pool_size=2),
keras.layers.Conv2D(32, kernel_size=3, padding="same", activation="selu"),
keras.layers.MaxPool2D(pool_size=2),
keras.layers.Conv2D(64, kernel_size=3, padding="same", activation="selu"),
keras.layers.MaxPool2D
(pool_size=2)
])
conv_decoder = keras.models.Sequential([
keras.layers.Conv2DTranspose(32, kernel_size=3, strides=2, padding="valid",
activation="selu",
input_shape=[3, 3, 64]),
keras.layers.Conv2DTranspose ...