torcheval.metrics.WindowedBinaryNormalizedEntropy¶
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class
torcheval.metrics.
WindowedBinaryNormalizedEntropy
(*, from_logits: bool = False, num_tasks: int = 1, max_num_updates: int = 100, enable_lifetime: bool = True, device: Optional[device] = None)[source]¶ The windowed version of BinaryNormalizedEntropy that provides both windowed and liftime values. Windowed value is calculated from the input and target of the last window_size number of update() calls. Lifetime value is calculated from all past input and target of update() calls.
Compute the normalized binary cross entropy between predicted input and ground-truth binary target.
Parameters: - from_logits (bool) – A boolean indicator whether the predicted value y_pred is a floating-point logit value (i.e., value in [-inf, inf] when from_logits=True) or a probablity value (i.e., value in [0., 1.] when from_logits=False) Default value is False.
- num_tasks (int) – Number of tasks that need BinaryNormalizedEntropy calculation. Default value is 1. BinaryNormalizedEntropy for each task will be calculated independently.
- max_num_updates (int) – The max window size that can accommodate the number of updates.
- enable_lifetime (bool) – A boolean indicator whether to calculate lifetime values.
Examples:
>>> import torch >>> from torcheval.metrics import WindowedBinaryNormalizedEntropy >>> metric = WindowedBinaryNormalizedEntropy(max_num_updates=2) >>> metric.update(torch.tensor([0.2, 0.3]), torch.tensor([1.0, 0.0])) >>> metric.update(torch.tensor([0.5, 0.6]), torch.tensor([1.0, 1.0])) >>> metric.update(torch.tensor([0.6, 0.2]), torch.tensor([0.0, 1.0])) >>> metric.num_examples, metric.windowed_num_examples (tensor([6.], dtype=torch.float64), tensor([[2., 2.]], dtype=torch.float64)) >>> metric.compute() (tensor([1.4914], dtype=torch.float64), tensor([1.6581], dtype=torch.float64)) >>> metric = WindowedBinaryNormalizedEntropy(max_num_updates=2, enable_lifetime=False) >>> metric.update(torch.tensor([0.2, 0.3]), torch.tensor([1.0, 0.0])) >>> metric.update(torch.tensor([0.5, 0.6]), torch.tensor([1.0, 1.0])) >>> metric.update(torch.tensor([0.6, 0.2]), torch.tensor([0.0, 1.0])) >>> metric.windowed_num_examples tensor([[2., 2.]], dtype=torch.float64) >>> metric.compute() tensor([1.6581], dtype=torch.float64) >>> metric = WindowedBinaryNormalizedEntropy(max_num_updates=2, num_tasks=2) >>> metric.update(torch.tensor([[0.2, 0.3], [0.5, 0.1]]), torch.tensor([[1.0, 0.0], [0.0, 1.0]])) >>> metric.update(torch.tensor([[0.8, 0.3], [0.6, 0.1]]), torch.tensor([[1.0, 1.0], [1.0, 0.0]])) >>> metric.update(torch.tensor([[0.5, 0.1], [0.3, 0.9]]), torch.tensor([[0.0, 1.0], [0.0, 0.0]])) >>> metric.num_examples, metric.windowed_num_examples (tensor([6., 6.], dtype=torch.float64), tensor([[2., 2.], [2., 2.]], dtype=torch.float64)) >>> metric.compute() (tensor([1.6729, 1.6421], dtype=torch.float64), tensor([1.9663, 1.4562], dtype=torch.float64))
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__init__
(*, from_logits: bool = False, num_tasks: int = 1, max_num_updates: int = 100, enable_lifetime: bool = True, device: Optional[device] = None) None [source]¶ Initialize a metric object and its internal states.
Use
self._add_state()
to initialize state variables of your metric class. The state variables should be eithertorch.Tensor
, a list oftorch.Tensor
, or a dictionary withtorch.Tensor
as values
Methods
__init__
(*[, from_logits, num_tasks, ...])Initialize a metric object and its internal states. compute
()Return the normalized binary cross entropy. load_state_dict
(state_dict[, strict])Loads metric state variables from state_dict. merge_state
(metrics)Merge the metric state with its counterparts from other metric instances. reset
()Reset the metric state variables to their default value. state_dict
()Save metric state variables in state_dict. to
(device, *args, **kwargs)Move tensors in metric state variables to device. update
(input, target, *[, weight])Update the metric state with the total entropy, total number of examples and total number of positive targets. Attributes
device
The last input device of Metric.to()
.