# Going with the Flow: An Introduction to Normalizing Flows  Normalizing Flows (NFs) (Rezende & Mohamed, 2015) learn an invertible mapping $f: X \rightarrow Z$, where $X$ is our data distribution and $Z$ is a chosen latent-distribution.

Normalizing Flows are part of the generative model family, which includes Variational Autoencoders (VAEs) (Kingma & Welling, 2013), and Generative Adversarial Networks (GANs) (Goodfellow et al., 2014). Once we learn the mapping $f$, we generate data by sampling $z \sim p_Z$ and then applying the inverse transformation, $f^{-1}(z) = x_{gen}$.

Note: $p_Z(z)$ is the probability density of sampling $z$ under the distribution $Z$.

In this blog to understand normalizing flows better, we will cover the algorithm’s theory and implement a flow model in PyTorch. But first, let us flow through the advantages and disadvantages of normalizing flows.

Note: If you are not interested in the comparison between generative models you can skip to ‘How Normalizing Flows Work’

## Why Normalizing Flows

With the amazing results shown by VAEs and GANs, why would you want to use Normalizing flows? We list the advantages below

Note: Most advantages are from the GLOW paper (Kingma & Dhariwal, 2018)

• NFs optimize the exact log-likelihood of the data, log($p_X$)
• VAEs optimize the lower bound (ELBO)
• GANs learn to fool a discriminator network
• NFs infer exact latent-variable values $z$, which are useful for downstream tasks
• The VAE infers a distribution over latent-variable values
• GANs do not have a latent-distribution
• Potential for memory savings, with NFs gradient computations scaling constant to their depth
• Both VAE’s and GAN’s gradient computations scale linearly to their depth
• NFs require only an encoder to be learned
• VAEs require encoder and decoder networks
• GANs require generative and discriminative networks

But remember what mother says, “There ain’t no such thing as a free lunch”.

Some of the downsides of normalizing flows are as follows,

• The requirements of invertibility and efficient Jacobian calculations restrict model architecture
• more on this later…
• Less resources/research on NFs compared to other generative models
• The reason for this blog!
• NFs generative results are still behind VAEs and GANs

Now let us get dirty in some theory!

# How Normalizing Flows Work

In this section, we understand the heart of Normalizing Flows.

## Probability Distribution Change of Variables

Consider a random variable $X \in \mathbb{R}^d$ (our data distribution) and an invertable transformation $f: \mathbb{R}^d \mapsto \mathbb{R}^d$

Then there is a random variable $Z \in \mathbb{R}^d$ which $f$ maps $X$ to.

Furthermore,

$P(X = x) = P(f(X) = f(x)) = P(Z = z)\tag{0}$

Now consider some interval $\beta$ over $X$. Then there exists some interval $\beta^{\prime}$ over $Z$ such that,

$P(X \in \beta) = P(Z \in \beta^{\prime})\tag{1}$ $\int_{\beta} p_X dx = \int_{\beta^{\prime}} p_Z dz\tag{2}$

For the sake of simplicity, we consider a single region.

$dx \cdot p_X(x) = dz \cdot p_Z(z) \tag{3}$ $p_X(x) = \mid\dfrac{dz}{dx}\mid \cdot p_Z(z) \tag{4}$

Note: We apply the absolute value to maintain the equality since by the probability axioms $p_X$ and $p_Z$ will always be positive.

$p_X(x) = \mid\dfrac{df(x)}{dx}\mid \cdot p_Z(f(x)) \tag{5}$ $p_X(x) = \mid det(\dfrac{df}{dx}) \mid \cdot p_Z(f(x)) \tag{6}$

Note: We use the determinant to generalize to the multivariate case ($d > 1$)

$\log(p_X(x)) = \log(\mid det(\dfrac{df}{dx}) \mid) + \log(p_Z(f(x))) \tag{7}$

Tada! To model our random variable $X$, we need to maximize the right-hand side of equation (7).

Breaking the equation down:

• $\log(\mid det(\dfrac{df}{dx}) \mid)$ is the amount of stretch/change $f$ applies to the probability distribution $p_X$.
• This term is the log determinant of the Jacobian matrix ($\dfrac{df}{dx}$). We refer to the determinant of the Jacobian matrix as the Jacobian.
• $\log(p_Z(f(x)))$ constrains $f$ to transform $x$ to the distribution $p_Z$.

Since there are no constraints on $Z$ we can choose $p_Z$! Usually, we choose $p_Z$ to be gaussian.

Now I know what your thinking, as a reader of this blog you strive for greatness and say,

‘Brennan, a single function does not satisfy me. I have a hunger for more.’

## Applying multiple functions sequentially

Fear not my readers! I will show you how we can sequentially apply multiple functions.

Let $z_n$ be the result of sequentially applying $n$ functions to $x \sim p_X$.

$z_n = f_n \circ \dots \circ f_1(x) \tag{8}$ $f = f_n \circ \dots \circ f_1 \tag{9}$

Using the handy dandy chain rule, we can modify equation (7) with equation (8) to get equation (10) as follows.

$\log(p_X(x)) = \log(\mid det(\dfrac{df}{dx}) \mid) + \log(p_Z(f(x))) \tag{7}$ $\log(p_X(x)) = \log(\prod_{i=1}^{n} \mid det(\dfrac{dz_i}{dz_{i-1}}) \mid) + \log(p_Z(f(x)))\tag{10}$

Where $x \triangleq z_0$ for conciseness.

