def make_kernel_bias_posterior_mvn_diag(kernel_shape,
bias_shape,
dtype=tf.float32,
kernel_initializer=None,
bias_initializer=None):
"""Create learnable posterior for Variational layers with kernel and bias."""
if kernel_initializer is None:
kernel_initializer = tf.initializers.glorot_normal()
if bias_initializer is None:
bias_initializer = tf.initializers.glorot_normal()
make_loc = lambda shape, init, name: tf.Variable( # pylint: disable=g-long-lambda
init(shape, dtype=dtype),
name=name + '_loc')
make_scale = lambda shape, name: TransformedVariable( # pylint: disable=g-long-lambda
tf.ones(shape, dtype=dtype),
Chain([Shift(1e-5), Softplus()]),
name=name + '_scale')
return JointDistributionSequential([
Independent(Normal(loc=make_loc(kernel_shape, kernel_initializer,
'posterior_kernel'),
scale=make_scale(kernel_shape, 'posterior_kernel')),
reinterpreted_batch_ndims=prefer_static.size(kernel_shape),
name='posterior_kernel'),
Independent(Normal(loc=make_loc(bias_shape, bias_initializer,
'posterior_bias'),
scale=make_scale(bias_shape, 'posterior_bias')),
reinterpreted_batch_ndims=prefer_static.size(bias_shape),
name='posterior_bias'),
])
def make_kernel_bias_posterior_mvn_diag(
kernel_shape,
bias_shape,
kernel_initializer=None,
bias_initializer=None,
kernel_batch_ndims=0, # pylint: disable=unused-argument
bias_batch_ndims=0, # pylint: disable=unused-argument
dtype=tf.float32,
kernel_name='posterior_kernel',
bias_name='posterior_bias'):
"""Create learnable posterior for Variational layers with kernel and bias.
Args:
kernel_shape: ...
bias_shape: ...
kernel_initializer: ...
Default value: `None` (i.e., `tf.initializers.glorot_uniform()`).
bias_initializer: ...
Default value: `None` (i.e., `tf.zeros`).
kernel_batch_ndims: ...
Default value: `0`.
bias_batch_ndims: ...
Default value: `0`.
dtype: ...
Default value: `tf.float32`.
kernel_name: ...
Default value: `"posterior_kernel"`.
bias_name: ...
Default value: `"posterior_bias"`.
Returns:
kernel_and_bias_distribution: ...
"""
if kernel_initializer is None:
kernel_initializer = nn_init_lib.glorot_uniform()
if bias_initializer is None:
bias_initializer = tf.zeros
make_loc = lambda init_fn, shape, batch_ndims, name: tf.Variable( # pylint: disable=g-long-lambda
_try_call_init_fn(init_fn, shape, dtype, batch_ndims),
name=name + '_loc')
# Setting the initial scale to a relatively small value causes the `loc` to
# quickly move toward a lower loss value.
make_scale = lambda shape, name: TransformedVariable( # pylint: disable=g-long-lambda
tf.fill(shape, value=tf.constant(1e-3, dtype=dtype)),
Chain([Shift(1e-5), Softplus()]),
name=name + '_scale')
return JointDistributionSequential([
Independent(Normal(loc=make_loc(kernel_initializer, kernel_shape,
kernel_batch_ndims, kernel_name),
scale=make_scale(kernel_shape, kernel_name)),
reinterpreted_batch_ndims=prefer_static.size(kernel_shape),
name=kernel_name),
Independent(Normal(loc=make_loc(bias_initializer, bias_shape,
kernel_batch_ndims, bias_name),
scale=make_scale(bias_shape, bias_name)),
reinterpreted_batch_ndims=prefer_static.size(bias_shape),
name=bias_name),
])
def make_kernel_bias_posterior_mvn_diag(kernel_shape,
bias_shape,
dtype=tf.float32,
kernel_initializer=None,
bias_initializer=None,
kernel_name='posterior_kernel',
bias_name='posterior_bias'):
"""Create learnable posterior for Variational layers with kernel and bias.
Args:
kernel_shape: ...
bias_shape: ...
dtype: ...
Default value: `tf.float32`.
kernel_initializer: ...
Default value: `None` (i.e., `tf.initializers.glorot_uniform()`).
bias_initializer: ...
Default value: `None` (i.e., `tf.zeros`).
kernel_name: ...
Default value: `"posterior_kernel"`.
bias_name: ...
Default value: `"posterior_bias"`.
Returns:
kernel_and_bias_distribution: ...
"""
if kernel_initializer is None:
kernel_initializer = tf.initializers.glorot_uniform()
if bias_initializer is None:
bias_initializer = tf.zeros
make_loc = lambda shape, init, name: tf.Variable( # pylint: disable=g-long-lambda
init(shape, dtype=dtype),
name=name + '_loc')
make_scale = lambda shape, name: TransformedVariable( # pylint: disable=g-long-lambda
tf.ones(shape, dtype=dtype),
Chain([Shift(1e-5), Softplus()]),
name=name + '_scale')
return JointDistributionSequential([
Independent(Normal(loc=make_loc(kernel_shape, kernel_initializer,
kernel_name),
scale=make_scale(kernel_shape, kernel_name)),
reinterpreted_batch_ndims=prefer_static.size(kernel_shape),
name=kernel_name),
Independent(Normal(loc=make_loc(bias_shape, bias_initializer,
bias_name),
scale=make_scale(bias_shape, bias_name)),
reinterpreted_batch_ndims=prefer_static.size(bias_shape),
name=bias_name),
])
def make_kernel_bias_prior_spike_and_slab(
kernel_shape,
bias_shape,
kernel_initializer=None, # pylint: disable=unused-argument
bias_initializer=None, # pylint: disable=unused-argument
kernel_batch_ndims=0, # pylint: disable=unused-argument
bias_batch_ndims=0, # pylint: disable=unused-argument
dtype=tf.float32,
kernel_name='prior_kernel',
bias_name='prior_bias'):
"""Create prior for Variational layers with kernel and bias.
