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python-kerasHow can I use Python and Keras to create a Variational Autoencoder (VAE)?

A Variational Autoencoder (VAE) is a type of generative model which can be used to reconstruct high-dimensional data points from a lower-dimensional latent space representation. This is done by using a neural network to learn the distribution of the data points in the latent space. To create a VAE using Python and Keras, we need to define the encoder and decoder networks, and then combine them into a VAE model.

Example code

# define the encoder network
inputs = Input(shape=(784,))
encoded = Dense(256, activation='relu')(inputs)
encoded = Dense(128, activation='relu')(encoded)
z_mean = Dense(2)(encoded)
z_log_var = Dense(2)(encoded)

# define the decoder network
latent_inputs = Input(shape=(2,))
decoded = Dense(128, activation='relu')(latent_inputs)
decoded = Dense(256, activation='relu')(decoded)
outputs = Dense(784, activation='sigmoid')(decoded)

# combine the encoder and decoder into a VAE
encoder = Model(inputs, [z_mean, z_log_var], name='encoder')
decoder = Model(latent_inputs, outputs, name='decoder')

outputs = decoder(encoder(inputs)[0])
vae = Model(inputs, outputs, name='vae')

The code above defines the encoder and decoder networks, and combines them into a VAE model. The encoder network takes an input of size 784 (corresponding to the flattened image size of 28x28) and outputs two values: z_mean and z_log_var. These two values are then used as inputs to the decoder network, which reconstructs the original image.

Code explanation

  1. Define the encoder network: inputs, encoded, z_mean, z_log_var
  2. Define the decoder network: latent_inputs, decoded, outputs
  3. Combine the encoder and decoder into a VAE: encoder, decoder, vae

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