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Hi I am trying to understand how the loss function for Bayesian Neural Networks (BNN) is computed. In the TensorFlow documentation they illustrate a BNN in practice where they train the network to minimise the negative of the ELBO (as seen below).

import tensorflow as tf
import tensorflow_probability as tfp

model = tf.keras.Sequential([
    tf.keras.layers.Reshape([32, 32, 3]),
    tfp.layers.Convolution2DFlipout(
        64, kernel_size=5, padding='SAME', activation=tf.nn.relu),
    tf.keras.layers.MaxPooling2D(pool_size=[2, 2],
                                 strides=[2, 2],
                                 padding='SAME'),
    tf.keras.layers.Flatten(),
    tfp.layers.DenseFlipout(10),
])

logits = model(features)
neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits(
    labels=labels, logits=logits)
kl = sum(model.losses)
loss = neg_log_likelihood + kl
train_op = tf.train.AdamOptimizer().minimize(loss)

However, they seem to be computing the KL divergence as the sum of the losses of the network weights i.e. kl = sum(model.losses). This is not how the KL divergence should be computed.

They repeat it here too :

  # Compute the -ELBO as the loss, averaged over the batch size.
  neg_log_likelihood = -tf.reduce_mean(labels_distribution.log_prob(labels))
  kl = sum(neural_net.losses) / mnist_data.train.num_examples
  elbo_loss = neg_log_likelihood + kl

Am I missing something very basic? The exact KL divergence is something which is quite difficult to compute unless you make assumptions about the underlying probability distribution of the weights such as it following a Normal distribution in which case the KL divergence would be computed as:

enter image description here

where $\theta$ is the weights, $\mu$ is the mean weight, and $\sigma$ is the standard deviation of the weight and $\mu'$ and $\sigma'$ are the means and standard deviations after the updates.

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tfp.layers computes the KL terms and adds them to model.losses automatically.

Those layers call this function here which ends up computing the KL value as you've written it out.

As you can see in the documentation, the prior defaults to the standard normal distribution, and the posterior is approximated with a mean field distribution.

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    $\begingroup$ I think the OP is struggling to understand what is done and why rather than looking for a code suggestion so I am not sure your answer is going to get him/her much further. $\endgroup$ – mdewey Dec 10 '18 at 17:05
  • $\begingroup$ i expanded a bit $\endgroup$ – shimao Dec 10 '18 at 17:55
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    $\begingroup$ Thanks for the answer but for convolutional neural networks how are they computing the KL divergence? Wouldn't the KL divergence of the filters of a convolutional neural network be different to the KL divergence of the weights on a multilayer perceptron? $\endgroup$ – Mellow Dec 10 '18 at 18:00
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    $\begingroup$ @Mellow I don't see how -- sure, the number of parameters changes, but you can use the same gaussian prior everywhere. $\endgroup$ – shimao Dec 10 '18 at 18:01
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    $\begingroup$ @Mellow i'm not an expert on bayesian DL but as far as I can tell the priors are the same for CNNs as well -- it's just that the algorithm for performing variational inference changes a bit. $\endgroup$ – shimao Dec 10 '18 at 18:15

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