This article is for teaching myself how to implement Adversarial Variational Bayes (AVB) as a beginner at Tensorflow. The code is heavily drawn on this blog post. We will be reproduce the synthetic dataset experiment in the original paper.

#

Graphical explanation

We will be constructing a neural network as the **Fig. 1** shows. The input data contains four types of 2x2 binary images (). Although they seem to be 2D image data, we have to stretch them into 1D arrays before feeding them to the neural network. Therefore, we will only use 1D arrays as input data throughput the implementation.

The encoder extracts features from the input data; the distribution of extracted features is called posterior, and operation of running input data through encoder is called inference in terms of probability. Accordingly, running data on latent space (where posterior exists) through decoder is called generation.

Prior is a distribution to which you want to fit the posterior. By training the whole network, the shape of posterior becomes closer and closer to that of prior, which is what you want. The point is that by training this AVB architecture, posterior becomes not only closer to prior but also more "expressive" than those posteriors that generated by other types of autoencoder architectures (such as variational autoencoder), meaning that posterior looks more self-organized (?) so that the decoded images can be sharper.

**Figure 1. Network architecture**

The details of the theories will be discussed elsewhere, so now we'll focus on the implementation.

#

Preparation for implementation

The following programs are supposed to be installed.

- Tensorflow

- Tensorflow probability

- Anaconda

The code was tested on python >3.6, ipython 6.5, Tensorflow >1.12 and Tensorflow probability 0.5.

##

Loading packages

```
import tensorflow as tf
```

import numpy as np

import tensorflow_probability as tfp

from tqdm import tqdm

from matplotlib import pyplot as plt
# function shorthand

tfc = tf.contrib

tfd = tfp.distributions

graph_replace = tfc.graph_editor.graph_replace

##

Parameters

```
batch_size = 512
```

latent_dim = 2 # dimension of latent space

input_dim = 4 # dimension of input data

n_layer = 2 # number of hidden layers

n_unit = 256 # number of hidden units

##

Input data

```
# number of data for each class; here we have 4 classes
```

points_per_class = batch_size / input_dim

# create labels for the 4 classes

labels = np.concatenate([[i] * int(points_per_class)

for i in range(input_dim)])

# create dataset

np_data = np.eye(input_dim, dtype=np.float32)[labels]

#

Model construction

We will now start constructing a neural network described as previous figure. Here you will learn some basics of how to use Tensorflow including:

- How to use
`variable_scope`

- How to use
`fully_connected`

layers - How to use
`layers.repeat`

to repeatedly pile up the same layer - How to use
`graph_replace`

to reuse the same network with different veriables - How to use
`tfp.distributions`

to randomly sample or calculate probability from a certain distribution - How to set up loss functions
- How to optimize only specific variables
- How to run
`tf.Session`

in a for loop

##

Making input data a constant

To be calculated by Tensorflow, all variables have to be tensors. Since `np_data`

, we created above, is in numpy format, we have to convert it to a tensor. Also because the input data is very simple & artificial (meaning we are not using real images), we can make the input data a constant, which makes coding a little easier (to understand).

```
x = tf.constant(np_data)
```

Check the shape on ipython

```
In [9]: np_data.shape
```

Out[9]: (512, 4)
In [9]: x.shape

Out[9]: TensorShape([Dimension(512), Dimension(4)])

##

Encoder

Now we need to create "noise" from a Gaussian distribution for the input of encoder (**Fig. 1**). We will create an object with `tfp.distributions`

, from which we can always grab (formally called "sample") some values/probability that are associated with a certain distribution. Since every input data has 4 pixels, we need to sample noise from a 4D Gaussian distribution.

We first start from creating a multivariate Gaussian distribution with `tfd.MultivariateNormalDiag`

. By plugging 4 values of means (`loc`

) and standard deviations (`scale_diag`

) in this function, it creates a 4D Gaussian distribution generator (named as `Gauss4D`

) with the designated means and standard deviations. Here, we set means and standard deviations to be all 0 and 1 respectively. Then, you can sample values equivalent to `batch_size`

from this 4D Gaussian distribution by running `Gauss4D.sample(batch_size)`

as shown below.

```
Gauss4D = tfd.MultivariateNormalDiag(loc=tf.zeros(input_dim),
```

scale_diag=tf.ones(input_dim))

noise = Gauss4D.sample(batch_size)

Note that `Gauss4D`

is not a tensor, so `Gauss4D`

cannot be used as a variable.

