RStudio AI Weblog: Moving into the circulation: Bijectors in TensorFlow Likelihood


    As of at the moment, deep studying’s best successes have taken place within the realm of supervised studying, requiring tons and many annotated coaching knowledge. Nonetheless, knowledge doesn’t (usually) include annotations or labels. Additionally, unsupervised studying is enticing due to the analogy to human cognition.

    On this weblog thus far, we have now seen two main architectures for unsupervised studying: variational autoencoders and generative adversarial networks. Lesser recognized, however interesting for conceptual in addition to for efficiency causes are normalizing flows (Jimenez Rezende and Mohamed 2015). On this and the subsequent publish, we’ll introduce flows, specializing in how you can implement them utilizing TensorFlow Likelihood (TFP).

    In distinction to earlier posts involving TFP that accessed its performance utilizing low-level $-syntax, we now make use of tfprobability, an R wrapper within the type of keras, tensorflow and tfdatasets. A be aware concerning this bundle: It’s nonetheless underneath heavy improvement and the API might change. As of this writing, wrappers don’t but exist for all TFP modules, however all TFP performance is out there utilizing $-syntax if want be.

    Density estimation and sampling

    Again to unsupervised studying, and particularly pondering of variational autoencoders, what are the primary issues they provide us? One factor that’s seldom lacking from papers on generative strategies are footage of super-real-looking faces (or mattress rooms, or animals …). So evidently sampling (or: era) is a crucial half. If we will pattern from a mannequin and procure real-seeming entities, this implies the mannequin has realized one thing about how issues are distributed on the earth: it has realized a distribution. Within the case of variational autoencoders, there may be extra: The entities are purported to be decided by a set of distinct, disentangled (hopefully!) latent components. However this isn’t the belief within the case of normalizing flows, so we’re not going to elaborate on this right here.

    As a recap, how will we pattern from a VAE? We draw from (z), the latent variable, and run the decoder community on it. The outcome ought to – we hope – appear to be it comes from the empirical knowledge distribution. It mustn’t, nevertheless, look precisely like several of the gadgets used to coach the VAE, or else we have now not realized something helpful.

    The second factor we might get from a VAE is an evaluation of the plausibility of particular person knowledge, for use, for instance, in anomaly detection. Right here “plausibility” is obscure on goal: With VAE, we don’t have a way to compute an precise density underneath the posterior.

    What if we would like, or want, each: era of samples in addition to density estimation? That is the place normalizing flows are available in.

    Normalizing flows

    A circulation is a sequence of differentiable, invertible mappings from knowledge to a “good” distribution, one thing we will simply pattern from and use to calculate a density. Let’s take as instance the canonical strategy to generate samples from some distribution, the exponential, say.

    We begin by asking our random quantity generator for some quantity between 0 and 1:

    This quantity we deal with as coming from a cumulative likelihood distribution (CDF) – from an exponential CDF, to be exact. Now that we have now a price from the CDF, all we have to do is map that “again” to a price. That mapping CDF -> worth we’re in search of is simply the inverse of the CDF of an exponential distribution, the CDF being

    [F(x) = 1 – e^{-lambda x}]

    The inverse then is

    F^{-1}(u) = -frac{1}{lambda} ln (1 – u)

    which suggests we might get our exponential pattern doing

    lambda <- 0.5 # decide some lambda
    x <- -1/lambda * log(1-u)

    We see the CDF is definitely a circulation (or a constructing block thereof, if we image most flows as comprising a number of transformations), since

    • It maps knowledge to a uniform distribution between 0 and 1, permitting to evaluate knowledge chance.
    • Conversely, it maps a likelihood to an precise worth, thus permitting to generate samples.

    From this instance, we see why a circulation ought to be invertible, however we don’t but see why it ought to be differentiable. This may turn into clear shortly, however first let’s check out how flows can be found in tfprobability.


    TFP comes with a treasure trove of transformations, referred to as bijectors, starting from easy computations like exponentiation to extra advanced ones just like the discrete cosine remodel.

