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cruyffturn
@cruyffturn
Hello guys, what does Turing complete probabilistic programming language means? If it means any computable probability distribution, then can we define an Undirected Graph in Edward? I think not all the undirected graphical models can be represented by directed models which is stated by a figure in Bishop in Pattern Recognition and Machine Learning.
Raza Habib
@Razcle
You can always l1
Shaowu Pan
@pswpswpsw
Hi all, a quick question: the KLqp documentation say it is using variational EM, is it just ADVI for the ELBO?
cruyffturn
@cruyffturn
if you look at the paper https://arxiv.org/abs/1610.09787 pg:14 as an example to Hybrid Algorithms they show the variational EM algorithm, it's a sub class of variational methods that can be implemented with Edward
Shaowu Pan
@pswpswpsw
@cruyffturn Thanks for the information. So I think the default KLqp is not doing the central algorithm described in ADVI Paper? Today I checked the API, it says KLqp is doing This class minimizes the objective by automatically selecting from a variety of black box inference techniques. So it is BBVI, not the better version of VI that use gradient in the model. ref http://edwardlib.org/api/ed/KLqp
Shaowu Pan
@pswpswpsw
But from the another API page, I think the reparametericGraident in KLqp is doing ADVI that uses the gradient information and score function is the BBVI. http://edwardlib.org/tutorials/klqp
Shaowu Pan
@pswpswpsw
Hello guys! Just curious, why do you prefer ADVI over Laplace approximation on the MAP? The latter is faster, cheaper. The ADVI paper mentions some difference between ADVI and Laplace approximation. But from my viewpoint, ADVI is still mode seeking so I don't know what is exactly the improvement for the past few decades since Laplace approximation is found.
Shaowu Pan
@pswpswpsw
update on the understanding of hyperprior setting in Edward: it is assumed that Q(w,alpha) = Q(w) Q(alpha), which is stated in some other paper about VI but not Edward/ADVI paper. It is kind of weird that alpha is the prior parameter for w but their posterior is assumed to be independent between w and alpha.
Results on some simple linear regression have been shown this approximation seems okay to produce reasonable results.
However, the most orthodox way (not VI to minimize KL) to do this is to compute the P(alpha|D), by first computing P(D|alpha) P(alpha), and integrating out the parameter in the likelihood P(D|w,a lpha) to get P(D|alpha). Then do the MAP on P(alpha|D), to get the best alpha. Still, I don't see any exact equivalent relation between the above classical way and the ADVI.
Shaowu Pan
@pswpswpsw
I will just leave it here, in case someone found similar problems or interesting things.
ahmedaltakrouri
@ahmedaltakrouri
Hello all, I have very general question I am not sure this is the right place to ask
Evan Krall
@EvanKrall
When doing inference, is there a way to map variables to distributions of different shapes? e.g. the prior is a mixture model for each of several variables, but the posterior is a multivariate normal distribution
Evan Krall
@EvanKrall
I ended up writing a probably-terrible thing for this: https://gist.github.com/EvanKrall/daab4d4abced844e6caef951e7fee06e
matthieu bulté
@matthieubulte_twitter

Hi, I'm looking into issue #271 which is about implementing IS / SMC inference and I was thinking of the two following options:

  1. Implement an ImportanceSampling class inheriting from MonteCarlo. The build_update method simply computes one sample from the prior and its the importance weight (likelihood ratio). At each iteration, I don't update the user's Empirical but store everything internally (let's ignore the how for now) and only in populate the user's Empirical in the finalize method by sampling the auto-normalized weighted approximation.
  2. Create a WeightedEmpirical distribution, super-classing Empirical (default weights of 1/N) and replace in MonteCarlo the Empirical requirement by WeightedEmpirical. Then at each iteration I can directly populate the WeightedEmpirical and auto-normalize the distribution in the finalize method.

It feels like the first option is more of a hack, but is easier to implement than the second option which would require refactoring some existing code. Please let me know which of these two options make more sense or if you have any comments about them.

Oh, sorry I forgot to mention that I'm just trying to implement the IS part of the ticket. Implementing SMC straight away would be a little too much for my first contribution ;)
Evan Krall
@EvanKrall
looks like the Independent distribution is what I'm looking for, though it seems to require that everything is identically distributed too
Matthew Feickert
@matthewfeickert
Hi. In Edwaard there is ed.models.Empirical. Does anyone know what the corresponding thing in Edward2 or TensorFlow Probability is? Maybe as_random_variable?
Matthew Feickert
@matthewfeickert
@cruyffturn I already read that, but it wasn't entirely clear to me why we would want to use softplus though. I can reread this though, so thanks for taking time to respond.
jjennings955
@jjennings955

I have a problem where I have an architecture vaguely similar to an auto-encoder, but I want the encoder to be probabilistic.

