def test_exact_posterior_recovery_no_transition_noise(self):
with self.test_session() as session:
stub_model, data, true_params = self._get_single_model()
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(data))
features, _ = input_fn()
model_outputs = stub_model.get_batch_loss(
features=features,
mode=None,
state=math_utils.replicate_state(
start_state=stub_model.get_start_state(),
batch_size=array_ops.shape(
features[feature_keys.TrainEvalFeatures.TIMES])[0]))
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
posterior_mean, posterior_var, posterior_times = session.run(
# Feed the true model parameters so that this test doesn't depend on
# the generated parameters being close to the variable initializations
# (an alternative would be training steps to fit the noise values,
# which would be slow).
model_outputs.end_state,
feed_dict=true_params)
coordinator.request_stop()
coordinator.join()
self.assertAllClose(numpy.zeros([1, 4, 4]),
posterior_var,
atol=1e-2)
self.assertAllClose(numpy.dot(
numpy.linalg.matrix_power(
stub_model.transition,
data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
true_params[stub_model.prior_state_mean]),
posterior_mean[0],
rtol=1e-1)
self.assertAllClose(
math_utils.batch_end_time(
features[feature_keys.TrainEvalFeatures.TIMES]).eval(),
posterior_times)
def test_exact_posterior_recovery_no_transition_noise(self):
with self.cached_session() as session:
stub_model, data, true_params = self._get_single_model()
input_fn = input_pipeline.WholeDatasetInputFn(
input_pipeline.NumpyReader(data))
features, _ = input_fn()
model_outputs = stub_model.get_batch_loss(
features=features,
mode=None,
state=math_utils.replicate_state(
start_state=stub_model.get_start_state(),
batch_size=array_ops.shape(
features[feature_keys.TrainEvalFeatures.TIMES])[0]))
variables.global_variables_initializer().run()
coordinator = coordinator_lib.Coordinator()
queue_runner_impl.start_queue_runners(session, coord=coordinator)
posterior_mean, posterior_var, posterior_times = session.run(
# Feed the true model parameters so that this test doesn't depend on
# the generated parameters being close to the variable initializations
# (an alternative would be training steps to fit the noise values,
# which would be slow).
model_outputs.end_state, feed_dict=true_params)
coordinator.request_stop()
coordinator.join()
self.assertAllClose(numpy.zeros([1, 4, 4]), posterior_var,
atol=1e-2)
self.assertAllClose(
numpy.dot(
numpy.linalg.matrix_power(
stub_model.transition,
data[feature_keys.TrainEvalFeatures.TIMES].shape[1]),
true_params[stub_model.prior_state_mean]),
posterior_mean[0],
rtol=1e-1)
self.assertAllClose(
math_utils.batch_end_time(
features[feature_keys.TrainEvalFeatures.TIMES]).eval(),
posterior_times)