Now we start constructing neural network.

We start by setting up `variable_scope`

, which is a name for functions and variables. Giving names to functions and variables is important particularly when you train a GAN-like architecture where variables are trained selectively in each round. By calling their names, you can selectively train specific functions and variables. With `with`

statement, all the variables/layers/functions share the same name. By turning on `reuse = tf.AUTO_REUSE`

, you can reuse the same name (but is not necessary in this article).

```
with tf.variable_scope('encoder', reuse=tf.AUTO_REUSE):
```

enc = tf.concat([x, noise], 1) # concatenate input data and noise

enc = tfc.layers.repeat(enc, n_layer, tfc.layers.fully_connected, n_unit)

enc = tfc.layers.fully_connected(enc, latent_dim, activation_fn = None)

We use `repeat`

to replicate & connect the same layer. Here, we generate & connect `n_layers = 2`

layers of `tfc.layers.fully_connected`

which has `n_unit = 256`

of outputs. When using `tfc.layers.fully_connected`

, the activation function is set to ReLU by default. Another `tfc.layers.fully_connected`

is used to reduce the output down to the dimension of latent space (which is `latent_dim`

). No activation function is given for this layer because it is only for reducing the dimension of output.

##

Decoder

Now we repeat the same thing to create decoder. Again, using `variable_scope`

to set up name, using `repeat`

to create 2-layer fully connected neural network. But this time we need the final layer to have sigmoid activation function because the output of decoder has to fit to input data which is binary images. The coefficients are to limit the range of the output value to prevent NaN.

```
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE):
```

dec = tfc.layers.repeat(enc, n_layer, tfc.layers.fully_connected, n_unit)

# clipping is necessary to prevent NaN when dec is too large

dec = 1e-6 + (1 - 2e-6) * tfc.layers.fully_connected(dec, input_dim,

activation_fn = tf.nn.sigmoid)

# calculate log_probability in Bernoulli distribution

log_probs = tfd.Bernoulli(probs=dec).log_prob(x)

Because each input image only allow 1 pixel to be 1 and the rest has to be 0, we use Bernoulli distribution to evaluate how generated image is close to input image. `tfd.Bernoulli(probs=dec)`

creates a Bernoulli distribution with its shape defined by `dec`

. By using `.log_prob(x)`

, it gives the log probability of sampling `x`

in such distribution.

##

Discriminator

As **Fig. 1** shows, discriminator discriminates posterior from prior which is another Gaussian distribution but this time in 2D. So we start by creating a 2D Gaussian distribution.

```
Gauss2D = tfd.MultivariateNormalDiag(loc=tf.zeros(latent_dim),
```

scale_diag=tf.ones(latent_dim))

prior = Gauss2D.sample(batch_size)

Posterior and prior are combined with input data when they are discriminated by discriminator (**Fig. 1**). So we first have to concatenate them with input data `x`

. To make them centered at 0, we perform a little ocnversion `2 * x - 1`

when they are concatenated. `x`

will range from 1 to -1. (This is not mandatory)

```
dis_pr = tf.concat([2*x-1, prior], 1)
```

dis_po = tf.concat([2*x-1, enc], 1)

with tf.variable_scope('discriminator', reuse=tf.AUTO_REUSE):

dis = tfc.layers.repeat(dis_pr, n_layer,

tfc.layers.fully_connected, n_unit)

log_d_prior = tfc.layers.fully_connected(dis, 1, activation_fn = None)
# use graph_replace to reuse the same network

dis2 = graph_replace(dis, {dis_pr: dis_po})

log_d_posterior = graph_replace(log_d_prior, {dis: dis2})

Although there are two inputs (posterior and prior) here, both of them have to be passed to a single network. This is made possible by using `graph_replace`

. This function works as if you replace the input variable with another: `graph_replace(<target function>, {<original var>: <new var>})`

.

##

Loss functions

Let's define the loss function for discriminator first because it's easier. Here, we want to perform binary classification, meaning that the labels will be only 0 or 1. We want `log_d_prior`

and `log_d_posterior`

and be close to 0 and 1 respectively, not the other way round. We will see the reason soon.

