March 2019
Intermediate to advanced
576 pages
20h 51m
Portuguese (Portugal, Brazil)
Content preview from Mãos à Obra: Aprendizado de Máquina com Scikit-Learn e TensorFlow
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436 | Capítulo 15: Autoencoders
Implementação do TensorFlow
É possível implementar um autoencoder empilhado muito parecido com um MLP profundo normal
aplicando as mesmas técnicas que utilizamos no Capítulo 11 para o treinamento de redes profundas.
Por exemplo, o código a seguir com a utilização da inicialização He, a função de ativação ELU e a
regularização ℓ
2
cria um autoencoder empilhado para o MNIST. O código deve parecer bem
familiar, porém não existem labels (sem
y):
from functools import partial
n_inputs = 28 * 28 # para MNIST
n_hidden1 = 300
n_hidden2 = 150 # códigos
n_hidden3 = n_hidden1
n_outputs = n_inputs
learning_rate = 0.01
l2_reg = 0.0001 ...
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