metrics.py
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	# Source: https://github.com/dmizr/phuber/blob/master/phuber/metrics.py
	import torch


	class LossMetric:
	"""Keeps track of the loss over an epoch"""

	def __init__(self) -> None:
	self.running_loss = 0
	self.count = 0

	def update(self, loss: float, batch_size: int) -> None:
	self.running_loss += loss * batch_size
	self.count += batch_size

	def compute(self) -> float:
	return self.running_loss / self.count

	def reset(self) -> None:
	self.running_loss = 0
	self.count = 0


	class AccuracyMetric:
	"""Keeps track of the top-k accuracy over an epoch
	Args:
	k (int): Value of k for top-k accuracy
	"""

	def __init__(self, k: int = 1) -> None:
	self.correct = 0
	self.total = 0
	self.k = k

	def update(self, out: torch.Tensor, target: torch.Tensor) -> None:
	# Computes top-k accuracy
	_, indices = torch.topk(out, self.k, dim=-1)
	target_in_top_k = torch.eq(indices, target[:, None]).bool().any(-1)
	total_correct = torch.sum(target_in_top_k, dtype=torch.int).item()
	total_samples = target.shape[0]

	self.correct += total_correct
	self.total += total_samples

	def compute(self) -> float:
	return self.correct / self.total

	def reset(self) -> None:
	self.correct = 0
	self.total = 0