with tf.variable_scope(country):
aux = tf.concat([global_inf.output, variation[tf.newaxis]],
axis=-1)
individ_variation = DFlow([
NVPFlow((VAR_DIM * 2 + 1) * VAR_DIM,
name='nvp_{}'.format(i),
aux_vars=aux) for i in range(6)
],
init_sigma=0.01)
ind = individ_variation.output[0] + global_inf.output[0]
indivs[country] = ind
tf.add_to_collection('logdensities', individ_variation.logdens)
tf.add_to_collection('priors',
individ_variation_prior.logdens(ind))
model = STACmodel(data,
mu=ind[tf.newaxis],
name='{}_model'.format(country),
var_dim=VAR_DIM,
current_year=current_year)
models.append(model)
graph = tf.get_default_graph()
prior = tf.reduce_sum([model.priors for model in models]) + tf.reduce_sum(
graph.get_collection('priors'))
logdensity = tf.reduce_sum([model.logdensities
for model in models]) + tf.reduce_sum(
graph.get_collection('logdensities'))
dtype=floatX,
reuse=tf.AUTO_REUSE):
for country, data in country_data.items():
with tf.variable_scope(country):
individ_variation = DFlow([
NVPFlow((VAR_DIM * 2 + 1) * VAR_DIM,
name='nvp_{}'.format(i),
aux_vars=global_inf.output) for i in range(6)
],
init_sigma=0.01)
ind = individ_variation.output[0]
indivs[country] = ind
indiv_logdens.append(individ_variation.logdens)
indiv_priors.append(individ_variation_prior.logdens(ind))
model = VARmodel(data,
name='{}_model'.format(country),
var_dim=VAR_DIM,
mu=ind[tf.newaxis],
current_year=current_year)
models.append(model)
graph = tf.get_default_graph()
prior = tf.reduce_sum([model.priors for model in models
]) + tf.reduce_sum(indiv_priors) + tf.reduce_sum(
graph.get_collection('priors'))
logdensity = tf.reduce_sum([model.logdensities for model in models