# Modeling sequential data

**URL:** <https://discourse.edwardlib.org/t/modeling-sequential-data/200>\
**Category:** General\
**Created:** [June 28, 2017, 2:27pm UTC](https://discourse.edwardlib.org/t/modeling-sequential-data/200 "2017-06-28T14:27:43Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![dasirra](https://avatars.discourse-cdn.com/v4/letter/d/a5b964/32.png) [@dasirra](https://discourse.edwardlib.org/u/dasirra)\
**Post date:** [June 28, 2017, 2:27pm UTC](https://discourse.edwardlib.org/t/modeling-sequential-data/200/1 "2017-06-28T14:27:43Z")

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Hi all,

I’m new to edward and I’m having difficulties to implement a sequential model. The main problem is that after inference procedure I’m getting the same value as initialization for latent variables. I’d like to infer **alpha1** , **alpha2** and **theta**. Here’s my code:

```
## DATA ##
data = np.load("../data/pickle/data.npz")
x = data["x"].astype(np.int32)
cx = data["cx"].astype(np.float32)
dx = data["dx"].astype(np.float32)
n_users,n_iters = x.shape

## MODEL ##
# beta priors
alpha1 = Beta(tf.ones(n_users), tf.ones(n_users), name="alpha1")
alpha2 = Beta(tf.ones(n_users), tf.ones(n_users), name="alpha2")

# initial variables
a = [1] * n_iters
b = [1] * n_iters
theta = [0] * n_iters
X = [0] * n_iters

# build sequential model
a[0] = tf.ones(n_users)
b[0] = tf.ones(n_users)
for n in range(1,n_iters):
    with tf.name_scope("iter_{}".format(n)):
        a[n] = alpha1*a[n-1] + (1.-alpha1)*cx[:,n-1]
        b[n] = alpha2*b[n-1] + (1.-alpha2)*dx[:,n-1]
        theta[n] = Beta(a[n],b[n], name="theta_{}".format(n))
        X[n] = Bernoulli(theta[n], name="bernoulli_{}".format(n))

# remove first element
theta = theta[1:]
X = X[1:]

## INFERENCE ##
# build latent variables
T = 1000
latent_vars = {}
qalpha1 = Empirical(tf.Variable(tf.([T, n_users])))
qalpha2 = Empirical(tf.Variable(tf.ones([T, n_users])))
qtheta = [0] * len(theta)
for i,t in enumerate(theta):
    qtheta[i] = Empirical(tf.Variable(tf.random_normal([T, n_users])))
    latent_vars.update({t: qtheta[i]})
latent_vars.update({alpha1: qalpha1})
latent_vars.update({alpha2: qalpha2})

# build data
data = {X_t:x_t for X_t,x_t in zip(X,x.T[1:,:])}

# make inference
inference = ed.HMC(latent_vars, data=data)
inference.run(logdir="hmc_train/")

## RESULT ##
qalpha1.eval()
array([[1., 1., 1., ..., 1., 1., 1.],
   [1., 1., 1., ..., 1., 1., 1.],
   [1., 1., 1., ..., 1., 1., 1.],
   ..., 
   [1., 1., 1., ..., 1., 1., 1.],
   [1., 1., 1., ..., 1., 1., 1.],
   [1., 1., 1., ..., 1., 1., 1.]], dtype=float32)

```

I’m getting unexpected results (basically latent variables are keeping their initial value). I’m sure i’m doing something wrong. Does anybody know how to model this kind of problems?

Thanks in advance!

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<div class="post-metadata">

**Author:** ![dustin](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.edwardlib.org/dustin/32/134_2.png) [@dustin](https://discourse.edwardlib.org/u/dustin)\
**Post date:** [July 1, 2017, 11:12pm UTC](https://discourse.edwardlib.org/t/modeling-sequential-data/200/2 "2017-07-01T23:12:08Z")

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Have you looked at HMC’s acceptance rate? It sounds like all samples are being rejected. I recommend tuning its hyperparameters (see API at [http://edwardlib.org/api/ed/HMC](http://edwardlib.org/api/ed/HMC)).