May 2020
Intermediate to advanced
576 pages
26h 55m
French
Content preview from Deep Learning avec Keras et TensorFlow
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344
Chapitre 9. Apprentissage de représentations et apprentissage génératif
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(16, kernel_size=3, strides=2, padding="same",
activation="selu"),
keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding="same",
activation="sigmoid"),
keras.layers.Reshape([28, 28])
])
conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
9.5 AUTOENCODEURS RÉCURRENTS
Si vous devez construire un autoencodeur pour des séquences, comme des séries
chronologiques ou ...
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