View source on GitHub |
Build a variational posterior that factors over model parameters.
tfp.sts.build_factored_surrogate_posterior(
model, batch_shape=(), seed=None, name=None
)
Used in the notebooks
Used in the tutorials |
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The surrogate posterior consists of independent Normal distributions for
each parameter with trainable loc
and scale
, transformed using the
parameter's bijector
to the appropriate support space for that parameter.
Args | |
---|---|
model
|
An instance of StructuralTimeSeries representing a
time-series model. This represents a joint distribution over
time-series and their parameters with batch shape [b1, ..., bN] .
|
batch_shape
|
Batch shape (Python tuple , list , or int ) of initial
states to optimize in parallel.
Default value: () . (i.e., just run a single optimization).
|
seed
|
PRNG seed; see tfp.random.sanitize_seed for details.
|
name
|
Python str name prefixed to ops created by this function.
Default value: None (i.e., 'build_factored_surrogate_posterior').
|
Examples
Assume we've built a structural time-series model:
day_of_week = tfp.sts.Seasonal(
num_seasons=7,
observed_time_series=observed_time_series,
name='day_of_week')
local_linear_trend = tfp.sts.LocalLinearTrend(
observed_time_series=observed_time_series,
name='local_linear_trend')
model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],
observed_time_series=observed_time_series)
To fit the model to data, we define a surrogate posterior and fit it by optimizing a variational bound:
surrogate_posterior = tfp.sts.build_factored_surrogate_posterior(
model=model)
loss_curve = tfp.vi.fit_surrogate_posterior(
target_log_prob_fn=model.joint_distribution(observed_time_series).log_prob,
surrogate_posterior=surrogate_posterior,
optimizer=tf.optimizers.Adam(learning_rate=0.1),
num_steps=200)
posterior_samples = surrogate_posterior.sample(50)
# In graph mode, we would need to write:
# with tf.control_dependencies([loss_curve]):
# posterior_samples = surrogate_posterior.sample(50)
For more control, we can also build and optimize a variational loss manually:
@tf.function(autograph=False) # Ensure the loss is computed efficiently
def loss_fn():
return tfp.vi.monte_carlo_variational_loss(
model.joint_distribution(observed_time_series).log_prob,
surrogate_posterior,
sample_size=10)
optimizer = tf.optimizers.Adam(learning_rate=0.1)
for step in range(200):
with tf.GradientTape() as tape:
loss = loss_fn()
grads = tape.gradient(loss, surrogate_posterior.trainable_variables)
optimizer.apply_gradients(
zip(grads, surrogate_posterior.trainable_variables))
if step % 20 == 0:
print('step {} loss {}'.format(step, loss))
posterior_samples = surrogate_posterior.sample(50)