class MvnGaussDataset(ToyDataset):
def __init__(self, mu, sigma, dim, static_data=None):
super(MvnGaussDataset, self).__init__(dim, static_data=static_data)
self.mu = mu
self.sigma = sigma
self.data_dist = DiagMvn(mu=[self.mu] * self.dim,
sigma=[self.sigma] * self.dim)
def gen_batch(self, batch_size):
return self.data_dist.get_samples(num_samples=batch_size)
class SizedMvnGaussDataset(ToyDataset):
def __init__(self, mu, sigma, dim, static_data=None):
super(SizedMvnGaussDataset, self).__init__(dim,
static_data=static_data)
self.mu = mu
self.sigma = sigma
self.data_dist = DiagMvn(mu=[self.mu] * self.dim,
sigma=[self.sigma] * self.dim)
self.num_samples = 1000
self.data = self.data_dist.get_samples(num_samples=self.num_samples)
def gen_batch(self, batch_size):
inds = np.random.choice(np.arange(self.num_samples), size=batch_size)
return self.data[inds, :].reshape((batch_size, ) + self.data.shape[1:])
from ade.common.distributions import DiagMvn
import ade.common.mcmc_net as mcmc_net
if __name__ == '__main__':
random.seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
tf.random.set_random_seed(cmd_args.seed)
true_mean = -1.0
true_std = 0.1
with tf.variable_scope('energy_func'):
energy_func = GaussianEnergy(dim=cmd_args.gauss_dim, init_mu=true_mean, init_sigma=true_std)
with tf.variable_scope('generator'):
mcmc = mcmc_net.HMCNet(cmd_args, dim=cmd_args.gauss_dim)
ph_z0 = tf.placeholder(tf.float32, shape=(1000, cmd_args.gauss_dim))
zt = mcmc(energy_func, ph_z0)
init_dist = DiagMvn(mu=[0.0] * cmd_args.gauss_dim,
sigma=[1.0] * cmd_args.gauss_dim)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
z0 = init_dist.get_samples(num_samples=1000)
z_samples = sess.run(zt, feed_dict={ph_z0: z0})
print(np.mean(z_samples, axis=0))
print(np.std(z_samples, axis=0))