Keras 变分自动编码器 - 潜在数据答案

【问题标题】：Keras Variational Autoenoder - Latent dataKeras 变分自动编码器 - 潜在数据
【发布时间】：2019-03-09 15:16:15
【问题描述】：

我在使用 Keras 变分自动编码器时遇到了困难，那是在他们的 GitHub 上。我正在尝试获取瓶颈中的数据，然后将其发送到解码器的输入端。我是否正确，在学习模型之后，您必须对输入数据进行编码，并查看下面的“Z”变量的结果：

# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
for i in range(2):
filters *= 2
    x = Conv2D(filters=filters,
           kernel_size=kernel_size,
           activation='relu',
           strides=2,
           padding='same')(x)

# shape info needed to build decoder model
shape = K.int_shape(x)

# generate latent vector Q(z|X)
x = Flatten()(x)
x = Dense(16, activation='relu')(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)

# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])

# instantiate encoder model
encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')

# build decoder model
latent_inputs = Input(shape=(latent_dim,), name='z_sampling')
x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)
x = Reshape((shape[1], shape[2], shape[3]))(x)

for i in range(2):
    x = Conv2DTranspose(filters=filters,
                    kernel_size=kernel_size,
                    activation='relu',
                    strides=2,
                    padding='same')(x)
    filters //= 2

outputs = Conv2DTranspose(filters=1,
                      kernel_size=kernel_size,
                      activation='sigmoid',
                      padding='same',
                      name='decoder_output')(x)

# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')

# instantiate VAE model
outputs = decoder(encoder(inputs)[2])
vae = Model(inputs, outputs, name='vae')

# __main__
models = (encoder, decoder)
reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))

reconstruction_loss *= original_dim
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='rmsprop')

vae.fit(x_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, None))

谢谢。

【问题讨论】：

标签： python tensorflow keras deep-learning autoencoder

【解决方案1】：

请按照 Keras 作者编写的 AutoEncoder 教程进行操作。最后一部分处理 VAE 并逐步解释。

https://blog.keras.io/building-autoencoders-in-keras.html

由于学习了分布 Z，您可以从中采样以生成新的数字。

【讨论】：