Note: Distribution scale is inversely related to regularization strength.
Consider a "Normal" prior; bigger scale corresponds to less L2 regularization.
I.e.,
```python
scale = (2. * l2weight)**-0.5
l2weight = scale**-2. / 2.
```
have a similar regularizing effect.
The std. deviation of each of the component distributions returned by this
function is approximately `1415` (or approximately `l2weight = 25e-6`). In
other words this prior is extremely "weak".
Args:
kernel_shape: ...
bias_shape: ...
kernel_initializer: Ignored.
Default value: `None` (i.e., `tf.initializers.glorot_uniform()`).
bias_initializer: Ignored.
Default value: `None` (i.e., `tf.zeros`).
kernel_batch_ndims: ...
Default value: `0`.
bias_batch_ndims: ...
Default value: `0`.
dtype: ...
Default value: `tf.float32`.
kernel_name: ...
Default value: `"prior_kernel"`.
bias_name: ...
Default value: `"prior_bias"`.
Returns:
kernel_and_bias_distribution: ...
"""
w = MixtureSameFamily(mixture_distribution=Categorical(probs=[0.5, 0.5]),
components_distribution=Normal(loc=0.,
scale=tf.constant(
[1., 2000.],
dtype=dtype)))
return JointDistributionSequential([
Sample(w, kernel_shape, name=kernel_name),
Sample(w, bias_shape, name=bias_name),
])
def make_kernel_bias_prior_spike_and_slab(kernel_shape,
bias_shape,
dtype=tf.float32,
kernel_initializer=None,
bias_initializer=None):
"""Create prior for Variational layers with kernel and bias."""
del kernel_initializer, bias_initializer
w = MixtureSameFamily(mixture_distribution=Categorical(probs=[0.5, 0.5]),
components_distribution=Normal(loc=0.,
scale=tf.constant(
[1., 2000.],
dtype=dtype)))
return JointDistributionSequential([
Sample(w, kernel_shape, name='prior_kernel'),
Sample(w, bias_shape, name='prior_bias'),
])
def random_walk_mvnorm_fn(covariance,
pu=0.95,
fixed_variance=0.01,
is_adaptive=1,
name=None):
"""Returns callable that adds Multivariate Normal (MVN) noise to the input.
Args:
covariance: Python `list` of `Tensor`s representing each covariance
matrix, size d x d, of the Multivariate Normal proposal. The number
of parameters is d.
pu: Python floating point number representing the bounded convergence
parameter. If equal to 1, then all proposals are drawn
from the MVN(0, `covariance`) distribution, if less than 1,
proposals are drawn from MVN(0, `covariance`) with probability `pu`,
and MVN(0, `fixed_variance`/d) otherwise.
Default value: 0.95.
fixed_variance: Python floating point number representing the variance of
the fixed proposal distribution of the form MVN(0, `fixed_variance`/d).
Default value: 0.01.
is_adaptive: Python list of `Tensor`s representing the type of proposal
where for each batch 0 represents a fixed proposal and 1 an adaptive
proposal.
Default value: 1.
name: Python `str` name.
Given the default value of `None` the name is set to `random_walk_mvnorm_fn`.
Returns:
random_walk_mvnorm_fn: A callable accepting a Python `list` of `Tensor`s
representing the state parts of the `current_state` and an `int`
representing the random seed to be used to generate the proposal. The
callable returns two quantities. First, a `Tensor` of type integer
representing whether each state part was updated using the fixed
(value=0) or adaptive (value=1) proposal. Second, a `list` of
`Tensor`s, with the same-type as the input state parts, which represents
the proposal for the Metropolis Hastings algorithm.
"""
dtype = dtype_util.base_dtype(covariance[0].dtype)
shape = tf.stack(covariance, axis=0).shape
# for numerical stability ensure covariance matrix is positive semi-definite
covariance = covariance + 1.0e-9 * tf.eye(
shape[1], batch_shape=[shape[0]], dtype=dtype)
scale_tril = tf.linalg.cholesky(covariance)
rv_adaptive = MultivariateNormalTriL(loc=tf.zeros([shape[0], shape[1]],
dtype=dtype),
scale_tril=scale_tril)
rv_fixed = Normal(
loc=tf.zeros([shape[0], shape[1]], dtype=dtype),
scale=tf.constant(fixed_variance, dtype=dtype) / shape[2],
)
def _fn(state_parts, seed):
with tf.name_scope(name or "random_walk_mvnorm_fn"):
def proposal():
# For parallel computation it is quicker to sample
# both distributions then select the result
rv = tf.stack(
[
rv_fixed.sample(seed=seed),
rv_adaptive.sample(seed=seed),
],
axis=1,
)
return tf.squeeze(tf.gather(rv,
is_adaptive,
axis=1,
batch_dims=1),
axis=1)
proposal_parts = tf.unstack(proposal())
new_state_parts = [
proposal_part + state_part for proposal_part, state_part in
zip(proposal_parts, state_parts)
]
return new_state_parts
return _fn