$\log(p_X(x)) = \sum_{i=1}^{n} \log(\mid det(\dfrac{dz_i}{dz_{i-1}}) \mid) + \log(p_Z(f(x))) \tag{11}$

We want the Jacobian term to be easy to compute since we will need to compute it $n$ times.

To efficiently compute the Jacobian, the functions $f_i$ (corresponding to $z_i$) are chosen to have a lower or upper triangular Jacobian matrix. Since the determinant of a triangular matrix is the product of its diagonal, which is easy to compute.

Now that you understand the general theory of Normalizing flows, lets flow through some PyTorch code.

# The Family of Flows

For this post we will be focusing on, real-valued non-volume preserving flows (R-NVP) (Dinh et al., 2016).

Though there are many other flow functions out and about such as NICE (Dinh et al., 2014), and GLOW (Kingma & Dhariwal, 2018). For keeners wanting to learn more, I will show you to the ‘More Resources’ section at the bottom of this post which includes blog posts with more flows which may interest you.

# R-NVP Flows

We consider a single R-NVP function $f: \mathbb{R}^d \rightarrow \mathbb{R}^d$, with input $\mathbf{x} \in \mathbb{R}^d$ and output $\mathbf{z} \in \mathbb{R}^d$.

To quickly recap, in order to optimize our function $f$ to model our data distribution $p_X$, we want to know the forward pass $f$, and the Jacobian $\mid det(\dfrac{df}{dx}) \mid$.

We then will want to know the inverse of our function $f^{-1}$ so we can transform a sampled latent-value $z \sim p_Z$ to our data distribution $p_X$, generating new samples!

## Forward Pass

$f(\mathbf{x}) = \mathbf{z}\tag{12}$

The forward pass is a combination of copying values while stretching and shifting the others. First we choose some arbitrary value $k$ which satisfies $0 < k < d$ to split our input.

R-NVPs forward pass is then the following

$\mathbf{z}_{1:k} = \mathbf{x}_{1:k} \tag{13}$ $\mathbf{z}_{k+1:d} = \mathbf{x}_{k+1:d} \odot \exp(\sigma(\mathbf{x}_{1:k})) + \mu(\mathbf{x}_{1:k})\tag{14}$

Where $\sigma, \mu: \mathbb{R}^k \rightarrow \mathbb{R}^{d-k}$ and are any arbitrary functions. Hence, we will choose $\sigma$ and $\mu$ to both be deep neural networks. Below is PyTorch code of a simple implementation.

## Log Jacobian

The Jacobian matrix $\dfrac{df}{d\mathbf{x}}$ of this function will be

$\begin{bmatrix}I_d & 0 \\ \frac{d z_{k+1:d}}{d \mathbf{x}_{1:k}} & \text{diag}(\exp[\sigma(\mathbf{x}_{1:k})]) \end{bmatrix} \tag{15}$

The log determinant of such a Jacobian Matrix will be

$\log(\det(\dfrac{df}{d\mathbf{x}})) = \log(\prod_{i=1}^{d-k} \mid\exp[\sigma_i(\mathbf{x}_{1:k})]\mid) \tag{16}$ $\log(\mid\det(\dfrac{df}{d\mathbf{x}})\mid) = \sum_{i=1}^{d-k} \log(\exp[\sigma_i(\mathbf{x}_{1:k})]) \tag{17}$ $\log(\mid\det(\dfrac{df}{d\mathbf{x}})\mid) = \sum_{i=1}^{d-k} \sigma_i(\mathbf{x}_{1:k}) \tag{18}$

## Inverse

$f^{-1}(\mathbf{z}) = \mathbf{x}\tag{19}$

One of the benefits of R-NVPs compared to other flows is the ease of inverting $f$ into $f^{-1}$, which we formulate below using the forward pass of equation (14)

$\mathbf{x}_{1:k} = \mathbf{z}_{1:k} \tag{20}$ $\mathbf{x}_{k+1:d} = (\mathbf{z}_{k+1:d} - \mu(\mathbf{x}_{1:k})) \odot \exp(-\sigma(\mathbf{x}_{1:k})) \tag{21}$ $\Leftrightarrow \mathbf{x}_{k+1:d} = (\mathbf{z}_{k+1:d} - \mu(\mathbf{z}_{1:k})) \odot \exp(-\sigma(\mathbf{z}_{1:k})) \tag{22}$

## Summary

And voilà, the recipe for R-NVP is complete!

To summarize we now know how to compute $f(\mathbf{x})$, $\log(\mid\det(\dfrac{df}{d\mathbf{x}})\mid)$, and $f^{-1}(\mathbf{z})$.

Below is the full jupyter notebook with PyTorch code for model optimization and data generation.

Jupyter Notebook

Note: In the notebook the multilayer R-NVP flips the input before a forward/inverse pass for a more expressive model.

### Optimizing Model

$\log(p_X(x)) = \log(\mid det(\dfrac{df}{dx}) \mid) + \log(p_Z(f(x)))$ $\log(p_X(x)) = \sum_{i=1}^{n} \log(\mid det(\dfrac{dz_i}{dz_{i-1}}) \mid) + \log(p_Z(f(x)))$

### Generating Data from Model

$z \sim p_Z$ $x_{gen} = f^{-1}(z)$

# Conclusion

In summary, we learned how to model a data distribution to a chosen latent-distribution using an invertible function $f$. We used the change of variables formula to discover that to model our data we must maximize the Jacobian of $f$ while also constraining $f$ to our latent-distribution. We then extended this notion to sequentially applying multiple functions $f_n \circ \dots \circ f_1(x)$. Lastly, we learned about the theory and implementation of the R-NVP flow.