import bayes_lib as bl
import autograd
import autograd.numpy as agnp
import autograd.scipy as agsp
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
#x_true = agnp.random.normal(3, 1, size = (100, 10))
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
train_images = mnist.train.next_batch(100)[0]
with bl.Model() as m:
X = bl.Placeholder('X', dimensions=agnp.array([100, 784]))
encoder = bl.ml.neural_network.DenseNeuralNetwork(
'Encoder',
X,
layer_dims=[784, 50, 20],
nonlinearity=bl.math.utils.sigmoid)
decoder = bl.ml.neural_network.DenseNeuralNetwork(
'Decoder',
encoder,
layer_dims=[20, 50, 784],
nonlinearity=bl.math.utils.sigmoid,
last_layer_nonlinearity=bl.math.utils.sigmoid)
y = bl.rvs.Bernoulli('obs', decoder, observed=X)
fit_params = agnp.loadtxt("pos.txt", delimiter=',')
m.set_param(fit_params)
out = m.evaluate(decoder, feed_dict={X: train_images})
for i in range(out.shape[1]):
logpdfs = []
#theta = agnp.random.multivariate_normal(agnp.array([3,3]), agnp.eye(2), size = n)
theta = agnp.random.uniform(0, 5, size=(n, 2))
data = []
for i in range(n):
y = agnp.random.multivariate_normal(theta[i, :], agnp.eye(2))
logpdfs.append(
agsp.stats.multivariate_normal.logpdf(y, theta[i, :], agnp.eye(2)))
data.append(y)
return logpdfs, theta, agnp.array(data)
#data = agnp.hstack([agnp.random.normal(3, 2, size = (1000,1)),agnp.random.normal(10, .1, size = (1000,1)), agnp.random.normal(-5, 3, size = (1000,1))])
with bl.Model() as m_surrogate_density:
#X = bl.Placeholder('X', dimensions = agnp.array([1000,3]))
X = bl.Placeholder('X', dimensions=agnp.array([100, 2]))
theta = bl.Placeholder('theta', dimensions=agnp.array([100, 2]))
made = bl.ml.made.ConditionalGaussianMADE('made',
2,
2,
theta, [20, 21],
X,
nonlinearity=bl.math.utils.relu)
init = agnp.random.normal(-3, 3, size=m_surrogate_density.n_params)
def iter_func(t, p, o):
print("Objective value: %f" % o)
for i in range(10):
_, pseudo_theta, pseudo_data = gen_data(100)