Entropy#
- class ignite.metrics.Entropy(output_transform=<function Metric.<lambda>>, device=device(type='cpu'))[source]#
Calculates the mean of entropy.
$H = \frac{1}{N} \sum_{i=1}^N \sum_{c=1}^C -p_{i,c} \log p_{i,c}, \quad p_{i,c} = \frac{\exp(z_{i,c})}{\sum_{c'=1}^C \exp(z_{i,c'})}$where $p_{i,c}$ is the prediction probability of $i$-th data belonging to the class $c$.
update
must receive output of the form(y_pred, y)
whiley
is not used in this metric.y_pred
is expected to be the unnormalized logits for each class. $(B, C)$ (classification) or $(B, C, ...)$ (e.g., image segmentation) shapes are allowed.
- Parameters
output_transform (Callable) – a callable that is used to transform the
Engine
’sprocess_function
’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as(y_pred, y)
or{'y_pred': y_pred, 'y': y}
.device (Union[str, device]) – specifies which device updates are accumulated on. Setting the metric’s device to be the same as your
update
arguments ensures theupdate
method is non-blocking. By default, CPU.
Examples
To use with
Engine
andprocess_function
, simply attach the metric instance to the engine. The output of the engine’sprocess_function
needs to be in the format of(y_pred, y)
or{'y_pred': y_pred, 'y': y, ...}
. If not,output_tranform
can be added to the metric to transform the output into the form expected by the metric.For more information on how metric works with
Engine
, visit Attach Engine API.from collections import OrderedDict import torch from torch import nn, optim from ignite.engine import * from ignite.handlers import * from ignite.metrics import * from ignite.metrics.regression import * from ignite.utils import * # create default evaluator for doctests def eval_step(engine, batch): return batch default_evaluator = Engine(eval_step) # create default optimizer for doctests param_tensor = torch.zeros([1], requires_grad=True) default_optimizer = torch.optim.SGD([param_tensor], lr=0.1) # create default trainer for doctests # as handlers could be attached to the trainer, # each test must define his own trainer using `.. testsetup:` def get_default_trainer(): def train_step(engine, batch): return batch return Engine(train_step) # create default model for doctests default_model = nn.Sequential(OrderedDict([ ('base', nn.Linear(4, 2)), ('fc', nn.Linear(2, 1)) ])) manual_seed(666)
metric = Entropy() metric.attach(default_evaluator, 'entropy') y_true = torch.tensor([0, 1, 2]) # not considered in the Entropy metric. y_pred = torch.tensor([ [ 0.0000, 0.6931, 1.0986], [ 1.3863, 1.6094, 1.6094], [ 0.0000, -2.3026, -2.3026] ]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['entropy'])
0.8902875582377116
Methods
Computes the metric based on its accumulated state.
Resets the metric to its initial state.
Updates the metric's state using the passed batch output.
- compute()[source]#
Computes the metric based on its accumulated state.
By default, this is called at the end of each epoch.
- Returns
- the actual quantity of interest. However, if a
Mapping
is returned, it will be (shallow) flattened into engine.state.metrics whencompleted()
is called. - Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed.