tfp.bijectors.RationalQuadraticSpline

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A piecewise rational quadratic spline, as developed in [1].

Inherits From: Bijector

tfp.bijectors.RationalQuadraticSpline(
    bin_widths, bin_heights, knot_slopes, range_min=-1, validate_args=False,
    name=None
)

This transformation represents a monotonically increasing piecewise rational quadratic function. Outside of the bounds of knot_x/knot_y, the transform behaves as an identity function.

Typically this bijector will be used as part of a chain, with splines for trailing x dimensions conditioned on some of the earlier x dimensions, and with the inverse then solved first for unconditioned dimensions, then using conditioning derived from those inverses, and so forth. For example, if we split a 15-D xs vector into 3 components, we may implement a forward and inverse as follows:

nsplits = 3

class SplineParams(tf.Module):

  def __init__(self, nbins=32):
    self._nbins = nbins
    self._built = False
    self._bin_widths = None
    self._bin_heights = None
    self._knot_slopes = None

  def _bin_positions(self, x):
    x = tf.reshape(x, [-1, self._nbins])
    return tf.math.softmax(x, axis=-1) * (2 - self._nbins * 1e-2) + 1e-2

  def _slopes(self, x):
    x = tf.reshape(x, [-1, self._nbins - 1])
    return tf.math.softplus(x) + 1e-2

  def __call__(self, x, nunits):
    if not self._built:
      self._bin_widths = tf.keras.layers.Dense(
          nunits * self._nbins, activation=self._bin_positions, name='w')
      self._bin_heights = tf.keras.layers.Dense(
          nunits * self._nbins, activation=self._bin_positions, name='h')
      self._knot_slopes = tf.keras.layers.Dense(
          nunits * (self._nbins - 1), activation=self._slopes, name='s')
      self._built = True
    return tfb.RationalQuadraticSpline(
        bin_widths=self._bin_widths(x),
        bin_heights=self._bin_heights(x),
        knot_slopes=self._knot_slopes(x))

xs = np.random.randn(1, 15).astype(np.float32)  # Keras won't Dense(.)(vec).
splines = [SplineParams() for _ in range(nsplits)]

def spline_flow():
  stack = tfb.Identity()
  for i in range(nsplits):
    stack = tfb.RealNVP(5 * i, bijector_fn=splines[i])(stack)
  return stack

ys = spline_flow().forward(xs)
ys_inv = spline_flow().inverse(ys)  # ys_inv ~= xs

For a one-at-a-time autoregressive flow as in [1], it would be profitable to implement a mask over xs to parallelize either the inverse or the forward pass and implement the other using a tf.while_loop. See tfp.bijectors.MaskedAutoregressiveFlow for support doing so (paired with tfp.bijectors.Invert depending which direction should be parallel).

References

[1]: Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios. Neural Spline Flows. arXiv preprint arXiv:1906.04032, 2019. https://arxiv.org/abs/1906.04032

bin_widths The widths of the spans between subsequent knot x positions, a floating point Tensor. Must be positive, and at least 1-D. Innermost axis must sum to the same value as bin_heights. The knot x positions will be a first at range_min, followed by knots at range_min + cumsum(bin_widths, axis=-1).
bin_heights The heights of the spans between subsequent knot y positions, a floating point Tensor. Must be positive, and at least 1-D. Innermost axis must sum to the same value as bin_widths. The knot y positions will be a first at range_min, followed by knots at range_min + cumsum(bin_heights, axis=-1).
knot_slopes The slope of the spline at each knot, a floating point Tensor. Must be positive. 1s are implicitly padded for the first and last implicit knots corresponding to range_min and range_min + sum(bin_widths, axis=-1). Innermost axis size should be 1 less than that of bin_widths/bin_heights, or 1 for broadcasting.
range_min The x/y position of the first knot, which has implicit slope 1. range_max is implicit, and can be computed as range_min + sum(bin_widths, axis=-1). Scalar floating point Tensor.
validate_args Toggles argument validation (can hurt performance).
name Optional name scope for associated ops. (Defaults to 'RationalQuadraticSpline').

bin_heights

bin_widths

dtype dtype of Tensors transformable by this distribution.
forward_min_event_ndims Returns the minimal number of dimensions bijector.forward operates on.
graph_parents Returns this Bijector's graph_parents as a Python list.
inverse_min_event_ndims Returns the minimal number of dimensions bijector.inverse operates on.
is_constant_jacobian Returns true iff the Jacobian matrix is not a function of x.
knot_slopes

name Returns the string name of this Bijector.
name_scope Returns a tf.name_scope instance for this class.
parameters Dictionary of parameters used to instantiate this Bijector.
range_min

submodules Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
list(a.submodules) == [b, c]
True
list(b.submodules) == [c]
True
list(c.submodules) == []
True

trainable_variables Sequence of trainable variables owned by this module and its submodules.
validate_args Returns True if Tensor arguments will be validated.
variables Sequence of variables owned by this module and its submodules.

Methods

forward

View source

forward(
    x, name='forward', **kwargs
)

Returns the forward Bijector evaluation, i.e., X = g(Y).

Args
x Tensor. The input to the 'forward' evaluation.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
Tensor.

Raises
TypeError if self.dtype is specified and x.dtype is not self.dtype.
NotImplementedError if _forward is not implemented.

forward_dtype

View source

forward_dtype(
    dtype, name='forward_dtype', **kwargs
)

Returns the dtype of the output of the forward transformation.

Args
dtype tf.dtype, or nested structure of tf.dtypes, of the input to forward.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
tf.dtype or nested structure of tf.dtypes of the output of forward.

forward_event_shape

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forward_event_shape(
    input_shape
)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as forward_event_shape_tensor. May be only partially defined.