I think I need this because the loss function I'm optimizing has a few 'hot spots' (good initial conditions) and many very 'cold spots' (zero gradient).

So, if I treat the output as something deterministic, just by the luck of initialization very few (maybe zero) of the encoder outputs may be hot spots. But if they are treated as a distribution with enough variance to cover the hot spots, I should be able to sample good encodings to find a good trajectory to optimize (as well as push the encoder distribution further towards hot spots during training).

Does this sound suited for probabilistic programming, and does anyone have any advice based on this description?

jjennings955
@jjennings955
so in essence I would like to treat the output of my encoder as parameters of a distribution, and calculate my loss based on (potentially many) samples from that distribution
liuchenbaidu
@liuchenbaidu
when i import edward ImportError: cannot import name 'set_shapes_for_outputs'
when i import edward ImportError: cannot import name 'set_shapes_for_outputs'
Ahmet Can Acar
@acanacar

Greetings, I m trying to use Multinomial Distribution in Edward to predict multiclass labels ( 3 class ) with neural network. I m confused about :

1-) how should i design label dataset as shape. I choose to way that converting label as [[0],[1],[0],[2]] to [[1,0,0],[0,1,0],[1,0,0],[0,0,2] ] .
2-) what conditions for my total_counts arg in multinomial function not being equal 1 when i use probs instead logits?

I m not too familiar with multinomial actually i used bernoulli easily but i cant handle multiclass network:/

After training my data i got predictions from test data.But when i try evaluate mse i m getting error:
ValueError: Dimensions must be equal, but are 145 and 3 for 'sub_2' (op: 'Sub') with input shapes: [145], [145,3].
Here my code :
and if u see some missing parts of me i m very happy to get advise.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
import edward as ed
from edward.models import Normal, Multinomial

num_labels = 3
(n_samples, n_iter) = (30, 2500)
symbol = 'A'
dataFrequency = '10'


X, Y = np.array(X), np.array(Y)
Y =(np.arange(num_labels) == Y[:,None]).astype(np.float32)

X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)

# X_train.shape  (578, 120)
# y_train.shape  (578, 3)
# X_test.shape  (145, 120)
# y_test.shape  (145, 3)

def neural_network(x):
    h = tf.tanh(tf.matmul(x, W_0) + b_0)
    h = tf.tanh(tf.matmul(h, W_1) + b_1)
    h = tf.tanh(tf.matmul(h, W_2) + b_2)
    h = tf.matmul(h, W_3) + b_3
    nn_result = tf.nn.softmax(h)
    return nn_result

D = X_train.shape[1]
N = X_train.shape[0]
N2 = X_test.shape[0]

W_0 = Normal(loc=tf.zeros([D, 10]), scale=tf.ones([D, 10]))
W_1 = Normal(loc=tf.zeros([10, 10]), scale=tf.ones([10, 10]))
W_2 = Normal(loc=tf.zeros([10, 5]), scale=tf.ones([10, 5]))
W_3 = Normal(loc=tf.zeros([5, 3]), scale=tf.ones([5, 3]))
b_0 = Normal(loc=tf.zeros(10), scale=tf.ones(10))
b_1 = Normal(loc=tf.zeros(10), scale=tf.ones(10))
b_2 = Normal(loc=tf.zeros(5), scale=tf.ones(5))
b_3 = Normal(loc=tf.zeros(3), scale=tf.ones(3))

x_ph = tf.placeholder(tf.float32, [None, D])
y = Multinomial(probs=neural_network(x_ph), total_count=1.)

qw_0 = Normal(loc=tf.get_variable("qw_0/loc", [D, 10]),
              scale=tf.nn.softplus(tf.get_variable("qw_0/scale", [D, 10])))
qb_0 = Normal(loc=tf.get_variable("qb_0/loc", [10]),
              scale=tf.nn.softplus(tf.get_variable("qb_0/scale", [10])))
qw_1 = Normal(loc=tf.get_variable("qw_1/loc", [10, 10]),
              scale=tf.nn.softplus(tf.get_variable("qw_1/scale", [10, 10])))
qb_1 = Normal(loc=tf.get_variable("qb_1/loc", [10]),
              scale=tf.nn.softplus(tf.get_variable("qb_1/scale", [10])))
qw_2 = Normal(loc=tf.get_variable("qw_2/loc", [10, 5]),
              scale=tf.nn.softplus(tf.get_variable("qw_2/scale", [10, 5])))
qb_2 = Normal(loc=tf.get_variable("qb_2/loc", [5]),
              scale=tf.nn.softplus(tf.get_variable("qb_2/scale", [5])))
qw_3 = Normal(loc=tf.get_variable("qw_3/loc", [5, 3]),
              scale=tf.nn.softplus(tf.get_variable("qw_3/scale", [5, 3])))
qb_3 = Normal(loc=tf.get_variable("qb_3/loc", [3]),
              scale=tf.nn.softplus(tf.get_variable("qb_3/scale", [3])))

