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A library for probabilistic modeling, inference, and criticism. http://edwardlib.org

- Aug 11 07:42mafattma commented #948
- Jul 05 16:16PhatBoy44 opened #948
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- Mar 12 07:14hyeon424 opened #946
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- Jan 29 13:52ursb2017 opened #945
- Jan 22 06:14cymqqqq opened #944
- Jan 16 08:22stenpiren commented #723

I ended up writing a probably-terrible thing for this: https://gist.github.com/EvanKrall/daab4d4abced844e6caef951e7fee06e

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

- 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.
- 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 ;)

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`

?
@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.
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?

when i import edward ImportError: cannot import name 'set_shapes_for_outputs'

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}))
```

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.

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

like Figure 1?

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.

I was able to find this in the Edward source code code-link:

Use TensorFlow ops such as

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.
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

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

If so, do you have a reference to specific runtime results comparing Edward to the other PPLs ?

Hi, I am facing an issue while running variational inference using KLqp on an RNN model. Here are the details of my code

def rnn_cell(hprev, x):

return tf.tanh(ed.dot(hprev, Wh) + ed.dot(x, Wx) + bh)

Wx = Normal(loc=tf.zeros([n_i, n_h]), scale=tf.ones([n_i,n_h]))

Wh = Normal(loc=tf.zeros([n_h, n_h]), scale=tf.ones([n_h, n_h]))

Wy = Normal(loc=tf.zeros([n_h, n_o]), scale=tf.ones([n_h, n_o]))

bh = Normal(loc=tf.zeros(n_h), scale=tf.ones(n_h))

by = Normal(loc=tf.zeros(n_o), scale=tf.ones(n_o))

x = tf.placeholder(tf.float32, [None, n_i], name='x')

h = tf.scan(rnn_cell, x, initializer=tf.zeros(n_h))

y = Normal(loc=tf.matmul(h, Wy) + by, scale = 1.0*tf.ones(N))

qWx = Normal(loc=tf.get_variable("qWx/loc", [n_i, n_h]),scale=tf.nn.softplus(tf.get_variable("qWx/scale", [n_i, n_h])))

qWh = Normal(loc=tf.get_variable("qWh/loc", [n_h, n_h]),scale=tf.nn.softplus(tf.get_variable("qWh/scale", [n_h, n_h])))

qWy = Normal(loc=tf.get_variable("qWy/loc", [n_h, n_o]),scale=tf.nn.softplus(tf.get_variable("qWy/scale", [n_h, n_o])))

qbh = Normal(loc=tf.get_variable("qbh/loc", [n_h]),scale=tf.nn.softplus(tf.get_variable("qbh/scale", [n_h])))

qby = Normal(loc=tf.get_variable("qby/loc", [n_o]),scale=tf.nn.softplus(tf.get_variable("qby/scale", [n_o])))

inference = ed.KLqp({Wx:qWx, Wh:qWh, Wy:qWy, bh:qbh, by:qby}, data={x: x_train, y: y_train})

inference.run(n_iter=1000, n_samples=5)

Getting the following ERROR

/Applications/anaconda/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.pyc in AddWhileContext(self, op, between_op_list, between_ops)

1257 if grad_state is None:

1258 # This is a new while loop so create a grad state for it.

-> 1259 outer_forward_ctxt = forward_ctxt.outer_context

1260 if outer_forward_ctxt:

1261 outer_forward_ctxt = outer_forward_ctxt.GetWhileContext()

AttributeError: 'NoneType' object has no attribute 'outer_context'

I am using Tensorflow 1.7.0 since I faced other issues with using Edward on higher versions. I have seen the same AttributeError reported on stack overflow for other use cases of TensorFlow, but not found a practical solution reported anywhere. Thanks in advance for the help.

Hi, does anyone have an idea about https://discourse.edwardlib.org/t/reproducing-cvae-in-edward-2-and-keras/1074 ? :)

@dustinvtran Are there any examples of MDN for classification on mnist? I've only seen one example http://edwardlib.org/tutorials/mixture-density-network which is about regression on a toy problem from the original paper.

I am trying to run a simple example of Edward (https://github.com/blei-lab/edward/blob/master/examples/bayesian_nn.py), but with TensorFlow 2. In TF2, there is no Edward, but only Edward 2 (https://www.tensorflow.org/probability/api_docs/python/tfp/edward2). Apparently,

`KLqp`

is not defined in TF2., so the call at line 89 of the example (https://github.com/blei-lab/edward/blob/master/examples/bayesian_nn.py#L89) produces the error `AttributeError: module 'tensorflow_probability.python.edward2' has no attribute 'KLqp'`

.
A new package has been released for model evaluation with metrics:

https://www.reddit.com/r/MachineLearning/comments/e8m4zr/open_source_metric_python_package_beta_release/?

https://www.reddit.com/r/MachineLearning/comments/e8m4zr/open_source_metric_python_package_beta_release/?

is tensorflow 2.0.0 not supported?

State of Art model Zoo, cross-platform tensorflow, pytorch, Gluon, .... :

https://github.com/arita37/mlmodels

https://github.com/arita37/mlmodels