多任务卷积变分自编码器实现无线电层析成像

Question

我想寻找有关用卷积变分自编码器实现无线电层析成像的示例及代码，最好是可以实现多任务的（层析成像、参数估计、噪声分类）

Answer 1

可以使用深度学习框架如TensorFlow或PyTorch来实现。比如这个是用TensorFlow实现的：

import tensorflow as tf
from tensorflow.keras import layers

latent_dim = 32

# Encoder
encoder_inputs = tf.keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(32, 3, activation="relu", strides=2, padding="same")(encoder_inputs)
x = layers.Conv2D(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Flatten()(x)
x = layers.Dense(16, activation="relu")(x)
z_mean = layers.Dense(latent_dim, name="z_mean")(x)
z_log_var = layers.Dense(latent_dim, name="z_log_var")(x)

# Sampling function
def sampling(args):
    z_mean, z_log_var = args
    batch = tf.shape(z_mean)[0]
    dim = tf.shape(z_mean)[1]
    epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
    return z_mean + tf.exp(0.5 * z_log_var) * epsilon
  
# Reparameterization trick
z = layers.Lambda(sampling)([z_mean, z_log_var])

# Decoder
decoder_inputs = layers.Input(shape=(latent_dim,))
x = layers.Dense(7 * 7 * 64, activation="relu")(decoder_inputs)
x = layers.Reshape((7, 7, 64))(x)
x = layers.Conv2DTranspose(64, 3, activation="relu", strides=2, padding="same")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu", strides=2, padding="same")(x)
decoder_outputs = layers.Conv2DTranspose(1, 3, activation="sigmoid", padding="same")(x)

# Define the CVAE model
cvae = tf.keras.Model(encoder_inputs, decoder_outputs)

# Define the loss function
def vae_loss(encoder_inputs, decoder_outputs, z_mean, z_log_var):
    reconstruction_loss = tf.keras.losses.binary_crossentropy(encoder_inputs, decoder_outputs)
    reconstruction_loss *= 28 * 28
    kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
    kl_loss = tf.reduce_sum(kl_loss, axis=-1)
    kl_loss *= -0.5
    return tf.reduce_mean(reconstruction_loss + kl_loss)

cvae.compile(optimizer=tf.keras.optimizers.Adam(), loss=vae_loss)

# Train the model
cvae.fit(train_data, train_data, epochs=10, batch_size=128)