inference = ed.KLqp({
    W_0: qw_0, b_0: qb_0,
    W_1: qw_1, b_1: qb_1,
    W_2: qw_2, b_2: qb_2,
    W_3: qw_3, b_3: qb_3,
}, data={x_ph: X_train, y: y_train})
inference.run(n_samples=n_samples, n_iter=n_iter,
              logdir='log/{}/{}/{}/{}'.format(symbol,
                                              dataFrequency,
                                              n_samples,
                                              n_iter)
              )

y_post = ed.copy(y, {
    W_0: qw_0, b_0: qb_0,
    W_1: qw_1, b_1: qb_1,
    W_2: qw_2, b_2: qb_2,
    W_3: qw_3, b_3: qb_3,
})

sess = ed.get_session()
predictions = sess.run(y_post, feed_dict={x_ph: X_test})

print('mse: ', ed.evaluate('mse', data={x_ph: X_test, y: y_test}))
Gaurav Shrivastava
@the-darklord
Hi there, I'm not sure this is the right place to ask but can anybody direct me to an example(script) of variational gaussian process or hierarchical variational models.
matrixbot
@matrixbot
vincenzoserio Yo ;)
Dmitriy Voronin
@VoroninDA
Hey guys, checking in from Richmond VA! Anybody have a second to lend an ear?
Dmitriy Voronin
@VoroninDA
@tscholak Hey, thanks for that great talk last year on Edward. Do you have a moment?
Torsten Scholak
@tscholak
sure, what’s up?
Dmitriy Voronin
@VoroninDA
I am trying to move my Bayesian Network from a PyMC3 implementation to Edward since Theano isn't able to handle the complexity of the network.
However, I can't seem to find a way to replicate a theano switch statement. Goal, use one distribution over another given the particular value sampled at run-time.
Torsten Scholak
@tscholak
a mixture model with two different base distributions?
Dmitriy Voronin
@VoroninDA
Latent Truth Model
If the Latent bernoulli variable is true, use the False Positive Rate beta var, if the latent variable is false, sample from respective Sensitivity beta
Thank you for your time, Mr. Scholak! Spacibo
Torsten Scholak
@tscholak
sounds like a mixture to me
like Figure 1?
Dmitriy Voronin
@VoroninDA
Exact paper I'm implementing. Haha!
Torsten Scholak
@tscholak
I don’t know right now if this can be done in Edward
Dmitriy Voronin
@VoroninDA
I have it working in PyMC3, without the collapsed gibbs sampling but using NUTS and BinGibbsMetropolis
Torsten Scholak
@tscholak
I have to go now, but I can give this some more thought later
Dmitriy Voronin
@VoroninDA
It was a pleasure getting a moment of your time, Mr. Scholak. Best wishes.
Dmitriy Voronin
@VoroninDA
Follow up: I don't think I'm able to use tf.where since it evaluates and doesn't wait for inferencing. Next step for me is to try is tf.cond and return from functions the respective dependant distributions. I am able to use theano.tensor.switch for the PyMC3 implementation built on top of theano.
Dmitriy Voronin
@VoroninDA
I was able to find this in the Edward source code code-link:
Use TensorFlow ops such as tf.cond to execute subgraphs conditioned on a draw from a random variable.
tf.cond to execute subgraphs conditioned on a draw from a random variable.
Evan Krall
@EvanKrall
this might be more of a TFP question, but hopefully someone here can help: why does this crash? what am I misunderstanding? https://gist.github.com/EvanKrall/fdc4e23e3688c809890d908e70737c9c
Evan Krall
@EvanKrall
(I had better luck with the AffineScalar bijector)
the issue seems to be that the Affine bijector has a forward_min_event_ndims of 1 even though the distribution it's operating on is a scalar distribution
Kenneth Lu
@krlu
Hi, I had a question about your paper. You mentioned in the abstract that it's much faster than Stan and PyMC3, is that true for the other PPLs that you cited?