# How to specify the dimension of Empirical

I define the prior as w = Normal(loc=tf.zeros([self.n_in, self.n_out]), scale=2.0 * tf.ones([self.n_in, self.n_out])), so w.shape is TensorShape([Dimension(3), Dimension(2)])

I also define the posterior as qw=Empirical(params=tf.Variable(np.zeros([self.n_in, self.n_out]))), then qw.shape becomes TensorShape([Dimension(2)])

When I try inference = ed.SGHMC({self.w: self.qw}, data={self.x: X_train}), there is a TypeError: Key-value pair in latent_vars does not have same shape: (3, 2), (2,)

So how to make qw the same dimension as w?

We recently updated the website to better display the API. Quoting the Empirical API’s args:

Args:

params: tf.Tensor. Collection of samples. Its outer (left-most) dimension determines the number of samples.

So you need to include an additional dimension determining the number of samples in the Empirical distribution.

Thank you, dustin. So in the case of Probabilistic PCA, I have to modify the code as below to do SGHMC inference. The syntaxe" qz=Empirical(params=tf.Variable(tf.zeros([N, N, K])))" have to include two N, both mean the number of samples？ It looks somewhat weired and the results of SGHMC looks not correct

``````N = 5000  # number of data points
D = 2  # data dimensionality
K = 1  # latent dimensionality

x_train = build_toy_dataset(N, D, K)

w = Normal(loc=tf.zeros([D, K]), scale=2.0 * tf.ones([D, K]))
z = Normal(loc=tf.zeros([N, K]), scale=tf.ones([N, K]))
x = Normal(loc=tf.matmul(w, z, transpose_b=True), scale=tf.ones([D, N]))

#For SGHMC inference
qw=Empirical(params=tf.Variable(tf.zeros([N, D, K])))
qz=Empirical(params=tf.Variable(tf.zeros([N, N, K])))

inference = ed.SGHMC({w: qw, z:qz}, data={x: x_train})
inference.run(step_size=1e-3)

#For KLqp inference
#qw = Normal(loc=tf.Variable(tf.random_normal([D, K])),
#            scale=tf.nn.softplus(tf.Variable(tf.random_normal([D, K]))))
#qz = Normal(loc=tf.Variable(tf.random_normal([N, K])),
#            scale=tf.nn.softplus(tf.Variable(tf.random_normal([N, K]))))

#inference = ed.KLqp({w: qw, z: qz}, data={x: x_train})
#inference.run(n_iter=500, n_print=100, n_samples=10)

w_post_mean=self.qw.mean().eval()``````

The outer dimension refers to the number of desired posterior samples. It has no relation to the number of observations.

re:SGHMC. SGMCMC algorithms can be very finicky. Have you tried tuning them and using longer chains (i.e., more posterior samples)? If you’re not doing data subsampling, `ed.HMC` is likely a better choice.