tf_agents.bandits.multi_objective.multi_objective_scalarizer.ScalarizerTraceType

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Class outlining the default Tracing Protocol for Scalarizer.

tf_agents.bandits.multi_objective.multi_objective_scalarizer.ScalarizerTraceType(
    value
)

If included as an argument, corresponding tf.function will always retrace for each usage.

Derived classes can override this behavior by specifying their own Tracing Protocol.

Methods

cast

cast(
    value, cast_context
) -> Any

Cast value to this type.

flatten

flatten() -> List['TraceType']

Returns a list of TensorSpecs corresponding to to_tensors values.

from_tensors

from_tensors(
    tensors: Iterator[core.Tensor]
) -> Any

Generates a value of this type from Tensors.

Must use the same fixed amount of tensors as to_tensors.

is_subtype_of

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is_subtype_of(
    _
)

Returns True if self is a subtype of other.

For example, tf.function uses subtyping for dispatch: if a.is_subtype_of(b) is True, then an argument of TraceType a can be used as argument to a ConcreteFunction traced with an a TraceType b.

Example:

class Dimension(TraceType):
  def __init__(self, value: Optional[int]):
    self.value = value

  def is_subtype_of(self, other):
    # Either the value is the same or other has a generalized value that
    # can represent any specific ones.
    return (self.value == other.value) or (other.value is None)

most_specific_common_supertype

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most_specific_common_supertype(
    _
)

Returns the most specific supertype of self and others, if exists.

The returned TraceType is a supertype of self and others, that is, they are all subtypes (see is_subtype_of) of it. It is also most specific, that is, there it has no subtype that is also a common supertype of self and others.

If self and others have no common supertype, this returns None.

Example:

 class Dimension(TraceType):
   def __init__(self, value: Optional[int]):
     self.value = value

   def most_specific_common_supertype(self, other):
      # Either the value is the same or other has a generalized value that
      # can represent any specific ones.
      if self.value == other.value:
        return self.value
      else:
        return Dimension(None)

placeholder_value

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placeholder_value(
    placeholder_context=None
)

Creates a placeholder for tracing.

tf.funcion traces with the placeholder value rather than the actual value. For example, a placeholder value can represent multiple different actual values. This means that the trace generated with that placeholder value is more general and reusable which saves expensive retracing.

For the Fruit example shared above, implementing:

class FruitTraceType:
  def placeholder_value(self, placeholder_context):
    return Fruit()

instructs tf.function to trace with the Fruit() objects instead of the actual Apple() and Mango() objects when it receives a call to get_mixed_flavor(Apple(), Mango()). For example, Tensor arguments are replaced with Tensors of similar shape and dtype, output from a tf.Placeholder op.

More generally, placeholder values are the arguments of a tf.function, as seen from the function's body:

@tf.function
def foo(x):
  # Here `x` is be the placeholder value
  ...

foo(x) # Here `x` is the actual value

to_tensors

to_tensors(
    value: Any
) -> List[core.Tensor]

Breaks down a value of this type into Tensors.

For a TraceType instance, the number of tensors generated for corresponding value should be constant.

__eq__

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__eq__(
    _
)

Return self==value.