Funcsgd

SGDState (tuple)

SGDState(center, velocity, center_learning_rate, momentum)

Source code in evotorch/algorithms/functional/funcsgd.py

class SGDState(NamedTuple):
    center: torch.Tensor
    velocity: torch.Tensor
    center_learning_rate: torch.Tensor
    momentum: torch.Tensor

__getnewargs__(self) special

Return self as a plain tuple. Used by copy and pickle.

Source code in evotorch/algorithms/functional/funcsgd.py

def __getnewargs__(self):
    'Return self as a plain tuple.  Used by copy and pickle.'
    return _tuple(self)

__new__(_cls, center, velocity, center_learning_rate, momentum) special staticmethod

Create new instance of SGDState(center, velocity, center_learning_rate, momentum)

__repr__(self) special

Return a nicely formatted representation string

Source code in evotorch/algorithms/functional/funcsgd.py

def __repr__(self):
    'Return a nicely formatted representation string'
    return self.__class__.__name__ + repr_fmt % self

sgd(*, center_init, center_learning_rate, momentum=None)

Initialize the gradient ascent/descent search and get its initial state.

Reference regarding the momentum behavior:

Polyak, B. T. (1964).
Some methods of speeding up the convergence of iteration methods.
USSR Computational Mathematics and Mathematical Physics, 4(5):1–17.

Parameters:

Name	Type	Description	Default
center_init	Union[torch.Tensor, numpy.ndarray]	Starting point for the gradient ascent/descent. Expected as a PyTorch tensor with at least 1 dimension. If there are 2 or more dimensions, the extra leftmost dimensions are interpreted as batch dimensions.	required
center_learning_rate	Union[numbers.Number, numpy.ndarray, torch.Tensor]	Learning rate (i.e. the step size) for gradient ascent/descent. Can be a scalar or a multidimensional tensor. If given as a tensor with multiple dimensions, those dimensions will be interpreted as batch dimensions.	required
momentum	Union[numbers.Number, numpy.ndarray, torch.Tensor]	Momentum coefficient, expected as a scalar. If provided as a scalar, Polyak-style momentum will be enabled. If given as a tensor with multiple dimensions, those dimensions will be interpreted as batch dimensions.	None

Source code in evotorch/algorithms/functional/funcsgd.py

def sgd(
    *,
    center_init: BatchableVector,
    center_learning_rate: BatchableScalar,
    momentum: Optional[BatchableScalar] = None,
) -> SGDState:
    """
    Initialize the gradient ascent/descent search and get its initial state.

    Reference regarding the momentum behavior:

        Polyak, B. T. (1964).
        Some methods of speeding up the convergence of iteration methods.
        USSR Computational Mathematics and Mathematical Physics, 4(5):1–17.

    Args:
        center_init: Starting point for the gradient ascent/descent.
            Expected as a PyTorch tensor with at least 1 dimension.
            If there are 2 or more dimensions, the extra leftmost dimensions
            are interpreted as batch dimensions.
        center_learning_rate: Learning rate (i.e. the step size) for gradient
            ascent/descent. Can be a scalar or a multidimensional tensor.
            If given as a tensor with multiple dimensions, those dimensions
            will be interpreted as batch dimensions.
        momentum: Momentum coefficient, expected as a scalar.
            If provided as a scalar, Polyak-style momentum will be enabled.
            If given as a tensor with multiple dimensions, those dimensions
            will be interpreted as batch dimensions.
    """
    center_init = torch.as_tensor(center_init)
    dtype = center_init.dtype
    device = center_init.device

    def as_tensor(x) -> torch.Tensor:
        return torch.as_tensor(x, dtype=dtype, device=device)

    velocity = torch.zeros_like(center_init)
    center_learning_rate = as_tensor(center_learning_rate)
    momentum = as_tensor(0.0) if momentum is None else as_tensor(momentum)

    return SGDState(
        center=center_init,
        velocity=velocity,
        center_learning_rate=center_learning_rate,
        momentum=momentum,
    )

sgd_ask(state)

Get the search point stored by the given SGDState.

Parameters:

Name	Type	Description	Default
state	SGDState	The current state of gradient ascent/descent.	required

Returns:

Type	Description
Tensor	The search point as a 1-dimensional tensor in the non-batched case, or as a multi-dimensional tensor if the search is batched.

Source code in evotorch/algorithms/functional/funcsgd.py

def sgd_ask(state: SGDState) -> torch.Tensor:
    """
    Get the search point stored by the given `SGDState`.

    Args:
        state: The current state of gradient ascent/descent.
    Returns:
        The search point as a 1-dimensional tensor in the non-batched case,
        or as a multi-dimensional tensor if the search is batched.
    """
    return state.center

sgd_tell(state, *, follow_grad)

Tell the gradient ascent/descent the current gradient to get its next state.

Parameters:

Name	Type	Description	Default
state	SGDState	The current state of gradient ascent/descent.	required
follow_grad	Union[torch.Tensor, numpy.ndarray]	Gradient at the current point of the search. Can be a 1-dimensional tensor in the non-batched case, or a multi-dimensional tensor in the batched case.	required

Returns:

Type	Description
SGDState	The updated state of gradient ascent/descent, with the given gradient applied.

Source code in evotorch/algorithms/functional/funcsgd.py

def sgd_tell(state: SGDState, *, follow_grad: BatchableVector) -> SGDState:
    """
    Tell the gradient ascent/descent the current gradient to get its next state.

    Args:
        state: The current state of gradient ascent/descent.
        follow_grad: Gradient at the current point of the search.
            Can be a 1-dimensional tensor in the non-batched case,
            or a multi-dimensional tensor in the batched case.
    Returns:
        The updated state of gradient ascent/descent, with the given gradient
        applied.
    """
    velocity, center = _sgd_step(
        follow_grad,
        state.center,
        state.velocity,
        state.center_learning_rate,
        state.momentum,
    )

    return SGDState(
        center=center,
        velocity=velocity,
        center_learning_rate=state.center_learning_rate,
        momentum=state.momentum,
    )