Args
input_shape TensorShape indicating event-portion shape passed into forward function.

Returns
forward_event_shape_tensor TensorShape indicating event-portion shape after applying forward. Possibly unknown.

forward_event_shape_tensor

View source

forward_event_shape_tensor(
    input_shape, name='forward_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args
input_shape Tensor, int32 vector indicating event-portion shape passed into forward function.
name name to give to the op

Returns
forward_event_shape_tensor Tensor, int32 vector indicating event-portion shape after applying forward.

forward_log_det_jacobian

View source

forward_log_det_jacobian(
    x, event_ndims, name='forward_log_det_jacobian', **kwargs
)

Returns both the forward_log_det_jacobian.

Args
x Tensor. The input to the 'forward' Jacobian determinant evaluation.
event_ndims Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.forward_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape rank(x) - event_ndims dimensions.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
Tensor, if this bijector is injective. If not injective this is not implemented.

Raises
TypeError if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError if neither _forward_log_det_jacobian nor {_inverse, _inverse_log_det_jacobian} are implemented, or this is a non-injective bijector.

inverse

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inverse(
    y, name='inverse', **kwargs
)

Returns the inverse Bijector evaluation, i.e., X = g^{-1}(Y).

Args
y Tensor. The input to the 'inverse' evaluation.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
Tensor, if this bijector is injective. If not injective, returns the k-tuple containing the unique k points (x1, ..., xk) such that g(xi) = y.

Raises
TypeError if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError if _inverse is not implemented.

inverse_dtype

View source

inverse_dtype(
    dtype, name='inverse_dtype', **kwargs
)

Returns the dtype of the output of the inverse transformation.

Args
dtype tf.dtype, or nested structure of tf.dtypes, of the input to inverse.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
tf.dtype or nested structure of tf.dtypes of the output of inverse.

inverse_event_shape

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inverse_event_shape(
    output_shape
)

Shape of a single sample from a single batch as a TensorShape.

Same meaning as inverse_event_shape_tensor. May be only partially defined.

Args
output_shape TensorShape indicating event-portion shape passed into inverse function.

Returns
inverse_event_shape_tensor TensorShape indicating event-portion shape after applying inverse. Possibly unknown.

inverse_event_shape_tensor

View source

inverse_event_shape_tensor(
    output_shape, name='inverse_event_shape_tensor'
)

Shape of a single sample from a single batch as an int32 1D Tensor.

Args
output_shape Tensor, int32 vector indicating event-portion shape passed into inverse function.
name name to give to the op

Returns
inverse_event_shape_tensor Tensor, int32 vector indicating event-portion shape after applying inverse.

inverse_log_det_jacobian

View source

inverse_log_det_jacobian(
    y, event_ndims, name='inverse_log_det_jacobian', **kwargs
)

Returns the (log o det o Jacobian o inverse)(y).

Mathematically, returns: log(det(dX/dY))(Y). (Recall that: X=g^{-1}(Y).)

Note that forward_log_det_jacobian is the negative of this function, evaluated at g^{-1}(y).

Args
y Tensor. The input to the 'inverse' Jacobian determinant evaluation.
event_ndims Number of dimensions in the probabilistic events being transformed. Must be greater than or equal to self.inverse_min_event_ndims. The result is summed over the final dimensions to produce a scalar Jacobian determinant for each event, i.e. it has shape rank(y) - event_ndims dimensions.
name The name to give this op.
**kwargs Named arguments forwarded to subclass implementation.

Returns
ildj Tensor, if this bijector is injective. If not injective, returns the tuple of local log det Jacobians, log(det(Dg_i^{-1}(y))), where g_i is the restriction of g to the ith partition Di.

Raises
TypeError if self.dtype is specified and y.dtype is not self.dtype.
NotImplementedError if _inverse_log_det_jacobian is not implemented.

with_name_scope

@classmethod
with_name_scope(
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
method The method to wrap.

Returns
The original method wrapped such that it enters the module's name scope.

__call__

View source

__call__(
    value, name=None, **kwargs
)

Applies or composes the Bijector, depending on input type.

This is a convenience function which applies the Bijector instance in three different ways, depending on the input:

  1. If the input is a tfd.Distribution instance, return tfd.TransformedDistribution(distribution=input, bijector=self).
  2. If the input is a tfb.Bijector instance, return tfb.Chain([self, input]).
  3. Otherwise, return self.forward(input)

Args
value A tfd.Distribution, tfb.Bijector, or a Tensor.
name Python str name given to ops created by this function.
**kwargs Additional keyword arguments passed into the created tfd.TransformedDistribution, tfb.Bijector, or self.forward.

Returns
composition A tfd.TransformedDistribution if the input was a tfd.Distribution, a tfb.Chain if the input was a tfb.Bijector, or a Tensor computed by self.forward.

Examples

sigmoid = tfb.Reciprocal()(
    tfb.AffineScalar(shift=1.)(
      tfb.Exp()(
        tfb.AffineScalar(scale=-1.))))
# ==> `tfb.Chain([
#         tfb.Reciprocal(),
#         tfb.AffineScalar(shift=1.),
#         tfb.Exp(),
#         tfb.AffineScalar(scale=-1.),
#      ])`  # ie, `tfb.Sigmoid()`

log_normal = tfb.Exp()(tfd.Normal(0, 1))
# ==> `tfd.TransformedDistribution(tfd.Normal(0, 1), tfb.Exp())`

tfb.Exp()([-1., 0., 1.])
# ==> tf.exp([-1., 0., 1.])