Using `sigmoid_cross_entropy_with_logits`

, you can calculate the cross entropy between `logits`

and `labels`

. Simply summing up those two sigmoid cross entropy followed by taking the mean of them makes the discriminator loss.

```
disc_loss = tf.reduce_mean(
```

tf.nn.sigmoid_cross_entropy_with_logits(

logits = log_d_posterior,

labels = tf.ones_like(log_d_posterior)) +

tf.nn.sigmoid_cross_entropy_with_logits(

logits = log_d_prior,

labels = tf.zeros_like(log_d_prior)))

Since `log_probs`

is the (log) probability of how the image generated by decoder is close to real input image, it will be trained to be close to 1. In the meanwhile, `log_d_posterior`

is fit to 1 as well. So both values will compete each other, making the whole network adversarial.

```
# decoder loss
```

recon_likelihood = tf.reduce_sum(log_probs, axis=1)

# generator loss

gen_loss = tf.reduce_mean(log_d_posterior) - tf.reduce_mean(recon_likelihood)

##

Optimization

Now we collect trainable variables separately by names because we want to train them separately.

```
qvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "encoder")
```

pvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "decoder")

dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "discriminator")

Then, we set up optimizers. Here you can define multiple optimizer with different parameters. By using `opt.minimize`

you can also indicate which variables in which functions to be optimized.

```
opt = tf.train.AdamOptimizer(2e-4, beta1=0.5)
```

opt2 = tf.train.AdamOptimizer(1e-3, beta1=0.5)

train_gen_op = opt.minimize(gen_loss, var_list = qvars + pvars)

train_disc_op = opt2.minimize(disc_loss, var_list = dvars)

#

Training

Now, everything is ready. Before start training, you have to create a session because Tensorflow is operated by session. Also, all variables have to be initialized if you want to freshly train a model. Create a session by calling `tf.Sess()`

, and run everything with `sess.run(<function or variables>)`

. Basically values in any Tensorflow tensors are not visible without running `sess.run()`

.

```
init_g = tf.global_variables_initializer()
```

init_l = tf.local_variables_initializer()

sess = tf.Session()

sess.run(init_g)

sess.run(init_l)

By running optimizer, you train the network. By running loss functions independently, you get outputs of losses each round. So recording these losses you can draw training curves. You can monitor any values by passing the variables to `sess.run()`

.

Note that we rung `train_gen_op`

and `train_disc_op`

together, but each optimizer actually optimize different trainable parameters as we designated before. That's why we don't need to worry about freezing parameters. Now you see how convenient to use `variable_scope`

to specifically call variables.

```
gen_loss_list = []
```

disc_loss_list = []

n_epoch = 2000

for i in tqdm(range(n_epoch)):

gl, dl, _, _ = sess.run([gen_loss, disc_loss, train_gen_op, train_disc_op])

gen_loss_list.append(gl)

disc_loss_list.append(dl)

By using `tqdm`

you can see a progress bar like this:

```
100%|██████████| 2000/2000 [00:08<00:00, 227.83it/s]
```

#

Results

After training ends, you can plot training curve and a latent space scatter plot.

```
plt.figure()
```

plt.plot(np.arange(n_epoch), np.asarray(gen_loss_list))

plt.plot(np.arange(n_epoch), np.asarray(disc_loss_list))

plt.legend(['generation loss','discrimination loss'])

plt.title('Training curve')

plt.xlabel('Epoch')

plt.ylabel('Loss')

We create latent space scatter plot by passing input data and randomly generated noise into encoder. Because there are only 512 data in input data (i.e. `x`

), which is a little too few, we repeat running `enc`

10 times to generate 5120 data points on latent space.

```
n_vis = 10
```

enc_test = np.vstack([sess.run(enc) for _ in range(n_vis)])

enc_test_label = np.tile(labels, (n_vis))

plt.figure()

for i in range(len(np.unique(labels))):

plt.scatter(enc_test[enc_test_label==i, 0], enc_test[enc_test_label==i, 1],

edgecolor='none', alpha=0.5, s=2)

plt.title('Latent space')

#

Reference

The following web sites (in English) significantly helped me understand AVB.

https://github.com/gdikov/adversarial-variational-bayes

https://www.inference.vc/variational-inference-with-implicit-models-part-ii-amortised-inference-2/

https://github.com/LMescheder/AdversarialVariationalBayes

Following web sites explaining the theories (in Japanese) also helped me a lot to achieve .

http://seiya-kumada.blogspot.com/2018/07/adversarial-variational-bayes.html

https://qiita.com/tmasada/items/dc9000eceff593bab213