base.py
import functools
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.python.ops import array_ops
from .config import default_float
DType = Union[np.dtype, tf.DType]
VariableData = Union[List, Tuple, np.ndarray, int, float]
TensorLike = object # Union[tf.Tensor, tf.Variable, np.ndarray], but doesn't work with multipledispatch
Transform = tfp.bijectors.Bijector
Prior = tfp.distributions.Distribution
def _IS_PARAMETER(o):
return isinstance(o, Parameter)
def _IS_TRAINABLE_PARAMETER(o):
return (_IS_PARAMETER(o) and o.trainable)
class Module(tf.Module):
@property
def parameters(self):
return tuple(self._flatten(predicate=_IS_PARAMETER))
@property
def trainable_parameters(self):
return tuple(self._flatten(predicate=_IS_TRAINABLE_PARAMETER))
class Parameter(tf.Module):
def __init__(self,
value,
*,
transform: Optional[Transform] = None,
prior: Optional[Prior] = None,
trainable: bool = True,
dtype: Optional[DType] = None,
name: Optional[str] = None):
"""
Unconstrained parameter representation.
According to standard terminology `y` is always transformed representation or,
in other words, it is constrained version of the parameter. Normally, it is hard
to operate with unconstrained parameters. For e.g. `variance` cannot be negative,
therefore we need positive constraint and it is natural to use constrained values.
"""
super().__init__()
self._transform = transform
if isinstance(value, tf.Variable):
self._unconstrained = value
else:
unconstrained_value = self.validate_unconstrained_value(value, dtype)
self._unconstrained = tf.Variable(unconstrained_value,
dtype=dtype, name=name, trainable=trainable)
self.prior = prior
def log_prior(self):
x = self.read_value()
y = self._unconstrained
if self.prior is not None:
out = tf.reduce_sum(self.prior.log_prob(x))
if self.transform is not None:
log_det_jacobian = self.transform.forward_log_det_jacobian(y, y.shape.ndims)
out += tf.reduce_sum(log_det_jacobian)
return out
else:
return tf.convert_to_tensor(0., dtype=self.dtype)
def value(self):
return _to_constrained(self._unconstrained.value(), self.transform)
def read_value(self):
return _to_constrained(self._unconstrained.read_value(), self.transform)
def experimental_ref(self):
return self
def deref(self):
return self
@property
def unconstrained_variable(self):
return self._unconstrained
@property
def transform(self):
return self._transform
@transform.setter
def transform(self, new_transform):
constrained_value = self.read_value()
self._transform = new_transform
self.assign(constrained_value)
@property
def trainable(self):
return self._unconstrained.trainable
@trainable.setter
def trainable(self, flag: Union[bool, int]):
self._unconstrained._trainable = bool(flag)
@property
def initial_value(self):
return self._unconstrained.initial_value
def validate_unconstrained_value(self, value: tf.Tensor, dtype: DType) -> tf.Tensor:
value = _cast_to_dtype(value, dtype)
unconstrained_value = _to_unconstrained(value, self.transform)
message = "gpflow.Parameter: unconstrained value of passed value " \
"has NaN or Inf and cannot be assigned."
return tf.debugging.assert_all_finite(unconstrained_value, message=message)
def assign(self, value: tf.Tensor, use_locking=False, name=None, read_value=True) -> tf.Variable:
"""
Assigns constrained `value` to the unconstrained parameter's variable.
It passes constrained value through parameter's transform first.
Example:
```
a = Parameter(2.0, transform=tfp.bijectors.Softplus())
b = Parameter(3.0)
a.assign(4.0) # `a` parameter to `2.0` value.
a.assign(tf.constant(5.0)) # `a` parameter to `5.0` value.
a.assign(b) # `a` parameter to constrained value of `b`.
```
:param value: Constrained tensor-like value.
:param use_locking: If `True`, use locking during the assignment.
:param name: The name of the operation to be created.
:param read_value: if True, will return something which evaluates to the new
value of the variable; if False will return the assign op.
"""
unconstrained_value = self.validate_unconstrained_value(value, self.dtype)
return self._unconstrained.assign(unconstrained_value,
use_locking=use_locking, name=name, read_value=read_value)
@property
def is_tensor_like(self):
"""
This method means that TensorFlow's `tensor_util.is_tensor` function
will return `True`
"""
return True
@property
def name(self):
return self._unconstrained.name
@property
def initializer(self):
return self._unconstrained.initializer
@property
def device(self):
return self._unconstrained.device
@property
def dtype(self):
return self._unconstrained.dtype
@property
def op(self):
return self._unconstrained.op
@property
def shape(self):
if self.transform is not None:
return self.transform.forward_event_shape(self._unconstrained.shape)
return self._unconstrained.shape
def numpy(self):
return self.read_value().numpy()
def get_shape(self):
return self.shape
def _should_act_as_resource_variable(self):
# needed so that Parameters are correctly identified by TensorFlow's
# is_resource_variable() in resource_variable_ops.py
pass # only checked by TensorFlow using hasattr()
@property
def handle(self):
return self._unconstrained.handle
def __repr__(self):
unconstrained = self.unconstrained_variable
constrained = self.read_value()
info = f"dtype={self.dtype.name} " \
f"unconstrained-shape={unconstrained.shape} " \
f"unconstrained-numpy={unconstrained.numpy()} " \
f"constrained-shape={constrained.shape} " \
f"constrained-numpy={constrained.numpy()}"
return f"<gpflow.Parameter {self.name!r} {info}>"
# Below
# TensorFlow copy-paste code to make variable-like object to work
@classmethod
def _OverloadAllOperators(cls): # pylint: disable=invalid-name
"""Register overloads for all operators."""
for operator in tf.Tensor.OVERLOADABLE_OPERATORS:
cls._OverloadOperator(operator)
# For slicing, bind getitem differently than a tensor (use SliceHelperVar
# instead)
# pylint: disable=protected-access
setattr(cls, "__getitem__", array_ops._SliceHelperVar)
@classmethod
def _OverloadOperator(cls, operator): # pylint: disable=invalid-name
"""Defer an operator overload to `ops.Tensor`.
We pull the operator out of ops.Tensor dynamically to avoid ordering issues.
Args:
operator: string. The operator name.
"""
tensor_oper = getattr(tf.Tensor, operator)
def _run_op(a, *args, **kwargs):
# pylint: disable=protected-access
return tensor_oper(a.read_value(), *args, **kwargs)
functools.update_wrapper(_run_op, tensor_oper)
setattr(cls, operator, _run_op)
# NOTE(mrry): This enables the Variable's overloaded "right" binary
# operators to run when the left operand is an ndarray, because it
# accords the Variable class higher priority than an ndarray, or a
# numpy matrix.
# TODO(mrry): Convert this to using numpy's __numpy_ufunc__
# mechanism, which allows more control over how Variables interact
# with ndarrays.
__array_priority__ = 100
Parameter._OverloadAllOperators()
tf.register_tensor_conversion_function(Parameter, lambda x, *args, **kwds: x.read_value())
def _cast_to_dtype(value: VariableData, dtype: Optional[DType] = None) -> tf.Tensor:
if dtype is None:
dtype = default_float()
if tf.is_tensor(value):
return tf.cast(value, dtype)
else:
return tf.convert_to_tensor(value, dtype)
def _to_constrained(value: VariableData, transform: Transform) -> tf.Tensor:
if transform is not None:
return transform.forward(value)
return value
def _to_unconstrained(value: VariableData, transform: Transform) -> tf.Tensor:
if transform is not None:
return transform.inverse(value)
return value
