Dear Edward’s users.
I do not understand something using Tensorboard: how can I plot distributions for two dimensional parameters?
For a simple two Gaussian Mixture model (code below) I would expected two means in 2D (one for each component), but when I see the Distributions figure using Tensorboard I only see one mean (in one dimension?) called, in my case, qmu_loc/0 … but the parameter to optimize in qmu distribution is
loc=tf.Variable(tf.random_normal([K,D]),name="qmu_loc),
i.e., shape [2,2]. I don’t understand what Tensorboard does with the dimensions of the parameters.
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import numpy as np
from scipy.stats import norm
import time
import tensorflow as tf
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plt.style.use('ggplot')
from edward.models import \
Categorical, Empirical, InverseGamma, Normal, Gamma, Beta, Dirichlet, MultivariateNormalDiag, ParamMixture, Mixture
def build_toy_dataset(N):
pi = np.array([0.3, 0.7])
mus = [[5, 5], [-1, -1]]
stds = [[1, 1], [0.1, 0.1]]
x = np.zeros((N, 2))
for n in range(N):
k = np.argmax(np.random.multinomial(1, pi))
x[n, :] = np.random.multivariate_normal(mus[k], np.diag(stds[k]))
return x
# dataset
N = 500 # number of data points
x_data = build_toy_dataset(N)
N, D = x_data.shape
K = 2 # number of components to test
with tf.name_scope("model"):
pi = Dirichlet(concentration=tf.ones(K), name="pi")
mu = Normal(loc=[[5.0, 5.0], [-1.0, -1.0]], scale=tf.ones([K,D]), name="mu")
sigmasq = InverseGamma(tf.ones(D), tf.ones(D), sample_shape=K, name="sigmasq")
z = Categorical(probs=pi, sample_shape=N, name="z")
components = [MultivariateNormalDiag(loc=mu[k], scale_diag=tf.sqrt(sigmasq[k])) for k in range(K)]
x = Mixture(cat=z, components=components, sample_shape=N, name="mixture")
with tf.name_scope("posterior"):
qpi = Dirichlet(tf.nn.softplus(tf.Variable(tf.random_normal([K]),name="qpi")))
qmu = MultivariateNormalDiag(loc=tf.Variable(tf.random_normal([K,D]),name="qmu_loc"), scale_diag=tf.sqrt([[10.0, 10.0],[10.0, 10.0]]), name="qmu/posterior")
qsigmasq = InverseGamma(concentration=tf.nn.softplus(tf.Variable(tf.random_normal([K,D]),name="concentration")), rate=tf.ones([K,D]), name="qsigmasq/posterior")
inference = ed.KLqp({pi: qpi, mu: qmu, sigmasq: qsigmasq}, data={x: x_data})
inference.run(n_iter=5000, n_samples=30, logdir='log2DMH')