    To get began, let’s use tfprobability to generate samples from the conventional distribution. There’s a bijector tfb_normal_cdf() that takes enter knowledge to the interval ([0,1]). Its inverse remodel then yields a random variable with the usual regular distribution:

    Conversely, we will use this bijector to find out the (log) likelihood of a pattern from the conventional distribution. We’ll test in opposition to an easy use of tfd_normal within the distributions module:

    x <- 2.01
    d_n <- tfd_normal(loc = 0, scale = 1) 
    d_n %>% tfd_log_prob(x) %>% as.numeric() # -2.938989

    To acquire that very same log likelihood from the bijector, we add two parts:

    • Firstly, we run the pattern by means of the ahead transformation and compute log likelihood underneath the uniform distribution.
    • Secondly, as we’re utilizing the uniform distribution to find out likelihood of a standard pattern, we have to observe how likelihood adjustments underneath this transformation. That is accomplished by calling tfb_forward_log_det_jacobian (to be additional elaborated on beneath).
    b <- tfb_normal_cdf()
    d_u <- tfd_uniform()
    l <- d_u %>% tfd_log_prob(b %>% tfb_forward(x))
    j <- b %>% tfb_forward_log_det_jacobian(x, event_ndims = 0)
    (l + j) %>% as.numeric() # -2.938989

    Why does this work? Let’s get some background.

    Likelihood mass is conserved

    Flows are primarily based on the precept that underneath transformation, likelihood mass is conserved. Say we have now a circulation from (x) to (z): [z = f(x)]

    Suppose we pattern from (z) after which, compute the inverse remodel to acquire (x). We all know the likelihood of (z). What’s the likelihood that (x), the remodeled pattern, lies between (x_0) and (x_0 + dx)?

    This likelihood is (p(x) dx), the density instances the size of the interval. This has to equal the likelihood that (z) lies between (f(x)) and (f(x + dx)). That new interval has size (f'(x) dx), so:

    [p(x) dx = p(z) f'(x) dx]

    Or equivalently

    [p(x) = p(z) * dz/dx]

    Thus, the pattern likelihood (p(x)) is decided by the bottom likelihood (p(z)) of the remodeled distribution, multiplied by how a lot the circulation stretches area.

    The identical goes in larger dimensions: Once more, the circulation is in regards to the change in likelihood quantity between the (z) and (y) areas:

    [p(x) = p(z) frac{vol(dz)}{vol(dx)}]

    In larger dimensions, the Jacobian replaces the by-product. Then, the change in quantity is captured by absolutely the worth of its determinant:

    [p(mathbf{x}) = p(f(mathbf{x})) bigg|detfrac{partial f({mathbf{x})}}{partial{mathbf{x}}}bigg|]

    In observe, we work with log possibilities, so

    [log p(mathbf{x}) = log p(f(mathbf{x})) + log bigg|detfrac{partial f({mathbf{x})}}{partial{mathbf{x}}}bigg| ]

    Let’s see this with one other bijector instance, tfb_affine_scalar. Beneath, we assemble a mini-flow that maps a number of arbitrary chosen (x) values to double their worth (scale = 2):

    x <- c(0, 0.5, 1)
    b <- tfb_affine_scalar(shift = 0, scale = 2)

    To match densities underneath the circulation, we select the conventional distribution, and have a look at the log densities:

    d_n <- tfd_normal(loc = 0, scale = 1)
    d_n %>% tfd_log_prob(x) %>% as.numeric() # -0.9189385 -1.0439385 -1.4189385

    Now apply the circulation and compute the brand new log densities as a sum of the log densities of the corresponding (x) values and the log determinant of the Jacobian:

    z <- b %>% tfb_forward(x)
    (d_n  %>% tfd_log_prob(b %>% tfb_inverse(z))) +
      (b %>% tfb_inverse_log_det_jacobian(z, event_ndims = 0)) %>%
      as.numeric() # -1.6120857 -1.7370857 -2.1120858

    We see that because the values get stretched in area (we multiply by 2), the person log densities go down. We will confirm the cumulative likelihood stays the identical utilizing tfd_transformed_distribution():

    d_t <- tfd_transformed_distribution(distribution = d_n, bijector = b)
    d_n %>% tfd_cdf(x) %>% as.numeric()  # 0.5000000 0.6914625 0.8413447
    d_t %>% tfd_cdf(y) %>% as.numeric()  # 0.5000000 0.6914625 0.8413447

    To date, the flows we noticed had been static – how does this match into the framework of neural networks?

    Coaching a circulation

    Provided that flows are bidirectional, there are two methods to consider them. Above, we have now principally burdened the inverse mapping: We would like a easy distribution we will pattern from, and which we will use to compute a density. In that line, flows are typically referred to as “mappings from knowledge to noise” – noise principally being an isotropic Gaussian. Nonetheless in observe, we don’t have that “noise” but, we simply have knowledge. So in observe, we have now to be taught a circulation that does such a mapping. We do that through the use of bijectors with trainable parameters. We’ll see a quite simple instance right here, and depart “actual world flows” to the subsequent publish.

