Class GradientTracker

machine_learning/automatic_differentiation.py:156–322

Class contains methods to compute partial derivatives of Variable based on the computation graph. Examples: >>> with GradientTracker() as tracker: ... a = Variable([2.0, 5.0]) ... b = Variable([1.0, 2.0]) ... m = Variable([1.0, 2.0]) ... c = a + b

class GradientTracker:
    """
    Class contains methods to compute partial derivatives of Variable
    based on the computation graph.

    Examples:

    >>> with GradientTracker() as tracker:
    ...     a = Variable([2.0, 5.0])
    ...     b = Variable([1.0, 2.0])
    ...     m = Variable([1.0, 2.0])
    ...     c = a + b
    ...     d = a * b
    ...     e = c / d
    >>> tracker.gradient(e, a)
    array([-0.25, -0.04])
    >>> tracker.gradient(e, b)
    array([-1.  , -0.25])
    >>> tracker.gradient(e, m) is None
    True

    >>> with GradientTracker() as tracker:
    ...     a = Variable([[2.0, 5.0]])
    ...     b = Variable([[1.0], [2.0]])
    ...     c = a @ b
    >>> tracker.gradient(c, a)
    array([[1., 2.]])
    >>> tracker.gradient(c, b)
    array([[2.],
           [5.]])

    >>> with GradientTracker() as tracker:
    ...     a = Variable([[2.0, 5.0]])
    ...     b = a ** 3
    >>> tracker.gradient(b, a)
    array([[12., 75.]])
    """

    instance = None

    def __new__(cls) -> Self:
        """
        Executes at the creation of class object and returns if
        object is already created. This class follows singleton
        design pattern.
        """
        if cls.instance is None:
            cls.instance = super().__new__(cls)
        return cls.instance

    def __init__(self) -> None:
        self.enabled = False

    def __enter__(self) -> Self:
        self.enabled = True
        return self

    def __exit__(