    The instance relies on half 1 of Eric Jang’s introduction to normalizing flows. The primary distinction (other than simplification to indicate the fundamental sample) is that we’re utilizing keen execution.

    We begin from a two-dimensional, isotropic Gaussian, and we wish to mannequin knowledge that’s additionally regular, however with a imply of 1 and a variance of two (in each dimensions).

    tfe_enable_eager_execution(device_policy = "silent")
    # the place we begin from
    base_dist <- tfd_multivariate_normal_diag(loc = c(0, 0))
    # the place we wish to go
    target_dist <- tfd_multivariate_normal_diag(loc = c(1, 1), scale_identity_multiplier = 2)
    # create coaching knowledge from the goal distribution
    target_samples <- target_dist %>% tfd_sample(1000) %>% tf$solid(tf$float32)
    batch_size <- 100
    dataset <- tensor_slices_dataset(target_samples) %>%
      dataset_shuffle(buffer_size = dim(target_samples)[1]) %>%

    Now we’ll construct a tiny neural community, consisting of an affine transformation and a nonlinearity. For the previous, we will make use of tfb_affine, the multi-dimensional relative of tfb_affine_scalar. As to nonlinearities, at present TFP comes with tfb_sigmoid and tfb_tanh, however we will construct our personal parameterized ReLU utilizing tfb_inline:

    # alpha is a learnable parameter
    bijector_leaky_relu <- perform(alpha) {
        # ahead remodel leaves optimistic values untouched and scales detrimental ones by alpha
        forward_fn = perform(x)
          tf$the place(tf$greater_equal(x, 0), x, alpha * x),
        # inverse remodel leaves optimistic values untouched and scales detrimental ones by 1/alpha
        inverse_fn = perform(y)
          tf$the place(tf$greater_equal(y, 0), y, 1/alpha * y),
        # quantity change is 0 when optimistic and 1/alpha when detrimental
        inverse_log_det_jacobian_fn = perform(y) {
          I <- tf$ones_like(y)
          J_inv <- tf$the place(tf$greater_equal(y, 0), I, 1/alpha * I)
          log_abs_det_J_inv <- tf$log(tf$abs(J_inv))
          tf$reduce_sum(log_abs_det_J_inv, axis = 1L)
        forward_min_event_ndims = 1

    Outline the learnable variables for the affine and the PReLU layers:

    d <- 2 # dimensionality
    r <- 2 # rank of replace
    # shift of affine bijector
    shift <- tf$get_variable("shift", d)
    # scale of affine bijector
    L <- tf$get_variable('L', c(d * (d + 1) / 2))
    # rank-r replace
    V <- tf$get_variable("V", c(d, r))
    # scaling issue of parameterized relu
    alpha <- tf$abs(tf$get_variable('alpha', record())) + 0.01

    With keen execution, the variables have for use contained in the loss perform, so that’s the place we outline the bijectors. Our little circulation now could be a tfb_chain of bijectors, and we wrap it in a TransformedDistribution (tfd_transformed_distribution) that hyperlinks supply and goal distributions.

    loss <- perform() {
     affine <- tfb_affine(
            scale_tril = tfb_fill_triangular() %>% tfb_forward(L),
            scale_perturb_factor = V,
            shift = shift
     lrelu <- bijector_leaky_relu(alpha = alpha)  
     circulation <- record(lrelu, affine) %>% tfb_chain()
     dist <- tfd_transformed_distribution(distribution = base_dist,
                              bijector = circulation)
     l <- -tf$reduce_mean(dist$log_prob(batch))
     # preserve observe of progress
     print(spherical(as.numeric(l), 2))

    Now we will really run the coaching!

    optimizer <- tf$prepare$AdamOptimizer(1e-4)
    n_epochs <- 100
    for (i in 1:n_epochs) {
      iter <- make_iterator_one_shot(dataset)
        batch <- iterator_get_next(iter)

    Outcomes will differ relying on random initialization, however it is best to see a gradual (if sluggish) progress. Utilizing bijectors, we have now really educated and outlined a bit of neural community.


    Undoubtedly, this circulation is just too easy to mannequin advanced knowledge, however it’s instructive to have seen the fundamental rules earlier than delving into extra advanced flows. Within the subsequent publish, we’ll try autoregressive flows, once more utilizing TFP and tfprobability.

    Jimenez Rezende, Danilo, and Shakir Mohamed. 2015. “Variational Inference with Normalizing Flows.” arXiv e-Prints, Could, arXiv:1505.05770.


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